6 Types of Transportation Big Data Every City Needs

6 Types of Transportation Big Data Every City Needs

In a changing transportation landscape, it’s more important than ever for planners to have access to detailed data and analytics. Given our increasingly complex transportation networks, planners must consider where to implement road diets, add EV charging infrastructure, establish tollways, expand access to multimodal transport, and so much more.

While traditional methods of data collection and analysis (think: surveys and sensors) can still offer important insights, today’s cities often seek the help of something bigger — Big Data. In transportation, Big Data involves large, complex data sets collected from numerous sources to provide a complete picture of today’s transportation networks, including various modes of transportation and how they interact. This type of detailed data is collected from trillions of pings from connected vehicles and the Internet of Things, combined with contextual data like the census and maps of the roadway network, which is anonymized, aggregated, and processed by machine learning engines to generate recognizable metrics like Annual Average Daily Traffic (AADT) and Vehicle Miles Traveled (VMT), which transportation planners use every day.

Having access to detailed real-time and historical transportation data empowers planners and transportation departments to develop better strategies, reduce costs, prioritize initiatives, and measure the effectiveness of each change or improvement. But which metrics are most important for smart planning and development? To make a wide range of informed decisions, planners typically rely on the following Big Data for transportation, which we’ll cover in this article:

Annual Average Daily Traffic
Origin-Destination
Turning Movement Counts
Vehicle Miles Traveled
Vehicles Hours of Delay
Vehicle Speeds

1. Annual Average Daily Traffic

Annual Average Daily Traffic (AADT) is perhaps the most foundational metric in transportation analytics. It measures the average daily volume of traffic on a given road during a given year, and it’s critical for evaluating road congestion, spotting safety concerns, and planning infrastructure updates. AADT also plays an integral role in shaping non-transportation decisions, such as developing new retail or investigating accident cases.

Historically, this fundamental piece of traffic data has been expensive and time-consuming to acquire. Traditional collection methods for AADT require working counters that can track traffic counts 24 hours a day, 365 days a year. In many jurisdictions, it’s simply not possible to collect this much data for every road.

Today, however, machine learning allows software like StreetLight InSight® to build models that calculate AADT for more roadways — and much more quickly. Instead of spending time and money installing sensors on every roadway or extrapolating annual numbers from a few days’ worth of manual counts, Big Data leverages large sample sizes and a continuous stream of data to quickly deliver the most reliable, up-to-date data on roadway volumes.

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2. Origin-Destination

Origin-Destination (O-D) patterns provide another essential piece of the puzzle in transportation data analytics. O-D data helps transportation professionals understand where trips begin and end, shedding light on commute patterns, areas of high travel demand, and locations that generate the most traffic. For these reasons, O-D data is often used by agencies to understand where infrastructure improvements, policy changes, or traffic management strategies can help optimize traffic flow, encourage mode shift to walking, biking, and transit, boost economic activity, and more.

An Origin-Destination analysis in StreetLight InSight® shows top origins and destinations, with a time distribution overlay contextualizing when most trips occur.

However, traditional O-D metrics are often incomplete and inefficient to collect, requiring extensive surveys which are typically costly and suffer from human bias and small sample size issues. Big Data can provide more detailed information — without the survey process — and go beyond typical O-D analyses to identify the most prevalent travel routes and allow planners to easily customize their analysis and contextualize O-D patterns with other data overlays like vehicle speeds or AADT.
For instance, planners in Sarasota, Florida, used StreetLight O-D data to prioritize bike routes and route directness and create a stronger multimodal network. Using detailed metrics without the need for surveys, Sarasota connected more bike and pedestrian routes to transit services and key destinations to reduce congestion and create a more equitable transportation system.

3. Turning Movement Counts

Turning Movement Counts (TMC) provide critical safety and congestion information about intersections. In simple terms, they demonstrate the volume of traffic entering and exiting an intersection at a given time.

While this data can be collected manually, this process is cumbersome and costly. Since the 1970s, pneumatic tube counters have typically provided these counts every 24 to 48 hours, but such tubes often provide inaccurate data due to vehicle angles in intersections. It’s also expensive to lay down enough counters to collect sufficient data — each single-lane tube costs roughly $5,000, while each four-lane tube costs around $9,000.¹

Planners can leverage Big Data to collect more frequent, accurate, and less expensive analytics on any intersection. Big Data can model 15-minute granularity for nearly every intersection — signalized or not — at any hour of the day, without the sample-size challenges of 48-hour counts. With this level of collection and analysis, planners can understand turning patterns and peak turning times for almost every road in the U.S. and Canada.

4. Vehicle Miles Traveled

To understand travel demand, measure emissions, evaluate multimodal infrastructure, and more, Vehicle Miles Traveled (VMT) is another critical metric for city transportation planners. VMT estimates the total mileage traveled by vehicles in the region or along specific corridors. This helps planners monitor changes in travel demand over time, allocate resources where they’re most needed, and understand how vehicle travel impacts road infrastructure and regional emissions.

With Big Data, planners have access to continuous, widespread VMT info for any road or region. This information makes it possible to build travel demand forecasts, plan for congestion relief, and direct regional and corridor traffic studies. VMT is also invaluable for detailed estimates of greenhouse gas emissions, fuel tax impacts, and more.

Big data transportation analytics platforms allow cities like Citrus Heights, California, to get comprehensive VMT metrics for every census block, meeting regional reporting requirements like SB 743, shedding light on city-generated transportation emissions, and guiding infrastructure improvements.

5. Vehicle Hours of Delay

Vehicle Hours of Delay (VHD) is an essential metric for measuring congestion issues and targeting traffic bottlenecks. It provides the total number of hours lost to traffic delays in a given area during a specific time period. Understanding VHD can help planners quantify the severity and investigate the causes of traffic delays, as well as predict how road construction, special events, or bad weather may impact travel times. It is also critical for evaluating the effectiveness of traffic management strategies and more accurately forecasting which road improvements will reduce delays.

With StreetLight InSight®, planners can access historical and monthly delay metrics, providing visibility into fluctuations over time. This type of traffic data is invaluable in highly congested urban areas like Los Angeles or Chicago, where the success of each transportation initiative hinges on understanding the exact sources and nature of each bottleneck or problem area.

6. Vehicle Speeds

Vehicle speed metrics help city planners understand a variety of road conditions, particularly road safety and congestion.

Vehicle speed data is critical to measuring and improving road safety, especially when it comes to protecting Vulnerable Road Users like cyclists and pedestrians. To put this into perspective, data from AAA Foundation shows pedestrians are five times more likely to die from crashes when cars are traveling 40 mph vs. 20 mph. Vehicle speed metrics help city planners understand where high speed vehicles threaten the safety of all road users, and evaluate roadways for potential traffic calming strategies like road diets or speed humps.

A data visualization shows where high vehicle speeds and high pedestrian activity overlap on a dangerous section of Oakland, California’s Grand Avenue.

Inversely, identifying areas with lower-than-expected vehicle speeds can help city planners identify areas of high congestion, and evaluate when that congestion is at its worst. Getting real-time data on vehicle speeds can even enable city traffic managers to quickly deploy congestion mitigation strategies like retiming smart traffic signals or deploying trained traffic controllers where needed.
While vehicle speeds can be collected from physical sensors like speed cameras or calculated based on the distance and travel time between two separate traffic counters, getting vehicle speed data for every roadway would require installing these types of sensors throughout a city’s entire road network, which is prohibitively expensive and time-consuming. Big data vehicle speed metrics can fill these gaps for cities that want to improve safety or congestion on city streets, even those without permanent counters installed.

Accessing the Data You Need, When You Need It

Solving regional transportation issues is a problem of massive scale and enormous import. Faced with millions of cars traveling thousands of miles per year and the pressing problems of urban congestion, transportation inequity, a pedestrian safety crisis, and greenhouse gas emissions, planners must have access to digestible, actionable metrics to understand and address the issues their constituents face. Injecting Big Data in transportation planning puts a better city mobility network within reach by delivering more information to demystify these common problems and their potential solutions.

With StreetLight InSight®, planners can access the most comprehensive suite of transportation data analytics on the market. While traditional collection methods and metrics are still useful for providing critical snapshots and verifying broader data sets, StreetLight’s Big Data analytics expand upon what’s possible with these traditional methods with 24/7 access to the metrics planners need to make more informed, data-driven decisions for any road and any mode.

To tap into the most comprehensive, up-to-the-minute transportation data on the market, get started with StreetLight today.

1. Federal Highway Administration. “A Summary of Vehicle Detection and Surveillance Technologies use in Intelligent Transportation Systems.”  https://www.fhwa.dot.gov/policyinformation/pubs/vdstits2007/04.cfm

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How Should We Build EV-Charging Infrastructure?

Electric vehicles (EVs) are a critical element of the fight against climate change. Compared to a typical gas-powered vehicle, the average EV produces less than half the amount of carbon pollution over its lifetime. [1] Even at today’s moderate levels of EV sales, electric cars are already reducing crude oil usage by 1.7 million barrels a day. [2]

Yet we have a long way to go to reach the tipping point, when EVs take over as the primary form of individual transportation. In the U.S., 10% of new vehicles registered in 2023 were electric, far from a majority. [3] Tipping the scales requires not only an increase in EV production, but a significant expansion of the nation’s EV charging infrastructure.

As of today, our systems are woefully underprepared for an EV-dominant future. What will it take to catch up? Although federal and state-level funding is essential, building an adequate charging infrastructure largely comes down to smart, data-driven planning.

In this article, we’ll explore what that looks like, covering the following:

How do EV charging stations work?
The current state of EV charging infrastructure
Optimizing charger placement in communities
Promoting sustainable energy sources

How Do EV Charging Stations Work?

Electric vehicle supply equipment (EVSE), as charging stations are commonly called, is fundamentally different from gas pumps. While both types of equipment are designed to refuel vehicles, EV charging stations are far less standardized than gas pumps.

EVSE comes in several different types, from basic (extremely slow) Level 1 chargers to high-speed, Level 3 DC fast chargers. Although the latter can recharge an empty car battery to 80% in less than an hour, most public EVSE consists of Level 2 chargers, which take anywhere from four to 10 hours to achieve a similar charge level. [4]

Not surprisingly, EV charging station costs vary widely based on type. You can plug into your home outlet for Level 1 charging, but it’ll cost anywhere from $1,000 for a basic home Level 2 charger to upwards of $50,000 for a commercial Level 3 charger. [5], [6]

Besides the cost of EV charging stations, planners must consider numerous other factors, including the connection type, interoperability with various vehicles, and the payment network of choice. All of these are critical factors in how to build EV charging station infrastructure.

The Current State of EV Infrastructure

In the U.S., the number of public and private EV charging stations has grown rapidly in recent years, thanks largely to available tax credits and incentives to help reduce upfront costs. However, the nation’s EV charging infrastructure has a long way to go to keep pace with near-term goals for vehicle electrification.

That’s especially true for public EVSE. Recent research by Stanford University sounded the alarm that relying on nighttime, home-based charging would put far too much demand on the electrical grid within the next decade. [7]

Currently, there are almost 10 times more home EV chargers than public ones, and that ratio needs to change quickly. [8] Data from the National Renewable Energy Laboratory (NREL) further supports the need for a massive uptick in public charging station installations. As of 2023, there were just over 168,000 public charging ports available in the U.S., but NREL research calls for nearly 1.2 million public ports to match EV demand by 2030.[9], [10]

massachusettes EV Charging infrastructure Gaps — *A screenshot from StreetLight’s EV Dashboard visualizes the largest EV charging infrastructure gaps across Massachusetts, based on vehicle activity and existing charger locations.*

It’s not just a numbers game, either. If electric vehicles are to become the norm, EV charging station infrastructure must be more accessible to everyone. That means charging equipment must become more interoperable with all types of EVs, stations must be available where people can conveniently use them, and EVSE must be reliable and easy to use.

Overall, reaching this level of accessibility requires more investment in both public and private (workplace) charging, along with a commitment to eradicating “charging deserts” in underserved communities. [11]

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Optimizing Charger Placement in Communities

The long-term goal may be accessible, reliable charging for everyone, but we are still many miles from that final destination. Accelerating our progress in the right direction requires near-term prioritization. Where does a given community need EV charging infrastructure next? What locations would make it practical and easy for more existing EV drivers to switch from charging at home overnight to charging at work or while shopping during the day?

To understand that, planners must gather and analyze a host of data. Targeting the ideal location for more EV chargers involves analyzing demographics and demand, accessibility concerns, and mobility patterns in various communities. It also requires an understanding of existing infrastructure and the differing needs of rural and urban locations.

For instance, planners in a dense urban area may need to evaluate traffic patterns in numerous parts of the city to understand how far people commute every day, where they tend to stop and for how long, and where people without driveways tend to park. In a rural area, conversely, data about long-range commutes and the most trafficked freeway corridors may be more relevant. They might also examine data about interstate corridors with heavy commercial freight transit to find ideal hubs for charging heavy-duty vehicles.

In the Silcon Valley, for example, planners used StreetLight InSight® to evaluate Origin-Destination (O-D) data, traveler demographics, and more to help choose locations for 400+ public EV chargers.

Planners must also attack the problem at a broader level, considering factors like how a specific location for EV charging stations will affect electricity demand in a particular area.

To prepare the electric grid for rising EV charging demand, Eversource, New England’s largest utility, used StreetLight to forecast where and when charging demand would be highest to plan substation upgrades and charging rates that would incentivize off-peak charging. Their demand analysis also allowed them to coordinate long-term electrification planning with public agencies.

Promoting Sustainable Energy Sources

Taking a wider scope, examining the best locations to maximize the impact of clean transportation can help planners prioritize where and how to build EV charging infrastructure. Although EVs reduce emissions regardless of where you deploy them, they offer the largest reduction in regions that rely on clean energy sources like wind, solar, or hydroelectric power.

For example, driving an EV in a coal-dependent state like West Virginia results in a 50% reduction in emissions compared to driving a gas car. In Texas, which is a national leader in solar and wind power generation, choosing an EV reduces emissions by over 77%. [12]

Practically, the application here is twofold: On one hand, it may be beneficial to develop more EV charging infrastructure in areas that already rely on clean energy sources. Yet, it’s also likely worth considering policies and incentive programs that will help municipalities and private companies go beyond simply installing EVSE to adding solar panels, wind turbines, or other clean energy sources that increase EVs’ environmental impact.

Get Charged Up With Big Data

EV charging infrastructure has come a long way in the U.S.—but there’s still a long road ahead to add enough EVSE to support a truly all-electric transportation system. Reliable data is crucial at this stage, regardless of where you’re looking to add more charging equipment. The pressing issue doesn’t simply come down to adding more chargers, but knowing where to put them so that they best serve real-world demand efficiently and equitably.

With purpose-built EV metrics and emissions analytics, StreetLight InSight® can help planners, policymakers, and businesses make smart decisions in this crucial sector. This software unlocks access to relevant transportation data, including O-D, vehicle miles traveled, traveler demographics, travel times, and more. This helps pinpoint where people are traveling and when traffic is highest to measure the potential GHG impact of adding EV charging stations to any specific location, while ensuring the grid can handle rising electric demand.

See how it works in the video below.

To learn more about how you can use big data to fight against climate change, download our Transportation Climate Data Solutions handbook.

To start using StreetLight to plan your EV charging infrastructure today, contact us here.

Yale Climate Connections. “Don’t get fooled: Electric vehicles really are better for the climate.”
BloombergnNEF. “ElectricVehicle Outlook 2024.”
International Energy Agency. “Global EV Outlook 2024: Trends in electric cars.”
U.S. Department of Transportation. “Charger Types and Speeds.”
J.D. Power. “What Does an EV Home Charger Cost?”
State of New York. “Exhibit I Cost of charging stations.”
Stanford University. “Charging cars at home at night is not the way to go, Stanford study finds.”
International Energy Agency. “Global Outlook 2024: Trends in electric vehicle charging.”
National Renewable Energy Laboratory. “The 2030 NationalCharging Network: Estimating U.S. Light-Duty Demand for Electric Vehicle Charging Infrastructure.”
Alternative Fuels Data Center.  ”U.S. Public Electric Vehicle Charging Infrastructure.”
World Resources Institute. “Many US Communities Face EV ‘Charging Deserts.’ 5 Strategies Can Help.”
Alternative Fuels Data Center. “Emissions from Electric Vehicles.”

traffic on highway interchange used for aadt calculation

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Public Data from NYCDOT Validates the Reliability of StreetLight’s Speed Metrics

NYC highway with vehicle speed data along multiple segments

Access to accurate vehicle speed data is critical for effective road safety interventions, congestion mitigation, and more. We compared StreetLight’s speed metrics to data from New York City’s OpenData portal to ensure we’re delivering the most reliable insights.

To collect data on vehicle speeds, many agencies rely on permanent roadway sensors, speed cameras, or manual speed studies. But tight budgets and project timelines prevent the installation of sensors on every road, and manual studies only capture a small snapshot of roadway conditions, while also putting workers at risk.

Meanwhile, businesses and firms may not have access to the already limited speed data that is available via these methods, limiting their ability to make informed decisions about real estate, traffic operations, or events management.

For these and other reasons, many agencies, firms, and businesses turn to analytics platforms like StreetLight that leverage a big data approach to deliver vehicle speed metrics for any road, at any time. But when sourcing your data, it’s important to understand how reliable it is compared to more traditional ground truth methods.

Thanks to the City of New York’s OpenData portal, with publicly available vehicle speed data provided by NYCDOT, we were able to perform a validation of StreetLight’s speed data against New York City’s documented speeds. We’ll explain how our speed data is collected, how it compares to NYCDOT’s data, and what that means for your own vehicle speed analyses.

How NYCDOT Collects Speed Data

The speed data available in NYC’s OpenData portal is collected through E-ZPass readers located on approximately 110 road segments throughout the city. Vehicle speeds are calculated based on the travel time and distance between two E-ZPass readers.

This approach captures average vehicle speed in real time, and the portal is updated with the most recent data several times per day.¹

How StreetLight Collects Speed Data

StreetLight’s Vehicle Speed metrics are derived from Aggregated GPS Data, which includes data from a blend of device navigation apps, traditional mobile data apps, and in-vehicle navigation apps.

This method has the advantage of strong penetration rates across various road sizes and regions for a highly representative sample, even on rural and lower-volume roads. StreetLight’s sample penetration rate averaged 27% nationally in 2023 and was observed as high as 40%+ in some locations.

To develop segment-level speed metrics, StreetLight maps this data onto the StreetLight InSight® Zone Library, derived from OpenStreetMap (OSM). Based on the length of the segment and how long it takes a vehicle to travel from one end to the other, we estimate the average vehicle speed along that segment.

For more information on how StreetLight collects, aggregates, and validates our vehicle speed metrics, you can download this white paper.

Comparing the Data: How Accurate Are StreetLight’s Speed Metrics?

To ensure an apples-to-apples comparison, StreetLight analysts first cleaned the NYCDOT data, removing certain obviously incorrect datapoints that may have been caused by malfunctioning E-ZPass readers. Next, the cleaned NYCDOT data was aggregated such that the mean speed could be calculated per segment by day of week and hour of day.

Using StreetLight’s Network Performance analysis, analysts obtained the average speeds of groups of OSM segments that aligned with NYCDOT’s segments, looking at data for October 2023. This allowed for a close comparison between StreetLight and NYCDOT average speeds on 11 NYCDOT segments.

In this example comparison for a portion of FDR Drive Northbound, analysts averaged vehicle speeds from 10 StreetLight OSM zones (middle) aligned to the corresponding NYCDOT segment (left). On the right, NYCDOT speeds by day and time are marked with a blue line, while StreetLight speeds are marked in yellow.

Because StreetLight’s OSM-based segments do not have a one-to-one correspondence with NYCDOT’s segments (which are derived based on the distance between E-ZPass readers), special care was taken to align StreetLight segments with those used by NYCDOT, but some discrepancies persist, which we will discuss further in the analysis below.

Speed comparisons by day of week and hour of day for RFK Bridge Southeast Bound (left), Staten Island Expressway Eastbound (middle) and Long Island Expressway Westbound (right) segments. Monthly Average Daily Traffic (MADT) for each segment is marked below its graph.

Vehicle speed data comparisons for Bronx Whitestone Bridge, Gowanus Expressway, and FDR Drive

Speed comparisons for Bronx Whitestone Bridge Southbound (left), Gowanus Expressway Southbound (middle), and FDR Drive Northbound (right).

Speed comparisons for Bruckner Expressway Westbound (left), Brooklyn-Queens Expressway (BQE) Southbound between Atlantic and 9th St (middle), and the Brooklyn Battery Tunnel Eastbound (right).

The above nine segment analyses showed StreetLight speed metrics closely aligned with speed data reported by NYCDOT. Where the data differs, StreetLight speeds tend to be slightly higher than those reported by NYCDOT.

Overall, StreetLight’s daily and hourly speed variations for each segment also track closely with the NYCDOT data, indicating that StreetLight’s speed metrics deliver reliable insights for real-world applications like safety and congestion studies, which can save agencies the considerable cost of installing physical sensors.

The two remaining segments (pictured below) display the greatest divergence between the StreetLight and NYCDOT datasets.

vehicle speed data comparisons for 12th Ave and Lincoln Tunnel

Speed comparisons for 12th Avenue Southbound (left) and Lincoln Tunnel Eastbound (right). These graphs show segments where StreetLight’s OSM zones could not be perfectly aligned to NYCDOT segments.

These discrepancies are likely caused, at least in part, by misaligned segment boundaries. As discussed above, sometimes StreetLight OSM zones could not be perfectly aligned to the NYCDOT segments.

In the case of 12th Avenue (AKA West Side Highway), this segment is part of a signalized corridor with closely spaced intersections, which could exacerbate the impact of the misaligned segments. Because the comparison segments do not have the same signalized-intersection approaches, this could lead to larger differences in average speed.

Despite these localized limitations in segment comparability, the overall results of our comparison show a high degree of alignment between StreetLight’s big data-based speed metrics and NYCDOT’s speed data derived from E-ZPass sensors.

More about StreetLight’s Vehicle Speed Data – Segment Speed and Spot Speeds

Because the vehicle speed metrics provided by StreetLight include average segment speeds, they can provide a helpful perspective, even for agencies that already collect speed data through physical sensors.

Unlike NYCDOT’s average segment speeds used in the above analysis, the speed data available to most agencies are spot speeds. Spot speeds capture vehicle speed at a specific location rather than the average vehicle speed along a whole segment.

Spot speeds and segment speeds each capture a different nuance of vehicle traffic, and comparing the two can help agencies better understand the causes of unsafe speeds or congestion, as well as their most effective solutions.

To ensure clients can take advantage of these nuanced speed insights, spot speeds are now available from StreetLight! To stay updated on all our product releases, consider subscribing to our newsletter.

1. City of New York. NYC OpenData. “DOT Traffic Speeds NBE.” https://data.cityofnewyork.us/Transportation/DOT-Traffic-Speeds-NBE/i4gi-tjb9/about_data

6 Ways to Drive Sustainability in Transportation

bicyclist on trail through lush natural scenery

Sustainability may be a marketing buzzword, but the concept has never been more critical in transportation. As climate change continues to charge forward, shifting our behaviors, habits, and societal systems to preserve and pass on a healthy environment is one of the most pressing challenges of our time.

Rethinking how we get around is a crucial aspect of this challenge. The transportation sector is responsible for more greenhouse gases (GHGs) — a leading driver of climate change — than any other sector in the United States. [1]  Small changes in this area, when multiplied by millions of people in major urban areas, could dramatically reduce emissions and create a more sustainable lifestyle for future generations.

For sustainability to be more than a mere buzzword, however, we must move past using it as a marketing term and find sustainable transportation solutions that can truly move the needle in the fight against climate change. In this article, we’ll cover six key ways to drive sustainability in transportation, including:

Promote electric and hybrid vehicles
Invest in public transportation infrastructure
Prioritize sustainable urban planning
Implement smart traffic management systems
Support active transportation modes
Develop sustainable freight transport systems

Promote Electric and Hybrid Vehicles

The data is clear: Over the full vehicle lifecycle, hybrid and electric vehicles (EVs) produce far lower levels of GHG emissions than gas-powered cars. This is true even when accounting for the varying sustainability of fuel sources used to produce electricity. According to data from the Alternative Fuels Center, the average hybrid vehicle produces 55% as much emissions over its life as a similar gas-powered vehicle. The typical electric vehicle, meanwhile, produces 22% as much emissions as a gas car. [2]

Harnessing these benefits at scale requires significant investment into electric and hybrid vehicle technologies. Altogether, EVs and hybrids represented 16% of U.S. light-duty vehicle sales in 2023 — a significant amount, but far from unseating gasoline as the primary source of fuel. [3]

Federal and state programs provide tax credits or other incentives to encourage EV adoption and EV charger installation, but more programs are needed to overcome consumer concerns about battery range and costs of ownership. Local governments can explore additional incentives and work with utility companies to support rebate programs for EV chargers and bolster the electrical grid to handle growing charging demands. Additionally private and public organizations must step up to lead the charge in electrifying fleets and offering public charging options. More research and investment in other alternative fuels, such as biofuels, propane, and natural gas, could also help cut into the market share of gasoline. [4]

Invest in Public Transportation Infrastructure

Public transportation is a multi-faceted sustainable transportation solution. Not only does it reduce GHG emissions — by as much as two-thirds compared to private vehicles — but it also drives sustainability in more holistic ways. For instance, a stronger public transportation infrastructure provides more equitable access to jobs, education, and services, raising the standard of living for some of the most disadvantaged members of society. It also promotes a more active lifestyle and reduces communities’ exposure to pollutants, both of which can improve health outcomes across the board. [5]

Urban planners can use transportation data to find ideal opportunities for enhancing public transportation, whether by adding more light rail or expanding bus routes. For instance, planners with TransLink in Vancouver, B.C. used data comparing commuter reliance on various modes of transportation to demonstrate the strength of existing bus ridership across the metro area. With such detailed data on various urban corridors, planners were able to make the case for greater investment in bus transportation throughout the region.

Support Active Transportation Modes

Active methods of transportation, such as biking and walking, also tackle the sustainability problem from multiple angles. Communities with higher levels of active transportation are happier and healthier, and biking and walking can drastically reduce emissions. Even choosing to ride a bike instead of driving once a day can reduce one person’s emissions by as much as 67%. [6]

However, far too few U.S. cities are designed with cyclists and pedestrians in mind. To see more commuters make these choices, planners must make streets safer and routes more direct for active modes of transportation. That means expanding bike lanes, adding sidewalks and crosswalks, and reducing the width of certain streets. Similarly, cities would benefit from development approaches that increase population density and support shorter commutes by placing essential services within biking and walking distance.

Once again, deciding on the best options for such changes requires an in-depth analysis of available traffic data. Understanding current traffic patterns can help planners pinpoint, for instance, where a road diet may help to divert or slow traffic and make room for a new bike lane. Or, as planners did in Portland, Oregon, cities can examine the data on average trip distance to find opportunities for adding safer active transportation options such as pedestrian bridges or strategic walking paths.

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Implement Smart Traffic Management Systems

Not every transportation improvement requires a major overhaul. Some sustainable transportation solutions are simple, especially with the technology and data available today. Smart traffic management systems leverage tools like signal timing, traffic cameras, and automated speed enforcement to improve traffic flow and reduce pollution from congestion and extended travel times. By reducing both extreme speeds and heavy idling, transportation planners can help improve fuel efficiency and lessen overall pollution. [7] These simple tools can also decrease speeding and related accidents, making transportation more sustainable in other important ways. [8]

Transportation planners and agencies can use detailed traffic data to find the best opportunities for these types of smart traffic solutions. For instance, higher traffic counts or average speeds at a specific intersection could warrant camera-enforced radar or improvements in signal timing methods.

smart city traffic light above a snowy intersection — *Smart traffic signals like the one pictured above can offer a cost-effective way to reduce idling time at intersections, while improving safety and travel times.*

Develop Sustainable Freight Transport Solutions

According to the EPA, large trucks are already the fastest-growing contributor to transportation emissions. On top of that, shipments of U.S. goods are expected to increase by 40% by 2040, pushing the growth of emissions from freight ahead of all other transportation categories over the next 15 years. [9] In other words, you can’t address the sustainability of transportation without considering the role of freight transport.

Transitioning more fleets to electric or alternative fuels could substantially increase sustainability in this sector, especially for delivery vehicles that travel fewer than 200 miles per day. [10] Additionally, a significant portion of freight emissions come from poor route planning and wasted miles — areas that can benefit from data analysis around metrics like trip length, travel time, and hours of delay. By making delivery routes more efficient, large companies can offset some of the impact of this growing portion of the transport sector.

Prioritize Sustainable Urban Planning

Cities account for between 60% and 80% of the world’s energy consumption and 75% of emissions, despite only occupying about 3% of its land. [11] Transportation may be one of the biggest contributors to urban emissions and energy usage, but it’s by no means the only one.

That said, transportation is linked to numerous other aspects of urban planning, and connecting transport and sustainability ultimately requires a broader approach to all aspects of city planning. For instance, transportation planners can think beyond the mechanics of roadways to consider how adding green spaces to urban corridors can enhance quality of life and improve air quality by adding more trees.

Likewise, urban planners must dive into transportation data to understand how a new development would affect traffic patterns and potentially help or harm larger sustainable development goals. Clear data on the traffic volume in a given corridor, for example, may help planners decide whether a road diet will ease or exacerbate traffic and pollution.

What Is the Future of Sustainable Transportation?

Setting the stage for greener transport is a critical component of the environmental movement. Without sustainable transportation, it’s difficult to imagine a sustainable future.

The good news is that there are several ways to make the transport sector more sustainable. And with big data at the center, planners can ensure these efforts achieve their maximum impact by making informed decisions about which initiatives are most urgent for their region.

Big data platforms like StreetLight InSight® provide valuable, actionable data that planners can use to evaluate vehicle emissions and understand how infrastructure changes could transform their area. With access to detailed metrics on origin and destination, vehicle volume, speed, turning movements, and more, you can look at potential solutions from every angle, both in terms of safety and sustainability.

In the video below, you can see how transportation professionals in Maine used these kinds of metrics to measure regional transportation emissions and equip cities and counties with data to inform local emissions reduction efforts.

To learn more about StreetLight’s methodology for measuring GHG emissions, and how StreetLight InSight® can help you capture this key data, check out our recent white paper.

And to see where your city ranks on eight major factors that contribute to transportation emissions, download our free eBook, Transportation Climate Impact Index: How the top 100 U.S. metros rank on core emissions factors.

U.S. Environmental Protection Agency. “Fast Facts on Transportation Greenhouse Gas Emissions.” https://www.epa.gov/greenvehicles/fast-facts-transportation-greenhouse-gas-emissions
Alternative Fuels Data Center. “Emissions from Electric Vehicles.” https://afdc.energy.gov/vehicles/electric-emissions
U.S. Energy Information Administration. “Electric vehicles and hybrids surpass 16% of total 2023 U.S. light-duty vehicle sales.” https://www.eia.gov/todayinenergy/detail.php?id=61344
University of Minnesota Transportation Futures Project. “Alternative Fuels & Vehicle Electrification.” https://www.minnesotago.org/application/files/5614/6376/6119/AlternativeFuels.pdf
World Resources Institute. “3 Ways to Reimagine Public Transport for People and the Climate.” https://www.wri.org/insights/3-ways-reimagine-public-transport-people-and-climate
UCLA. “How Riding A Bike Benefits the Environment.” https://www.wri.org/insights/3-ways-reimagine-public-transport-people-and-climate
Northeast Ohio Areawide Coordinating Agency. “Impacts of Idling.” https://www.noaca.org/regional-planning/air-quality-planning/transportation-emissions/impacts-of-idling
U.S. Department of Transportation. “ITS Fast Facts.” https://www.its.dot.gov/resources/fastfacts.htm
U.S. Environmental Protection Agency. “Why Freight Matters to Supply Chain Sustainability.” https://www.epa.gov/smartway/why-freight-matters-supply-chain-sustainability
Alternative Fuels Data Center. “Electric Vehicles for Fleets.” https://afdc.energy.gov/vehicles/electric-fleets
United Nations Foundation. “5 Statistics on Why Urban Development Matters.” https://unfoundation.org/blog/post/5-statistics-on-why-sustainable-urban-development-matters/

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How Can We Reduce Emissions From Urban Transportation?

Emissions of greenhouse gases (GHGs) and other pollutants are a pressing concern for environmental and human health. Despite recent declines in the U.S., the global level of emissions remains at historic levels, leading to alarm among public health advocates and climate change activists alike. [1]

Although there are many factors behind these historically high emissions levels, none is as significant as transportation. In the U.S., the transportation sector accounts for 29% of all GHG emissions, ahead of even electricity generation and industry. [2] These emissions are highly concentrated in urban areas. According to the United Nations, 60% of GHGs come from cities, where cars and other modes of transportation relying on Internal Combustion Engines (ICE) are especially prominent. [3]

This makes urban transportation a strategic target for reducing emissions and curbing their impact on the environment and public health. The good news is that more than 10,000 cities have already committed to reducing carbon emissions by 2050. [4] Still, if history is any indication, curbing transportation emissions is easier said than done. Ultimately, city planners, transportation agencies, and many other stakeholders must come together with a strategic plan.

What will it take to reduce emissions from transportation, and just how important is this task? In this article, we’ll explore:

The cost of urban emissions
Shifting the urban transportation paradigm
Picturing the future with big data

The Cost of Urban Emission

Emissions are more than a nuisance — they exact a heavy toll on the global economy. In the U.S. alone, pollution accounts for around 5% of the nation’s gross domestic product in damages each year, or $1.3 trillion in 2023. More than mere dollars and cents, however, the costs of pollution are particularly prominent in terms of public and environmental health. [5]

Damaging Public Health

By any estimation, pollution is a serious public health concern. According to one in-depth study, fine particulate matter from numerous toxic pollutants contributes to between 100,000 and 200,000 deaths in the U.S. each year. The transportation sector is responsible for the second-largest number of these deaths, behind only industrial and commercial activity. [6]

No matter who is involved, such a large number of deaths is tragic. Yet, the tragedy is made worse by inequity, as pollution disproportionately impacts already vulnerable Americans. Children, pregnant people, older adults, people of color, and those living in poverty are among those most at risk for adverse outcomes from pollution. [7]

smog over NYC — *Transportation emissions are a primary source of city smog impacting residents’ health.*

Accelerated Climate Change

GHG emissions are the single largest contributor to climate change since the mid-20th century. [8] Research has connected emissions from human activity to a host of environmental events, including temperature extremes, surges in precipitation, more frequent droughts and wildfires, and more devastating weather patterns.

The risk of these events continues to grow, and the Intergovernmental Panel on Climate Change (IPCC) warns of serious peril for major ecosystems if global temperatures aren’t brought under control. If global averages reach temperatures of at least 1.5 degrees Celsius above pre-industrial levels, the effect on human, plant, and animal life may be irreversible, even catastrophic. [9]

Shifting the Urban Transportation Paradigm

In light of such devastating consequences, reducing carbon emissions is becoming a top priority for many involved in public policy and planning. Urban transportation represents an important target for these changes, as small adjustments in this sector could have an outsized impact on reducing pollution.

Realizing these outcomes requires three critical shifts in how we approach transportation in urban areas.

Move People First, Not Cars

The first and most important step in reducing urban transportation emissions is to shift away from a car-centric approach to transportation planning. The purpose of any type of transportation is to move people from one place to another, but many of our cities focus on moving cars.

Instead of merely building more and wider roads designed only for vehicles, planners can focus on building complete streets — ones that make room for all kinds of commuters, including pedestrians, bikers, and users of public transit. Centering multimodal transportation will help incentivize and enable more commuters to use these alternative methods.

Reducing reliance on household vehicles could have a substantial effect on urban emissions. According to the United Nations, each person who switches from cars to public transport could reduce their carbon emissions by up to 2.2 tons per year. [10] Another study shows that while public transit cuts GHG emissions by 58% compared to cars, cycling lowers them by 98% — meaning both offer substantial emissions reduction potential. [11]

The more transportation planners can leverage detailed data to inform their plans for new or updated roads, the more effective these changes can be. For instance, planners in Oregon’s largest special park district, the Tualatin Hills Park & Recreation District, were able to use detailed origin-destination data to confirm the value and potential impact of installing a bike-pedestrian bridge to move more commuters over a busy highway — without adding more car traffic.

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Emphasize Electric

Although it’s possible to reduce emissions and other urban transportation problems by shifting the focus away from vehicles, it’s not feasible to entirely eliminate the need for cars in our cities. Where they are still needed, then, it’s critical to accelerate the move toward electric vehicles (EVs) and away from gas-powered vehicles.

One recent study showed that adopting EVs would reduce carbon emissions significantly in every state. In states like Washington or Vermont, which already rely on clean electricity sources, EV usage could reduce pollution from carbon emissions by more than 90%. Even in states like Kentucky and West Virginia, where electricity generation relies heavily on fossil fuels, emissions would drop by over 30% with a full transition to EVs. [12]

With more federal support for the EV initiative than ever, now is an ideal time for cities to encourage drivers to increase fuel efficiency and electrify their driving. In addition to the National Electric Vehicle Infrastructure (NEVI) grant program introduced by the Bipartisan Infrastructure Law (BIL), massive federal tax credits are also available for EVs and chargers, and many cities and states are taking a step further by providing credits of their own or encouraging utility companies to create rebate programs and other incentives. Cities themselves can also leverage such programs to expand public charging installations and electrify public transit.

*Public EV chargers in cities help overcome barriers to accelerated EV adoption.*

Rethink City Planning

As essential as investments in electrification and multimodal transportation are to reducing carbon emissions, they aren’t sufficient solutions to the problem. Urban planners must think bigger, considering land use, transportation, operations, policy, and more in a comprehensive approach to emissions-reducing city planning.

With a holistic view, city planners can make progress by focusing on initiatives such as investing in green buildings, expanding renewable energy production, and improving waste management. [13] They can also consider the best ways to invest in tomorrow’s transportation infrastructure.

This requires thoughtfulness and intentionality. The BIL provides historic levels of funding for cities to upgrade their transportation infrastructure, but studies show that these investments could actually lead to increased emissions if not used properly. For instance, the Georgetown Climate Center recommends that planners focus on a “fix it first approach” of maintaining existing roads and investing in public transit, EVs, and other low-carbon options — rather than building more roads or expanding existing ones, which could induce demand and bring more pollution. [14]

Again, choosing the right updates and planning initiatives requires access to extensive data, both in terms of transportation patterns and existing urban emissions levels. Only when properly informed can planners choose initiatives that will result in successful emissions reductions.

Picturing the Future With Big Data

At every turn in the fight against carbon emissions, data is critical for making informed, effective decisions. In transportation, planners must have access to a wide range of emissions-related metrics, such as:

Vehicle Miles Traveled (VMT)
Annual Average Daily Traffic (AADT)
Vehicle Hours of Delay (VHD)
Origin-Destination (O-D) and routing patterns
Average trip speed and duration
Electric vehicle usage
Changes in walking and biking activity
Truck traffic by vehicle class (light-, medium-, and heavy-duty)

As urbanization continues to transform U.S. cities, this data has never been more critical for the decision-making process. Big data providers like StreetLight are helping to fill data gaps that would otherwise prevent planners from understanding their city’s impact on the climate. That’s how the  Twin Cities Metropolitan Council was able to share critical emissions data with local governments, equipping them with crucial insights for local planning, rather than generic national numbers.

The Southern Maine Planning and Development Commission took a similar approach, using big data to power urban planning that reduces emissions. In the video below, see how they measured statewide VMT to develop regional mitigation strategies.

To learn more about how you can use data to cut emissions and improve your city’s climate impact, download our eBook, Measure & Mitigate: Transportation Climate Data Solutions.

Stanford. “Global carbon emissions from fossil fuels reached record high in 2023.” https://sustainability.stanford.edu/news/global-carbon-emissions-fossil-fuels-reached-record-high-2023
United States Environmental Protection Agency. “Fast Facts on Transportation Greenhouse Gas Emissions.” https://www.epa.gov/greenvehicles/fast-facts-transportation-greenhouse-gas-emissions
United Nations. “Generating power.” https://www.un.org/en/climatechange/climate-solutions/cities-pollution
United Nations. “Seven Ways Cities Can Take Climate Action.” https://unfccc.int/news/seven-ways-cities-can-take-climate-action#
Standford. “How much does air pollution cost the U.S.?” https://sustainability.stanford.edu/news/how-much-does-air-pollution-cost-us
Environmental Science and Technology Letters. “Reducing Mortality from Air Pollution in the United States by Targeting Specific Emission Sources.”  https://pubs.acs.org/doi/10.1021/acs.estlett.0c00424
American Lung Association. “Who is Most Affected by Outdoor Air Pollution?” https://www.lung.org/clean-air/outdoors/who-is-at-risk
United States Environmental Protection Agency. “Climate Change Indicators: Greenhouse Gases.” https://www.epa.gov/climate-indicators/greenhouse-gases
Intergovernmental Panel on Climate Change. “Climate Change 2022: Impacts, Adaptation and Vulnerability.” https://www.ipcc.ch/report/ar6/wg2/
United Nations. “Your guide to climate action: Transport.” https://www.un.org/en/actnow/transport
ScienceDirect. “The climate change mitigation effects of daily active travel in cities.” https://www.sciencedirect.com/science/article/pii/S1361920921000687
Yale Climate Connections. “Electric vehicles reduce carbon pollution in all U.S. states.” https://yaleclimateconnections.org/2023/09/electric-vehicles-reduce-carbon-pollution-in-all-u-s-states/
National League of Cities. “The Top 5 Ways Cities Are Addressing Climate Change.” https://www.nlc.org/article/2022/04/22/the-top-5-ways-cities-are-addressing-climate-change/
Georgetown Climate Center. “Issue Brief: Estimating the Greenhouse Gas Impact of Federal Infrastructure Investments in the IIJA.” https://www.georgetownclimate.org/articles/federal-infrastructure-investment-analysis.html

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The Data Behind How Speed Cameras Curbed Crash Rates on One of Philadelphia’s Most Dangerous Roads

To fix deadly speeding on one of America’s most dangerous roads, Philadelphia installed speed cameras along eight sections of Roosevelt Blvd. Now, before-and-after analysis by StreetLight reveals how effective the strategy really was, and whether it could save lives in other cities.

Roosevelt Boulevard (US Route 1) in Philadelphia, PA has been dubbed one of America’s most dangerous roads. This twelve-lane highway is host to both local and commuter traffic, with at-grade express and local lanes traveling along its length.

Dozens of crashes resulting in severe injury or death occurred on the boulevard in 2020 alone, many of them involving pedestrians. [1] And because Northeast Philadelphia is home to a number of densely populated disadvantaged communities, the victims this boulevard claims are disproportionately people of color, whose communities have long been bisected by these twelve lanes of fast-moving vehicles.

In 2020, the city took measures to address the boulevard’s high crash rates, installing speed cameras along eight sections of Roosevelt Blvd. To understand how these cameras impacted safety conditions on the corridor, StreetLight used its transportation data platform to analyze vehicle speeds before and after the camera installation, looking at data from before COVID, during the pandemic, and as recently as 2024.

Then we investigated where high vehicle speeds persist on the boulevard, revealing potential locations for the next set of traffic calming interventions.

In this article, we’ll explore:

Roosevelt Boulevard’s speed problem
What Philadelphia is doing to reduce speeding
Whether speed cameras made Roosevelt Blvd safer (and how much)
How cities can choose the right traffic calming measures and evaluate their impact

Roosevelt Boulevard’s Speed Problem

At the heart of Roosevelt Blvd’s high crash rates is a history of dangerous vehicle speeds. Although the posted speed limit for much of the highway is 45 miles per hour, residents have complained that drivers on Roosevelt Blvd routinely exceed this limit. In one extreme case in 2013, four pedestrians, including three children, were struck and killed by two motorists traveling over 40mph above the posted speed limit. [2]

Data from PennDOT corroborates residents’ testimony, indicating that prior to 2020, 55% of crashes on the boulevard were attributed to speeding and aggressive driving.

A number of factors make the boulevard’s speed problem particularly deadly. Surrounding the 12-lane freeway, a growing population of Northeast Philadelphians generate significant pedestrian traffic as they access goods and services from the businesses that call Roosevelt Blvd home. Because many of these residents are from Disadvantaged Communities (DAC), they are also less likely to have access to a car, making them reliant on more vulnerable modes of transportation like walking and biking.

In the image below, StreetLight’s Justice 40 map layer highlights in purple the many Disadvantaged Community census tracts that surround Roosevelt Blvd.

Roosevelt Boulevard with Justice40 communities highlighted — *Roosevelt Boulevard (in blue) is flanked by clusters of Disadvantaged Communities (in purple), shown by the StreetLight Insight® Justice 40 map layer.*

Meanwhile, the roadway design has limited infrastructure designed to improve pedestrian safety or slow vehicles, such as pedestrian islands, bulb-outs, or signalized crossings, dramatically increasing the risk pedestrians face on the stroad.

Considering that pedestrians are five times more likely to die from crashes when cars are traveling 40 mph vs. 20 mph, according to data from the AAA Foundation, any vehicle exceeding the boulevards’ posted speed limit of 45 is likely to kill any pedestrian it strikes. [3]

Philadelphia’s Plan to Reduce Speeding

Now for the good news: a number of safety improvement projects are already in the works to address high crash rates on Roosevelt Blvd.

The City of Philadelphia has secured $10 million in state grants from PennDOT to be used on curb extensions, realignments to crosswalks, traffic lanes, and turning lanes, upgraded traffic signals, and other projects. Another $2 million will go toward the planning of future road design improvements as part of the city’s Route for Change program. [4]

While some of these improvements will be completed as far out as 2040, speed cameras offered the city a faster way to curb dangerous vehicle speeds in the short term.

In 2020, the City of Philadelphia, along with the Philadelphia Parking Authority, installed speed cameras along eight particularly dangerous stretches of Roosevelt Blvd to automate speed enforcement and ticket offenders.

Did Speed Cameras Make Roosevelt Blvd Safer?

Initial reports from the city have shown positive impacts from the speed cameras, with a 90% reduction in excessive speeding, a 36% drop in car crashes, and 50% fewer traffic deaths in the first seven months. [5]

How did speed cameras achieve such a dramatic effect, and will they continue to positively impact crash rates on the boulevard beyond their initial install? Furthermore, will the tactic be as effective in other cities, or along other roadways in Philadelphia’s high-injury network? Finally, are additional safety improvements needed to achieve the city’s Vision Zero goals for the boulevard?

To investigate these questions, we used StreetLight’s Network Performance tool to look back in time at speed conditions before cameras were installed, track the changes in average speeds (as well as rates of speeding) after cameras were installed, and follow up on where speeds are at now, in 2024, to identify where additional safety improvements may still be critical.

Establishing a Baseline

To understand how speed cameras impacted speeds on Roosevelt Blvd, we need to look back at speed conditions prior to their installation in June 2020. Because StreetLight’s Network Performance tool offers five years of comparable data, we can go all the way back in time to March 2019 to establish our baseline.

This timeframe is particularly useful as a baseline, because it allows us to look at typical speed conditions before the COVID pandemic disrupted traffic patterns across the country (we’ll look at how COVID impacted speeds in the next section).

To establish our baseline, we’ve chosen to analyze a typical Tuesday during the peak morning commute hours (8-9 a.m.). (Notably, this section of Roosevelt Blvd is relatively uncongested so even during peak hours, speeds are not tamped down significantly due to traffic.)

Roosevelt Blvd Speed Distribution map in 2019 — *A map of average traffic speeds along Roosevelt Blvd. Higher speeds appear in red, while lower speeds appear in green.*

In the map above, we can already see that average speeds exceed the 45 mph speed limit along many segments of the boulevard, and we can see where speeding is at its worst, with segments near Pennypack Park, Northeast Philadelphia Airport, and the Woodhaven Rd (PA-63) interchange standing out.

Roosevelt Blvd speed distribution graph 2019 — Speed distribution by hour of day and day of week in March 2019 on the Southbound express lane over Pennypack Creek. A beige line marks the mean speed, while the 85th percentile speed is shown with a golden line.

In the image above, we zoom in on a segment of Roosevelt where multiple fatal crashes have occurred — the Southbound express lane over Pennypack Creek. Looking at speed distribution by hour of day reveals that at 8 a.m. on an average Tuesday, the mean speed on this segment is 51 mph. Meanwhile, the 85th percentile speed (i.e. the speed that 85% of vehicles on the corridor are travelling at or below), which is commonly used to estimate rates of speeding, is 59 mph.

Bearing in mind that the posted speed limit is 45 mph along most of the boulevard, these figures reveal that speeding was indeed a significant issue in 2019. And that was before the COVID road safety crisis.

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How COVID Worsened Speeding

It has now been well established that as roads emptied out during the March 2020 stay-at-home orders and subsequent rise in remote work and social distancing, vehicle speeds increased. Unsurprisingly, deadly crashes also spiked. This phenomenon is likely due to the relationship between road capacity and vehicle speed — as capacity increases, drivers tend to take advantage of the extra space to speed up. (This is also why road diets, with their reduction of lane capacity, are considered an effective safety intervention.)

Roosevelt Boulevard was no exception. Looking at the same deadly segment of roadway above Pennypack Creek, we can see that rates of speeding increased above their already high levels.

Roosevelt Blvd speed distribution in 2020 — *Speed distribution by hour of day and day of week in March 2020 on the Southbound express lane over Pennypack Creek.*

During the peak a.m. hours, average speeds remain the same compared to 2019 (51 mph), but the 85th percentile speed has increased slightly to 60 mph. We also observe that speeds tend to spike even higher during non-peak hours, especially during the late evening.

To address this crisis, just a few months later, in June 2020, the City of Philadelphia installed speed cameras along eight particularly deadly stretches of Roosevelt.

Slowing Down: How Cameras Curbed Dangerous Speeding

Looking at average and 85th percentile speeds along Roosevelt Blvd in March 2022, our analysis corroborates the city’s initial reports of reduced speeding.

Roosevelt Blvd speed distribution graph in 2022 — *Speed distribution by hour of day and day of week in March 2022 on the Southbound express lane over Pennypack Creek, 21 months after speed cameras were installed.*

After over a year of automated speed enforcement from the new cameras, average and 85th percentile speeds on the Southbound express lane over Pennypack Creek dropped significantly. The mean speed of 46 mph nearly matches the 45 mph posted speed limit. Meanwhile, the 85th percentile speed has been reduced to 52 mph – just 1 mph higher than the mean speed two years prior.

Following Up: Did Reduced Speeds Stick?

A look at recent data from March 2024 can help confirm whether the speed reduction observed in 2022 has continued, and where further safety interventions may still be critical to saving lives.

Roosevelt Blvd speed distribution graph 2020-2024 — Speed cumulative frequency distribution from 2020 to 2024, highlighting the change in speed profile on the Southbound express lane over Pennypack Creek before and after the installation of speed cameras.

In the graph above, we chart speed distributions from each year analyzed (except 2019, which was identical to 2020 above the 30th percentile). The leftward shift highlights that overall speed continued to drop between 2022 and 2024. As of March 2024, a much larger percentage of vehicles are now traveling at or below the posted speed limit of 45 mph.

So has the boulevard’s speed problem been fixed? While rates of dangerous speeding have significantly dropped — and fatal crash rates along with them, according to city reports — some segments of the corridor may still need further intervention.

Roosevelt Speed Distribution map 2024 — *Segments of Roosevelt Blvd. with average traffic speed above 40 mph on a typical Tuesday between 8 a.m. and 9 a.m., March 2024.*

Using the data trimming tool in StreetLight’s Network Performance product, we can zero in on sections of the boulevard where high-speed traffic still poses significant risk to pedestrians. The map above highlights in red the segments with average vehicle speeds above 40 mph during peak morning hours on an average Tuesday.

Although some of these segments have average vehicle speeds that fall below the 45 mph speed limit, we chose to highlight all segments with average speeds above 40 mph because these speeds fall within the range that is particularly deadly for pedestrians, according to the AAA Foundation.

At least nine highway segments of varying lengths emerge as potential candidates for further safety intervention. As we observed in 2019, segments near Pennypack Park, Northeast Philadelphia Airport, and the Woodhaven Rd (PA-63) interchange are among these high-speed areas.

Insights like these could help city officials determine where to prioritize state grant funds slated for additional traffic calming measures along the boulevard.

Spot Speeds on Roosevelt Boulevard

While the bulk of this analysis examines segment speeds, which are derived from a vehicle’s travel time from one end of a roadway segment to another (and the distance between those points), it can also be useful to examine spot speeds at specific locations along a corridor when evaluating potential safety improvements and the success of past projects.

Spot speeds measure a vehicle’s speed at a specific point in time and space, rather than the average speed across a given segment. This means spot speeds are particularly useful when analyzing safety or congestion on smaller roadway segments, such as a single intersection. In our case, they can also help shed light on exactly where drivers slow down and speed up, revealing whether and how quickly drivers speed back up after they’ve passed a speed camera.

In the data viz below, 15 spot speeds taken on a typical Tuesday between 8 and 9 a.m. in March 2024 show vehicles slow down after passing Pennypack Creek as they approach a speed camera located near Strahle Street. Drivers then speed up again as they approach Solly Ave, slowing once more as they approach an intersection with pedestrian crosswalks at Rhawn Street. These granular insights can help cities like Philadelphia determine the most effective safety measures to further the benefits of speed cameras.

spot speed data for Roosevelt Blvd 2024 — *Colored dots show spot speeds along Roosevelt Blvd. near Pennypack Creek on a typical Tuesday between 8 a.m. and 9 a.m., March 2024.*

More About StreetLight’s Network Performance Tool

StreetLight’s Network Performance tool is ideal for before-and-after analyses like this. It offers five years of data comparability so cities can look back in time to understand how roadway conditions have changed over time, including traffic patterns from before COVID, which are often sought out as a baseline to understand “typical” past conditions. They can also analyze the impact of policy interventions to show the public the efficacy of their work.

Since many roadways lack permanent traffic counters (or only recently had counters installed), this ability to access historical traffic data for any road unlocks before-and-after analyses that would otherwise be impossible.

As we’ve demonstrated in our analysis above, agencies can use this Network Performance tool to proactively identify locations with a trend of excessive speeding, particularly where it overlaps with high crash rates, pedestrian/bicycle activity, or Justice40 communities.

With the data trimming option shown in the section above, agencies can easily pinpoint problematic road segments instead of relying on anecdotal observations about excessive speeding, or worse, waiting for the next crash to identify an unsafe traffic pattern. Likewise, this tool offers agencies the ability to monitor the impacts of changes in land use (e.g., new development), infrastructure (e.g. lane additions), traveler behaviors (e.g. work-from-home patterns due to COVID), traffic calming measures (e.g. speed limit reductions or speed cameras), and more.

The ability to analyze both segments speeds and spot speeds also offers added granularity that can be useful in understanding driver behaviors and diagnosing dangerous locations along a roadway.

To learn more about StreetLight’s Network Performance tool, check out our white paper: Network Performance Analysis Methodology and Validation.

And for more ways to implement data-driven safety improvements in your city, download our free eBook: Practitioner’s Guide to Solving Transportation Safety.

Delaware Valley Regional Planning Commission. Crash Statistics for the DVRPC Region. https://www.dvrpc.org/webmaps/crash-data/
CBS News. “Philadelphia’s Roosevelt Blvd. Among most dangerous roads in US” July 10, 2023. https://www.cbsnews.com/philadelphia/video/philadelphias-roosevelt-blvd-among-most-dangerous-roads-in-us/
AAA Foundation for Traffic Safety. “Impact Speed and a Pedestrian’s Risk of Severe Injury or Death.” September 2011.
Michaela Althouse. Philly Voice. “Philadelphia gets $19.3 million for road safety projects from PennDOT, most directed to Roosevelt Boulevard work.” February 3, 2024. https://www.phillyvoice.com/roosevelt-boulevard-traffic-safety-projects-philadelphia-grants-penndot/
Philadelphia Parking Authority. Roosevelt Boulevard Automated Speed Camera Annual Report. April 2023. https://philapark.org/wp-content/uploads/2023-Speed-Camera-Report-Final-32023.pdf

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What is AADT, why does it matter, and how does big data make it more powerful?

As agencies grapple with budget and staff limitations, big data analytics enable officials to overcome gaps in Annual Average Daily Traffic (AADT) data for local streets and low-volume roads. But first, what is AADT, and how does it inform transportation decisions?

In recent years, infrastructure improvement has become a hot topic in the US.

At the center of that interest are our streets, roads, and highways. Road conditions are a key factor in an area’s quality of life, economic dynamism, as well as access to schools, jobs, and healthcare.

Jurisdictions across the US are gearing up to improve their road networks, in part thanks to the federal Bipartisan Infrastructure Law (BIL), also referred to as the Infrastructure Investment and Jobs Act (IIJA), which makes $110 billion available for these improvements.

Behind the scenes, there’s one transport metric that is fundamental to nearly every federal funding request or routine budgeting at the state or local level: Average Annual Daily Traffic (AADT).

So what is AADT and why is it so central to transportation planning and funding? We unpack what AADT is, how it differs from other fundamental roadway metrics, and how measurement is going digital to fill data gaps and add richness to planning and modeling.

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What is AADT?

Annual Average Daily Traffic (AADT) is the transportation industry’s most essential metric for analyzing and forecasting traffic volume. Among other things, it’s used for the planning and design of infrastructure, tracking traffic congestion, estimating road safety, and as an empirical measure to help allocate highway funds.

In its simplest form, AADT takes in all vehicle trips on a segment of road or highway during a yearlong interval, in both directions, and then divides the total by 365 days to arrive at the average number of daily trips.

In addition to infrastructure and transport planning, AADT data is applied in many other contexts as a way to measure traffic flow and allow for “apples-to-apples” comparisons of traffic volume. For example:

Retailers and commercial real-estate professionals consider AADT data when evaluating sites for development and for marketing and pricing properties
Lawyers and law enforcement officials use the metric in accident cases and investigations
Lawmakers use AADT to inform budgeting and prioritization of transport budgets and projects

But AADT is also a simple measure, which flattens away seasonal and weekday variations in traffic patterns. Nonetheless, AADT remains the most widely-referenced benchmark for how busy a road or street is.

AADT vs. ADT

There are two main types of traffic measurement: Annual Average Daily Traffic (AADT) and Average Daily Traffic (ADT).

These measures are starkly different in how they are calculated and applied. They are often confused in casual discussions so it’s important to understand the differences.

As described above, AADT is the total volume of vehicle travel on a road for an entire year, divided by 365.
ADT is the average number of vehicles traveling through a location during a period shorter than a year. For example, it may be applied to a season, or a selected month or week, a short span of days, or a specific day in the year.

AADT is generally used to measure long-term trends or changes in travel demand, while ADT is more useful for short-term planning and operations.

For example, if a city wants to know how many people use a particular bridge to estimate wear-and-tear and traffic loads, it would use AADT. But if that same city needs to know how many cars will cross the bridge during a summer weekday to plan construction, it would use ADT calculated from a sample taken in the target interval.

Both measures have their advantages and disadvantages.

AADT can obscure seasonal variation (e.g., fewer people travel in winter), special events (e.g., increased travel for holidays or road closures within the measurement period), and day-to-day variation in demand (e.g., less travel on weekends). This can sometimes make it difficult to compare year-over-year changes or identify important micro-trends.

ADT can pick up these kinds of fluctuations, but they will not reflect overall demand on a road since by definition they only consider shorter periods. On the other hand, they have a limited lens: they will not reflect in any variations occurring outside of the measurement period.

As discussed below, AADT data is relatively difficult and expensive to collect since it requires real-world data spanning an entire year. Even when using calculation methods that don’t require 365 days of data, the need for accurate and continuous data is still relatively burdensome.

ADT is more forgiving from a measurement standpoint since by definition it requires the counting of traffic during shorter intervals.

How to calculate AADT

To calculate Annual Average Daily Traffic (AADT) in its simplest form as described above, analysts must know total traffic volume on the target road segment for every day in a given year.

This baseline method isn’t simple or cheap to implement since by definition it requires permanent devices known as counters or ATRs (automatic traffic recorders) detecting passing vehicles and collecting complete trips data for 365 continuous days in a year. Even missing a few hours of traffic means the calculation can be thrown off. Not to mention the recorders are not easy or inexpensive to install and maintain. During the COVID-19 lockdown, agencies saw their ability to collect counter data hamstrung by lockdown orders that kept workers off the road.

Even when permanent counters are in place and putting out continuous AADT traffic data, sometimes special events such as roadwork or adverse weather can distort averages with outlier days of abnormally low or high trip counts.

Finally, and most commonly, there are simply too many road segments in any jurisdiction to allow for comprehensive AADT data from permanent counters.

In the video below, Keith Nichols explains why Hampton Roads TPO commonly encounters data gaps with traditional AADT data collection methods, and how the agency supplements traditional methods for long-range transportation planning.

Traditional methods of closing AADT data gaps

Statistical methods, complemented with temporary data collection, are commonly deployed to address common data-collection issues. When officials turn to these methods, they do so knowing they are sacrificing accuracy for savings.

It’s worth understanding how these methods work together, and some of the tradeoffs.

Short-term expansion

One industry-standard method for arriving at Annual Average Daily Traffic (AADT) in the absence of permanent counters is known as “short-term expansion.”

In this method, a road section’s traffic is calculated on the basis of a temporary counter collecting two days or more of data. That incomplete short-term data is then “expanded,” or scaled up, to calculate AADT. To do this, analysts derive scaling “factors” from a nearby permanent traffic counter that has a year’s worth of data.

Ideally, that permanent counter used as a reference has same-year data. But it’s not uncommon for transport analysts to be forced to rely on permanent counter data from past years when calculating AADT using this method.

Obviously, even when the short-term expansion model bakes in some math, this tactic relies on the differences between the target road’s short-term traffic data versus the same-day measurements collected by the nearby permanent counter on a different road.

If these differences are not consistent across the year, i.e. if the short-term data was not taken on representative days, AADT accuracy will be compromised.

In the instances where permanent counters are in place but there are small gaps in the annual data or road closures and other outlier events that may throw off the averages, a separate established method is to limit the number of days for which complete datasets are required.

The AASHTO method

In one industry-recognized approach, officials collect total traffic volume on seven separate days in each month that correspond to the different days of the week. This method leaves planners with 84 days of data to work with that nonetheless will account for variations in traffic across different weekdays and on weekends.

Then, they take an average for each day of the week sampled across the year, giving them seven averages, and then they take the average of those averages for an AADT.

The American Association of State Highway Transportation Officials (AASHTO) has promoted this technique, known as an “average of averages” method.

Other methods

Many other estimation methods can be used depending on what data and counters are available and affordable. In fact, the proliferation of methods adds to the complexity faced by transport planners as they consider approaches for data collection and AADT calculation.

In fact, one in-depth review of the relevant academic literature on AADT estimation identified 30 separate methods just for estimating AADT on low-volume roads.

Adding to the difficulty, these techniques were hardly one-size-fits-all.

“Some AADT estimation techniques are only applicable in specific locations,” write the authors, Edmund Baffoe-Twum and Eric Asa of the West Virginia University Institute of Technology, along with Bright Awuku from North Dakota State University. “Others require significant data to provide accurate estimates. Several processes to adjust models for a location may be needed for other locations.”

Traditional methods vs. traffic analytics

Arguably the biggest change for Annual Average Daily Traffic (AADT) in recent years is the availability of instant up-to-date AADT estimates right from a computer through traffic-analytics providers.

On-demand traffic-analytics platforms reduce the need for expensive and sometimes hazardous fieldwork and are able to fill data gaps whenever they arise.

As a result, jurisdictions have more flexibility in the extent to which they rely on permanent and temporary counters for AADT metrics.

For example, recently StreetLight helped fill gaps in AADT data by the Indian Nations Council of Governments in Tulsa, Oklahoma. Due to budget issues, many jurisdictions in the area had stopped reporting traffic counts to INCOG, a metropolitan planning organization or MPO. In minutes, StreetLight was able to generate traffic counts for all of the untracked road segments.

In the video below, we explore other ways agencies commonly use on-demand traffic analytics platforms to leverage AADT metrics, such as developing crash rates and understanding the traffic impact of road or lane closures.

How do traffic-analytics platforms come up with their AADT measurements?

Typically, traffic analytics rely on connected devices and Internet of Things data, and then layer in parcel data and road-network data for a complete picture. With this data, it’s possible to create analytics that model vehicle trips on a stretch of road in the absence of temporary or permanent counters.

The real technical challenge for deriving AADT from traffic analytics is not just in collecting and organizing the large volume of location data, but in the next steps:

Algorithms are needed to match this raw data to vehicle trips
The dataset, for greater accuracy, is enhanced with additional sources of data such as US Census Data or street-map data to account for changes in demographics and road networks
The resulting AADT model must be tweaked for greater accuracy by testing results against real-world “ground truth” data, which should encompass different road and vehicle types, e.g. heavy trucks

In the case of StreetLight, which provides AADT metrics for 4.5 million miles of roadway in the US and Canada, all these steps were important and detailed in the whitepaper, “AADT 2023: U.S. Methodology and Validation.”

By comparing their own AADT metrics to AADT produced by thousands of permanent counters nationwide, StreetLight was able to determine that their AADT figures fall within a 98% prediction interval for all road types.

Challenges in measuring AADT: local and low-volume roads

AADT measurement is increasingly being shaped by many jurisdictions’ need for more granular and complete coverage of road systems.

What’s driving this demand? The answer is a set of interrelated traffic, demographic, and environmental concerns that are only growing stronger with time.

First, local streets are increasingly seeing overflow traffic from overburdened highways and multi-lane roads.

“Estimating AADT on local streets becomes a necessity as local street traffic continues to grow and the capacity of arterial roads becomes insufficient,” write Peng Chen, Songhua Hu, Qing Shen, Hangfei Lin, and Chie Xie. They are the authors of a 2019 paper on AADT measurements in the Seattle area, published in the Transportation Research Record.

Second, jurisdictions are also newly anxious to track traffic increases on low-volume roads in rural and semi-rural areas. Many of these areas have seen a major influx of short- and long-term visitors and new residents arrive over the last two years.

A 2022 analysis by StreetLight showed how this trend had impacted the resort town of Jackson Hole, Wyoming. The analysis showed that AADT on a section of unpaved road —an access point to Grand Teton National Park — had already significantly surpassed 2019 levels in 2021, even after dipping dramatically during the COVID pandemic.

Annual Average Daily Traffic (AADT) counts for Moose-Wilson Road — *AADT counts for Moose-Wilson Road from StreetLight suggest 2021 traffic volume had already exceeded 2019.*

Thirdly, environmental and road-safety concerns are also behind the need for more comprehensive AADT data. Since a significant proportion, if not a majority, of vehicle miles in a state are driven on these roads, it’s impossible to form a complete picture of emissions or accident trends without it.

Despite the demand, coverage of low-volume and local streets poses a formidable challenge for traditional AADT calculation.

As we’ve seen, it is cost-prohibitive to deploy permanent counters widely on local streets or low-volume roads. Not to mention, rural roads are far-flung and cover many miles of sometimes difficult terrain. Local roads in urban areas are dense and highly varied in traffic patterns, which would mean putting counting stations on virtually every corner.

Many jurisdictions, in the cases where they have the budget to take the measurements at all, turn to temporary counts and estimation methods to measure AADT on these segments.

For these reasons, traffic analytics–based AADT metrics are a cost-effective and simple solution for filling data gaps on local and low-volume roads.

For example, StreetLight’s AADT metrics include urban and rural roads — even unpaved roads, as seen in the case study from Jackson Hole. Analyses covering hundreds of low-volume road segments can be run in minutes.

Annual Average Daily Traffic (AADT) and technology

Traffic-analytics platforms relying on big data approaches are only the latest tech innovations to transform how Annual Average Daily Traffic (AADT) is collected and calculated.

In the 1930s, AADT was based solely on manual counts, which required considerable manpower and intensive fieldwork, according to David Albright in a 1991 article on the history of AADT measurement. Counting devices only began to be used in the 1940s, and became widespread only after a couple of decades, with methods continuing to evolve in the years since.

More recently, computers and algorithms have helped run some of the sophisticated statistical models used by transport engineers and planners for AADT estimation. As discussed, StreetLight itself uses advanced software algorithms to process data, tie it to vehicle trips, and enhance it with U.S. Census and OpenStreetMap data.

While conceptually AADT has remained the same metric over all this time, and surprisingly resilient as a keystone metric in transport planning, technology has completely transformed data collection, calculation, and estimation methods. It’s a good bet that technology, and specifically big data and software-driven algorithms, will continue to drive innovation around AADT in coming years.

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How Advanced Traffic Counts are Powering Better Business Decisions

Understanding traffic patterns is critical when choosing a store location, designing automotive products, or analyzing property values. Now, businesses can get the most up-to-date traffic counts on 2.5x more roads to drive more profitable decisions.

For many businesses, transportation intelligence is critical to making the right decisions. The number of vehicles and pedestrians on a given road — and how they move throughout a region — can dramatically impact how many visits a store gets, where drivers need to fuel up, or if EVs need to charge.

But historically, commercial decisionmakers have had to rely on incomplete and outdated traffic counts for information on where and how potential customers drive.

Now, StreetLight offers Advanced Traffic Counts to inform real estate decisions and products for professionals across industries like:

Commercial real estate
Retail and restaurants
Software
Electric vehicles
New mobility

These counts are updated frequently and cover millions of road segments across the U.S. and Canada, so you can make confident decisions based on the most comprehensive, up-to-date information on travel patterns.

StreetLight Advanced Traffic Counts is designed specifically for the commercial sector, to understand how potential customers move so you can choose the right street corner for a new store, select EV charger locations based on nearby traffic patterns, or develop new products and services based on real customer travel behaviors.

Here’s how it works.

StreetLight Advanced Traffic Counts offer pre-processed traffic volumes for over 25 million road segments, totaling over five million miles in the U.S. and Canada including motorways and trunk roads, arterials, and on/off ramps for comprehensive coverage. But it doesn’t end with traffic volumes. This data can be further contextualized with additional details like trip characteristics and historical demographics so you can make much more nuanced business decisions.

StreetLight Advanced Traffic Counts offer pre-processed traffic volumes for over 25 million road segments, totaling over five million miles in the U.S. and Canada including motorways and trunk roads, arterials, on/off ramps, and residential streets for comprehensive coverage.

coverage of advanced traffic counts in Chicago, Illinois

A visualization of traffic counts in the broader Chicago metropolitan area. StreetLight’s extensive coverage provides traffic counts for millions of road segments, enabling more granular location intelligence to power the most profitable business decisions.

This means that as you begin to identify the next corner for your coffee shop, convenience store, or electric vehicle (EV) charger location, you can take into consideration factors like vehicle traffic, trip purpose (e.g. home-to-office vs. non-commute trips), and the overall demographics (e.g. income, education, family status, and more) of travelers passing by your potential location.

Likewise, these same factors can inform portfolio management for existing commercial real estate locations, diagnosing why some locations perform better than others, and where certain locations should be closed, where open hours should be extended or shortened, or where downsizing or expansion would help maximize overall revenue.

Market research firms and consultancies also benefit from these same insights when advising clients on commercial real estate decisions.

visualization of traffic counts in Chicago, zoomed in

A zoomed-in view of Chicago traffic counts highlights roadways with the most trips (seen in dark red) and the least trips (in yellow).

How StreetLight’s Traffic Counts Support Better Site Selection and Operations

Because of the extensive coverage of our traffic counts, and the ability to filter counts by time of day and day of week in the relevant month or year, retailers, real estate professionals, investors, and other customers can now more easily identify promising store locations along nearly any roadway in the U.S. or Canada.

Importantly, businesses that rely on foot traffic (such as retail and restaurants) can also view historical pedestrian traffic counts to understand where high foot traffic will translate into more sales, while businesses that rarely benefit from foot traffic (such as car washes) can narrow their focus to vehicle traffic counts exclusively.

Importantly, businesses that rely on foot traffic (such as retail and restaurants) can also view historical pedestrian traffic counts to understand where high foot traffic will translate into more sales, while businesses that rarely benefit from foot traffic (such as car washes) can narrow their focus to vehicle traffic counts exclusively.

These same insights can also be used to forecast sales at new and existing stores. Portfolio managers can now understand where traffic is most likely to drive store visits and sales. Using traffic counts by time of day and day of week, they can also determine if store hours or staff operations should be shifted to capture demand when it is highest.

Trip and Traveler Attributes Help Determine Where Demand is High

Traffic counts are just the tip of the iceberg when it comes to site selection, research and development, and more. That’s why StreetLight Advanced Traffic Counts also include trip speeds, trip distance, and historical trip purposes and traveler demographics to further inform important decisions impacting your business.

Visualization of traffic volume with trip and traveler characteristics for two locations

For example, a commercial real estate professional evaluating potential locations for a new coffee shop could zero in on road segments that morning commuters take on their way to the office. Similarly, someone looking to open up a new location for a budget-friendly grocery store chain could pinpoint road segments that are frequently used by travelers with lower household incomes.

Likewise, adding the context of trip characteristics can further inform commercial real estate decisions. For example, traffic volume may not tell the full story for brands who rely on drivers traveling slowly enough to read their signage and turn into their location.

Luckily, with StreetLight Advanced Traffic Counts, these customers can take trip characteristics like speed into consideration to ensure the traffic at the site they select isn’t speeding by too quickly to bring in business.

Likewise, trip characteristics like direction of travel can inform which street corner or side of the road is most advantageous for a new store.

Ensuring Reliable Data for Confident Business Decisions

We’ve already discussed how StreetLight Advanced Traffic Counts allow commercial customers to access recent and historical data for a full-picture view of mobility patterns impacting their business. But how reliable are the metrics we provide?

Our data validation processes for StreetLight Advanced Traffic Counts follow the same trusted methodology used for StreetLight InSight®, the most-adopted big data platform for transportation. Metrics are validated against permanent traffic counters and by transportation industry professionals.

Every month, we ingest, index, and process vast amounts of location data from connected devices and the Internet of Things, then add context from numerous other sources like parcel data and digital road network data to develop a view into North America’s vast network of roads, bike lanes, and sidewalks.

This data is then delivered in bulk through either a data file (such as a CSV) or via API, to integrate seamlessly into your existing data analysis platforms.

To understand how your customers move and get more information on the travel patterns impacting your business, click the banner below to get started.

What Is a Smart City?

Smart cities use innovative technologies to improve operations, expand access to goods and services, and get things done more efficiently. At the center of it all, transportation data is changing how cities make decisions that impact how people move.

smart city aerial view - NYC lights at night

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Today, nearly everything has become “smart,” from phones to fridges, doorbells, and cars. And cities are no exception. Across the globe, cities large and small are adopting new technologies to improve quality of life, automate essential services, and integrate with the digital world.

So how exactly are “smart city” technologies changing how we live? And what role does transportation data play in making our cities “smarter”?

In this article, we’ll discuss:

What is a smart city?
Smart city technologies in use today
How smart cities use transportation data
Real-world examples of smart city transportation projects

What Is a Smart City?

A smart city is a city that uses data to optimize operations, planning, and governance. Data collected from residents and visitors, electronic devices and systems, and the broader Internet of Things is analyzed to understand patterns, monitor how things are going, communicate with constituents, and more.

If this all sounds incredibly broad, it is. Most cities today are smart cities, because data collection and analysis have become ingrained in just about everything we do.

Still, the extent and sophistication of data usage varies greatly from city to city. Below, we’ll explore some of the common ways smart cities are using this data.

*Ride hailing apps for taxis, microtransit, and rideshare services are common in today’s smart cities.*

Smart City Technologies

There are many types of smart cities technologies in use today. Some common examples include:

Real-Time Transit Tracking – GPS data from buses, subway cars, etc. are used to calculate wait times for public transit, communicated via mobile apps or station signage.
Energy & Utilities Optimization – usage data for electric grids, water, and more can be monitored and in some cases automatically adjusted to prevent service disruptions, detect leaks, anticipate the need for infrastructure improvements, or aid conservation efforts.
Smart Lighting – light detection technology can be used to automatically turn on overhead lights for roadways, parking lots, and more during poor lighting conditions (not just at nighttime).
Smart Traffic Signals – smart traffic lights use real-time data on the volume and speeds of vehicles approaching an intersection to automatically adjust the timing of signals to reduce idling time, improve traffic flow, or prevent unsafe conditions.
Air Quality Monitoring – sensors may be deployed throughout the city to monitor air quality, detect potential sources of pollution, communicate current conditions with the public, and inform air quality improvement efforts.

These and other technologies are becoming more and more common in cities across the globe. But one of the most frequently used forms of smart city technology is transportation intelligence. Below, we’ll discuss some of the key ways smart cities are using transportation data to improve mobility, air quality, climate impact, and more.

See how data can help you fix traffic jams, speeding, and more

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How Smart Cities Use Transportation Data

Data on how people and vehicles move can help cities plan roadway infrastructure improvements, make land-use decisions, plan for special events, reduce traffic fatalities, monitor public health, boost economic activity, reduce commute times, mitigate greenhouse gas emissions, and so much more.

In many cases, cities will install physical sensors on the road that capture data on vehicle volumes, speeds, turning movements, and more. But these sensors are too expensive to install on every roadway, and they may not capture non-vehicle traffic such as bikes and pedestrians.

To get more comprehensive and cost-effective insights on how people move, cities often supplement physical roadway sensors with big data analytics. Big data platforms like StreetLight collect, process, anonymize, and validate massive numbers of datapoints from connected devices and the Internet of Things to make precise inferences about how people move.

Data privacy assurances are crucial when collecting big data on mobility. That’s why StreetLight’s Route Science® algorithm anonymizes and aggregates all datapoints, to ensure individuals can never be identified or tracked.

In addition to smart traffic signals (mentioned above), which typically use data on vehicle volumes and speeds, there are many other ways cities are using transportation data. Common examples include:

Vehicle volumes to fix traffic jams, ensure smooth traffic flow during special events, and prioritize infrastructure investments
Speed data to understand where congestion or unsafe speeds are impacting mobility for residents and visitors
Turning movement counts to help time traffic signals
Origin-Destination and Routing patterns to measure tourism and plan detours, bus routes, or new EV charger locations
Annual Average Daily Traffic (AADT) counts to anticipate the need for repairs and inform safety planning
Bike and pedestrian activity to figure out where to install bike lanes, implement traffic calming measures, or add crosswalks
Truck activity and routing data to move goods efficiently, highlight emissions hotspots, or reduce dangerous cut-through traffic in residential areas

Real-World Examples of Smart City Transportation Projects

Cities across the globe are using transportation data to make smarter decisions. Here are just a few examples of smart city transportation projects in the U.S. and Canada.

Temecula Retimes Traffic Signals

After securing safety funds for new smart signals, Temecula, California needed intersection data to understand which signals were most in need of synchronization.

They used StreetLight’s turning movement counts to diagnose congestion across 40 intersections within a few hours, saving at least three months on data collection and analysis, and deploying funds effectively to optimize signal timing.

Vancouver Makes the Case for Bus Infrastructure

Multimodal transportation options help reduce congestion, improve climate impact, and ensure equitable access to essential goods and services. But with many transit agencies facing exceptionally tight budgets, proving the value of bus infrastructure investments is crucial to getting improvements implemented.

In Vancouver, city analysts used big data to reveal that bus mode share was much higher than previously understood, helping them gain political buy-in for new bus lanes and rapid lines to provide commuters accessible alternatives to personal vehicle use.

Richmond Measures Equitable Access to Multimodal Transportation

Transportation equity is gaining more attention, with grant programs like Reconnecting Communities and Safe Streets and Roads for All incentivizing projects that expand equitable access. But getting reliable measurements of transportation access is often a barrier to diagnosing and addressing existing inequities impacting city residents, especially among communities of color, low-income households, and people with disabilities.

In the video below, Alex Bell of Renaissance Planning explains how data from StreetLight helped city planners for Richmond, Virginia develop measures of multimodal accessibility and understand who was most underserved by existing infrastructure. This analysis revealed where infrastructure investments were most critical to improve transportation equity throughout the city.

To learn more about how big data transportation analytics can make your city smarter, download our free eBook, Any Road, Any Mode: Your Guide to the Transportation Data Revolution.

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How Can a Road Diet Improve Safety for Everyone on the Road?

road diet blog header - new york street with center turn , bike lane, and parking

America’s roads just keep getting wider. According to a Washington Post report, over a third of U.S. states spent more than $19 billion on expanding existing highways and road infrastructure in 2019. [1]

It’s a common way to attempt to curb the traffic congestion plaguing U.S. cities, but many transportation buffs say it’s the wrong approach. In fact, America’s road safety troubles may be directly related to its bloated urban corridors. The data appears to indicate that, in many cases, our roads don’t need to be fed more pavement — they need a diet.

The Federal Highway Administration (FHWA) points out that undivided four-lane roads — so ubiquitous in American cities — play host to a wide range of dangerous collisions, from lane-changing side swipes to bike and pedestrian crashes. These crashes are directly related to the volume and speed of traffic, but they’re also tied to the width and layout of the road. And, while it may seem counterintuitive, the path to increased safety without worsening congestion often lies in reducing lanes, rather than adding them, through a method called road diets. [2]

In this article, we’ll explore how road diets work to increase safety not only for vehicles, but also for pedestrians and cyclists. We’ll tackle the following:

What is a road diet?
Key safety considerations
How effective is a road diet?
Considering the alternatives
Finding the best approach with big data

So, What Is a Road Diet?

A road diet, or lane reduction, is a type of traffic calming measure designed to reduce speed and traffic congestion on an existing undivided roadway. Instead of expanding the existing road width with additional lanes, planners reduce the number of standard traffic lanes to make room for other types of lanes or road features.

Road diets are most common on four-lane highways. Typically, planners exchange the existing four driving lanes for a center left-turn lane flanked by two standard driving lanes and bike or pedestrian lanes. Other road diet examples include replacing two lanes with a tree-lined median or adding parking on the sides and narrowing existing lanes.

VDOT road diet example — *Photo source: Virginia DOT, reprinted in the U.S. DOT’s Road Diet Informational Guide*

Whatever the specific approach, the overarching goal of a road diet is simple: to lower traffic speed and thus reduce the number and severity of crashes. In the process, this measure can also enhance the overall quality of life and appeal of an urban area by making more room for cyclists and pedestrians, expanding common areas, and even adding more green space to popular thoroughfares.

Key Safety Considerations

Understanding the value of road diets first requires an appreciation of just how threatening many of our busy four-lane, undivided roadways are. These popular road layouts are riddled with safety concerns for drivers, passengers, pedestrians, and cyclists alike. Before making any change to an established roadway, it’s essential to understand the dangers of the existing layout.

For Vehicles

In urban and suburban areas, four-lane, undivided highways are hot zones for vehicle-to-vehicle crashes. There are simply too many potential collision points for drivers to track. Such corridors are commonly home to all sorts of collisions:

Rear-end collisions from sudden stops or vehicles waiting to turn
Sideswipe crashes during lane changes
T-bone or angle collisions from cars turning left across oncoming traffic
Multi-car crashes at intersections

These collisions occur due to a range of factors, many of which can be addressed through road diets. High speed differential between lanes or intersecting streets can lead to sudden stops or miscalculations that result in collisions. Left turns across multiple lanes are also inherently dangerous. [2]

The specific traffic patterns and risks vary by location. When assessing the potential benefits of lane reductions, planners need data that allows them to analyze metrics like average speed, speed differentials, average traffic volumes, and typical turn counts at intersections.

For Pedestrians

As problematic as four-lane roads are for vehicle-to-vehicle crashes, they’re even more hazardous for pedestrians. Drivers hit and killed more pedestrians in 2022 than in any year since 1981, and these types of roads are at the center of many such collisions. [3] As one recent study revealed, 97% of the hot spots for pedestrian deaths in the U.S. are multi-lane roads. [4]

pedestrians crossing multilane highway — *Pedestrians cross a multilane roadway.*

Crashes on such roads occur for many reasons, from a lack of sidewalks to pedestrians misjudging oncoming vehicles when crossing multiple lanes. However, the central problem on wide urban streets is vehicle speed. Studies show that pedestrians are five times more likely to die from crashes when cars are traveling at 40 miles an hour than at 20 miles an hour.

Here again, data is crucial for determining the right solution. Understanding vehicle speeds, the volume of pedestrian traffic, and the frequency of pedestrian collisions can help planners assess whether a road diet — and which type — would provide an effective solution.

For Cyclists

According to the National Safety Council, more than 850 cyclists died in collisions with vehicles in 2021. [5] Many of these incidents were linked to unsafe road conditions. [6] The FHWA reports that the simple addition of a bike lane could reduce bike crashes by 49% on four-lane roads. [7]

Beyond being an argument for bike lane additions, this is a strong point in favor of considering road diets on cyclist-heavy city roadways. Road diets are designed to reduce traffic speed, and they can make room for more extensive road modifications such as protected bike lanes, which are physically separated from vehicle traffic.

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How Effective Is a Road Diet?

Overall, the data shows that road diets are often highly effective at increasing safety. Based on broad studies, the FHWA states that road diets can reduce crashes by between 19% and 47%. [2] In Seattle, a 2008 road diet reduced pedestrian collisions by 80%. The same project saw a 35% increase in cyclist traffic without any increase in bicycle-involved crashes.

To project the impact in a specific area, planners must account for various factors, particularly existing traffic volume. Lane reductions tend to be most effective on roads with between 8,000 and 20,000 average daily vehicles. [8]

Despite these benefits, many people are skeptical of road diets. In Los Angeles, the firefighters union recently launched a campaign against proposed road diets, claiming they would hinder emergency vehicles by increasing congestion. [9] Others argue they would slow down public transportation or simply divert heavy traffic elsewhere. [8]

Broadly speaking, the evidence to support these claims is slim. Although lane-reduction projects occasionally get pulled back, research shows that these road diet failures often occur due to a disconnection between public perception and reality. In other words — the project accomplishes its goal, even if the public thinks it’s not working.

That’s not to say these tactics are the right solution for every safety or traffic congestion problem. However, the evidence does indicate that when planners closely evaluate the many variables of a situation and decide to implement a road diet, the resulting safety enhancements are usually significant. [10]

Finding the Best Approach With Big Data

As noted, road diet success is much more likely with a deep analysis of traffic patterns. Understanding metrics like turning movement counts, average daily traffic volume and speed, speed differentials, and stopping frequency is essential for an accurate assessment of any potential project.

Big data platforms like StreetLight InSight® can expedite data collection when evaluating a potential road diet, providing detailed visualizations of traffic patterns and trends to help planners conduct a thorough road safety audit before making any decisions. This approach also makes the data collection itself safer because agency staff need not put themselves at risk to take manual counts along dangerous roadways. Furthermore, big data can provide a more complete view of roadway conditions than manual counts, which are usually limited to a small snapshot of traffic during peak driving hours on a particular day or a few days.

Many agencies are already using big data to evaluate potential road diets, as well as the success of past road diets. As the video below explains, Maine’s Department of Transportation used Streetlight tools to conduct a detailed analysis of its Bangor Street road diet proposal, filling in critical data gaps to understand turning movement counts, side-road traffic, and complex roundabout traffic flows.

Similarly, a StreetLight analysis of roadway capacity, vehicle speeds, routing patterns, and pedestrian activity suggested that a road diet on Oakland’s Grand Avenue could help address safety concerns without causing major congestion.

potential road diet locaiton on Grand Avenue in Oakland — *Grand Avenue, part of Oakland’s High Injury Network, could be a good candidate for a road diet treatment.*

After implementing a road diet, big data transportation analytics also allow planners to easily measure any changes in traffic volumes, routing, safety, and critical metrics like Vehicle Hours of Delay (VHD). These insights simplify the process of assessing project potential and outcomes so planners can do their work more effectively.

For more information on how big data can help you find the best safety solutions for your streets, check out our Safety Data Handbook.

The Washington Post. “Infrastructure plan calls for fixing the nation’s existing roads. Some states are still focused on expansion.” https://www.washingtonpost.com/transportation/2021/05/23/highway-funding-infrastructure/
U.S. Federal Highway Administration. “Road Diet Informational Guide.” https://safety.fhwa.dot.gov/road_diets/guidance/info_guide/ch1.cfm
Governors Highway Safety Administration. “Pedestrian Traffic Fatalities by State: 2022 Preliminary Data.” https://www.ghsa.org/resources/Pedestrians23
Journal of Transport and Land Use. “United States fatal pedestrian crash hot spot locations and characteristics.” https://jtlu.org/index.php/jtlu/article/view/1825
National Safety Council. “Bicycle Deaths.” https://injuryfacts.nsc.org/home-and-community/safety-topics/bicycle-deaths/#:~:text=In%202021%2C%20most%20deaths%20occurred,for%20Health%20Statistics%20mortality%20data
NPR. “More cyclists are being killed by cars. Advocates say U.S. streets are the problem.” https://www.npr.org/2022/05/25/1099566472/more-cyclists-are-being-killed-by-cars-advocates-say-u-s-streets-are-the-problem
U.S. Federal Highway Administration. “Bicycle Lanes.” https://highways.dot.gov/safety/proven-safety-countermeasures/bicycle-lanes
AARP. “Road Diets: A Livability Fact Sheet.” https://www.aarp.org/content/dam/aarp/livable-communities/livable-documents/documents-2014/Livability%20Fact%20Sheets/Road-Diets-Fact-Sheet.pdf
Los Angeles Times. “Firefighters launch campaign against Measure HLA, saying ‘road diets’ threaten safety.” https://www.latimes.com/california/story/2024-02-14/firefighters-launch-campaign-against-measure-hla
Scientific Research. “A Comprehensive Study of a Road Diet Implementation in the US and Abroad.” https://www.scirp.org/journal/paperinformation?paperid=127799#:~:text=The%20road%20diets%20were%20implemented,were%20implemented%20across%2066%20projects

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6 Types of Transportation Big Data Every City Needs

6 Types of Transportation Big Data Every City Needs

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1. Annual Average Daily Traffic

Test-drive StreetLight's AADT metrics

2. Origin-Destination

3. Turning Movement Counts

4. Vehicle Miles Traveled

5. Vehicle Hours of Delay

6. Vehicle Speeds

Accessing the Data You Need, When You Need It

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How Should We Build EV-Charging Infrastructure?

How Should We Build EV-Charging Infrastructure?

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How Do EV Charging Stations Work?

The Current State of EV Infrastructure

See what emissions reduction tactics your city needs most

Optimizing Charger Placement in Communities

Promoting Sustainable Energy Sources

Get Charged Up With Big Data

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Public Data from NYCDOT Validates the Reliability of StreetLight’s Speed Metrics

Public Data from NYCDOT Validates the Reliability of StreetLight’s Speed Metrics

Access to accurate vehicle speed data is critical for effective road safety interventions, congestion mitigation, and more. We compared StreetLight’s speed metrics to data from New York City’s OpenData portal to ensure we’re delivering the most reliable insights.

How NYCDOT Collects Speed Data

How StreetLight Collects Speed Data

Comparing the Data: How Accurate Are StreetLight’s Speed Metrics?

More about StreetLight’s Vehicle Speed Data – Segment Speed and Spot Speeds

6 Ways to Drive Sustainability in Transportation

6 Ways to Drive Sustainability in Transportation

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Promote Electric and Hybrid Vehicles

Invest in Public Transportation Infrastructure

Support Active Transportation Modes

See what emissions reduction tactics your city needs most

Implement Smart Traffic Management Systems

Develop Sustainable Freight Transport Solutions

Prioritize Sustainable Urban Planning

What Is the Future of Sustainable Transportation?

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How Can We Reduce Emissions From Urban Transportation?

How Can We Reduce Emissions From Urban Transportation?

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The Cost of Urban Emission

Damaging Public Health

Accelerated Climate Change

Shifting the Urban Transportation Paradigm

Move People First, Not Cars

See what emissions reduction tactics your city needs most

Emphasize Electric

Rethink City Planning

Picturing the Future With Big Data

See how StreetLight's transportation analytics can help you improve mobility outcomes

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The Data Behind How Speed Cameras Curbed Crash Rates on One of Philadelphia’s Most Dangerous Roads

The Data Behind How Speed Cameras Curbed Crash Rates on One of Philadelphia’s Most Dangerous Roads

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Roosevelt Boulevard’s Speed Problem

Philadelphia’s Plan to Reduce Speeding

Did Speed Cameras Make Roosevelt Blvd Safer?

Establishing a Baseline

See how dangerous traffic speeds impact walking and biking in your region

How COVID Worsened Speeding

Slowing Down: How Cameras Curbed Dangerous Speeding

Following Up: Did Reduced Speeds Stick?

Spot Speeds on Roosevelt Boulevard

More About StreetLight’s Network Performance Tool

Investigate local traffic trends with a free trial of StreetLight InSight®

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What is AADT, why does it matter, and how does big data make it more powerful?

What is AADT, why does it matter, and how does big data make it more powerful?

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AADT 2023 is here! Get access to traffic counts and stay ahead of reporting deadlines.

What is AADT?

AADT vs. ADT

How to calculate AADT

Traditional methods of closing AADT data gaps