Before and After Congestion Pricing: From Staten Island to NJ to Manhattan, How Travel Times Are Changing
ANALYSIS
Before and After Congestion Pricing: From Staten Island to NJ to Manhattan, How Travel Times Are Changing
Is NYC’s congestion pricing working? StreetLight analyzed travel times on ten key routes to see how traffic conditions have changed during rush hour and beyond, including areas where the tolling program faced some resistance.
On January 5, 2025, New York’s MTA launched the Congestion Relief Zone tolling program, charging drivers a fee to enter the notoriously congested streets below 60th St. in Manhattan, excluding key highways and connector roads. The new toll, which includes peak and off-peak pricing, aims to reduce area congestion, air pollution, and safety risk, while raising revenue for the MTA. The tolling effort has implications not only for congestion in the immediate tolled zone but many surrounding areas, as well. (Federal administrators recently said they were rescinding approval of the tolling program, but as of this writing the tolls remain in effect.)
The MTA released initial data from week one of congestion pricing showing improved speeds on many of the bridges and tunnels entering the zone as well as on key bus routes.1 Overall, most of the routes studied by the MTA have seen travel times improve.
StreetLight is now using its Traffic Monitor product, which helps planners and engineers monitor recent speed and congestion changes, to deepen the picture on congestion tolling with more data since the fee went into effect.
For a bird eye’s view of how traffic looked on a single day three weeks into the launch of congestion pricing, StreetLight used Traffic Monitor to create the gif below, showing the change in atypical speeds over the course of the day on January 28th, as compared to similar days in January 2024. Green, thicker lines show improved speeds while red segments indicate decreased (i.e slower) speeds.
Year-over-Year speed changes on January 28th in Manhattan and the surrounding region.
Of course, no single day provides a perfect measurement of traffic, as any day can be affected by crashes, weather, tourist activity, construction, and other disruptions.
To further contribute to the public’s understanding, StreetLight analyzed change in travel times over a three-week study period in January on ten distinct routes in the NYC metro area. You can see the map of the routes studied below.
StreetLight studied north-south routes, crosstown routes, and routes traversing areas outside the toll zone, in places where some have raised concerns about increased congestion from rerouting vehicles. StreetLight also included trips ending at major hospitals, as improving emergency vehicle travel times has been a stated goal of the program.
StreetLight’s analysis finds that most routes studied did see travel times improve. Six of the ten routes saw travel times decrease during both peak and off-peak tolling hours, including routes through New Jersey and Queens where there has been some resistance to congestion tolling.
Both Manhattan-based hospital routes – from Times Square to NYU Langone and the West Village to Memorial Sloan Kettering – saw peak hour travel times decline by 10% and 6%, respectively, a positive indicator for emergency travel within the zone.
For the routes where travel times worsened, the effect was small. Even during peak hours, the increase in travel times was less than a minute on all negatively impacted routes. This may be expected regardless of policy change as vehicle miles traveled have been steadily rising since 2021.2
Routes from New Jersey to Columbus Circle saw an interesting trend. Travel over the George Washington Bridge from Ridgefield Park, NJ to the northern edge of the congestion tolling zone slowed down by a slight 30 seconds during peak hours, as compared to a year earlier. However, travel via the Lincoln Tunnel from East Rutherford, NJ to Columbus Circle improved significantly, by over 3 minutes during peak hours.
Analyzing Impact by Time of Day for Targeted Interventions
StreetLight allows planners and engineers to analyze travel at highly granular geographic and spatial scale. For example, if city planners are particularly focused on improving bottlenecks during the weekday AM or PM peak, that analysis is simple and straightforward. The impact of the MTA’s congestion charging will change over time as residents and visitors adjust, and as other trends impacting NYC arise. Many analyses will and should be done! StreetLight’s goal is to enable planners to understand and adapt to the complexities of managing congestion.
In the chart below, StreetLight compares the change in travel time on the Times Square to NYU Langone route by weekday only, looking at weekday all day vs. weekday peak AM and weekday peak PM. Peak AM travel times see the biggest improvement as compared to peak PM and all weekday.
Methodology
The analysis compares travel on select routes between January 5-25, 2025 and the same time of day and day of week for the month of January 2024. Travel times are based on sample count speed data.
Routes selected are not comprehensive of traffic in any one area. They represent travel between major destinations and aim to contribute to the picture of congestion pricing’s impact.
___
1. Metropolitan Transportation Authority (MTA). Congestion Relief Zone Tolling: Week One Update. January 13, 2025. https://www.mta.info/document/162396
2. U.S. Federal Highway Administration, Moving 12-Month Total Vehicle Miles Traveled [M12MTVUSM227NFWA], retrieved from FRED, Federal Reserve Bank of St. Louis; https://fred.stlouisfed.org/series/M12MTVUSM227NFWA, March 10, 2025.
Get traffic flowing with nine data-driven congestion solutions
Explore the resources listed above and don’t hesitate to reach out if you have any questions. We’re committed to fostering a collaborative community of transportation professionals dedicated to building a better future for our cities and communities.
This blog is for people who really enjoy getting into the weeds about data methodologies!
With StreetLight’s end-of-year product updates (if you’re an existing customer you can see release notes here) we included even more new data months, and we updated some of our methodologies for processing data. Our methodologies are documented in detail on our white papers page but we thought it would be helpful to summarize the updates here and discuss how the methodology changes might affect results of certain analyses, using some of our own previous blogs and ebooks as examples.
While these changes make our results better, we know methodology updates can be tricky for customers, so we want to be as transparent and helpful as possible, so customers can better navigate and understand any differences they may see. If you have questions about any of your own analyses, please contact our support team by visiting help.streetlightdata.com and selecting “Contact Support” or clicking the “Contact Support” link under the Help menu in your StreetLight InSight® account.
TL;DR: Overall the changes are moderate and the updates mean our results more accurately reflect the real world. The most significant impacts are:
Improved spot speeds and sample sizes for road segments analyzed with Network Performance in lieu of Segment Analysis.
Improved All Vehicles Volume estimates for data periods in 2019 nationally, as well as volume estimates for late 2023 for select states.
Improved differentiation between weekday and weekend vehicle volume metrics.
For Analyzing Speeds and Volumes on a Road Segment – Moving to our New Network Performance Analysis Type
During 2024, many users will have seen a new analysis type called “Network Performance” in StreetLight InSight®. This offers many of the same outputs as “Segment Analysis” but with improved methodology and data inputs, ultimately yielding better results. We are now recommending that clients analyzing vehicular movement on road segments transition to Network Performance, in particular for use cases involving measuring changes across time.
What’s Different: Network Performance relies on Aggregated GPS data (AGPS) as its underlying data source. Segment Analysis relies on a combination of Connected Vehicle Data (CVD) and Location-Based Services (LBS) data sources, both of which have smaller sample sizes than AGPS.
AGPS data has a few benefits, including a very high sample size (18-40% of vehicles on the road) and availability in both the US and Canada. Most notably, AGPS data has been continuously available since 2019, allowing for a better comparison across time since “data source” is no longer a variable that could account for differences measured. In addition, Segment Analysis metrics are not available beyond May 2023.
NOTE – As of January 2025, Network Performance can only be run on OpenStreetMap (OSM) segments. While it cannot currently be run on a customer’s LRS if they’ve uploaded that to the system, we are working on adding this capacity as soon as possible. Additionally, you can always work with our services team for a custom analysis that matches the metrics to your LRS.
Exploring Impact: Taylor Swift’s Eras Tour analysis
To explore the impact of shifting to Network Performance, we reran the results of our all-time-most-popular blog, which originally analyzed the Taylor Swift Eras Tour traffic jams using Segment Analysis (you can read the updated Eras Tour analysis here).
The analysis of congestion on typical days across each of the cities shows almost no difference between the two methodologies (Segment Analysis and Network Performance). The only notable difference where Network Performance shows less delay on a typical day is in New York City. We think this reflects Network Performance’s better differentiation of cars from subways and buses, and thus is an improvement.
On Eras Tour days, for most cities, Network Performance picked up a little more of an impact from the concerts as shown in Figure 1. Again, we consider this a positive reflection of Network Performance, as the data source is showing improved differentiation between typical activity and disruptive activity. This is one of the key benefits of big data — to analyze and react when events do not follow typical patterns.
Notably, these changes aren’t big enough to impact the overall story: Looking at excess VHD, the concerts in Vegas followed by Dallas, then Phoenix and then Tampa had the biggest impact on traffic compared to a typical day. The concert in New York City (with the most transit alternatives to driving) had the smallest. Figure 1 shows the changes.
Figure 1: Scatter plot showing Vehicle Hours of Delay on Concert Dates for the newer Network Performance (X-axis) and Segment Analysis (Y-Axis). A dot that is “below” the line indicates that Network Performance found more delay on these concert dates than Segment Analysis.
Looking at excess VHD % change (i.e., the percentage difference between typical VHD and VHD on the day of the concert), the Boston concert shows the biggest percent change for both methodologies, followed by Dallas-Fort Worth and Phoenix. New York City still shows the smallest change. The table below shows how these rankings vary between Segment Analysis and Network Performance, with venue positions shifting by 1 rank at most.
Metro Area
Segment Analysis Rank How much worse (by percent) was traffic on Eras Tour days?
Network Performance Rank How much worse (by percent) was traffic on Eras Tour days?
Boston (Foxborough, MA)
1 (Biggest impact)
1
Dallas-Fort Worth, TX
2
3
Phoenix, AZ
3
2
Houston, TX
4
5
Philadelphia, PA
5
4
Nashville, TN
6
6
Tampa, FL
7
8
Las Vegas, NV
8
7
Atlanta, GA
9
9
New York City, NY
10 (Least Impact)
10
Network Performance Volume Model Updates
In our end-of-year release, we also updated our U.S. Network Performance Volume model for all road segments in the U.S. for all months starting in 2019.
What’s Different: The volume estimates are derived from a machine learning model trained on over 14,000 unique permanent vehicle counts across all states in the contiguous U.S. The updated model uses more training locations than the first version of the model as more states published 2023 data after our initial release. We also used more historical data from 2019 and 2020 to refine the algorithms for those years. In general, these improvements yield:
Reduced bias and improved error in all years, especially on low volume roads
Improvements to the volume model for 2019
Improved weekday vs. weekend comparisons for all years
Figures 3 and 4 compare MAPE (Mean Absolute Percent Error) for various bins of roads for each data year. Deeper dive white papers are available here.
The new release also includes Network Performance volume estimates for Canada.
Figure 3: Nationwide model improvements in v2 (released November 2024) for 2019 data months. Improvements mainly show up in improved MAPE on smaller roads. This indicates that any given road, when run in v2, is likely to have more accurate estimation especially if that road is smaller.Figure 4: Nationwide model improvements in v2 (released November 2024) for early 2024 data months. The two models are much closer in performance, indicating that any given road is less likely to see big swings in volume estimation, because v1 was already strong.
Exploring Impact: VMT Report
Last fall, we published a report ranking VMT changes from Spring 2019 – Spring 2023 for metro areas in the U.S. VMT relies on our volume model, and when the report was published, we were still using our V1 model. In hindsight, for a metric as critically important as VMT, we should not have developed a report with V1 when we knew V2 was coming soon! It created unnecessary confusion for our customers. This was an error we regret and will not repeat. We may publish a more comprehensive update of that report with v2 metrics in the future.
When we reran the results with our improved volume model, we saw some changes:
A number of metro areas showed increased VMT totals for 2019, while most had similar results for 2024. This means that the percentage change in some metros between 2019 and 2024 was overstated in our initial report (Overall, Spring 2019 was in fact closer to Spring 2024, than initially reported by approximately 4-7 percentage points depending on region).
The increase in 2019 was most often attributable to improvements in low/medium volume road accuracy.
This granularity ensures agencies of all sizes, as well as firms and businesses, can get actionable insights to prioritize projects, evaluate impact, and anticipate future needs. It’s also particularly important for transportation modeling, which requires granular, empirical data to help predict how conditions will change over time, or in response to specific infrastructural and policy changes.
Let’s use a few metros in Connecticut to illustrate the change.
Area
V1 2019-2024 Spring Change in VMT
V2 2019-2024 Spring Change in VMT
Bridgeport-Stamford-Norwalk, CT
6.3%
0.4%
Hartford-West Hartford-East Hartford, CT
3.5%
-0.7%
New Haven-Milford, CT
6.0%
0.4%
Norwich-New London, CT
5.6%
-2.2%
Torrington, CT
13.3%
5.8%
Worcester, MA-CT
-2.2%
-1.1%
Connecticut – Statewide
4%
-0.6%
VMT Musings: How do we know what is “right” or “better”?
For our volume metrics, we can publish very precise estimates of overall accuracy based on thousands of “ground truth” permanent counters, as shown in Figure 5.
But VMT over a large area is trickier — there’s no such thing as ground truth. Instead, there are various methodologies, and thoughtful comparisons can be made based on known strengths and weaknesses of each one.
FHWA publishes two different reports on statewide VMT (and individual states have their own methodologies): the Traffic Volume Trends (TVT) and within the Highways Statistics Series (HSS). These are published at the state level, not MSA.
Method Summary:
The TVT is updated faster than HSS and is based on Continuous Count Stations (CCS), extrapolating changes seen on them to the rest of the roads.
For the Highway Statistics Series (HSS), FHWA “estimates national trends by using State reported Highway Performance and Monitoring System (HPMS) data, fuel consumption data (MF-21), vehicle registration data (MV-1), other data such as the R. L. Polk vehicle data, and a host of modeling techniques” and since HSS hasn’t come out yet for 2023, we can’t compare the most recent data.
Like the TVT, StreetLight uses CCS counters from the state in question as well as from similar roads (similar by volume, rural/urban context, weather patterns, and more) in nearby states to create a machine learning model to scale up a ~25% sample to a full count. We estimate each individual road segment’s volume independently using this method, multiply that volume by road segment length, then sum all road segment VMT values up in a given area to estimate that area’s total VMT.
Sticking with Connecticut to illustrate differences:
Figure 6: FHWA TVT, HSS, and StreetLight Annual Connecticut VMT change compared to 2019. The TVT shows more variability than HSS or StreetLight, particularly between 2022 and 2024. The TVT variability is too high compared to common sense in these years, in our opinion. HSS shows far less year over year variation, which builds confidence in its 21/22 estimates, but we don’t think the 2020 number matches common-sense COVID experience.
Looking at these two FHWA methods, we feel our V2 model performs well based on common sense and COVID experience.
Methodologies like TVT often extrapolate growth in VMT in a region from measured growth on CCS counters (which are most often on busy roads). If the balance of growth between highways and more local roads has changed since the pandemic — and we believe it has — then our industry needs to update the methodology used to estimate region-wide VMT. We believe the big data–driven approach offers just such an opportunity and we will explore this in future publications and posts.
And, as always, the more up-to-date, well-maintained permanent counters that are available (especially on lower volume roads) the better everyone’s estimates will be!
Swift Streets? Complete Rankings for Traffic Management at Every Stadium in Taylor Swift’s U.S. Eras Tour
Swift Streets? Complete Rankings for Traffic Management at Every Stadium in Taylor Swift’s U.S. Eras Tour
In a study of traffic delays across the entire U.S. Eras Tour, StreetLight found delaysat least doubledat most of the 23 stadiums where Swift performed — but there were some notable outliers.At one venue, trafficactually improved.This reportupdates and expandsStreetLight’s prior analysis ofnine stadiumsthat hosted Eras Tour concertsin March–May 2023.
When Taylor Swift announced her first live tour since 2018, the rush on tickets by fans made national headlines (and earned a congressional hearing).
For transportation and transit agencies, and stadium operators, a very different challenge emerged: Managing traffic from the legions of fans who would descend on the stadiums for the Eras Tour.
Event operations pose a special challenge as they put a dramatic tax on roadway operations over a narrow time block, which local transportation infrastructure is not built to support during a typical day. As a result, stadium operations groups often work in close coordination with local transportation agencies to manage traffic, as well as ingress and egress from the stadium.
So when it comes to the Eras tour, how have the stadiums and agencies fared at managing fan traffic and keeping the roadways flowing? StreetLight ran the numbers to find out. Then, we look at how transportation and operations professionals can use analytics for more effective events traffic management.
Key Findings:
Vehicle Hours of Delay (VHD) on roadways adjacent to the concert venues at least doubled during most Eras Tour concerts. On average, vehicle delays were 277% higher across all stadiums compared to delay hours at comparable times on non-concert dates.
Only four out of 23 venues saw traffic delays increase by less than 100%: MetLife Stadium in East Rutherford, NJ ; Mercedez-Benz Stadium in Atlanta, GA; Empower Field at Mile High in Denver, CO; and Acrisure Stadium in Pittsburgh, PA.
Traffic around MetLife Stadium, which invested heavily in transit access, actually decreased compared to usual delays. This is the only venue where traffic decreased.
The worst venue for increased traffic delays (based on % change from typical conditions) was Gillette Stadium in Foxborough, MA. This is a location where typical VHD is relatively low compared to many of the other venues studied.
Eras Tour Traffic Winners & Losers
To understand the traffic impacts from the U.S. Eras Tour concerts, StreetLight analyzed Vehicle Hours of Delay (VHD) on all non-local roadway segments within a one-mile radius of each stadium during the peak arrival hour of 5-6 p.m. on each concert date. VHD measures the difference in vehicle travel time on a segment during congested versus free-flowing conditions, multiplied by the number of vehicles traveling on that roadway.
This same process was repeated for the same days of week within that month (concert dates and holidays excluded) to determine a baseline VHD for a typical travel day. You can read more about StreetLight’s data here.
Overall, across all 23 stadiums and 62 concerts, average delay hours were 277% higher than typical. In fact, all but four stadiums saw delay hours at least double on average over the course of the concerts.
Two major success stories emerged, however: Atlanta’s Mercedes-Benz Stadium and New Jersey’s MetLife Stadium saw average delays well under 100%.
Atlanta only saw a 32% increase in traffic delays. But NJ’s MetLife Stadium was the real standout.
VHD actually decreased during the concerts, by 27% on average over the course of the three nights. Notably, both Atlanta and New Jersey’s concert venues were given high marks for their emphasis on public transit options to the concert. Atlanta’s Metropolitan Rapid Transit Authority System (MARTA) reported seeing three times the usual ridership during the concert days at stations near the stadium, according to CBSNews. NJTransit, which ran extra service around the stadium, carried 80,000 riders via train and bus to the concert, according to NJ.com.
Of note, on a normal day, both MetLife Stadium and Mercedez-Benz Stadium see higher baseline congestion than most of the other stadiums studied here (with the sole exception of Vegas’ Allegiant Stadium).
Philadelphia also placed a big emphasis on public transit. This may have paid off for the stadium on two of the concert nights. The Friday and Sunday shows in May 2023 at Philadelphia’s Lincoln Financial Field saw below average increases in delays compared to the other stadiums, with VHD 200% and 186% higher than typical for streets around the stadium, respectively.
However, on Saturday night Philadelphia’s Lincoln Financial Field encountered huge snarls, with a 599% increase in hours of delay. This dragged down the stadium’s average across the three nights. It’s also a signal of how tenuous traffic management at an event like this can be, and how easy it is for delays to compound.
But by far the worst increase in traffic delays occurred at Gillette Stadium in Foxborough, MA, near Boston. It saw delays 1,270% higher than typical on average over three nights in May 2023. Typical VHD near the stadium is low compared to many of the other venues in this study, perhaps because Foxborough, MA is a small town of just over 18k residents as of 2022, though its stadium regularly hosts sold out football games as the home of the New England Patriots, and is the largest stadium in the Greater Boston metro area.
Next highest for percent increase in traffic delays, at 737% higher than typical, was Kansas City, MO’s Geha Field at Arrowhead Stadium. Like Gillette Stadium, this venue also sees relatively low typical VHD.
Like Philadelphia’s Lincoln Financial Field, several other venues also saw dramatic differences in excess VHD depending on the concert date, including AT&T Stadium in Arlington, TX, Gillette Stadium in Foxborough, MA, and Geha Field at Arrowhead Stadium in Kansas City, MO.
Among these venues, Saturdays and Sundays tended to see the worst increase in delays, with Fridays relatively lower. This could be influenced by commuter traffic on Friday evenings peaking between 5 and 6 p.m., driving up typical VHD on Friday evenings, resulting in lower increases comparatively.
How Transportation, Events, & Operations Professionals Can Manage Event Traffic Better
Events like the hotly anticipated concerts of Swift’s Eras Tour test the limits of everyday traffic operations, and often demand temporary strategies that reduce congestion, encourage shared transportation modes, and keep concert-goers safe.
But anticipating and mitigating traffic issues from special events is far from simple. To minimize delays, promote smooth traffic flow, and ensure safety, planners and operators need to know which routes attendees will travel, the modes they will use on the way, the intersections where they’ll be turning, and what alternate routes people may take as primary routes become congested.
Complicating these challenges is the time and financial cost of gathering the right data needed to understand all these factors. While certain major arterials may benefit from permanent traffic counters, many roadways lack these counters, such as residential or other local roads that may experience cut-through traffic when larger roadways become gridlocked.
This makes it impossible to get historical data with the granularity needed to understand past events or even average seasonal roadway conditions. Meanwhile, collecting data on complex roundabouts, intersections, or weaving segments can also be difficult, even if manual counts or surveys are deployed in advance of the event.
Big Data and Special Events Traffic Planning
A big data approach to special events planning can help fill crucial data gaps to anticipate their traffic impacts. Whether it’s used to inform broader travel demand models or applied for analysis of traffic operations during specific events, access to on-demand transportation analytics expedites special events planning without needing to put staff in harm’s way for manual counts and surveys that only capture a snapshot of traffic during a short period of time.
This expedited process allows planners and operators to proactively evaluate alternative traffic management strategies and communicate their decisions with the public in advance of special events.
Moreover, analyzing Origin-Destination of traffic, and routing to and from event venues can be particularly difficult when using traditional data collection methods, but it can also be one of the best starting points to understanding where and why congestion hotspots occur while also revealing underutilized road segments that could be used to free up traffic.
A StreetLight Top Routes analysis shows the most-used routes traveling to State Farm Stadium near Phoenix, AZ. Top-used road segments appear in red.
Big data makes analyzing top routes quick and simple so that traffic operations managers or planners have the best tools to ensure traffic flows smoothly.
When analyzing historical traffic data for special events planning, the following metrics can be helpful:
Origin-Destination (O-D) and Top Routes – to anticipate where attendees are coming from, which roadways can expect the largest increase in travelers, and which less-used segments could be candidates for traffic rerouting.
Turning Movements – to understand where and when people turn into and near the event venue during typical conditions and special events.
Traffic Volumes – to understand where roadways may reach capacity and identify potential detour routes.
VHD – to anticipate the impact and severity of traffic congestion during special events compared to average conditions.
Speed – to evaluate safety conditions and crash risk near the venue, especially for vulnerable road users like pedestrians and cyclists.
Travel Time – to understand how special events impact not just attendees but other road users and communicate expected delays to the public.
Bike and Pedestrian activity – to identify common walking and cycling routes to and from the venue.
Transit ridership – to understand available capacity for shared transportation modes that can help ease congestion.
A StreetLight Origin-Destination analysis shows where trips headed to Tampa’s Raymond James Stadium for the Eras Concert began, with darker blues representing higher concentrations of trip starts.
Planners and traffic engineers can use these metrics to anticipate how traffic conditions will change during special events and prioritize traffic management strategies that will keep traffic flowing and protect the safety of all road users.
For example, examining turning movements at key intersections leading to the event venue could inform temporary signal retiming on the day(s) of the event to offer more opportunities for attendees to make their turns toward the venue. Likewise, identifying increased traffic volumes on residential or other local streets not suited for high-volume traffic could signal the need for signage directing event attendees to preferred alternate routes toward the venue.
Traffic operations managers can now also leverage real-time or near real-time data to monitor traffic disruptions as they develop and compare current speed and volume conditions to historical data to diagnose slow-downs or safety concerns and how to deploy the best solution quickly. StreetLight’s Traffic Monitor product can equip agencies and firms with real-time insights for any road, even newly constructed roads and other roads without physical counters. The gif below shows an example of atypical volumes around Las Vegas’ Allegiant Stadium during the 2024 Super Bowl.
StreetLight Traffic Monitor product users can view a time lapse of traffic trends measured by atypical volume, speed, atypical speed, and atypical delay. This Super Bowl time lapse shows atypical volumes. Higher volumes appear in red while lower volumes are in blue.
If you read StreetLight’s original analysis, covering the first nine venues of the Eras Tour in March–May of 2023, you may have noticed some differences in the results from the original analysis.
To learn more about the methodological changes driving those differences and why the new data reflected in the above analysis improves upon the reliability of congestion insights, check out our new blog on Data and Methodology updates for February 2025. There you’ll find an in-depth explanation of how StreetLight’s new Network Performance analysis type compares to the Segment Analysis data we used for the original nine-venue analysis — and where stadium rankings differed slightly between the two methodologies. You’ll also find information on other recent reliability improvements to metrics like vehicle volumes and VMT.
Get traffic flowing with nine data-driven congestion solutions
Explore the resources listed above and don’t hesitate to reach out if you have any questions. We’re committed to fostering a collaborative community of transportation professionals dedicated to building a better future for our cities and communities.
Investigating how trucks impact social equity with new freight data
Investigating how trucks impact social equity with new freight data
How doestruck activity impact disadvantaged communitiescompared to their non-disadvantaged neighbors?StreetLight uses new truck data to show how you can investigate inequitable truck impacts in your community.
Truck activity can have a huge impact on local emissions and traffic congestion, but not all communities are equally affected. Now, new truck data from StreetLight helps analysts investigate how truck traffic impacts disadvantaged communities (DACs)across the U.S., and how factors like urban density, vehicle weight class, and industry type contribute – and help diagnose – inequitable impact.
Key Takeaways
In New York state, StreetLight’s data indicatesdisadvantaged communities are impacted by worse truck travel delays than non-disadvantaged communities,potentially exacerbatingnegative outcomes, such as air pollution and emissions.
Communities in the urban coreare the most impacted by truck travel delays and the added emissions and pollution they often generate.
Overall, medium-duty trucks show the most unequal impact on disadvantaged communities. Trucks in this category from the public administration and transportation and warehousing industries may warrant special attention for DACs.
Trucks are some of the worst offenders when it comes to vehicle emissions. Although they are indispensable to today’s freight logistics, they also emit more CO2 and other air pollutants than cars because they typically emit more GHGs per mile in addition to traveling much longer average distances.
Compounding trucks’ oversized climate impact is a dramatic spike in freight activity. A 2021 analysis by USDOT’s Bureau of Transportation Statistics predicted total U.S. freight activity would grow 50% by 2050, with trucks accounting for 65% of that total.1 This proliferation of truck traffic also challenges existing road capacity in many communities, exacerbating rising congestion and safety issues.
And these ramifications often come down hardest on disadvantaged communities — people who live in low-income neighborhoods where high congestion, noise pollution, and poor air quality are common. For this reason, analyzing how truck activity impacts disadvantaged communities is critical to reducing harms.
Now, freight planners, fleet operators, and businesses can use new truck data from StreetLight to understand how freight activity impacts different communities, as well as investigate related questions about who truck activity serves, which industries are most represented, and how travel delays impact logistics, emissions, and equity.
Below, StreetLight analyzes commercial vehicle activity in New York, comparing its impact on DACs vs. non-disadvantaged communities.
How Trucks Impact Disadvantaged Communities in New York
Since 2021, disadvantaged communities (DACs) have been a key focus of efforts to improve transportation equity. These communities are “marginalized by underinvestment and overburdened by pollution,” as defined by Executive Order 14096, which established the Justice40 Initiative, a policy promising to funnel at least 40% of federal investments in clean energy, clean transit, sustainable housing, and similar programs into low-income census tracts that qualify as disadvantaged communities.2
In the state of New York, about 36% of all census tracts — accounting for about 35% of the state population — are considered to be DACs.
While all truck traffic can negatively impact a community’s air quality, traffic congestion, and local emissions levels, analyzing how different weight classes impact DACs vs. non-DACs can add helpful nuance to planning efforts aimed at mitigating this impact and targeting policy interventions.
To unpack the impact of trucks on DACs. vs. non-DACs, StreetLight’s analyzed truck activity in New York state by weight class and how it differed in these community types. Streetlight finds that light-duty and medium-duty truck activity have a disproportionate impact on disadvantaged communities per mile. Meanwhile, heavy-duty activity per roadway mile is slightly higher within non-DACs.
Truck Activity Per Roadway Mile is segmented by commercial vehicle weight class to compare trucks’ impact on DACs vs. non-DACs. See our Methodology section at the end for more details.
Going further and factoring in travel delays among trucks, the picture changes substantially.
The chart below illustrates the inequitable impact of congestion, with DACs encountering significantly more traffic delays per roadway mile, even among truck classes that have a lesser presence in DACs compared to non-DACs. In fact, DACs are faced with more traffic delays across every class of truck, suggesting that DACs may also be exposed to disproportionately high emissions from both gas-powered and diesel-powered trucks as these vehicles linger through delays, contributing to poor health outcomes.
Measuring the average delay from free-flow speed per roadway mile reveals that traffic delays impact disadvantaged communities significantly more, regardless of which classes of truck are driving in their community.
While trucks are not necessarily the cause of these traffic delays, congestion mitigation efforts can still help reduce the impact that trucks — and indeed all vehicles — have on the communities they travel through.
Because these delays are averaged across the entire available roadway network, the per-mile delay impacts appear small. Nevertheless, the fact that average delays are higher for DACs than non-DACs suggests that analyzing congestion is a worthy step when evaluating the impact of truck traffic within disadvantaged communities.
While traffic delays are a nuisance for communities in and of themselves, lengthening commute times and making it harder to access essential goods and services, delays also intensify other negative impacts of vehicle activity like emissions and air pollution.
Where TruckDelays Have the BiggestImpact by Density
For agencies to manage truck activity in a way that improves transportation equity, it’s also important to understand that DACs are diverse in makeup, as are non-DACs – no two communities look exactly the same. For example, urban density can have a significant impact on how communities are impacted by trucks.
To understand these nuances, let’s first establish how DACs are distributed across different urban densities – from rural locations to the urban core.
The map above highlights important nuances to consider when comparing census tracts—firstly, rural census tracts cover far more area, and therefore far more roadway network miles, than urban tracts. For this reason, StreetLight normalizes Truck Activity by roadway mile for this analysis (see Methodology section for more details).
Furthermore, the map shows that DACs are concentrated in urban areas. Zooming in on New York City reveals just how many DACs call the city home compared to rural areas throughout the state. The chart below explores this distribution by population.
Since DACs are concentrated in urban areas, you might expect to find that truck delays are only an issue for DACs in urban and urban core locations.
However, in the chart below, we can see that while delays are worse in urban DACs, even in rural and suburban areas, DACs are impacted by more travel time delays than their non-DAC counterparts, highlighting the importance of targeting congestion mitigation efforts and improving freight planning within disadvantaged communities across all urban densities.
How Different Truck Weight Classes Impact Communities
To further contextualize StreetLight’s truck findings by density, let’szero in on the roles different truck weight classes play in each type of tract.
Truck Weight Class Distribution by Urban Density
How Different Industries Contribute to Truck Impact, by Weight Class
Given the disproportionate impactmedium-duty vehicles have on DACs by overall activity, and especially when factoring in travel delays, it’shelpful to understand what roles these vehicles play in communities, and which industries drive medium-duty truck activity.
As the image above depicts, commercial vehicles range from class 1 to class 8, with classes 1 and 2 considered “light-duty” vehicles, ranging from commercial vans to pickup trucks. Classes 7 and 8 are “heavy-duty” vehicles, including garbage trucks, city transit buses, and traditional semi-trailer trucks. Everything in between (classes 3-6) is considered “medium-duty,” ranging from local delivery trucks to school buses.3
Based on mileage, medium-duty truck activity in New York’s DACs is predominantly comprised of Public Administration and Transportation and Warehousing vehicles, with Real Estate and Rental and Leasing (the front-runner for medium-duty activity in non-DACs) trailing just behind.
Meanwhile, Real Estate and Rental and Leasing and Transportation and Warehousing also make a strong showing within the heavy-duty vehicle activity breakdown.Although heavy-duty vehicles show more truck activity per roadway mile within non-DACs compared to their DAC neighbors, these trucks still have a significant impact on DACs, especially as they contribute more emissions and noise pollution per mile traveled compared to smaller trucks. For this reason, understanding the industry breakdown among heavy-duty trucks could also generate valuable lessons for equity-focused freight planning.
Based on these findings, medium- and heavy-duty trucks serving the transportation and warehousing industrycould warrant special attention from freight planners. These trucksoften travel to and from larger hubs of freight activity, such as distribution warehouses and ports, making communities impacted by these high-traffic freight routes that much more likely to experience air and noise pollution due tonearby truck activity. Moreover, much of this truck traffic may not be ending in these communities, merely passing through. For this reason, the Bipartisan Infrastructure Law (BIL) provides special funding opportunities for decarbonization efforts that target ports.4
Key Takeaways for Managing Truck Traffic Impacts
To improve outcomes for disadvantaged communities, planners must consider the inequitable impacts of truck traffic as they work to mitigate congestion, reduce emissions, and route trucks efficiently.
StreetLight’s analysis highlights that some classes of truck impact DACs more than their non-DAC counterparts, suggesting that analyzing this truck activity and targeting electrification efforts toward these weight classes and industries in particular may help address inequitable impact and target the most impactful improvements.
Additionally, the analysis shows traffic delays are a problem disproportionately impacting DACs across all urban densities, but especially in the urban core. Though these delays are not necessarily caused by trucks themselves, they can exacerbate the impacts trucks have on local emissions and air quality, making efforts to mitigate congestion and route trucks more efficiently in disadvantaged communities especially critical.
Finally, the Transportation and Warehousing industry emerges as a significant contributor to trucks’ impact on disadvantaged communities. This industry could warrant special attention from planners or businesses looking to address freight’s equity impact. And for businesses with large logistics operations, placing emphasis on improved routing and electrification could mitigate congestion and emissions impacts on disadvantaged communities.
This analysis includes truck data for March 2024 within the state of New York, including residential roadways.
To measure Truck Activity, StreetLight analyzes sample Vehicle Miles Traveled (VMT) for commercial vehicles per mile of available roadway network in each census tract.
To analyze traffic delay, StreetLight uses a weighted average of travel time delay per mile of available roadway network in each of the urban density and DAC/non-DAC categories. The weight is the segment truck sample count, so that segments with higher numbers of trips have their delay represented proportionately. In other words, the travel time delay value for each of these categories represents how much delay a driver can expect when travelling one mile within that category.
StreetLight analyzes the urban density of census tracts based on the density of their roadway networks.
Census tracts are labeled “disadvantaged” vs. “non-disadvantaged” based on how they are classified by the Justice40 Initiative. In general, census tracts labeled “disadvantaged” meet a threshold for “environmental, climate, or other burdens” and “an associated socio-economic burden.”5
1. USDOT Bureau of Transportation Statistics. “Freight Activity in the U.S. Expected to Grow Fifty Percent by 2050.” November 22, 2021. https://www.bts.gov/newsroom/freight-activity-us-expected-grow-fifty-percent-2050
2. The White House. “Justice40, a whole-of-government initiative.” https://www.whitehouse.gov/environmentaljustice/justice40/
3. U. S. Department of Energy, Alternative Fuels Data Center. “Maps and Data – Vehicle Weight Classes & Categories.” https://afdc.energy.gov/data/10380
4. Office of Energy Efficiency & Renewable Energy. “Federal Funding Opportunities for Port Low- to Zero-Emissions Technologies.” https://www.energy.gov/eere/federal-funding-opportunities-port-low-zero-emission-technologies
5. Office of Energy Justice and Equity. “Justice40 Initiative.” https://www.energy.gov/justice/justice40-initiative
Ready to investigate trucks' equity impact in your community?
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.
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.
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.
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
How Can We Reduce Emissions From Urban Transportation?
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]
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.
See what emissions reduction tactics your city needs most
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:
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.
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
See how StreetLight's transportation analytics can help you improve mobility outcomes
Explore the resources listed above and don’t hesitate to reach out if you have any questions. We’re committed to fostering a collaborative community of transportation professionals dedicated to building a better future for our cities and communities.
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
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 (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.)
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.
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.
See how dangerous traffic speeds impact walking and biking in your region
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.
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.
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.
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.
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.
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.
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
Investigate local traffic trends with a free trial of StreetLight InSight®
Explore the resources listed above and don’t hesitate to reach out if you have any questions. We’re committed to fostering a collaborative community of transportation professionals dedicated to building a better future for our cities and communities.
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?
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).
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:
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.
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.
Test-drive StreetLight AADT metrics today with a free trial
Explore the resources listed above and don’t hesitate to reach out if you have any questions. We’re committed to fostering a collaborative community of transportation professionals dedicated to building a better future for our cities and communities.
How Advanced Traffic Counts are Powering Better Business Decisions
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.
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.
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?
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.
New truck data reveals how port traffic is changing
New truck data reveals how port traffic is changing
In the first four months of the year,data on heavy-duty truck traffic at some of the biggest ports in North America indicatea sector in transition, with divergingtrendsby geography and sector. New truck traffic data from StreetLightenables the private sector across diverse industries, including retail site selection, vehicle electrification, ports and distribution hub planning, and insurance risk assessors, as well as public sector agencies,to keep pace with these trends as they happen.
Heavy-duty truck traffic between January through April at some of North America’s largest ports reveals a supply chain and logistics system in transition, with increases in truck activity year-over-year at ports in Georgia and Texas, versus contraction or mixed results at ports in Los Angeles and Vancouver, British Columbia.
The analysis comes from StreetLight’s newest truck product, which enables private sector businesses to stay up to date on the most recent changes in supply and logistics ground travel patterns and adjust accordingly.
The data indicates that it has been a bumpy few months for truck traffic around the major North American ports. Heavy-duty truck stops and starts around the boundaries of Georgia’s port region saw an initial contraction in activity before bouncing back to growth in February. The port at Vancouver, Canada’s largest facility, saw a similar but more pronounced trend, a dramatic decline in truck activity growth through March before climbing back to the positive in April.
The port of Los Angeles on the West Coast stands out as the facility where truck traffic has contracted throughout the entire first third of the year, with double-digit declines in heavy-duty truck stops and starts for the past six weeks, compared to the year prior.
However, diving deeper into the data we can see it is a mixed bag by industry, with some sectors remaining strong at the Port of LA, while others dip.
Among the five largest industries for truck activity at the Port of LA, one of the largest declines is in manufacturing, down 40–50% year-over-year, according to StreetLight’s data. This downturn aligns with other third-party indicators, such as a drop in the ISM Manufacturing Index and contraction in industrial production in Q1 2025.
That said, manufacturing only accounts for about 7% of trucking activity at the port, whereas transportation and warehouse, at 46% of truck stop and starts, is by far the largest slice of trucking activity at the port. This sector, alongside wholesale trade, has seen trucking activity remain positive around the port.
Of course, there are many varied signals at every port. While StreetLight finds a dip in heavy-duty truck activity around the Port of LA, alongside the drop in the manufacturing index, total cargo volumes increased in the first three months of the year, according to Freight Waves. This points to the complexity of factors impacting ports, and the importance for businesses interconnected to the supply chain and logistics industry to keep pace with fast-changing trends.
StreetLight’s data can help shine a light on the most recent shifts in trucking activity coming out of these ports and throughout the U.S. and Canada. Clients looking to dive deeper into these and segment-level trucking activity can access nuanced metrics and travel pattern data to better understand shifts in the flow of ground transport that affect their industry.
How New Truck Data Can Help Businesses Adjust Dynamically to Changing Conditions
As the trucking industry faces a changing landscape, private sector companies are also grappling with a persistent challenge: a lack of detailed, reliable, and up-to-date truck data across the U.S. and Canada.
Whether it’s understanding where commercial trucks are traveling to optimize sites for truck fueling or service stations or estimating exposure for insurance risk modeling, many businesses require robust, granular insights into truck activity—especially in an era marked by electrification, regulatory shifts, and evolving freight patterns.
But much of this data is either too outdated, has gaps in coverage, lacks detailed insights such as industry and route type, or is simply not available in a useful format for private organizations.
StreetLight has a suite of truck data products tailored specifically for the private sector to deliver unmatched commercial truck insights through a range of tools designed to answer real business questions—fast.
To predict customer demand at potential new truck stops, fuel locations, convenience stores, and more, understanding nearby truck activity is key. StreetLight allows you to:
Compare truck activity and volumes between potential sites
Compare all vehicle activity vs. truck activity to further contextualize demand
Monitor truck activity around current sites in your real estate portfolio
Monitor truck activity around competitors’ sites
Analyze truck activity by fuel type, weight class, route type (long-haul vs. Local) and more to understand demand for specific fuels or truck infrastructure
Data for Truck Fleet Electrification
StreetLight’s truck data can also help you effectively electrify truck fleets and plan charging infrastructure that supports their use.
Information on truck trip lengths, high-volume routes, dwell times, and more can help you anticipate charging needs, choose locations for charging stations, and plan routes efficiently.
Truck Data for Market Research and Product Development
The ability to segment nationwide truck activity data by weight class, trip types, and industry segments supports detailed market research and product development efforts, helping you go beyond overall truck activity to understand what your customers need.
For example, you might look at nationwide truck activity to forecast future truck market demand based on weight class, industry, and trip type. Meanwhile, the same metrics can also be used to inform product development roadmaps or comply with regulations like California’s Advanced Clean Trucks.
Understanding Insurance Risk and Exposure
Want to understand how truck activity impacts risks to health or property in different locations? StreetLight’s truck data can help you analyze truck activity along specific roads or within an entire region to inform risk assessment. Plus, you can compare truck activity to overall vehicle activity for further context.
Truck Data for Port and Distribution Hub Planning
The analysis above illustrates a few useful ways to analyze truck activity around ports and hubs to understand industry trends. Understanding the fast-changing trends in port and distribution hub activity, as well as how different ports and hubs compare to one another, and which industries may be most impacted, can help you optimize logistics and anticipate supply chain fluctuations.
Freight Corridor and Bottleneck Insights to Reduce Delays and Improve Trucking Infrastructure
While some decisions require nationwide trucking insights (which our data also supports), others may require a closer look at specific freight corridors. Understanding where freight bottlenecks are happening and how goods flow along key corridors helps you avoid delivery delays and understand where infrastructure improvements may be needed.
Turning Truck Metrics into Actionable Insights at Any Scale
StreetLight offers a variety of data delivery methods to fit the scale of your questions and your team’s level of data science expertise.
Data can be delivered pre-processed for the entire U.S. or county for teams needing data at scale in a digestible format to fit into any workflow. This includes truck volume with class breakdown and VMT.
For companies that need deeper insights, detailed truck activity segmented by route type, industry, and trip attributes (e.g., long-haul vs. local) is available. Users can access key metrics like:
Congestion
Speed
Travel Time Reliability
Industry (e.g. manufacturing, construction, retail)
Intra- vs. Inter-State/Province Travel
Long-Haul vs. Local Travel
Additionally, for businesses looking to understand multi-trip analytics, StreetLight’s Services team provides tailored analytics such as:
Dwell time and stop counts at truck stops
Fuel type breakdowns
Vehicle type classification
Truck tours
Ensuring Reliable Truck Data for Confident Business Decisions
Knowing your decisions are based on recent and reliable information is crucial, so StreetLight always validates metrics rigorously before they ever make it into the hands of our users. To learn more about how StreetLight’s trucking data is collected, processed, and validated, download our Truck Volume Methodology and Validation white paper.
If you’re part of the private sector and looking to make smarter decisions about site selection, risk modeling, or infrastructure planning, reach out to understand how StreetLight’s truck data solutions can give you a competitive advantage.
If you are looking for more info about public sector truck data, check out freight planning solutions here.
