Where Road Traffic Counter Methods Fall Short — And How Transportation Analytics Can Fill in the Gaps

The COVID pandemic demonstrated how fast traffic trends can change and how crucial it is to have tools that are able to capture these changes quickly and track them over time. 

While traditional traffic counter sensors and surveys aren’t going away anytime soon, it’s important to be aware of their limitations when it comes to planning, implementing, and evaluating the success of transportation projects. Thankfully, transportation analytics can fill gaps in traffic counter data as well as add richness to transportation planning and modeling.

Transportation analytics from StreetLight Insight® provide access to multimodal data on traffic activity segmented by vehicle class, trip length, and more.

Traffic Counter Sensors Give An Incomplete View of the Transportation Ecosystem

The traditional way to gather traffic counts is to send staff onto a handful of targeted roadways to either manually count vehicles or install a temporary “tube” sensor across the roadway to capture counts for the vehicles that drive over it. Some areas install permanent traffic counters on priority roadways.

Unfortunately, transportation experts are well acquainted with the limitations of this type of traffic counter data, which include: 

• Lower-trafficked and rural roads are often overlooked, which can skew the data for city-wide, regional, or national analyses 
• Sending staff onto busy roadways is dangerous to workers and distracts drivers. 
• Small sample sizes can skew modeled results.
• Temporary traffic counters can drive inaccurate results.
• Permanent traffic counters are expensive to install and maintain.

Traffic Surveys Under-Sample

To help round out traffic count data, planners also use survey data, asking respondents questions about their travel routes and habits. But surveys may fall short in gathering sufficient traffic counter data for several reasons:

• Surveys can be expensive, costing hundreds of dollars per household.
• Results are based on small sample sizes (often around 1% or less) and small sample periods (usually 1-5 days).
• Participants are increasingly difficult to recruit due to increased privacy concerns, and fewer households using landline phones.
• Hard-to-reach populations are systematically under-sampled.
• Individuals/households tend to underreport travel, especially for short trips, active transportation modes, and non-work purposes. 
• Error can be introduced via the weighting and expansion process. 

That doesn’t mean traffic surveys – or traffic counters – have no place in the transportation planning toolkit. However, due to these limitations, surveys are more powerful tools for gathering subjective rather than objective data, while traffic counters are becoming important for use in validating transportation analytics insights that cover a much wider range of roads, modes, and time periods. 

Precise Modeling Requires Highly Granular Datasets

Traffic count data obtained from sensors and surveys has long provided transportation professionals with the necessary inputs for data modeling. Modelers assist planners by developing quantitative analyses that can create short- and long-term travel demand forecasts. 

Historically, data to develop and validate models has been limited by availability, frequency, or acquisition costs and time. These limitations are compounded by the level of detail that sophisticated models require. 

Models not only use traffic volume data to forecast trip generation (the number of trips to be made), they also require details like route information, trip characteristics (e.g. speed, length, and purpose), travel mode (e.g. driving vs. cycling), and Origin-Destination (O-D) Metrics to generate models that accurately predict how traffic patterns change in response to factors like seasonality, time of day, and day of week.

This level of granularity often requires multiple sources of data collection and results in large, complex datasets, exacerbating the affordability and availability limitations that modelers face. Transportation analytics make it easier for modelers to source large datasets contextualized with trip characteristics, traveler demographics, and more, resulting in a more up-to-date and easy-to-use source of travel and traffic counter data to improve, calibrate, and validate models.

StreetLight InSight® allows transportation professionals to contextualize traffic data and build sophisticated models with aggregated demographics, trip attributes, and more.

Moreover, some agencies do not have sufficient resources to develop models at scale, yet they do need simplified models for occasional projects. In such cases, an agency can use transportation analytics as building blocks to develop a simplified model to support planning and policy decision-making.

Existing Datasets Aren’t Flexible Or Easily Comparable

Even with the afore-mentioned tools like modeling, sensors, and surveys, there remain some gaps in data from traditional traffic counters. One example is before-and-after scenarios. For example, say that residents react strongly to restrictions put into place to combat cut-through traffic on a specific street. A transportation department looking to evaluate the success of those measures can’t compare “before and after” scenarios unless they planned ahead and captured traffic volume and route information before putting the restrictions into place. 

Imagine also that planners want to prepare for a special event that will draw a large amount of visitors from outside the area. Traditional transportation counters can’t measure past events to determine traffic load and optimal re-routing. For this reason, they’re also limited when it comes to measuring the impact of external factors that may dramatically change traffic patterns and event attendance — a challenge highlighted by the COVID pandemic. In these cases, planners need both historical and up-to-date traffic data in order to inform how they prepare for events.

Until recently, these methods were the best we had, in spite of their limitations. But transportation analytics can now help address these gaps, giving transportation professionals information that they didn’t even realize they could access. Download our eBook to learn more about how transportation analytics enhance the insights offered by sensors, surveys, and modeling to provide full metrics for today’s transportation challenges.