Quantifying Congestion Through Dynamic Applications of Travel Time Data

In our last blog post, The Role of Emerging Data in Traffic Data Collection Programs we illustrated why emerging data sources belong in traffic data programs. One of the main reasons is that emerging sources provide insights across all areas, 24 hours a day, 365 days a year. In other words, they provide flexibility that traditional data sources do not offer. This added flexibility significantly enhances the ability to get quick and timely traffic insights, such as travel time.

Well, what is travel time?

Having an accurate, up-to-date pulse on how long it takes vehicles to get from point A to point B is fundamental for transportation professionals to understand how their road segments are performing. In its most basic sense, the Federal Highway Administration defines travel time as “the time necessary to traverse a route between any two points of interest.” For transportation professionals, the importance of having an accurate and reliable pulse on how travel time fluctuates hour-to-hour and day-to-day can’t be understated.

At a high-level, the metric helps ensure traffic cues remain within a reasonable level for the travelling public. If this is not the case, transportation professionals are able to make changes to the road network to decrease travel time. Some common ways to achieve this is through changing signal timing at intersections, introducing cue warning systems, or making changes to the built environment. However, in order to implement these changes, it’s critical to have a quick, flexible way to measure travel time. So, if it’s that important, how do we measure it?

Collecting travel time data

Traditional methods of travel time data collection include methods such as ‘floating car’, license plate matching, as well as sensors and probes. These methods are widely used and well researched through wide application in the field. However, they generally take extensive time to deploy, and once they start collecting data, information is limited only to areas where equipment is deployed. Although they generally provide accurate measurements, they do not offer transportation professionals the ability to look at travel time beyond pre-planned road segments. This poses significant challenges for those needing to get a read on congestion without the significant upfront planning generally required. For example, deciding on whether or not to close a road for construction is increasingly difficult when there are no sensors, and ultimately no ability to actively quantify how surrounding road networks will react. 

As a result, agencies have been testing new, dynamic data sources and collection methods. These emerging methods typically collect data through large networks of connected vehicles and network-enabled devices such as smartphones. For a comprehensive look at these data sources and collection methods, visit our eBook, The Definitive Guide to Mobility Data Sources for Government. Although these sources can seem foreign and somewhat daunting, they offer meaningful alternatives for transportation professionals to collect accurate, reliable travel time data on-demand. Instead of lengthy timelines to deploy equipment or coordinate ‘floating vehicles’, officials can tap into a network of suppliers to get instant, accurate travel time. For example, the City of Ottawa, Ontario, used emerging data as a flexible, on-demand way to quantify travel time on roadways surrounding their Stage 2 Light Rail Transit (LRT) Project

How have dynamic sources of travel time been used? 

The City of Ottawa’s Rail Construction Program needed a way to actively measure and manage traffic congestion along construction corridors in near-real time. More specifically, they needed a way to easily quantify pre-construction travel times across key corridors, so they could establish baselines to measure against over the course of their Stage 2 Light Rail Transit Project construction. 

However, they recognized the limitations of obtaining hardware sensors to collect travel time data. As a result, the City of Ottawa used the UrbanLogiq platform to measure and manage congestion along construction corridors in near-real-time using location-based services data. This data came pre-populated in UrbanLogiq’s cloud-based platform, where Ottawa staff can easily interact with and create data visualizations and summary charts.

Having access to up-to-date and timely data helped City staff to understand when lane closures had a nominal effect on traffic flow, making project scheduling more efficient and helping the project remain on time and on budget. Similarly, access to a constant stream of reliable travel time information through the UrbanLogiq platform enabled the City to optimize road and lane closures.

Want to learn more?

Want to learn more about how UrbanLogiq provides transportation professionals with accurate and reliable travel time data? Contact our team at info@urbanlogiq.com.