Developing Novel Performance Measures for Traffic Congestion Management and Operational Planning Based on Connected Vehicle Data

Khadka, Swastik; Li, Pengfei “Taylor”; Wang, Qichao · 2022 · OpenAlex-citations

DOI: 10.1061/(asce)up.1943-5444.0000835

archive: archived pipeline: cataloged verified

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Summary

This paper addresses the limitations of traditional traffic monitoring methods, which rely on fixed-spot infrastructure sensors that are costly to maintain and prone to spatial bias. To overcome these challenges, the authors explore the use of Internet-Connected Vehicle (ICV) data, a crowdsourced dataset containing high-resolution GPS waypoints and abnormal vehicle events (e.g., hard braking). The research aims to develop novel performance measures for traffic congestion management and operational planning on both freeways and arterials, leveraging ICV data that represents 10% to 15% of moving vehicles in the Dallas-Fort-Worth area. The study employs a scalable data processing framework to handle the massive volume of raw ICV data. This framework includes a data reduction method using rasterized GIS maps and the OpenCV library to filter waypoints within specific areas of interest, and a grid-based map-matching algorithm to assign vehicle trajectories to road links efficiently. The authors propose two new performance metrics: a time-dependent link speed map integrated with slow vehicle movements to identify queue propagation, and the Degree of Speed Harmonization (DSH), which measures speed variability to indicate stop-and-go traffic patterns associated with safety hazards and emissions. Two case studies demonstrate the application of these metrics. In Case Study I, focusing on a freeway segment of I-20 in Arlington, Texas, the authors used ICV data to directly measure queue length and propagation at bottlenecks. By identifying waypoints with speeds below 40 MPH as "slow movements," they constructed time-space diagrams that visualized spatio-temporal bottleneck characteristics, such as queue formation at ramps and propagation upstream. The results showed that DSH values decreased within queues due to constrained vehicle movement, while the speed-slow movement metric effectively highlighted congestion zones caused by road construction during the pandemic lockdown period. In Case Study II, the authors applied ICV data to arterial congestion management. They combined ICV trajectories with high-resolution traffic signal control logs to generate "ground-truth" time-space diagrams. This approach allowed for the direct measurement of actual vehicle delay based on slow movement percentages, eliminating the need to assume non-delay travel speeds. The findings indicate that ICV data provides a cost-effective, high-resolution alternative to traditional sensors, enabling precise identification of hidden bottlenecks and detailed analysis of traffic signal performance. The study concludes that ICV data holds significant potential for improving traffic congestion management and operational planning by offering comprehensive, continuous coverage of traffic conditions.

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• traffic density