Evaluation of Arterial Signal Coordination with Commercial Connected Vehicle Data: Empirical Traffic Flow Visualization and Performance Measurement

Abstract

Emerging connected vehicle (CV) data sets have recently become commercially available, enabling analysts to develop a variety of powerful performance measures without deploying any field infrastructure. This paper presents several tools using CV data to evaluate traffic progression quality along a signalized corridor. These include both performance measures for high-level analysis as well as visualizations to examine details of the coordinated operation. With the use of CV data, it is possible to assess not only the movement of traffic on the corridor but also to consider its origin-destination (O-D) path through the corridor. Results for the real-world operation of an eight-intersection signalized arterial are presented. A series of high-level performance measures are used to evaluate overall performance by time of day, with differing results by metric. Next, the details of the operation are examined with the use of two visualization tools: a cyclic time-space diagram (TSD) and an empirical platoon progression diagram (PPD). Comparing flow visualizations developed with different included O-D paths reveals several features, such as the presence of secondary and tertiary platoons on certain sections that cannot be seen when only end-to-end journeys are included. In addition, speed heat maps are generated, providing both speed performance along the corridor and locations and the extent of the queue. The proposed visualization tools portray the corridor's performance holistically instead of combining individual signal performance metrics. The techniques exhibited in this study are compelling for identifying locations where engineering solutions such as access management or timing plan change are required. The recent progress in infrastructure-free sensing technology has significantly increased the scope of CV data-based traffic management systems, enhancing

Comparison at Scale of Traffic Signal Cycle Split Failure Identification from High-Resolution Controller and Connected Vehicle Trajectory Data

“…Over the last few years, crowdsourced connected vehicle (CV) trajectory data has emerged as a new dataset capable of producing and expanding existing traffic signal and arterial performance measures [7,[22][23][24][25][26][27][28][29][30][31][32][33]. With over 500 billion records generated each month in the United States, CV data enable the development of highly scalable techniques since no detection or communication equipment is required.…”