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

Mahmud, Shoaib; Day, Christopher M. · 2023 · OpenAlex-citations

DOI: 10.4236/jtts.2023.133016

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Summary

This study addresses the challenge of evaluating arterial signal coordination quality using commercially available connected vehicle (CV) data, eliminating the need for field infrastructure. The authors aim to develop tools for both high-level performance measurement and detailed empirical traffic flow visualization. The research is motivated by the increasing availability of disaggregate CV trajectory data from commercial providers, which allows for the assessment of traffic progression across various origin-destination (O-D) paths rather than just dominant through movements. The study focuses on a 2.6-mile, eight-intersection signalized corridor on US 20 in Dubuque, Iowa. The methodology utilizes CV trajectory data comprising approximately 53,656 unique vehicle journeys and 3.9 million waypoints collected over twenty weekdays in October 2021. Data processing involved geofencing, filtering for journey continuity, and map-matching to convert GPS coordinates into linear distances along the corridor. The authors developed delay-based metrics, including travel time index and delay, as well as the Smoothness of the Flow of Traffic (SOFT) metric, which quantifies platoon progression by analyzing the frequency content of speed data via Fourier transforms. Additionally, the study introduces two visualization tools: a cyclic time-space diagram (TSD) and an empirical platoon progression diagram (PPD). These visualizations aggregate trajectories by transforming time-of-day into "time in cycle" to reveal patterns of stopping, slowing, and queuing across the corridor. The results demonstrate that CV data effectively captures corridor performance across different O-D paths. High-level metrics revealed varying performance levels by time of day. The visualization tools provided deeper insights than aggregated metrics alone; specifically, comparing flow visualizations for different O-D paths uncovered secondary and tertiary platoons on certain corridor sections that were invisible when analyzing only end-to-end journeys. Speed heat maps further identified specific locations and extents of queues. The study found that the low penetration rate of CV data (3–6% of annual average daily traffic) was successfully mitigated by aggregating data across multiple days with consistent signal timing plans. The significance of this work lies in demonstrating the utility of infrastructure-free CV data for holistic traffic management. By combining quantitative metrics with empirical visualizations, transportation agencies can identify specific locations requiring engineering solutions, such as access management or timing plan adjustments. The proposed tools allow for a more accurate assessment of corridor operations by considering all O-D paths, offering a robust alternative to traditional detector-based methods and supporting more informed decision-making in traffic signal coordination.

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• v2x connected vehicle