Day, Christopher M., H. Li, L.M. Richardson, J. Howard, T. Platte, J.R. Sturdevant, and D.M. Bullock, “Detector-Free Optimization of Traffic Signal Offsets with Connected Vehicle Data,” Transportation Research Record: Journal of Transportation Research Board, No. 2620, Transportation Research Record of the National Academies, Washington, D.C., pp 54-68. 2017 (Received TRB AHB25 Committee 2017 Best Paper Award).




It has recently been shown that signal offset optimization is feasible using vehicle trajectory data at low levels of market penetration. This study performs offset optimization on two corridors using this type ofdata. Six weeks oftrajectory splines were processed for two corridors including 25 signalized intersections, in order to create vehicle arrival profiles, using a proposed procedure called "virtual detection." After processing and filtering the data, penetration rates between 0.09-0.80% were observed, varying by approach. The arrival profiles were statistically compared against those measured with physical detectors, with the majority of the approaches showing statistically significant goodness-of-fit at a 90% confidence level. Finally, the virtual detection arrival profiles were used to optimize offsets, and compared against a solution derived from physical detector arrival profiles. The results demonstrate that virtual detection can produce good quality offsets with current market penetration rates of probe data. The study also includes a sensitivity analysis to the sample period, which shows that two weeks of data may be sufficient for data collection at current penetration rates.


Traffic signals, connected vehicles, vehicle trajectories, offset optimization

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