This paper proposes a connected vehicle-based traffic signal control scheme that seeks to improve both vehicle and pedestrian operations. Real-time information on vehicle speeds and locations is combined with knowledge of pedestrian arrivals to optimize signal timings that minimize a weighted average of vehicle and pedestrian delays. Such real-time pedestrian information might be available using existing sensors—such as pedestrian push buttons or infrared detectors—as well as in a connected environment. The algorithm implements a rolling-horizon optimization framework that optimizes signal phase sequences over some period but only implements the first phase in the optimized sequence. The results reveal that considering pedestrians in the optimization can improve delays to both pedestrians and vehicles compared with ignoring pedestrians. Within the proposed framework, average vehicle delay increases and average pedestrian delay decreases as more weight is assigned to pedestrian delay in the optimization. In general, the average person delay can be minimized when the relative weight between vehicle and pedestrian delay is consistent with the average occupancy rate of cars. However, a different weight may be chosen to prioritize pedestrian movement, if desired. These results are robust under varying demand levels and demand patterns. The effectiveness of the algorithm decreases as the information level of pedestrian arrivals decreases, and the algorithm becomes ineffective when information from fewer than 60% pedestrians is available. However, the detection of more than 60% of pedestrians can likely be achieved using existing technologies and thus would likely be available in a connected environment.
A real-time network-level traffic signal control methodology with partial connected vehicle information
