The time-of-day (TOD) mode is the most widely used strategy for traffic signal control with fluctuating flows. Most studies determine TOD breakpoints based on traffic volumes collected by infrastructure-based detectors (e.g., loop detectors). However, these infrastructure-based detectors have low coverage and high maintenance cost. With the deployment of probe vehicles, vehicle trajectory data has become available, providing a new data source for signal control. This paper proposes an approach to identify TOD breakpoints at an isolated intersection based on the trajectory data of probe vehicles, instead of conventional traffic volumes, with under-saturated traffic conditions. It is shown that the speeds of queueing shockwaves capture the characteristics of the traffic volumes according to the queueing shockwave theory. Data from multiple sampling days are aggregated to compensate for the limitations of low market penetration rates and long sampling intervals. Queue joining vehicles are then identified to obtain the speeds of queueing shockwaves. The bisecting K-means algorithm is applied to cluster periods, which are characterized by queueing shockwave speeds, to identify TOD breakpoints. The numerical studies validate that the speeds of queueing shockwaves capture the trend of traffic volumes. The clustering algorithm identifies the same TOD breakpoints for queueing shockwave speeds and traffic volumes. As long as the number of sampling days is large enough, the proposed method can handle low penetration rates (e.g., 2%) and long sampling intervals (e.g., 20 s), and thus achieve a comparable performance to the ideal conditions with high penetration rates (e.g., 100%) and short sampling intervals (e.g., 1 s).
Macroscopic Traffic Flow Control Via State Estimation
