Characterizing the Uncertainty of Link Progression Speed Using Low-Frequency Probe Vehicle Data

Link progression speed is a key characteristic of urban traffic flow and is essential to developing effective signal coordination schemes. Typically, it is empirically determined as a fixed value regardless of its stochastic nature. Previous studies have proposed both analytical and simulation models to investigate the vehicle progression delay caused by different sources (i.e., midblock traffic, pedestrian crossing, and on-street parking). However, these models need explicit modeling efforts and an intensive collection of traffic data. This paper provides a practical approach that is easy to implement and accurate enough to characterize link-specific progression speed and its uncertainty. The pure input is the low-frequency probe vehicle data which is the most widely available and large-scale data source to provide spatiotemporal traffic information. By assuming the effects of progression delay are relatively stable during the same period of a day, the instantaneous speed of probe vehicles is aggregated to calculate progression speed at different locations of a link. To describe the continuous vehicle progression process and capture realistic driving behaviors, two latent progression states (i.e., normal and cautious driving states) and their spatial correlation are encoded to each cell of a link and formulated in a Hidden Markov Model (HMM). The effectiveness of the proposed method has been validated using field data, and the concept of reliable green bandwidth is also demonstrated to discuss the application of the proposed method to enhance the reliability of signal coordination.