Travel Time Distributions on Urban Streets: Their Estimation with a Hierarchical Bayesian Mixture Model and Application to Traffic Analysis Using High-Resolution Bus Probe Data

摘要

This paper develops a hierarchical Bayesian mixture travel time model to capture the interrupted nature of urban traffic flows. It uses high-resolution bus probe data to estimate travel times on urban streets for short links rather than for long paths, and reveals predominantly bimodal travel time distributions at the link level, with one mode corresponding to travels without delays and the other travels with delays. This bimodal travel time distribution is then used to analyze traffic operations and identify congestion. The advantage of the mixture model is demonstrated using empirical bus probe data, and the results are encouraging.