Vehicle Index Estimation for Signalized Intersections Using Sample Travel Times

We introduce in this paper the concept of vehicle indices in a cycle at a signalized intersection which are the positions of vehicles in the departure process of the cycle. We show that vehicle indices are closely relate to the vehicle arrival and the departure processes at the intersection. Based on vehicle indices and sample travel times collected from mobile sensors, a three-layer Bayesian Network model is constructed to describe the stochastic intersection traffic flow by capturing the relationship of vehicle indices, and the arrivals and departure processes. The non-homogeneous Poisson process and log- normal distributions are used respectively to model the stochastic arrival and departure processes. The methods of parameter learning and vehicle index inference are presented based on the observed intersection travel times. Simplification to the methods is discussed to reduce the computational effort of parameter learning and vehicle index estimation. The model is tested using data from NGSIM, field test, and simulation with reasonable results.