Trip Travel Time Distribution Prediction for Urban Signalized Arterials

Travel time prediction is a challenge, especially if we consider urban trips. For freeways well-known models for traffic flow and speeds are applicable, e.g., based on physical models inspired by hydrodynamic or statistical models ranging from more conventional to more advanced AI approaches. While for urban trips the traffic flow models are more complicated because, next to vehicle-vehicle interaction, also the influence of traffic signals has to be modeled. In this paper, a trip travel time distribution model for urban roads with fixed-time controlled intersections is introduced. The model explicitly considers urban traffic characteristics, including stochastic traffic processes at intersections, stochastic properties of traffic flow and signal coordination between intersections. Based on the proposed model, a trip travel time distribution prediction procedure is discussed, which considers time-varying demand and traffic control schemes. The model predicted results are further compared with those from VISSIM simulation data. It shows that the proposed trip travel time distribution prediction model can perform well both for undersaturated conditions and oversaturated conditions.