Routes And Trajectories Based Dynamic Models For Traffic Prediction And Control

Abstract

Network traffic assignment/equilibrium models have been widely used for transportation planning. For traffic management, one is interested on how traffic patterns change dynamically since equilibrium cannot be reached instantaneously. This dissertation focuses on modeling short-term traffic patterns in a transportation network, and addresses these topics: (1) comparison of loading route flows versus link flows, (2) development of a mesoscopic model for loading route flows in a network, (3) estimation of route flows based on the mesoscopic model, and (4) optimizing traffic signal timings based on the estimated route flows.With regard to the first topic, many models propagate flows through a network using link flows and independent turning probabilities (ITP) at nodes. Chapter 2 describes the effects of the ITP assumption on the traffic patterns that occur based on route flow loading; this provides the motivation for using route flows in this research.Route trajectories are the spatial-temporal realizations of vehicle route flow demands. This dissertation proposes a mesoscopic simulation platform where route flows are propagated through the network and dynamic trajectories are computed. Under interrupted and uninterrupted flow conditions, route trajectories from the mesoscopic model are compared with ones from a microscopic model, the latter model being used to provide realistic data since real data at this level of detail is not currently available. Results from both models match well; the corresponding traffic patterns are very similar, both graphically and statistically.In chapter 5, a model is presented for estimating temporal route flow demands when real-time data is available. The model is formulated as a bi-level optimization problem where a least-square model is constructed at the upper level and the mesoscopic model is utilized at the lower level to relate flow demands and route trajectories. Based on real-time measurements, this model estimates dynamic route flows that are consistent with observed traffic patterns. Computational results using a micro-simulator for "real" data show that the model estimates well the route flows loaded in the micro-simulation model.Finally, in chapter 6, a traffic signal control optimizer is developed based on the mesoscopic model and a gradient search to optimize any given performance index such as average delay. A numerical example shows that the optimizer significantly improves the performance index and the approach may be used for on-line traffic signal control.