Author: Renxin Zhong

Publisher:

Published: 2011

Total Pages: 544

ISBN-13:

For the surveillance part, this thesis concentrates on the development of a macroscopic traffic flow model to capture traffic dynamics on networks influenced by demand and supply uncertainties that are suitable for real-time traffic monitoring and control applications. To fulfill these objectives, a stochastic macroscopic dynamic traffic model, the stochastic cell transmission model (SCTM), which is based on the modified cell transmission model (MCTM) and the switching mode model (SMM), is proposed. The SCTM inherits the advantages of the MCTM and the SMM. However, there are several key differences between them, e.g. the MCTM and the SMM admit deterministic demand and stationary flow-density fundamental diagram while the SCTM accepts the random inflows (uncertain demand) as well as random parameters of the fundamental flow-density diagram (uncertain supply functions) with known means and variances of the freeway segment as exogenous inputs. Under the SCTM framework, the uncertain wavefronts are captured by probabilities of occurrence of operational modes which describe different congestion levels. The SCTM is calibrated and validated by several empirical studies. We also compare the performance of the SCTM with Monte Carlo Simulation of the MCTM (MCS-MCTM). The results confirm that the SCTM outperforms the MCS-MCTM. We apply the SCTM to estimate the queues and delays at signalized intersections and compare the results with some well-known delay and queue estimation formulas, e.g., Webster, Beckmann, McNeil, and Akcelik. The comparison results show a good consistency between the SCTM and these formulas. In addition, the SCTM describes the temporal behavior of the queue and delay distributions at signalized junctions with stochastic supply functions and (non-stationary) arrivals. In the traffic management part, optimal and robust decision making problems for managing uncertain network traffic are investigated. The proposed SCTM is applied to describe traffic dynamics on networks influenced by demand and supply uncertainties. The traffic management problems are formulated as stochastic dynamic programming problems. A closed form of optimal control law is derived in terms of a set of coupled generalized recursive Riccati equations. The robust decision making problem, which aims to act robustly with respect to the supply uncertainty and to attenuate the effect of demand uncertainty, can be recognized as an equivalent optimal decision making problem. Another implication of the proposed methodology is to make benefit from the inherent uncertainties, which is achieved by extending the conventional LQ optimal control theory to consider the indefinite terms of the state and input weighting matrices. The multiagent system (MAS) approach to access the traffic management for a general traffic network is discussed. The applications of the proposed methods to incident management are also highlighted. In conclusion, this thesis contributes to the literature on dynamic traffic assignment, stochastic dynamic traffic modeling and management, and to support further analysis and development in this area.