Y. Hawas

COMPARATIVE ANALYSIS OF ROBUSTNESS OF CENTRALIZED AND DISTRIBUTED NETWORK ROUTE CONTROL SYSTEMS IN INCIDENT SITUATIONS

A procedure for providing real-time route guidance in congested vehicular traffic networks is described. The control procedure, implementable in a decentralized scheme, envisions a set of distributed local controllers in the network in which every controller can extract only limited information from detectors. The assignment logic is driven by informed local search procedures and heuristics. A simulation-assignment model was developed and used to assess the effectiveness and robustness of the procedure in dealing with normal as well as incident traffic conditions. A comparative study was undertaken to gauge the performance of the distributed control structure against a benchmark scenario of the time-dependent system-optimal logic in a centralized architecture. The assessment was conducted under various operational scenarios of different incident links, durations, and severity. The findings of the simulation-based experiments indicated that the distributed scheme is more robust than the centralized time-dependent scheme under incident conditions.

Real-Time Dynamic Traffic Assignment for Route Guidance: Comparison of Global Predictive vs. Local Reactive Strategies Under Stochastic Demands

Abstract This paper describes two strategies for real-time route guidance of equipped motorists in a congested traffic network, and presents a comparative assessment of the strategies’ effectiveness and robustness under stochastic origin-destination (O-D) demands. The first approach consists of a global network-level system optimal assignment algorithm implemented in a rolling-horizon framework, with predicted O-D demands. The second consists of simpler locally-oriented rules that react to measured conditions (no prediction). Results of numerical experiments using a simulation-assignment model (DYNASMART) under different scenarios of demand variability and forecast error are presented with regard to (1) sensitivity of the RH solution to stage length, roll period and forecast errors, and (2) comparative performance and robustness of the two approaches under stochastically varying demands and imperfect forecasts.