Real-time dynamic traffic assignment for route guidance: comparision of global predictive vs. local reactive strategies under stochastic demands

摘要

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 simple locally-oriented rules that react to measured conditions (no prediction). Results of numerical experiments usihg 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.