Path comparisons for a priori and time-adaptive decisions in stochastic,time-varying networks |
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| Affiliation: | 1. Department of Civil and Environmental Engineering, 212 Sackett Building, The Pennsylvania State University, University Park, PA 16802, USA;2. Department of Civil Engineering, ECJ 6.2, The University of Texas at Austin, Austin, TX 78712, USA;1. CIRRELT and HEC Montréal 3000, chemin de la Côte-Sainte-Catherine, Montréal, Canada H3C 3J7;2. CIRRELT and Department of Management and Technology, Université du Québec à Montréal, 315 rue Sainte-Catherine est, Montréal, Canada H2X 3X2;3. CIRRELT and Department of Mathematics and Industrial Engineering, École Polytechnique de Montréal, C.P. 6128, succursale Centre-ville, Montréal, Canada H3C 3J7;1. Faculty of Electric Power Engineering, Kunming University of Science and Technology, 650500, Kunming, China;3. College of Engineering, Shantou University, 515063, Shantou, China;4. College of Electric Power, South China University of Technology, 510640, Guangzhou, China |
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| Abstract: | Travel times in congested transportation networks are time-varying quantities that can at best be known a priori probabilistically. In such networks, the arc weights (travel times) are represented by random variables whose probability distribution functions vary with time. These networks are referred to herein as stochastic, time-varying, or STV, networks. The determination of “least time” routes in STV networks is more difficult than in deterministic networks, in part because, for a given departure time, more than one path may exist between an origin and destination, each with a positive probability of having the least travel time. In this paper, measures for comparing time-varying, random path travel times over a time period are given for both a priori optimization and time-adaptive choices (where a driver may react to revealed arrival times at intermediate nodes). The resulting measures are central to the development of methodologies for determining “optimal” paths in STV networks. |
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