On the variance of recurrent traffic flow for statistical traffic assignment |
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Affiliation: | 1. Department of Civil Engineering and Architecture, University of Catania, Via Santa Sofia 64, Catania 95123, Italy;1. Department of Civil and Environmental Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, United States;2. Heinz College, Carnegie Mellon University, Pittsburgh, PA 15213, United States |
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Abstract: | This paper generalizes and extends classical traffic assignment models to characterize the statistical features of Origin-Destination (O-D) demands, link/path flow and link/path costs, all of which vary from day to day. The generalized statistical traffic assignment (GESTA) model has a clear multi-level variance structure. Flow variance is analytically decomposed into three sources, O-D demands, route choices and measurement errors. Consequently, optimal decisions on roadway design, maintenance, operations and planning can be made using estimated probability distributions of link/path flow and system performance. The statistical equilibrium in GESTA is mathematically defined. Its multi-level statistical structure well fits large-scale data mining techniques. The embedded route choice model is consistent with the settings of O-D demands considering link costs that vary from day to day. We propose a Method of Successive Averages (MSA) based solution algorithm to solve for GESTA. Its convergence and computational complexity are analyzed. Three example networks including a large-scale network are solved to provide insights for decision making and to demonstrate computational efficiency. |
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Keywords: | Statistical traffic assignment Probability distribution Demand variance Route choice variance Variance decomposition Data driven |
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