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.
Original language | English |
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Pages (from-to) | 57-82 |
Number of pages | 26 |
Journal | Transportation Research Part C: Emerging Technologies |
Volume | 81 |
DOIs | |
Publication status | Published - Aug 2017 |
Externally published | Yes |
Keywords
- Data driven
- Demand variance
- Probability distribution
- Route choice variance
- Statistical traffic assignment
- Variance decomposition
ASJC Scopus subject areas
- Civil and Structural Engineering
- Automotive Engineering
- Transportation
- Computer Science Applications