Abstract
To model a driver's route choice behavior under inherent traffic system stochasticity and to further provide better route guidance with travel-time reliability guarantees, this paper examines two models to evaluate the travel-time robustness: absolute robust shortest path (ARSP) and α-percentile robust shortest path (PRSP) problems. A Lagrangian relaxation approach and a scenario-based representation scheme are integrated to reformulate the minimax and percentile criteria under day-dependent random travel times. The complex problem structure is decomposed into several subproblems that can be efficiently solved as standard shortest path problems or univariate linear programming problems. Large-scale numerical experiments with real-world data are provided to demonstrate the efficiency of the proposed algorithms.
Original language | English (US) |
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Article number | 6490060 |
Pages (from-to) | 943-954 |
Number of pages | 12 |
Journal | IEEE Transactions on Intelligent Transportation Systems |
Volume | 14 |
Issue number | 2 |
DOIs | |
State | Published - 2013 |
Externally published | Yes |
Keywords
- Algorithms
- route guidance
- traffic information systems
- traffic planning
ASJC Scopus subject areas
- Automotive Engineering
- Mechanical Engineering
- Computer Science Applications