Abstract
Traffic demands at intersections vary across various periods of a day and from day to day. Generally, fixed time traffic signals are designed considering the average traffic flows across multiple days over a predetermined time interval. This approach overlooks the day to day variability in traffic demand, leading to inefficient and unreliable signal control performance. A signal plan should be robust such that it is less sensitive to demand variations and can maintain near-optimal performance during varying traffic demand. To address this need, the paper presents a new offline scenario-based framework, named Metaheuristic Robust plan Approach (MHRA), that identifies a robust plan for fixed time signals. MHRA includes a heuristic that considers optimum signal plan for various demand scenarios and corresponding costs to find a robust solution. The numerical experiments are performed using realistic traffic demand scenarios on an arterial corridor to verify the MHRA framework. The outcomes concluded that the framework produces a robust signal plan that outperforms a nominal signal plan based on average traffic demand and maintains stable performance under varying demand. Benchmarking MHRA with other scenario-based approaches proposed in the literature such as mean-variance optimization and conditional value at risk minimization confirms better efficiency for MHRA.
Original language | English |
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Journal | Journal of Intelligent Transportation Systems: Technology, Planning, and Operations |
DOIs | |
Publication status | Accepted/In press - 2021 |
Keywords
- Day to day variation
- fixed time traffic signals
- metaheuristic approach
- robust traffic signal
- varying traffic demand
ASJC Scopus subject areas
- Control and Systems Engineering
- Software
- Information Systems
- Automotive Engineering
- Aerospace Engineering
- Computer Science Applications
- Applied Mathematics