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Computational Algorithms and Modeling for the Traffic Flow
Objectives: Traffic congestions are faced with almost everyone living in big cities. This study focuses on the two main social costs in big cities i.e., traffic congestion and emissions. And also the minimization of both issues contradicts with each other. Methods/Statistical Analysis: In view of NP-hardness, heuristic methods might solve a kind of traffic congestion problem. And also Traffic equilibrium problem is one of nondeterministic combinational optimization problem which reduces the overall cost by finding the optimum paths with the shortest distance. Findings: The traffic congestion had been mainly increasing the air pollution problems, and also it might cause the bad traffic conditions such as idling, acceleration, and braking. It can be considered as the typical common tragedy in big cities because the limited roads are overused until traffic congestion into a jam. Traffic congestion mainly comes from traffic growth on the road even if there are no car accidents, and repairing and maintenance on the road. In this case, a traffic congestion problem can be solved with an estimation of traffic flow. An optimization model in this paper is described, which was applied on the traffic equilibrium problem. Improvements/Applications: The results are applied to solve some traffic equilibrium problems and several samples also are described.
Phantom Congestion, Pivotal Decomposition Algorithm, Shortest Path, Traffic Equilibrium, Traffic Network.
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