Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
VISSIM: a multi-parameter sensitivity analysis
Proceedings of the 38th conference on Winter simulation
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This paper presents how an optimal optimization method, Genetic Algorithm (GA), is applied for finding a suitable combination of VISSIM parameters. The North-South (N-S) Expressway is investigated and simulated in VISSIM platform using field data obtained from Traffic Information Collecting System (TICS) in Shanghai. Numerous simulation tests indicate that the following main parameters have affected simulation precision most deeply, such as Desired Speed in Reduced Speed Area (DSRSA), Desired Lane-Change Distance (DLCD), and Wiedemann99 car-following parameters, the average desired distance between stopped cars (CCO), the headway time (in second) that a driver wants to keep at a certain speed (CC1), and safety distance a driver allows before he intentionally moves closer to the car in front (CC2). The prepositional parameter combination of DSRSA, DLCD, CCO, CC1 and CC2 is 40,500, 1.5, 0.8 and 3.50 for peak time traffic.