A hybrid genetic algorithm-parallel simulation model for lane closure scheduling

Abstract

This paper introduces GAPSIM, a hybrid Genetic Algorithm-Parallel SIMulation model for scheduling of lane closure requests that aims to minimize total traffic delay in a network over a 24-hour period. Genetic algorithm is used as the search engine while in microscopic traffic simulation tool is used to estimate traffic delay under a set of lane closure schedule. To save computing time, traffic simulations are run in parallel in the different processors of a multi-processor machine. To speed up the convergence to the recommended lane closure schedule, three improvement techniques, namely precondition, standard error criterion, and termination criterion, are also implemented. GAPSIM has been tested in an example problem involving 20 lane closure requests in a day in a suburban road network consisting of 986 links, 397 nodes and 22 origin-destination zones. The results show that, the parallel simulations, combined with the improvement techniques, produces 89% saving in computing time.