Mathematical modeling for optimizing skip-stop rail transit operation strategy using genetic algorithm.

PDFTech ReportRail transitGenetic algorithmsMathematical modelsMathematical methodsUnited StatesNational Center for Transportation Management, Research and Development (U.S.)Lee, Young-JaeMorgan State University. Dept. of Transportation and Urban Infrastructure StudiesNational Center for Transportation Management, Research and Development (U.S.)US Transportation Collection"With skip-stop rail transit operation, transit agencies can reduce their operating costs and fleet size,and passengers can experience reduced in-transit travel times without extra track and technologicalimprovement. However, since skip-stop operation does not serve all the stations, passengers atexclusive stopping stations can possibly experience increased access time, waiting time, total traveltime, and transfer. Only when the stopping stations are carefully coordinated can skip-stop servicesbenefit passengers and transit agencies.This research developed an optimization model using a Genetic Algorithm that coordinated thestopping stations for skip-stop rail operation. Using the flexibility of the Genetic Algorithm, thismodel included many realistic conditions, such as different access modes, different stoppingscenarios, different collision constraints, different objective functions, and etc.For this research, the Seoul Metro system\ue2\u20ac\u2122s line No. 4 was used as an example. With skip-stopoperation, total travel time became about 17-20 percent shorter than with original all-stop operation,depending on the stopping constraints. In-vehicle travel time became about 20-26 percent shorter dueto skipping stations, although waiting, transfer, and additional access times increased by 24-38percent.