Optimizing Skip-Stop Rail Transit Stopping Strategy using a Genetic Algorithm

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 technological improvement. However, since skip-stop operation does not serve all stations, passengers for certain origins-destinations could experience increased access time, waiting time, total travel time, and/or transfer. Only when the stopping and skipping stations are carefully coordinated can skipstop service benefit passengers and transit agencies. This research developed a mathematical model using a Genetic Algorithm that coordinated the stopping and skipping stations for skip-stop rail operation. Using the flexibility of a Genetic Algorithm, this model included many realistic conditions, such as different access modes, different stopping scenarios, different collision constraints, and different objective functions. Passengers were put into three types and nine groups depending on their origin-destination pairs and the station and transferchoices. Four types of collision constraints were developed depending on the skipstop strategy. For this research, Seoul Metro system Line No. 4 was used as an example. With skipstop operation, 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 due to skipping stations, although waiting, transfer, and additional access times increased by 24–38 percent