Optimal train scheduling under a flexible skip-stop scheme for urban rail transit based on smartcard data

Skip-stop operation is a low cost approach to improving transit operating speed. This paper proposes a new methodology to design a skip-stop strategy for urban rail lines aiming to minimize total travel time while at the same time serve the maximum number of passengers in off-peak hours. Different from the conventional “A/B” scheme, the proposed new scheme called the Flexible Skip-Stop Scheme (FSSS) can provide more combinations of stop modes to accommodate passenger demand. The train headway is optimized, while the train capacity is taken into consideration. A Genetic Algorithm is developed to efficiently search for the optimal solution. Based on one-way real passenger origin-destination (O-D) pairs and entry and exit time data at the stations obtained from the smartcard system, a case study on a single metro rail line in the City of Shenzhen has been conducted. Kinds of scenarios are designed, the privilege of the station which able to be skipped is limited to specific stations according to their passenger flow. Results show that the high demand station trends to achieve the optimal under the All-stop scheme, while skip a low demand station would bring sizable savings for total passenger travel time. This can effectively limit the feasible solution set and reduce the computation time. In addition, tests show that the model can produce skip-stop schedules that not only decrease the total passenger travel time at a given service level, but it also generates significant transit agency cost savings, while the number of skipped stations of the optimized skip-stop operation scheme remains stable.