Konferensartikel

Optimizing the pedestrian facilities plan in transfer stations is the problem of adjusting the facilities on the layout of pedestrian flow route and the number of machines in service to service to meet the level of services requirements. In the practice, the operation of pedestrian facilities plan is always associated with the staff assignment. Hence, we develop a machine learning based integrated pedestrian facilities planning and staff assignment optimization model in transfer stations to schedule the pedestrian facilities plan and the staff assignment together. It aims to minimize the staff assignment cost and the deviation of working time of each employee of the station. The minimizing of the deviation gains the fairness of the assignment plan. The facilities plan is enforced by the level-of-services requirement in three performance indicators including transfer capacity, transfer average time and level-of-service. The performance indicators of facilities plans are evaluated by a simulation-based machine learning method. Based on simulation results, the random forest method fits a quantitative relationship among performance indicators of the facilities plans with operation scenario attributes and facilities plan attributes. The experiments on the case study of Xipu station show the integrated model can return pedestrian facilities plans which meet the level of service requirements and assign employees fairly of each period in a day and minimize the labor cost. The solutions of pedestrian facilities plan and staff assignment plan for possible operation scenarios in future are also suggested to station manager by our integrated method.

Transfer stations, Facilities plan, Staff assignment, Simulation, Random forest