Retrospective Dynamic Evaluation of Bus Scheduling Performance Considering Input-Output Relationship

Abstract:

Real-time scheduling plays an important decision-making role in public transport operation organization because of its flexibility. Although the current scheduling （or dispatcher） performance evaluation can assess the implementation of the bus schedule， it ignores the passenger experience and enterprise input-output evaluation of the scheduling performance. It also does not support backtracking the implementation performance of the scheduling in the past. In order to enable dispatchers to accurately improve their dispatching skills and performance， solve problems such as wasted capacity caused by lagging dispatching decisions and increase passenger satisfaction， from the two dimensions of input-output and retrospective dynamic evaluation， this paper proposes a performance evaluation method for bus scheduling based on the three-stage Super-SBM （super-efficiency slack-based measurement） model. Considering the impact of road conditions and passenger flow on the scheduling performance， and based on the existing indicators， three output indicators， i.e.， travel speed， waiting time change rate， and standard deviation of load factor， are added from the perspective of passengers to build an evaluation system. The retrospective evaluation of the actual scheduling of two bus routes in Shanghai verifies that the evaluation method proposed in this paper can more accurately distinguish the difference in scheduling performances. The results show that the flexible scheduling strategy in peak periods can produce more obvious benefits than that in off-peak periods， and the scheduling strategy shows different applicability in different space-time scenarios. The retrospective analysis of the performance of historical scheduling records， on the one hand， can provide scheduling decision support for dispatchers in different scenarios， and on the other hand， can select appropriate learning samples for the real-time bus scheduling strategy generation technology based on machine learning， to improve the efficiency of bus operation and organization.