Demand-driven integrated train timetabling and rolling stock scheduling on urban rail transit line

Abstract

This paper aims to simultaneously optimise train timetabling and rolling stock scheduling in a bidirectional urban rail transit line. A novel variable train composition strategy is adopted to respond to time-dependent passenger demand by allowing modular train units to split and couple with each other at depots. We mathematically model this problem in an arrival time revision framework, simplifying the problem structure and rendering the model solved by a commercial solver. In particular, the proposed model considers two realistic first-in-first-out rules to capture passenger transportation accurately in the oversaturated urban rail transit system. The efficiency of the proposed strategy and model is validated on an illustrative example and a real-world instance in the Yizhuang Line of Beijing Subway. Results show that adopting the variable train composition strategy can save 63.6% and 44.6% of total passenger waiting time compared to the current fixed train composition strategy with eight and six units.