A Short-Turning Strategy for the Management of Bus Bunching Considering Variable Spatial-Temporal Running Time

Abstract

Bus bunching could seriously damage the stability of transit system. This resultant instability always causes a dissatisfying performance of transit system. Traditional bus bunching control methods (e.g., holding control strategy) add slack to schedules or adapt cruising speed. The control methods can alleviate bus bunching in theory, but it is difficult to apply to actual operation, especially in busy traffic. The short-turning strategy only deals with spatial concentration of demand in the existing literatures. We find that the short-turning strategy is also very effective in alleviating bus bunching. In this study, based on the passenger arrival rate of each stop and the spatial-temporal running time, a short-turning model with bunching penalty is developed, and the waiting time of passengers and the operation cost are also considered. Based on data from Beijing Transportation Information Center, we take the Yuntong 111 bus line of Beijing as an example. Compared with the currently used timetable, it is found that a 46.78% reduction in bus bunching is achieved by using the optimal timetable, and there is no increase in operating costs.

Robustness and disturbances in public transport

“…( 2018b ). Short turning may also be used in connection with bus bunching as investigated in Tian ( 2021 ) and Tian et al. ( 2022 ).…”

“…( 2019a ) Determine factors related to the time of initial bunching incidents for a streetcar system Case study using AVL data for Toronto (Canada) Sajikumar and Bijulal ( 2021 ) Schedule planning at certain entry points Consideration of multiple-origins bus operation Sethuraman et al. ( 2019 ) Platooning Impact analysis regarding traffic control and energy consumption Sun and Schmöcker ( 2018 ) Passenger behavior Considering overtaking being allowed (or not) Tian ( 2021 ) Use of short-turning strategy to alleviate bus bunching Case for Beijing (China) Varga et al. ( 2019 ) Model-predictive control Includes a passenger waiting model Verbich et al.…”