A short-turning strategy to alleviate bus bunching

Some stops on busy bus lines regularly suffer from bus bunching, which refers to a bus arriving with a little headway to its predecessor. This phenomenon increases scheduling difficulties and has a negative impact on the passenger experience due to unreasonable scheduling. The conventional holding strategy aims to alleviate this problem by holding buses at control points. However, the holding strategy has the drawbacks of creating large deviations from the original schedule and prolonging passenger waiting time when confronted with traffic congestion. This study proposes an innovative short-turning strategy to alleviate bus bunching by the deliberate conversion of a few regular trips to short-turning trips. A nonlinear optimisation model is developed by rescheduling a set of trips using the short-turning strategy to minimise schedule deviation from the original schedule. The nonlinear short-turning model is then converted into a linear form that is solvable by CPLEX. Based on real data from the Yuntong 111 bus line in Beijing, China, the proposed short-turning strategy is deployed in a simulation experiment. The results show that the short-turning strategy is superior at alleviating bus bunching than the alternatives of no control strategy and the holding strategy. Compared with no control strategy, the short-turning strategy can achieve a more than 43.44% reduction in schedule deviation and significantly reduce total passenger waiting time by up to 8.99%.

[1]  Erik Jenelius,et al.  Real-time short-turning in high frequency bus services based on passenger cost , 2017, 2017 5th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS).

[2]  Nigel H. M. Wilson,et al.  Real-time holding control for high-frequency transit with dynamics , 2016 .

[3]  Chao Lei,et al.  Multiline Bus Bunching Control via Vehicle Substitution , 2019, Transportation Research Part B: Methodological.

[4]  Satish T. S. Bukkapatnam,et al.  Optimal Slack Time for Schedule-Based Transit Operations , 2006, Transp. Sci..

[5]  Ricardo Giesen,et al.  Real-Time Control of Buses in a Transit Corridor Based on Vehicle Holding and Boarding Limits , 2009 .

[6]  Kari Watkins,et al.  A real-time bus dispatching policy to minimize passenger wait on a high frequency route , 2015 .

[7]  Arun Mani,et al.  Real-Time Vehicular Traffic Analysis using Big Data Processing and IoT based Devices for Future Policy Predictions in Smart Transportation , 2019, 2019 International Conference on Communication and Electronics Systems (ICCES).

[8]  Ricardo Giesen,et al.  Analysis of real-time control strategies in a corridor with multiple bus services , 2015 .

[9]  Tao Tang,et al.  A Short Turning Strategy for Train Scheduling Optimization in an Urban Rail Transit Line: The Case of Beijing Subway Line 4 , 2018, Journal of Advanced Transportation.

[10]  Donald D. Eisenstein,et al.  A self-coördinating bus route to resist bus bunching , 2012 .

[11]  Avishai Ceder,et al.  Public Transit Planning and Operation: Theory, Modeling and Practice , 2007 .

[12]  Alejandro Tirachini,et al.  Optimal design and benefits of a short turning strategy for a bus corridor , 2011 .

[13]  Gilbert Laporte,et al.  A short-turning policy for the management of demand disruptions in rapid transit systems , 2016, Ann. Oper. Res..

[14]  Avishai Ceder,et al.  Public Transit Planning and Operation , 2007 .

[15]  Cristián E. Cortés,et al.  Hybrid predictive control for real-time optimization of public transport systems' operations based on evolutionary multi-objective optimization , 2010 .

[16]  Ziyou Gao,et al.  Robust dynamic bus controls considering delay disturbances and passenger demand uncertainty , 2019, Transportation Research Part B: Methodological.

[17]  G. F. Newell Control of Pairing of Vehicles on a Public Transportation Route, Two Vehicles, One Control Point , 1974 .

[18]  Yanfeng Ouyang,et al.  Dynamic bus substitution strategy for bunching intervention , 2018, Transportation Research Part B: Methodological.

[19]  Oded Cats,et al.  Railway disruption timetable: Short-turnings in case of complete blockage , 2016, 2016 IEEE International Conference on Intelligent Rail Transportation (ICIRT).

[20]  Lujie Chen,et al.  The application of big data analytics in optimizing logistics: a developmental perspective review , 2019, Journal of Data, Information and Management.

[21]  Hefu Liu,et al.  The role of big data analytics in enabling green supply chain management: a literature review , 2020, Journal of Data, Information and Management.

[22]  Qiang Meng,et al.  Robust optimization model of schedule design for a fixed bus route , 2012 .

[23]  Omar J. Ibarra-Rojas,et al.  Planning, operation, and control of bus transport systems: A literature review , 2015 .

[24]  Rob M.P. Goverde,et al.  A microscopic model for optimal train short-turnings during complete blockages , 2017 .

[25]  Zhiyuan Liu,et al.  Bus stop-skipping scheme with random travel time , 2013 .

[26]  Ricardo Giesen,et al.  How much can holding and/or limiting boarding improve transit performance? , 2012 .

[27]  Fahim Arif,et al.  Real-time data processing scheme using big data analytics in internet of things based smart transportation environment , 2019, J. Ambient Intell. Humaniz. Comput..

[28]  Liping Fu,et al.  Optimization of headways with stop‐skipping control: a case study of bus rapid transit system , 2015 .

[29]  Yunlong Zhang,et al.  Analyzing Urban Bus Service Reliability at the Stop, Route and Network Levels , 2009 .

[30]  Liping Fu,et al.  Design and Implementation of Bus–Holding Control Strategies with Real-Time Information , 2002 .