Real-Time Control of Buses in a Transit Corridor Based on Vehicle Holding and Boarding Limits

Abstract

A real-time mathematical programming model of buses operating on a transit corridor that incorporates vehicle-capacity constraints is proposed. The objective for the model is to minimize the total times experienced by all passengers in the system, from the moment they arrive at a stop to the moment they reach their destination. Two control policies are considered: (a) vehicle holding, which is applicable at any stop, and (b) boarding limits that constrain the number of passengers entering a vehicle even when the vehicle is at less than physical capacity, to increase operating speed. The objective function is quadratic, but not convex with linear constraints. This problem is solved by using MINOS in a reasonable amount of computation time. A case study in a high-demand scenario shows that the proposed control achieves reductions in the objective function of more than 22% and 12% compared with no control and only holding strategies, respectively.

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References

Ortúzar

J. de D.

Nested Logit Models for Mixed-Mode Travel in Urban Corridors. Transportation Research A, Vol. 17, No. 4 1983, pp. 283–299.

Eberlein

X.-J.

Real Time Control Strategies in Transit Operations: Models and Analysis. PhD thesis. Massachusetts Institute of Technology, Cambridge, 1995.

Barnett

On Controlling Randomness in Transit Operations. Transportation Science, Vol. 8, No. 2 1974, pp. 101–116.

Turnquist

and Blume

Evaluating Potential Effectiveness of Headway Control Strategies for Transit Systems. In Transportation Research Record 746, TRB, National Research Council, Washington, D.C., 1980, pp. 25–29.

Eberlein

X.-J.

Wilson

N. H. M.

and Bernstein

The Holding Problem with Real-Time Information Available. Transportation Science, Vol. 35, No. 1 2001, pp. 1–18.

Sun

and Hickman

The Holding Problem at Multiple Holding Stations. Proc., 9th International Conference on Computer-Aided Scheduling of Public Transport (CASPT), San Diego, Calif., 2004. http://fugazi.engr.arizona.edu/caspt/sun.pdf. Accessed March 15, 2008.

Zhao

Bukkapatnam

and Dessouky

Distributed Architecture for Real-Time Coordination of Bus Holding in Transit Networks. IEEE Transactions on Intelligent Transportation Systems, Vol. 4, No. 1 2003, pp. 43–51.

Zolfaghari

Azizi

and Jaber

A Model for Holding Strategy in Public Transit Systems with Real Time Information. International Journal of Transport Management, Vol. 2, 2004, pp. 99–110.

Puong

and Wilson

N. H. M.

A Train Holding Model for Urban Rail Transit Systems. Proc., 9th International Conference on Computer-Aided Scheduling of Public Transport (CASPT), San Diego, Calif., 2004. http://fugazi.engr.arizona.edu/caspt/puong.pdf. Accessed March 15, 2008.

10.

Suh

Chon

and Rhee

Effect of Skip-Stop Policy on a Korean Subway System. In Transportation Research Record: Journal of the Transportation Research Board, No. 1793, Transportation Research Board of the National Academies, Washington, D.C., 2002, pp. 33–39.

11.

and Liu

Real-Time Optimization Model for Dynamic Scheduling of Transit Operations. In Transportation Research Record: Journal of the Transportation Research Board, No. 1857, Transportation Research Board of the National Academies, Washington, D.C., 2003, pp. 48–55.

12.

Sun

and Hickman

The Real-Time Stop-Skipping Problem. Journal of Intelligent Transportation Systems, Vol. 9, No. 2 2005, pp. 91–109.

13.

Shen

and Wilson

N. H. M.

An Optimal Integrated Real-Time Disruption Control Model for Rail Transit Systems. Computer-Aided Scheduling of Public Transport ( Vob

and Daduna

, eds.), Springer-Verlag, New York, 2001, pp. 335–363.

14.

Sáez

Cortés

Sáez

Núñez

and Tirachini

Hybrid Predictive Control Strategy for a Public Transport System with Uncertain Demand. Proc., Triennial Symposium on Transportation Analysis (TRISTAN) VI, Phuket, Thailand, 2007.