Simulation Model for Estimating Bus Dwell Time by Simultaneously considering Numbers of Disembarking and Boarding Passengers

Abstract

Dwell time at a bus stop is one of the major components of bus travel time, and it is highly correlated with numbers of boarding and alighting passengers. With more resources from federal, state, and local governments currently being devoted to improving public transit services, transit ridership rates are expected to increase. Consequently, dwell time needs to be modeled in terms of ridership to allow accurate estimations of bus travel time. Typically, passengers can board a bus only through the front door but can choose to exit the vehicle through either the front or rear door. To estimate dwell time accurately, this study used data collected from Florida's Broward County Transit system to develop a binary door choice model predicting the proportion of alighting passengers who will use the front or rear door to disembark from the bus. Because the model explicitly considered passengers disembarking through the front and rear doors as well as passengers boarding through the front door, it was more effective in quantifying transit dwell time than the existing simulation algorithms available in CORSIM and VISSIM.

Get full access to this article

View all access options for this article.

References

Maloney

and Boyle

. Components of Travel Time on the Glen-dale Beeline Bus Network. In Transportation Research Record: Journal of the Transportation Research Board, No. 1666, TRB, National Research Council, Washington, D.C., 1999, pp. 23–27.

Special Report 209: Highway Capacity Manual, 3rd ed. TRB, National Research Council, Washington, D.C., 1985.

TCRP Report 100: Transit Capacity and Quality of Service Manual. TRB, National Research Council, Washington, D.C., 1999.

Dial

R. B.

Levinsohn

and Rutherford

G. S.

. Integrated Transit-Network Model (INET): A New Urban Transportation Planning System Program. In Transportation Research Record 761, TRB, National Research Council, Washington, D.C., 1980, pp. 33–40.

Guenthner

R. P.

and Sinha

K. C.

. Modeling Bus Delays due to Passenger Boardings and Alightings. In Transportation Research Record 915, TRB, National Research Council, Washington, D.C., 1983, pp. 7–13.

McKnight

C. E.

Passwell

R. E.

Ali

Kamga

and Cruz

. Impact of Congestion on New York Bus Service. New York City Transit, New York, NY, 1997.

Aashtiani

H. Z.

and Iravani

. Application of Dwell Time Function in Transit Assignment Model. In Transportation Research Record: Journal of the Transportation Research Board, No. 1817, Transportation Research Board of the National Academies, Washington, D.C., 2002, pp. 88–92.

Khasnabis

Karnati

R. R.

and Rudraraju

R. K.

. NETSIM-Based Approach to Evaluation of Bus Preemption Strategies. In Transportation Research Record 1554, TRB, National Research Council, Washington, D.C., 1996, pp. 80–89.

Abdelfattah

A. M.

and Khan

A. M.

. Models for Predicting Bus Delays. In Transportation Research Record 1623, TRB, National Research Council, Washington, D.C., 1998, pp. 8–15.

10.

TSIS/CORSIM User's Manual, Version 5.1. Federal Highway Administration, Washington, D.C., 2003.

11.

VISSIM User Manual, Version 4.10. PTV AG, Karlsruhe, Germany, 2005.

12.

Dueker

K. J.

Kimpel

T. J.

and Strathman

J. G.

. Determinants of Bus Dwell Time. Journal of Public Transportation, Vol. 7, No. 1 2004, pp. 21–40.

13.

ZRandom 1.2. www.zrandom.com/zrand/. Accessed July 15, 2005.