Acceptable Wait Time Models at Transit Bus Stops

S. A. Arhin, A. Gatiba, M. Anderson, B. Manandhar, M. Ribbisso

Abstract


This study aimed at determining patrons’ acceptable wait times beyond the bus scheduled arrival time at bus stops in Washington, DC and to develop accompanying prediction models to provide decision-makers with additional tools to improve patronage. The research primarily relied on a combination of manual and video-based data collection efforts. Manual field data collection was used for surveying patrons to obtain their suggested acceptable wait times at bus stops, while video-based data collection was used to obtain bus stop characteristics and operations. In all, 3,388 bus patrons at 71 selected bus stops were surveyed. Also, operational data for 2,070 bus arrival events on 226 routes were extracted via video playback. Data were collected for AM peak, PM peak and mid-day periods of nine-month duration from May 2018 through January 2019. The results of the survey showed that the minimum acceptable wait time beyond the scheduled arrival time was reported to be 1 minute, while the maximum acceptable wait time was reported to be 20 minutes. Regression analyses were conducted to develop models to predict the maximum acceptable wait time based on factors including temperature, presence of shelter at the bus stops, average headway of buses, and patrons’ knowledge of bus arrival times. The models were developed for A.M., P.M. and mid-day periods. The F-Statistics for the models were determined to be statistically significant with p values


Keywords


crashes; unsignalized intersection; artificial neural network; injury severity

Full Text:

PDF

References


T. Reed, “Reduction in the burden of waiting for public transit due to real time schedule information: A conjoint analysis study”, Pacific Rim TransTech Conference. 1995 Vehicle Navigation and Information Systems Conference Proceedings. 6th International VNIS. A Ride into the Future, Seattle, USA, July30-August 2, 1995

P. I. Welding, “The instability of a close-interval service”, Journal of the Operational Research Society, Vol. 8, pp. 133–142, 1957

P. G. Furth, T. H. J. Muller, “Service reliability and optimal running time schedules”, Transportation Research Record, Vol. 2034, No. 1, pp. 55-61, 2007

T. Firew, Analysis of Service Reliability of Public Transportation in the Helsinki Capital Region: The Case of Bus Line 550, MSc Thesis, Aalto University, 2016

W. Weber, The Travel Time of the Passengers of Public Transport Depending on Railway Type and Location, Traffic Science Institute at the Technical University Stuttgart, 1966

E. Osuna, F. Newell, “Control strategies for an idealized bus system”, Transportation Science, Vol. 6, No. 1, pp. 52-71, 1972

P. Seddon, M. Day, “Bus passenger waiting times in Greater Manchester”, Traffic Engineering and Control, Vol. 15, No. 9, pp. 442-445, 1974

K. Joliffe, T. Hutchinson, “A behavioral explanation of the association between bus and passenger arrivals at a bus stop”, Transportation Science, Vol. 9, pp. 248-282, 1975

L. C. Cham, Understanding Bus Service Reliability: A Practical Framework Using AVL/APC Data, MSc Thesis, Massachusetts Institute of Technology, 2006

M. Luethi, A. Weidmann, A. Nash, “Passenger arrival rates at public transport stations”, TRB 86th Annual Meeting Compendium of Papers. Transportation Research Board, Washington, USA, January 21-25, 2007

S. Guo, L. Yu, X. Chen, Y. Zhang, “Modeling Waiting Time for Passengers Transferring from Rail to Buses”, Transportation Planning and Technology, Vol. 34, No. 8, 29, pp. 795-809, 2007

H. Gong, X. Chen, “An application-oriented model of passenger waiting time based on bus departure time intervals”, Transportation Planning and Technology, Vol. 39, No. 4, pp. 424-437, 2016

I. Kaparias, C. L. Rossetti, V. Trozzi, “Analyzing passenger arrivals rates and waiting time at bus stops”, 94th Annual Meeting of the Transportation Research Board, Washington, DC, USA, January 11-15, 2015

Z. K. Gurmu, W. Fan, “Artificial Neural Network Travel Time Prediction Model for Buses Using Only GPS Data”, Journal of Public Transportation, Vol. 17, No. 2, pp. 45-65. 2014




eISSN: 1792-8036     pISSN: 2241-4487