Decomposition and Sensitivity Analysis of Bus Travel Times

Abstract

Transit service reliability is important for transit planning and operations as well as passenger experience. Large travel time variations increase operating costs and negatively affect passenger satisfaction. Existing literature focuses on specific aspects of transit travel times but less on how these aspects interact with each other. This paper proposes to combine previous research efforts by further decomposing observed trip travel times into four elements using 3 months of archived vehicle location and fare transaction data. Departure times and inter-stop travel times are obtained from vehicle locations. Dwell times at stops are estimated from fare transaction data using a dwell time model. Red-light waiting times are calculated using the vehicle locations and estimated signal timing plans. Then, using these as inputs, we identify important trip elements affecting the overall travel time variation, as well as how much variation can be attributed to each trip element using variance-based and one-at-a-time sensitivity analyses. The overall travel times and red-light waiting times are more affected by interaction effects between trip elements, whereas the overall inter-stop times and dwell times are mainly affected by large individual variations. The results suggest that planners must consider potential chain reactions where small variations in one trip element can lead to significant changes in the overall trip times as a result of interaction effects with varying cycle lengths in fixed signal timing plans. These findings will help planners better integrate available data sets, carry out comprehensive analyses, and pinpoint the determinants affecting travel time variation on each route.

Keywords

transit planning transit travel times travel time variations sensitivity analysis

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References

Danaher

Wensley

Dunham

Orosz

Avery

Cobb

Watkins

, et al. Minutes Matter: A Bus Transit Service Reliability Guidebook. The National Academies Press, Washington, DC, 2020. https://doi.org/10.17226/25727.

Carrel

Halvorsen

Walker

J. L.

Passengers’ Perception of and Behavioral Adaptation to Unreliability in Public Transportation. Transportation Research Record, Vol. 2351, No. 1, 2013, pp. 153–162. https://doi.org/10.3141/2351-17.

Balcombe

Mackett

Paulley

Preston

Shires

Titheridge

Wardman

White

The Demand for Public Transport: A Practical Guide. Transportation Research Laboratory, Wokingham, Berkshire, UK, 2004.

Daskalakis

N. G.

Stathopoulos

Users’ Perceptive Evaluation of Bus Arrival Time Deviations in Stochastic Networks. Journal of Public Transportation, Vol. 11, No. 4, 2008, pp. 25–38. https://doi.org/10.5038/2375-0901.11.4.2.

Muller

T. H.

Furth

P. G.

Trip Time Analyzers: Key to Transit Service Quality. Transportation Research Record, 2001. 1760: 10–19. https://doi.org/10.3141/1760-02.

Dueker

K. J.

Kimpel

T. J.

Strathman

J. G.

Callas

Determinants of Bus Dwell Time. Journal of Public Transportation, Vol. 7, No. 1, 2004, pp. 21–40. https://doi.org/10.5038/2375-0901.7.1.2.

Furth

P. G.

Saeidi-Razavi

Obeng-Amoako

N. D.

Tahmasebi

TSP-Friendly Underlying Traffic Signal Control: An Essential Complement to Transit Signal Priority. Future Transportation, Vol. 5, No. 4, 2025. https://doi.org/10.3390/futuretransp5040160

Fayazi

S. A.

Vahidi

Mahler

Winckler

Traffic Signal Phase and Timing Estimation From Low-Frequency Transit Bus Data. IEEE Transactions on Intelligent Transportation Systems, Vol. 16, No. 1, 2015, pp. 19–28. https://doi.org/10.1109/TITS.2014.2323341.

Saltelli

Ratto

Andres

Campolongo

Cariboni

Gatelli

Saisana

Tarantola

Global Sensitivity Analysis: The Primer. John Wiley & Sons, Chichester, West Sussex, UK, 2008.

10.

National Academies of Sciences, Engineering, and Medicine and others. Transit Capacity and Quality of Service Manual, 3rd ed. The National Academies Press, Washington, D.C., 2013. https://doi.org/10.17226/24766.

11.

Abkowitz

Engelstein

Methods for Maintaining Transit Service Regularity. Transportation Research Record, 1984. 961: 1–8.

12.

Berkow

El-Geneidy

A. M.

Bertini

R. L.

Crout

Beyond Generating Transit Performance Measures: Visualizations and Statistical Analysis with Historical Data. Transportation Research Record, 2009. 2111: 158–168. https://doi.org/10.3141/2111-18.

13.

Ferreira

Mesbah

A Framework for the Development of Bus Service Reliability Measures. Proc., 36th Australasian Transport Research Forum (ATRF), Brisbane, Australia, 2013.

14.

Kieu

L.-M.

Bhaskar

Chung

Public Transport Travel-Time Variability Definitions and Monitoring. Journal of Transportation Engineering, Vol. 141, No. 1, 2015, p. 04014068. https://doi.org/10.1061/(ASCE)TE.1943-5436.0000724.

15.

Rashidi

Ataeian

Ranjitkar

Estimating Bus Dwell Time: A Review of the Literature. Transport Reviews, Vol. 43, No. 1, 2023, pp. 32–61. https://doi.org/10.1080/01441647.2021.2023692.

16.

Currie

Delbosc

Harrison

Sarvi

Impact of Crowding on Streetcar Dwell Time. Transportation Research Record, 2013. 2353: 100–106. https://doi.org/10.3141/2353-10.

17.

Lin

Yang

Zou

Franz

Transit Signal Priority Control at Signalized Intersections: A Comprehensive Review. Transportation Letters, Vol. 7, No. 3, 2015, pp. 168–180. https://doi.org/10.1179/1942787514Y.0000000044.

18.

Diab

E. I.

El-Geneidy

A. M.

Variation in Bus Transit Service: Understanding the Impacts of Various Improvement Strategies on Transit Service Reliability. Public Transport, Vol. 4, No. 3, 2013, pp. 209–231. https://doi.org/10.1007/s12469-013-0061-0.

19.

Wang

van Zuylen

H. J.

Sze

N. N.

Chen

Wang

Predictive Strategy for Transit Signal Priority at Fixed-Time Signalized Intersections: Case Study in Nanjing, China. Transportation Research Record, 2012. 2311: 124–131. https://doi.org/10.3141/2311-12.

20.

Altun

S. Z.

Furth

P. G.

Scheduling Buses to Take Advantage of Transit Signal Priority. Transportation Research Record, 2009. 2111: 50–59. https://doi.org/10.3141/2111-07.

21.

Urbanik

Tanaka

Lozner

Lindstrom

Lee

Quayle

Beaird

, et al. Signal Timing Manual, Vol. 1. Transportation Research Board, Washington, D.C., 2015.

22.

Ester

Kriegel

H.-P.

Sander

A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise. Proc., Second International Conference on Knowledge Discovery and Data Mining, Vol. 34, AAAI Press, Portland, OR, 1996, pp. 226–231.

23.

Czitrom

One-Factor-at-a-Time versus Designed Experiments. The American Statistician, Vol. 53, No. 2, 1999, pp. 126–131. https://doi.org/10.1080/00031305.1999.10474445.

24.

Arriagada

Gschwender

Munizaga

M. A.

Trépanier

Modeling Bus Bunching Using Massive Location and Fare Collection Data. Journal of Intelligent Transportation Systems, Vol. 23, No. 4, 2019, pp. 332–344. https://doi.org/10.1080/15472450.2018.1494596.

25.

Luethi

Weidmann

Nash

Passenger Arrival Rates at Public Transport Stations. TRB 86th Annual Meeting Compendium of Papers. Transportation Research Board, Washington, D.C., 2007, p. 07-0635. https://doi.org/10.3929/ethz-a-005704674.