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Bus Travel Time: Experimental Evidence and Forecasting

Author

Listed:
  • Antonio Comi

    (Department of Enterprise Engineering, University of Rome Tor Vergata, 00118 Rome, Italy)

  • Antonio Polimeni

    (Department of Enterprise Engineering, University of Rome Tor Vergata, 00118 Rome, Italy)

Abstract

Bus travel time analysis plays a key role in transit operation planning, and methods are needed for investigating its variability and for forecasting need. Nowadays, telematics is opening up new opportunities, given that large datasets can be gathered through automated monitoring, and this topic can be studied in more depth with new experimental evidence. The paper proposes a time-series-based approach for travel time forecasting, and data from automated vehicle monitoring (AVM) of bus lines sharing the road lanes with other traffic in Rome (Italy) and Lviv (Ukraine) are used. The results show the goodness of such an approach for the analysis and reliable forecasts of bus travel times. The similarities and dissimilarities in terms of travel time patterns and city structure were also pointed out, showing the need to take them into account when developing forecasting methods.

Suggested Citation

  • Antonio Comi & Antonio Polimeni, 2020. "Bus Travel Time: Experimental Evidence and Forecasting," Forecasting, MDPI, vol. 2(3), pages 1-14, August.
    • Handle: RePEc:gam:jforec:v:2:y:2020:i:3:p:17-322:d:405406
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    References listed on IDEAS

    as
    1. Lam, Terence C. & Small, Kenneth A., 2003. "The Value of Time and Reliability: Measurement from a Value Pricing Experiment," University of California Transportation Center, Working Papers qt47s4z7z5, University of California Transportation Center.
    2. Small, Kenneth A., 2001. "The Value of Pricing," University of California Transportation Center, Working Papers qt0rm449sx, University of California Transportation Center.
    3. Lam, Terence C. & Small, Kenneth A., 0. "The value of time and reliability: measurement from a value pricing experiment," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 37(2-3), pages 231-251, April.
    4. Bates, John & Polak, John & Jones, Peter & Cook, Andrew, 0. "The valuation of reliability for personal travel," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 37(2-3), pages 191-229, April.
    5. Francesco Russo & Antonio Comi, 2020. "Investigating the Effects of City Logistics Measures on the Economy of the City," Sustainability, MDPI, vol. 12(4), pages 1-11, February.
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    Cited by:

    1. Sonia Leva, 2021. "Editorial for Special Issue: “Feature Papers of Forecasting”," Forecasting, MDPI, vol. 3(1), pages 1-3, February.
    2. Tamás DUSEK & Lívia ABLONCZY-MIHà LYKA & Petra KECSKÉS & Zsuzsanna Pà LFFY, 2022. "Time-Space Asymmetry Versus Time-Space Symmetry," Theoretical and Empirical Researches in Urban Management, Research Centre in Public Administration and Public Services, Bucharest, Romania, vol. 17(4), pages 68-81, November.
    3. Moeinaddini, Amin & Habibian, Meeghat, 2024. "Acceptability of transportation demand management policy packages considering interactions and socio-economic heterogeneity," Research in Transportation Economics, Elsevier, vol. 103(C).
    4. Irene Mariñas-Collado & Ana E. Sipols & M. Teresa Santos-Martín & Elisa Frutos-Bernal, 2022. "Clustering and Forecasting Urban Bus Passenger Demand with a Combination of Time Series Models," Mathematics, MDPI, vol. 10(15), pages 1-16, July.

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