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Evaluating alternative methods to estimate bus running times by archived automatic vehicle location data

Evaluating alternative methods to estimate bus running times by archived automatic vehicle location data

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Bus running times are a key element of time reliability for bus operators and passengers. Hence, their evaluation is crucial in order to build reliable schedules for transit operations. This study analyses three alternative offline methods for estimating bus running times using automatic vehicle location (AVL) data: the average method, the percentile method and an adjusted Kalman filter method, which is amended in order to be implemented for offline use. Experiments are conducted using ∼92,000 real-world archived AVL records, which are provided by an Italian bus operator. The results can be used to revise scheduled running times along a specific route by these methods.

References

    1. 1)
      • 1. Ceder, A.: ‘Public transit planning and operation. Theory, modelling and practice’ (Butterworth-Heinemann, Elsevier, Oxford, UK, 2007).
    2. 2)
      • 2. Shalaby, A., Farhan, A.: ‘Bus travel time prediction model for dynamic operations control and passenger information systems’ (Transportation Research Board, Washington D.C., USA, 2003), pp. 116.
    3. 3)
      • 3. Kumar, B.A., Vanajakshi, L., Subramanian, S.C.: ‘Bus travel time prediction using a time-space discretization approach’, Transp. Res. C, 2017, 79, pp. 308332.
    4. 4)
      • 4. Lin, W.H., Zeng, J.: ‘Experimental study of real-time bus arrival time prediction with GPS data’, Transp. Res. Rec., 1999, 1666, pp. 101109.
    5. 5)
      • 5. Xinghao, S., Jing, T., Guojun, C., et al: ‘Predicting bus real-time travel time basing on both GPS and RFID data’, Procedia Soc. Behav. Sci., 2013, 96, pp. 22872299.
    6. 6)
      • 6. Lee, W.C., Si, W., Chen, L.J., et al: ‘HTTP: a new framework for bus travel time prediction based on historical trajectories’. Int. Conf. Advances in Geographic Information Systems, Redondo Beach, CA, USA, 2012, pp. 279288.
    7. 7)
      • 7. Tiesyte, D., Jensen, C.S.: ‘Assessing the predictability of scheduled-vehicle travel times’. ACM SIGSPATIAL Int. Conf. Advances in Geographic Information Systems, Seattle, WA, USA, 2009, pp. 416419.
    8. 8)
      • 8. Kalman, R. E.: ‘A new approach to linear filtering and prediction problems’, J. Basic Eng., 1960, 82, (1), pp. 3545.
    9. 9)
      • 9. Fan, W., Gurmu, Z.: ‘Dynamic travel time prediction models for buses using only GPS data’, Int. J. Transp. Sci. Technol., 2015, 4, (4), pp. 353366.
    10. 10)
      • 10. Chen, X., Huibo, G., Wang, J.: ‘BRT vehicle travel time prediction based on SVM and Kalman filter’, J. Transp. Syst. Eng. Inf. Technol., 2012, 12, (4), pp. 2934.
    11. 11)
      • 11. Vanajakshi, L., Subramanian, S.C., Sivanandan, R.: ‘Travel time prediction under heterogeneous traffic conditions using global positioning system data from buses’, IET Intell. Transp. Syst., 2009, 3, (1), pp. 19.
    12. 12)
      • 12. Wall, Z., Dailey, D.J.: ‘An algorithm for predicting the arrival time of mass transit vehicles using automatic vehicle location data’. Annual Meeting of the Transportation Research Board, Washington DC, 1999.
    13. 13)
      • 13. Chien, S.I.J., Ding, Y., Wei, C.: ‘Dynamic bus arrival time prediction with artificial neural networks’, J. Transp. Eng., 2002, 128, (5), pp. 429438.
    14. 14)
      • 14. Kalaputapu, R., Demetsky, M.J.: ‘Modeling schedule deviations of buses using automatic vehicle-location data and artificial neural networks’, Transp. Res. Rec., 1995, 1497, pp. 4452.
    15. 15)
      • 15. Jeong, R., Rilett, L.: ‘Bus arrival time prediction using artificial neural network model’. IEEE Intelligent Transportation Systems Conf., Washington D.C., USA, 2004, pp. 988993.
    16. 16)
      • 16. Chen, M., Liu, X., Xia, J., et al: ‘A dynamic bus-arrival time prediction model based on APC data’, Comput. Aided Civil Infrastruct. Eng., 2004, 19, (5), pp. 364376.
    17. 17)
      • 17. Mazloumi, E., Rose, G., Currie, G., et al: ‘Prediction intervals to account for uncertainties in neural network predictions: methodology and application in bus travel time prediction’, Eng. Appl. Artif. Intell., 2011, 24, (3), pp. 534542.
    18. 18)
      • 18. Amita, J., Jain, S.S., Garg, P.K.: ‘Prediction of bus travel time using ANN: a case study in Delhi’, Transp. Res. Procedia, 2016, 17, pp. 263272.
    19. 19)
      • 19. Yu, B., Lam, W.H., Tam, M.L.: ‘Bus arrival time prediction at bus stop with multiple routes’, Transport. Res. C, 2011, 19, (6), pp. 11571170.
    20. 20)
      • 20. Abkowitz, M.D., Engelstein, I.: ‘Factors affecting running time on transit routes’, Transport. Res. A, 1983, 17, (2), pp. 107113.
    21. 21)
      • 21. Tetreault, P.R., El-Geneidy, A.M.: ‘Estimating bus run times for new limited-stop service using archived AVL and APC data’, Transport. Res. A, 2010, 44, pp. 390402.
    22. 22)
      • 22. Hawas, Y.E.: ‘Simulation-based regression models to estimate bus routes and network travel times’, J. Public Transport., 2013, 16, (4), pp. 107130.
    23. 23)
      • 23. Muller, T., Furth, P.: ‘Trip time analyzers: key to transit service quality’, Transp. Res. Rec., 2001, 1760, pp. 1019.
    24. 24)
      • 24. Furth, P.G., Hemily, B., Muller, T., et al: ‘Using archived AVL-APC data to improve transit performance and management’. Transit Cooperative Research Program (TRCP) 113, Transp. Res. Board (TRB), Washington, DC, USA, 2006.
    25. 25)
      • 25. Moreira-Matias, L., Mendes-Moreira, J., de Sousa, J. F., et al: ‘Improving mass transit operations by using AVL-based systems: A survey’, IEEE Trans. Intell. Transp. Syst., 2015, 16, (4), pp. 16361653.
    26. 26)
      • 26. Furth, P.G., Hemily, B., Muller, T.H.J., et al: ‘Uses of archived AVL–APC data to improve transit performance and management: review and potential’, TCRP Document 23 (appendices to TCRP Report 113).
    27. 27)
      • 27. Furth, P.G., Hemily, B., Muller, T.H.J., et al: ‘Designing automated vehicle location systems for archived data analysis’, Transp. Res. Rec., 2004, 1887, pp. 6270.
    28. 28)
      • 28. Barabino, B., Di Francesco, M., Mozzoni, S.: ‘An offline framework for handling automatic passenger counting raw data’, IEEE Trans. Intell. Transp. Syst., 2014, 15, (6), pp. 24432456.
    29. 29)
      • 29. CTM ‘carta della mobilità 2016-2017’, http://www.ctmcagliari.it/2017, accessed 07 December 2017.
    30. 30)
      • 30. Tilocca, P., Farris, S., Angius, S., et al: ‘Managing data and rethinking applications in an innovative mid-sized bus fleet’, Transp. Res. Procedia, 2017, 25, pp. 19041924.
    31. 31)
      • 31. Mandelzys, M, Hellinga, B.: ‘Identifying causes of performance issues in bus schedule adherence with automatic vehicle location and passenger count data’, Transp. Res. Rec., 2010, 2143, pp. 915.
    32. 32)
      • 32. Barabino, B., Di Francesco, M., Mozzoni, S.: ‘An offline framework for the diagnosis of time reliability by automatic vehicle location data’, IEEE Trans. Intell. Transp. Syst., 2017, 18, (3), pp. 583594.
    33. 33)
      • 33. Barabino, B., Lai, C., Casari, C., et al: ‘Rethinking transit time reliability by integrating automated vehicle location data, passenger patterns, and web tools’, IEEE Trans. Intell. Transp. Syst., 2017, 18, (4), pp. 756766.
    34. 34)
      • 34. ] Kittelson & Associates Inc, KFH Group Inc, Parsons Brinckerhoff Quade, Douglass Inc,Zaworski, K.H.: ‘Transit capacity and quality of service manual’ (TRB, Washington, DC, 2003, 2nd edn), Tech. Rep. TRCP 100.
    35. 35)
      • 35. Tofallis, C.: ‘A better measure of relative prediction accuracy for model selection and model estimation’, J. Oper. Res. Soc., 2015, 66, (8), pp. 13521362.
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