access icon free Regularity analysis on bus networks and route directions by automatic vehicle location raw data

Bus regularity is a key element for high-frequency transportation systems: it represents a measure of service quality for both users and transit agencies. Therefore, evaluating the regularity is highly important, but may also be a complex task in medium-size cities, because of the huge amount of data, which must be collected and processed effectively. Automatic vehicle location (AVL) technologies can address the data collection problem, but they involve several challenges such as correcting anomalies in gathered raw data and processing information efficiently. In this study, the authors propose a methodology to handle AVL raw data in order to measure the level of service of bus regularity in each route direction of a transit network, as well as in every bus stop and time period. The results are represented by easy-to-read control dashboards. The authors discuss the experimentation of this methodology to provide a detailed characterisation of bus regularity. The methodology is applied to about 800 000 data records of the bus operator CTM in Cagliari (Italy).

Inspec keywords: vehicle routing

Other keywords: Italy; automatic vehicle location raw data; easy-to-read control dashboards; bus regularity service level; bus networks; AVL technologies; CTM transport agency; data collection problem; transit network; route directions; Cagliari; service quality measure; anomaly correction; high-frequency transportation systems; information processing; transit time period; bus regularity analysis; bus stop; medium-size cities

Subjects: Systems theory applications in transportation

References

    1. 1)
      • 11. Ruan, M., Lin, J.: ‘An investigation of bus headway regularity and service performance in Chicago bus transit system’. Paper presented at the Transport Chicago, Annual Conf., 2009.
    2. 2)
      • 25. Daganzo, C.F., Pilachowski, J.: ‘Reducing bunching with bus-to-bus cooperation’, Transp. Res. B, 2011, 45, (1), pp. 267277 (doi: 10.1016/j.trb.2010.06.005).
    3. 3)
      • 9. Camus, R., Longo, G., Macorini, C.: ‘Estimation of transit reliability level-of-service based on automatic vehicle location data’, Transp. Res. Record, 2005, 1927, pp. 277286 (doi: 10.3141/1927-31).
    4. 4)
      • 19. Hounsell, N.B., Shrestha, B.P.: ‘AVL based bus priority at traffic signals: A review and case study of architectures’, Eur. J. Transp. Infrastruct. Res., 2005, 5, (1), pp. 1329.
    5. 5)
      • 17. Muller, T.H.J., Furth, P.: ‘Trip time analyzers: key to transit service quality’, Transp. Res. Record, 2001, 1760, pp. 1019 (doi: 10.3141/1760-02).
    6. 6)
      • 10. Lin, J., Wang, M.L., Barnum, P.D.: ‘A quality control framework for bus schedule reliability’, Transp. Res. E, 2007, 44, (6), pp. 10861098 (doi: 10.1016/j.tre.2007.10.002).
    7. 7)
      • 12. Chen, X., Yu, L., Zhang, Y., Gou, J.: ‘Analyzing urban bus service reliability at the stop, route, and network levels’, Transp. Res. A, 2009, 43, (8), pp. 722734.
    8. 8)
      • 1. CEN/TC 320: ‘Transportation – Logistics and services. European Standard EN 13816: 2002. Public passenger transport – Service quality definition, targeting and measurement’, 2002.
    9. 9)
      • 5. Marguier, P.H.J., Ceder, A.: ‘Passenger waiting strategies for overlapping bus routes’, Transp. Sci., 1984, 18, (3), pp. 207230 (doi: 10.1287/trsc.18.3.207).
    10. 10)
      • 13. Feng, W., Figliozzi, M., ‘Using archived AVL/APC bus data to identify spatial-temporal causes of bus bunching’. Compendium of papers of 90th Transportation Research Board, Annual Meeting, Washington, DC, 2011.
    11. 11)
      • 31. Barabino, B., Salis, S., Assorgia, A.: ‘Application of mobility management: a web structure for the optimisation of the mobility of working staff of big companies’, IET Intell. Transp. Syst., 2012, 6, (1), pp. 8795, (doi: 10.1049/iet-its.2010.0168).
    12. 12)
      • 3. Turnquist, M.A.: ‘Strategies for improving bus transit service reliability’, Transp. Res. Record, 1982, 818, pp. 713.
    13. 13)
      • 27. Mandelzys, M., Hellinga, B.: ‘Identifying causes of performance issues in bus schedule adherence with automatic vehicle location and passenger count data’, Transp. Res. Record, 2010, 2143, pp. 915 (doi: 10.3141/2143-02).
    14. 14)
      • 29. Strathman, J.G., Kimpel, T.J., Callas, S.: ‘Headway deviation effects on bus passenger loads: analysis of Tri-Met's archived AVL-APC data’. Technical Report PR126, Center for Urban Studies, Portland, OR, 2003.
    15. 15)
      • 15. Henderson, G., Adkins, H., Kwong, P.: ‘Toward a passenger-oriented model of subway performance’, Transp. Res. Record, 1990, 1266, pp. 221228.
    16. 16)
      • 20. Dalla Chiara, B.: ‘Role of automatic vehicle location systems and localization accuracy in freight transport: an analytical estimation of gained operational times’, IET Intell. Transp. Syst., 2010, 4, (4), pp. 365374, (doi: 10.1049/iet-its.2009.0138).
    17. 17)
      • 14. Transportation Research Board: ‘Transit capacity and quality of service manual, 2nd edn’. Transit Cooperative Research Program, Report 100, TRB, Washington, DC, 2003.
    18. 18)
      • 26. Cortés, C.E., Gibson, J., Gschwender, A., Munizaga, M., Zúñiga, M.: ‘Commercial bus speed diagnosis based on GPS-monitored data’, Transp. Res. C, 2011, 19, (4), pp. 695707 (doi: 10.1016/j.trc.2010.12.008).
    19. 19)
      • 16. Cham, L.: ‘Understanding bus service reliability: a practical framework using AVL/APC data’. Master's Thesis Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, 2006.
    20. 20)
      • 24. Berkow, M., El-Geneidy, A., Bertini, R.L., Crout, D.: ‘Beyond generating performance measures: visualizations and statistical analysis using historical data’, Transp. Res. Record, 2009, 2111, pp. 158168 (doi: 10.3141/2111-18).
    21. 21)
      • 28. Barabino, B., Di Francesco, M., Mozzoni, S.: ‘Regularity diagnosis by automatic vehicle location raw data’, Public Transp., 2013, 4, (3), pp. 187208 (doi: 10.1007/s12469-012-0059-z).
    22. 22)
      • 2. Ceder, A.: ‘Public transit planning and operation, theory, modelling and practice’ (Elsevier press, 2007).
    23. 23)
      • 21. McLeod, F.N.: ‘Estimating bus passenger waiting times from incomplete bus arrivals data’, J. Oper. Res. Soc., 2007, 58, (11), pp. 15181525 (doi: 10.1057/palgrave.jors.2602298).
    24. 24)
      • 30. Barabino, B., Corona, G., Tilocca, P.: ‘A telematic platform to manage private and public transport: a case study in Italy’. Proc. Transport Research Arena, Ljubljana, Slovenia, 2008.
    25. 25)
      • 7. Transportation Research Board: ‘A guidebook for developing a transit performance-measurement system’. Transit Cooperative Research Program Report 88, TRB, Washington, DC, 2003.
    26. 26)
      • 6. Nakanishi, Y.J.: ‘Bus performance indicators’, Transp. Res. Record, 1997, 1571, pp. 313 (doi: 10.3141/1571-01).
    27. 27)
      • 23. Muller, T.H.J., Knoppers, P.: ‘TRIp time analysis in public transport’, available at: http://www.tritapt.nl/, 2005.
    28. 28)
      • 4. Furth, P.G., Muller, T.H.J.: ‘Service reliability and hidden waiting time: insights from automated vehicle location data’, Transp. Res. Record, 2006, 1955, pp. 7987 (doi: 10.3141/1955-10).
    29. 29)
      • 8. Trompet, M., Liu, X., Graham, D.J.: ‘Development of a key performance indicator to compare regularity of service between urban bus operators’, Transp. Res. Record, 2011, 2216, pp. 3341 (doi: 10.3141/2216-04).
    30. 30)
      • 18. Furth, P., Hemily, B., Muller, T.H.J., Strathman, J.: ‘Using archived AVL-APC data to improve transit performance and management’. Transit Cooperative Research Program Report 113, TRB, Washington, DC, 2006.
    31. 31)
      • 22. McKone, T., Partridge, E., Martin, J.: ‘Eliminating bus bunching – building a process, information source, and tool box for improving service’. Proc. Bus & Paratransit Conf. & Int. Bus Roadeo/Bus Rapid Transit Conf., Seattle, 2009.
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