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An Indicator-Based Method for Bus Routing Adjustment

  • Yi-ling Deng
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 503)

Abstract

Increasing the ridership of bus transit through bus routing adjustment is one of the main tasks of transit agencies. A new method to characterize and classify bus routes according to their coverage, transfer and overlap level in meeting the needs of public transit planning is described. Geographic transit network and land use data are used to calculate seven indicators for each bus route: residential area coverage, non-residential area coverage, land use mix, transfer index to/from bus, overlap index with bus, transfer index to/from metro and overlap index with metro. Hierarchical clustering analysis is used to indicate where the largest potential scope exists for adjusting the bus transit system and hence can be used to guide transit planners. The method was applied in Nanjing, China aiming to identify improvable bus routes. Six bus route clusters were generated using hierarchical clustering. The profiles of each cluster are analysed and corresponding strategies for bus routing adjustment are proposed. ANOVA test shows there is a significant difference at the route-level ridership among these clusters, which verifies the reasonability of the method.

Keywords

Bus routing adjustment Hierarchical clustering Coverage Transfer Overlap 

References

  1. 1.
    Bus routing and coverage (2004) Transportation Research BoardGoogle Scholar
  2. 2.
    Asadi Bagloee S, Ceder AA (2011) Transit-network design methodology for actual-size road networks. Transp Res Part B: Methodol 45(10):1787–1804CrossRefGoogle Scholar
  3. 3.
    Currie G (2004) Gap analysis of public transport needs: measuring spatial distribution of public transport needs and identifying gaps in the quality of public transport provision. Transp Res Rec: J Transp Res Board 1895:137–146CrossRefGoogle Scholar
  4. 4.
    Currie G (2010) Quantifying spatial gaps in public transport supply based on social needs. J Transp Geogr 18(1):31–41CrossRefGoogle Scholar
  5. 5.
    Polzin SE, Pendyala RM, Navari S (2002) Development of time-of-day-based transit accessibility analysis tool. Transp Res Rec: J Transp Res Board 1799:35–41CrossRefGoogle Scholar
  6. 6.
    Al Mamun S, Lownes NE (2011) Measuring service gaps: accessibility-based transit need index. Transp Res Rec: J Transp Res Board 2217:153–161CrossRefGoogle Scholar
  7. 7.
    Minocha I, Sriraj PS, Metaxatos P et al (2008) Analysis of transit quality of service and employment accessibility for the greater Chicago, Illinois, region. Transp Res Rec: J Transp Res Board 2042:20–29CrossRefGoogle Scholar
  8. 8.
    Making effective fixed-guideway transit investments: indicators of success (2014) Transportation Research BoardGoogle Scholar
  9. 9.
    Elements needed to create high ridership transit systems (2005) Transportation Research BoardGoogle Scholar
  10. 10.
    Weber county to salt lake city commuter rail project (2005) Utah Transit AuthorityGoogle Scholar
  11. 11.
    The transit competitive index web tool: development and applications. Cambridge Systematics and Transportation AnalyticsGoogle Scholar
  12. 12.
    Jiménez F, Román A, López JM (2013) Methodology for kinematic cycle characterization of vehicles with fixed routes in urban areas. Transp Res Part D: Transp Environ 22:14–22CrossRefGoogle Scholar
  13. 13.
    Jiménez F, Serradilla F, Román A et al (2014) Bus line classification using neural networks. Transp Res Part D: Transp Environ 30:32–37CrossRefGoogle Scholar
  14. 14.
    André M, Villanova A (2004) Characterisation of an urban bus network for environmental purposes. Sci Total Environ 334:85–99CrossRefGoogle Scholar
  15. 15.
    Zhang H, Chen X, Li X (2011) Trip rate and travel time: a perspective in china city. Transportation Research Board 90th Annual Meeting. Washington, D.C.Google Scholar
  16. 16.
    Bus coverage of chinese cities [EB/OL]. http://www.beijingcitylab.com/ranking/, 6/20/2016
  17. 17.
    Walkscore [EB/OL]. http://www.walkscore.com/, 6/20/2016
  18. 18.
    Hastie T, Tibshirani R, Friedman J et al (2009) The elements of statistical learning, 2nd edn. Springer, BerlinGoogle Scholar
  19. 19.
    China City Statistical Yearbook (2016) China city statistical yearbook. China Statistics Press, Beijing, ChinaGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.College of Civil Engineering and ArchitectureZhejiang University of TechnologyHangzhouChina

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