Advertisement

Public Transport

, Volume 10, Issue 3, pp 499–527 | Cite as

DRT route design for the first/last mile problem: model and application to Athens, Greece

  • Anastasios Charisis
  • Christina Iliopoulou
  • Konstantinos KepaptsoglouEmail author
Original Paper

Abstract

The first/last mile problem in urban transportation services refers to limited connectivity and accessibility to high capacity commuter lines. This is often encountered in low-density residential areas, where low flexibility and resources of traditional public transportation systems lead to reduced service coverage. Demand-responsive transit (DRT) offers an alternative for providing first/last mile feeder services to low density areas, because of its flexibility in adjusting to different demand patterns. This paper presents a mathematical model and a genetic algorithm for efficiently designing DRT type first/last mile routes. The model is applied for the case of a residential area in Athens, Greece and results are discussed.

Keywords

Feeder bus Transit network design Demand-responsive transport Genetic algorithms 

References

  1. Ambrosino G, Nelson JD, Romanazzo M (2004) Demand responsive transport services: towards the flexible mobility agency. ENEA, Italian National Agency for New Technologies, Energy and the Environment, RomeGoogle Scholar
  2. Banister D (2008) The sustainable mobility paradigm. Transp Policy 15(2):73–80CrossRefGoogle Scholar
  3. Brake J, Mulley C, Nelson JD, Wright S (2007) Key lessons learned from recent experience with flexible transport services. Transp Policy 14(6):458–466CrossRefGoogle Scholar
  4. Chandra S, Quadrifoglio L (2013) A model for estimating the optimal cycle length of demand responsive feeder transit services. Transp Res Part B Methodol 51:1–16CrossRefGoogle Scholar
  5. Chapman L (2007) Transport and climate change: a review. J Transp Geogr 15(5):354–367CrossRefGoogle Scholar
  6. Chien S, Yang Z (2000) Optimal feeder bus routes on irregular street networks. J Adv Transp 34(2):213–248CrossRefGoogle Scholar
  7. Ciaffi F, Cipriani E, Petrelli M (2012) Feeder bus network design problem: a new metaheuristic procedure and real size applications. Procedia Soc Behav Sci 54:798–807CrossRefGoogle Scholar
  8. Davison L, Enoch M, Ryley T, Quddus M, Wang C (2014) A survey of demand responsive transport in Great Britain. Transp Policy 31:47–54CrossRefGoogle Scholar
  9. Deng LB, Gao W, Zhou WL, Lai TZ (2013) Optimal design of feeder-bus network related to urban rail line based on transfer system. Procedia Soc Behav Sci 96:2383–2394CrossRefGoogle Scholar
  10. Eiben AE, Smith JE (2003) Introduction to evolutionary computing. Springer, BerlinCrossRefGoogle Scholar
  11. Ewing RH (2008) Characteristics, causes, and effects of sprawl: a literature review. In: Urban ecology: an international perspective on the interaction between humans and nature. Springer, Boston, MA, pp 519–535CrossRefGoogle Scholar
  12. Faroqi H, Sadeghi-Niaraki A (2016) GIS-based ride-sharing and DRT in Tehran city. Public Transp 8(2):243–260CrossRefGoogle Scholar
  13. Golden B, Raghavan S, Wasil EA (eds) (2008) The vehicle routing problem: latest advances and new challenges. Springer, BerlinGoogle Scholar
  14. Häll CH, Andersson H, Lundgren JT, Värbrand P (2009) The integrated dial-a-ride problem. Public Transp 1(1):39–54CrossRefGoogle Scholar
  15. Häll CH, Lundgren JT, Voß S (2015) Evaluating the performance of a dial-a-ride service using simulation. Public Transp 7(2):139–157CrossRefGoogle Scholar
  16. Haupt RL, Haupt SE (2004) Practical genetic algorithms. Wiley, New YorkGoogle Scholar
  17. Holland J (1975) Adaptation in natural and artificial systems. MIT Press, CambridgeGoogle Scholar
  18. Hu Y, Zhang Q, Wang W (2012) A model layout region optimization for feeder buses of rail transit. Procedia Soc Behav Sci 43:773–780CrossRefGoogle Scholar
  19. Jerby S, Ceder A (2006) Optimal routing design for shuttle bus service. Transp Res Rec 1971:14–22CrossRefGoogle Scholar
  20. Jih W-R, Hsu Y-J (2004) A family competition genetic algorithm for the pickup and delivery problems with time window. Bull Coll Eng 90:121–130Google Scholar
  21. Konak A, Coit DW, Smith AE (2006) Multi-objective optimization using genetic algorithms: a tutorial. Reliab Eng Syst Saf 91(9):992–1007CrossRefGoogle Scholar
  22. Kuah GK, Perl J (1988) Optimization of feeder bus routes and bus-stop spacing. J Transp Eng 114(3):341–354CrossRefGoogle Scholar
  23. Kuah GK, Perl J (1989) The feeder-bus network-design problem. J Oper Res Soc 40:751–767CrossRefGoogle Scholar
  24. Kuan SN, Ong HL, Ng KM (2004) Applying metaheuristics to feeder bus network design problem. Asia Pac J Oper Res 21(4):543–560CrossRefGoogle Scholar
  25. Kuan SN, Ong HL, Ng KM (2006) Solving the feeder bus network design problem by genetic algorithms and ant colony optimization. Adv Eng Softw 37(6):351–359CrossRefGoogle Scholar
  26. Lane BW (2012) On the utility and challenges of high-speed rail in the United States. J Transp Geogr 22:282–284CrossRefGoogle Scholar
  27. Laws R, Enoch MP, Ison SG, Potter S (2009) Demand responsive transport: a review of schemes in England and Wales. J Public Transp 12(1):19–37CrossRefGoogle Scholar
  28. Lenstra JK, Rinnooy Kan AHG (1981) Complexity of vehicle routing and scheduling problems. Networks 11(2):221–227CrossRefGoogle Scholar
  29. Li X, Quadrifoglio L (2011) 2-Vehicle zone optimal design for feeder transit services. Public Transp 3(1):89–104CrossRefGoogle Scholar
  30. Li Y, Wang J, Chen J, Cassidy MJ (2007) Design of a demand-responsive transit system. California PATH program (No. UCB-ITS-PWP-2007-4). Institute of Transportation Studies, University of California at Berkeley, BerkeleyGoogle Scholar
  31. Litman T, Burwell D (2006) Issues in sustainable transportation. Int J Glob Environ Issues 6(4):331–347CrossRefGoogle Scholar
  32. Martins CL, Pato MV (1998) Search strategies for the feeder bus network design problem. Eur J Oper Res 106(2):425–440CrossRefGoogle Scholar
  33. Qiu F, Shen J, Zhang X, An C (2015) Demi-flexible operating policies to promote the performance of public transit in low-demand areas. Transp Res Part A Policy Pract 80:215–230CrossRefGoogle Scholar
  34. Shrivastava P, O’Mahony M (2006) A model for development of optimized feeder routes and coordinated schedules—a genetic algorithms approach. Transp Policy 13(5):413–425CrossRefGoogle Scholar
  35. Shrivastava P, O’Mahony M (2007) Design of feeder route network using combined genetic algorithm and specialized repair heuristic. J Public Transp 10(2):109–133CrossRefGoogle Scholar
  36. Steg L, Gifford R (2005) Sustainable transportation and quality of life. J Transp Geogr 13(1):59–69CrossRefGoogle Scholar
  37. Stiglic M, Agatz N, Savelsbergh M, Gradišar M (2016) Enhancing urban mobility: integrating ride-sharing and public transit. Comput Oper Res 90:12–21CrossRefGoogle Scholar
  38. Talbi EG (2009) Metaheuristics: from design to implementation, vol 74. Wiley, New YorkCrossRefGoogle Scholar
  39. Toth P, Vigo D (eds) (2014) Vehicle routing: problems, methods, and applications. MOS-SIAM Series on Optimization, 2nd edn. SIAM, PhiladelphiaGoogle Scholar
  40. Zhao P (2010) Sustainable urban expansion and transportation in a growing megacity: consequences of urban sprawl for mobility on the urban fringe of Beijing. Habitat Int 34(2):236–243CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Rural and Surveying EngineeringNational Technical University of AthensZografouGreece

Personalised recommendations