Advertisement

Public Transport

, Volume 11, Issue 1, pp 159–187 | Cite as

Mitigation of overcrowding in buses through bus planning

  • Hemant Kumar SumanEmail author
  • Nomesh B. Bolia
Original Paper

Abstract

Mathematical models have been developed to address overcrowding in buses to incentivize their use. The models capture real-life requirements of bus planning, are computationally tractable, and easy to understand by decision makers. First, the current level of bus services on the given network is assessed. Then the models are developed to allocate the existing buses optimally and determine the minimum number of buses needed to satisfy the existing and future demand. Our results demonstrate that significant benefits can be obtained by the use of these models. The models also incorporate decision-making flexibility by allowing policy makers to adjust the policy parameters according to their requirements. As a result, they can be useful decision-making tools for city transport anywhere in the world, especially Delhi and other cities with similar problems.

Keywords

Incentivize bus transport Delhi Overcrowding Mathematical modelling Decision support 

Notes

Acknowledgements

This research has been partially supported by the Department of Science and Technology, Government of India with grant number RS/FTP/ETA/0025/2011. We thank the Delhi Integrated Multi-Modal Transit System (DIMTS) for providing the ticketing data. We also thank Maansi Gupta and Abhishek Bhatnagar for their contributions. We would also like to express our gratitude to Rama Shankar and Premchand for providing logistical support in data collection, and to all the experts and organizations that participated in our research.

References

  1. Agrawal J, Mathew TV (2004) Transit route network design using parallel genetic algorithm. J Comput Civil Eng 18(3):248–256Google Scholar
  2. Ahmad S, Balaban O, Doll CN, Dreyfus M (2013) Delhi revisited. Cities 31:641–653Google Scholar
  3. Andaleeb SS, Haq M, Ahmed RI (2007) Reforming innercity bus transportation in a developing country: a passenger-driven model. J Public Transp 10(1):1–25Google Scholar
  4. Baaj MH, Mahmassani HS (1995) Hybrid route generation heuristic algorithm for the design of transit networks. Transp Res Part C Emerg Technol 3(1):31–50Google Scholar
  5. Badami MG (2001) A multiple-objectives approach to address motorized two-wheeled vehicle emissions in Delhi, India. The University of British Columbia Phd Thesis, (April), pp 1–341Google Scholar
  6. Badami MG, Haider M (2007) An analysis of public bus transit performance in Indian cities. Transp Res Part A Policy Pract 41(10):961–981Google Scholar
  7. Beevers SD, Carslaw DC (2005) The impact of congestion charging on vehicle emissions in London. Atmos Environ 39(1):1–5Google Scholar
  8. Beirão G, Cabral JS (2007) Understanding attitudes towards public transport and private car: a qualitative study. Transp Policy 14(6):478–489Google Scholar
  9. Bhattacharyya U, Salim DR (2015) Modeling the dynamic air transport industry aviation fuel demand in India. Int J Supply Chain Manag 4(2):35–54Google Scholar
  10. Blum JJ, Mathew TV (2011) Intelligent agent optimization of urban bus transit system design. J Comput Civil Eng 25(5):357–369Google Scholar
  11. Borndörfer R, Grötschel M, Pfetsch ME (2007) A column-generation approach to line planning in public transport. Transp Sci 41(1):123–132Google Scholar
  12. Cantwell M, Caulfield B, O’Mahony M (2009) Examining the factors that impact public transport commuting satisfaction. J Public Transp 12(2):1–21Google Scholar
  13. Carrese S, Gori S (2002) An urban bus network design procedure. In: Transportation planning. Springer, New York, pp 177–195Google Scholar
  14. Ceder A (1984) Bus frequency determination using passenger count data. Transp Res Part A Gen 18(5–6):439–453Google Scholar
  15. Ceder A (2002) Urban transit scheduling: framework, review and examples. J Urban Plan Dev 128(4):225–244Google Scholar
  16. Ceder A, Wilson NH (1986) Bus network design. Transp Res Part B Methodol 20(4):331–344Google Scholar
  17. Census-India (2011) Census of India 2011 primary census abstract: NCT of DelhiGoogle Scholar
  18. Cervero R, Kang CD (2011) Bus rapid transit impacts on land uses and land values in Seoul, Korea. Transp Policy 18(1):102–116Google Scholar
  19. Chakroborty P, Wivedi T (2002) Optimal route network design for transit systems using genetic algorithms. Engin Optim 34(1):83–100Google Scholar
  20. Chauhan V, Suman HK, Bolia N (2016) Binary logistic model for estimation of mode shift into Delhi Metro. Open Transp J 10(1):124–136Google Scholar
  21. Cipriani E, Gori S, Petrelli M (2012) Transit network design: a procedure and an application to a large urban area. Transp Res Part C Emerg Technol 20(1):3–14Google Scholar
  22. Das A, Parikh J (2004) Transport scenarios in two metropolitan cities in India: Delhi and Mumbai. Energy Convers Manage 45(15–16):2603–2625Google Scholar
  23. DES (2014) Statistical abstract of Delhi. Directorate of Economics and Statistics, Government of NCT of DelhiGoogle Scholar
  24. DIMTS (2016). Welcome to Delhi Integrated Multi Modal Transit System Ltd. http://www.dimts.in/
  25. Duarte A, Garcia C, Giannarakis G, Limão S, Polydoropoulou A, Litinas N (2010) New approaches in transportation planning: happiness and transport economics. NETNOMICS: Econ Res Electron Netw 11(1):5–32Google Scholar
  26. Eboli L, Mazzulla G (2008) A stated preference experiment for measuring service quality in public transport. Transp Plan Technol 31(5):509–523Google Scholar
  27. Eliasson J (2008) Lessons from the Stockholm congestion charging trial. Transp Policy 15(6):395–404Google Scholar
  28. Fan W, Machemehl RB (2006) Optimal transit route network design problem with variable transit demand: genetic algorithm approach. J Transp Eng 132(1):40–51Google Scholar
  29. Farahani RZ, Miandoabchi E, Szeto WY, Rashidi H (2013) A review of urban transportation network design problems. Eur J Oper Res 229(2):281–302Google Scholar
  30. Fusco G, Gori S, Petrelli M (2002) A heuristic transit network design algorithm for medium size towns. In: Proceedings of the 13th mini-euro conference, BariGoogle Scholar
  31. Gilbert A (2008) Bus rapid transit: is Transmilenio a miracle cure? Transp Rev 28(4):439–467Google Scholar
  32. Giuliano G (1992) Transportation demand management: promise or panacea? J Am Plan Assoc 58(3):327–335Google Scholar
  33. Goel R, Tiwari G (2016) Access–egress and other travel characteristics of metro users in Delhi and its satellite cities. IATSS Res 39:164–172Google Scholar
  34. Goldman T, Gorham R (2006) Sustainable urban transport: four innovative directions. Technol Soc 28(1–2):261–273Google Scholar
  35. Goyal SK, Ghatge SV, Nema PS, Tamhane SM (2006) Understanding urban vehicular pollution problem vis-a-vis ambient air quality–case study of a megacity (Delhi, India). Environ Monit Assess 119(1–3):557–569Google Scholar
  36. Guihaire V, Hao JK (2008) Transit network design and scheduling: a global review. Transp Res Part A Policy Pract 42(10):1251–1273Google Scholar
  37. Han SS (2010) Managing motorization in sustainable transport planning: the Singapore experience. J Transp Geogr 18(2):314–321Google Scholar
  38. Han AF, Wilson NH (1982) The allocation of buses in heavily utilized networks with overlapping routes. Transp Res Part B Methodol 16(3):221–232Google Scholar
  39. Ibeas A, Alonso B, dell’Olio L, Moura JL (2013) Bus size and headways optimization model considering elastic demand. J Transp Eng 140(4):04013021Google Scholar
  40. Ibrahim MF (2003) Improvements and integration of a public transport system: the case of Singapore. Cities 20(3):205–216Google Scholar
  41. Jain D, Tiwari G (2016) How the present would have looked like? Impact of non-motorized transport and public transport infrastructure on travel behavior, energy consumption and CO2 emissions–Delhi, Pune and Patna. Sustain Cities Soc 22:1–10Google Scholar
  42. Jain S, Aggarwal P, Kumar P, Singhal S, Sharma P (2014) Identifying public preferences using multi-criteria decision making for assessing the shift of urban commuters from private to public transport: a case study of Delhi. Transp Res Part F Traffic Psychol Behav 24:60–70Google Scholar
  43. Katz D, Rahman MM (2010) Levels of overcrowding in bus system of Dhaka, Bangladesh. Transp Res Rec 2143(1):85–91Google Scholar
  44. Kennedy CA (2002) A comparison of the sustainability of public and private transportation systems: study of the Greater Toronto Area. Transportation 29:459–493Google Scholar
  45. Khanna P, Jain S, Sharma P, Mishra S (2011) Impact of increasing mass transit share on energy use and emissions from transport sector for National Capital Territory of Delhi. Transp Res Part D Transp Environ 16(1):65–72Google Scholar
  46. Kottenhoff K, Freij KB (2009) The role of public transport for feasibility and acceptability of congestion charging–the case of Stockholm. Transp Res Part A Policy Pract 43(3):297–305Google Scholar
  47. Koutsopoulos HN, Odoni A, Wilson NH (1985) Determination of headways as a function of time varying characteristics on a transit network. Comput Sched Public Transp 2:391–413Google Scholar
  48. Lau HC, Sim M, Teo KM (2003) Vehicle routing problem with time windows and a limited number of vehicles. Eur J Oper Res 148(3):559–569Google Scholar
  49. Lei D, Yan X (2007) Urban transit route network design problem using tabu search algorithm. In: International conference on transportation engineering 2007, pp 3929–3934Google Scholar
  50. Li Z, Hensher DA (2011) Crowding and public transport: a review of willingness to pay evidence and its relevance in project appraisal. Transp Policy 18(6):880–887Google Scholar
  51. Li Z, Hensher DA (2013) Crowding in public transport: a review of objective and subjective measures. J Public Transp 16(2):107–134Google Scholar
  52. Liu H, Yang X (2007) Bus transit route network design using genetic algorithm. In: International conference on transportation engineering 2007, pp 1135–1141Google Scholar
  53. Liu C, Zheng Z (2013) Public acceptance towards congestion charge: a case study of Brisbane. Proc Soc Behav Sci 96:2811–2822Google Scholar
  54. Mishra S, Mathew TV, Khasnabis S (2010) Single-stage integer programming model for long-term transit fleet resource allocation. J Transp Eng 136(4):281–290Google Scholar
  55. Nahry SS (2000) Optimal scheduling of public transport fleet at network level. J Adv Transp 34(2):297–323Google Scholar
  56. Nesheli MM, Ceder AA, Brissaud R (2017) Public transport service-quality elements based on real-time operational tactics. Transportation 44(5):957–975Google Scholar
  57. Nikitas A, Karlsson M (2015) A worldwide state-of-the-art analysis for bus rapid transit: looking for the success formula. J Public Transp 18(1):1–33Google Scholar
  58. Parra D, Gomez L, Pratt M, Sarmiento OL, Mosquera J, Triche E (2007) Policy and built environment changes in Bogotá and their importance in health promotion. Indoor Built Environ 16(4):344–348Google Scholar
  59. Pattnaik SB, Mohan S, Tom VM (1998) Urban bus transit route network design using genetic algorithm. J Transp Eng 124(4):368–375Google Scholar
  60. Polzin SE, Baltes MR (2002) Bus rapid transit: a viable alternative? J Public Transp 5(2):47–70Google Scholar
  61. Pucher J, Korattyswaropam N, Mittal N, Ittyerah N (2005) Urban transport crisis in India. Transp Policy 12(3):185–198Google Scholar
  62. Raux C, Souche S, Pons D (2012) The efficiency of congestion charging: some lessons from cost-benefit analyses. Res Transp Econ 36(1):85–92Google Scholar
  63. Rozycki C Von, Koeser H, Schwarz H (2003) Ecology profile of the german high-speed rail passenger transport system, ICE. Int J Life Cycle Assess 8(2):83–91Google Scholar
  64. Salzborn FJ (1972) Optimum bus scheduling. Transp Sci 6(2):137–148Google Scholar
  65. Silman LA, Barzily Z, Passy U (1974) Planning the route system for urban buses. Comput Oper Res 1(2):201–211Google Scholar
  66. Singh SK (2006) Future mobility in India: implications for energy demand and CO2 emission. Transp Policy 13(5):398–412Google Scholar
  67. Suman HK, Bolia NB (2019) A review of service assessment attributes and improvement strategies for public transport. Transp Dev Econ 5(1):1Google Scholar
  68. Suman HK, Bolia NB, Tiwari G (2016) Analysis of the factors influencing the use of public buses in Delhi. J Urban Plan Dev 142(3):04016003Google Scholar
  69. Suman HK, Bolia NB, Tiwari G (2017) Comparing public bus transport service attributes in Delhi and Mumbai: policy implications for improving bus services in Delhi. Transp Policy 56:63–74Google Scholar
  70. Suman HK, Bolia NB, Tiwari G (2018) Perception of potential bus users and impact of feasible interventions to improve quality of bus services in Delhi. Case Stud Transp Policy 6(4):591–602Google Scholar
  71. Thynell M, Mohan D, Tiwari G (2010) Sustainable transport and the modernisation of urban transport in Delhi and Stockholm. Cities 27(6):421–429Google Scholar
  72. Tirachini A, Hensher DA, Rose JM (2013) Crowding in public transport systems: effects on users, operation and implications for the estimation of demand. Transp Res Part A Policy Pract 53:36–52Google Scholar
  73. Tiwari G (2009) Public transport research challenges in India. Indian Institute of Technology, DelhiGoogle Scholar
  74. Van Nes R, Hamerslag R, Immers BH (1988) Design of public transport networks. Transp Res Rec 1202:74–83Google Scholar
  75. Wall G, McDonald M (2007) Improving bus service quality and information in Winchester. Transp Policy 14(2):165–179Google Scholar
  76. Wan QK, Lo HK (2003) A mixed integer formulation for multiple-route transit network design. J Math Model Algorithms 2(4):299–308Google Scholar
  77. Yu B, Yang Z, Yao J (2010) Genetic algorithm for bus frequency optimization. J Transp Eng 136(6):576–583Google Scholar
  78. Zhang YJ, Peng HR, Liu Z, Tan W (2015) Direct energy rebound effect for road passenger transport in China: a dynamic panel quantile regression approach. Energy Policy 87:303–313Google Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Transport Engineering and LogisticsPontificia Universidad Catolica de ChileSantiagoChile
  2. 2.Department of Mechanical EngineeringIndian Institute of TechnologyDelhiIndia

Personalised recommendations