Relationship Between Mobility and Pedestrian Traffic Safety in India

Abstract

Pedestrians are the most vulnerable road users. In India and other developing countries, pedestrian fatalities constitute a significant proportion of traffic-related fatalities. As these countries’ economies grow and their populations become more mobile, there may be repercussions for pedestrians. While improved mobility is generally considered a positive outcome of economic growth, its impact on pedestrian traffic safety has not been studied in detail, especially at a region-wide level. Since pedestrian behavior and vehicle ownership characteristics in low-and middle-income countries are substantially different than in high-income countries, it is necessary to explore the relationship between mobility and pedestrian traffic safety in the context of a middle-income country. In this study, India serves as a case study to explore such a relationship. A time-series regression methodology and mobility indices were used to quantify regional mobility over time. The results suggest that improvements in mobility are detrimental to pedestrian traffic safety in India. This study should emphasize to decision-makers the importance of investing in safety features for pedestrians, whose needs have been neglected for decades, often in favor of motorized transport.

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References

  1. 1.

    WHO (2015) Global status report on road safety, 2015. Violence and Injury Prevention. World Health Organization (WHO). https://www.who.int/violence_injury_prevention-/road_safety_status/2015/en/. Accessed 5 June 2017

  2. 2.

    World Bank Group (2017) Global economic prospects, a fragile recovery. https://www.worldbank.org/en/publication/global-economic-prospects. Accessed 10 Mar 2017

  3. 3.

    NCRB (2015) Accidental Deaths and Suicides in India, 2015. Tables. Traffic accidents. Chapter and Table No. 1A.4. Mode of Transport—wise Number of Persons Died in Road Accidents during 2015 (State/UT & City-wise). National Crime Records Bureau (NCRB). Ministry of Home Affairs, Government of India, New Delhi-110066. https://ncrb.gov.in/StatPublications/ADSI/ADSI2015/ADSI2015.asp. Accessed 30 Apr 2017

  4. 4.

    Davis G (2001) Relating severity of pedestrian injury to impact speed in vehicle-pedestrian crashes: simple threshold model. Transp Res Record J Transp Res Board 1773:108–113

    Article  Google Scholar 

  5. 5.

    Kröyer HR (2015) Is 30km/ha ‘safe’speed? Injury severity of pedestrians struck by a vehicle and the relation to travel speed and age. IATSS Res 39(1):42–50

    Article  Google Scholar 

  6. 6.

    Poorfakhraei A, Samimi A, Ermagun A (2014) Investigating the effect of impact speed on pedestrian fatality in traffic accidents using a hazard based duration model. In: Transportation research board 93rd Annual Meeting (no. 14–4658)

  7. 7.

    Rosen E, Stigson H, Sander U (2011) Literature review of pedestrian fatality risk as a function of car impact speed. Accid Anal Prev 43(1):25–33

    Article  Google Scholar 

  8. 8.

    Gaca S, Kiec M (2015) Assessment of pedestrian risk at crossings with kinematic-probabilistic model. Transp Res Record J Transp Res Board 2514:129–137

    Article  Google Scholar 

  9. 9.

    Jin N, Guibing L, Jikuang Y, Xuenong Z, Chao Z, Xiaoping Y, Meichuan W (2012) A Study of injury risk of bicyclist and pedestrian in traffic accidents in Changsha of China. In: Proceedings of 5th international conference on ESAR

  10. 10.

    Rosen E, Sander U (2009) Pedestrian fatality risk as a function of car impact speed. Accid Anal Prev 41(3):536–542

    Article  Google Scholar 

  11. 11.

    Chikaraishi M, Fischbeck P, Chen, M (2013) Traffic safety and mobility: reviewing the ethical aspects and revisiting the definition of accident risks. In: Proceedings of the Eastern Asia society for transportation studies, p 9

  12. 12.

    Dulisse B (1997) Methodological issues in testing the hypothesis of risk compensation. Accid Anal Prev 29(3):285–292

    Article  Google Scholar 

  13. 13.

    Yannis G, Antoniou C, Papadimitriou E (2011) Autoregressive nonlinear time-series modeling of traffic fatalities in Europe. Eur Transport Res Rev 3(3):113–127

    Article  Google Scholar 

  14. 14.

    Quddus MA (2008) Time series count data models: an empirical application to traffic accidents. Accid Anal Prev 40(5):1732–1741

    Article  Google Scholar 

  15. 15.

    Brijs T, Karlis D, Wets G (2008) Studying the effect of weather conditions on daily crash counts using a discrete time-series model. Accident Anal Prevent 40(3):1180–1190

    Article  Google Scholar 

  16. 16.

    Saar I (2015) Do alcohol excise taxes affect traffic accidents? Evidence Estonia Traffic Injury Prevent 16(3):213–218

    Article  Google Scholar 

  17. 17.

    Meirambayeva A, Vingilis E, McLeod AI, Elzohairy Y, Xiao J, Zou G, Lai Y (2014) Road safety impact of Ontario street racing and stunt driving law. Accident Anal Prevent 71:72–81

    Article  Google Scholar 

  18. 18.

    Liu C, Chen CL (2004) Time series analysis and forecast of annual crash fatalities National center for statistics and analysis, Washington DC

  19. 19.

    Gururaj G, Thomas AA, Reddi MN (2000) Underreporting of road traffic injuries in Bangalore. Implications for road safety policies and programmes. In: Proceedings of the 5th world conference on injury prevention and control (p. 54). New Delhi, India: Macmillan India Ltd.

  20. 20.

    Mohan D (2004) The road ahead: traffic injuries and fatalities in India. In: Transportation research and injury prevention programme. Indian Institute of Technology, Delhi, p 8

  21. 21.

    NCRB 2003–2015 (2017) Accidental deaths and suicides in India 2003–2015. Tables. Accidents in India. Chapter and Table No. 1.8. Incidence of Road Accidental Deaths in type of vehicles (State, UT & City-wise). National Crime Records Bureau (NCRB). Ministry of Home Affairs, Government of India, New Delhi, p 110066. https://ncrb.nic.in/StatPublications/ADSI/PrevPublications.htm. Accessed 30 Apr 2017

  22. 22.

    Bhalla K, Khurana N, Bose D, Navaratne KV, Tiwari G, Mohan D (2017) Official government statistics of road traffic deaths in India under-represent pedestrians and motorised two wheeler riders. Injury Prevent 23(1):1–7

    Article  Google Scholar 

  23. 23.

    Levulytė L, Baranyai D, Sokolovskij E, Török Á (2017) Pedestrians’ role in road accidents. Int J Traffic Transport Eng 7:3

    Google Scholar 

  24. 24.

    Shinar D (2012) Safety and mobility of vulnerable road users: pedestrians, bicyclists, and motorcyclists. Accid Anal Prev 44(1):1–2

    Article  Google Scholar 

  25. 25.

    Census 2011 Provisional population totals (2016) 31st March, 2011. Ministry of Home Affairs. Office of the Registrar General and Census Commissioner of India. https://censusindia.gov.in/2011-prov-results/indiaatglance.html. Accessed 30 Oct 2016

  26. 26.

    MoRTH (2003–2015a) Basic Road Statistics of India. 2003–2015. Ministry of Road Transport & Highways (MoRTH). Transport Research Wing, Government of India, New Delhi, 110001. https://morth.nic.in/index2.asp?slid=314&sublinkid=142&lang=1. Accessed 20 Jan 2017

  27. 27.

    MoRTH (2003–2015b) Road Transport Year Book. 2003–2015. Ministry of Road Transport & Highways (MoRTH). Transport Research Wing, Government of India, New Delhi, 110001. https://morth.nic.in/index2.asp?slid=314&sublinkid=142&lang=1. Accessed 20 Jan 2017

  28. 28.

    Box GE, Jenkins GM, Reinsel GC (2013) Time Series Analysis: Forecasting and Control. Wiley, Hoboken

    Google Scholar 

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Acknowledgements

The authors acknowledge the opportunity provided by the 3rd Conference on Recent Advances in Traffic Engineering (RATE 2018) held at SVNIT Surat, India between during 11–12 August 2018 to present this work, that forms the basis of this manuscript.

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Correspondence to Vinod Vasudevan.

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Agarwala, R., Vasudevan, V. Relationship Between Mobility and Pedestrian Traffic Safety in India. Transp. in Dev. Econ. 6, 15 (2020). https://doi.org/10.1007/s40890-020-00103-2

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Keywords

  • Pedestrian fatality
  • Mobility
  • Time series model
  • Developing countries