International Journal of Business Analytics and Intelligence

Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

Distribution of Traffic Accident Times in India - Some Insights Using Circular Data Analysis


Affiliations
1 Indian Institute of Management, Ahmedabad, Gujarat, India
2 Saurashtra University, Rajkot, Gujarat, India
     

   Subscribe/Renew Journal


Traffic accidents are a major hazard for travellers on Indian roads. These are caused by a variety of reasons including the bad condition of roads, traffic density, lack of proper training of drivers, slack in enforcement of traffic rules, poor road lighting etc. It is further known that certain times of the day are more prone to traffic accidents than others. In this paper we investigate the distribution of traffic accident times using the data published annually by the National Crime Records Bureau (NCRB) over the period 2001-2014 using the tools of circular data analysis. It is seen that the observed distribution of the traffic accident times in most years is bimodal. Thus, several modelling strategies for bimodal distributions are tried which include fitting of mixture of von-Mises distributions and mixture of Kato-Jones distribution. It is seen from this analysis that the distribution of the traffic accident times are changing over the years. Notably, the proportion of accidents happening in late night has reduced over the years while the same has increased for late evening hours. Some more insights obtained from this analysis are also discussed.

Keywords

Circular Statistics, Kato-Jones Distribution, Mixture Distribution, Traffic Accidents, Von-Mises Distribution.
Subscription Login to verify subscription
User
Notifications
Font Size


  • Aderamo, A. J. (2012). Assessing the trends in road traffic accident casualties on Nigerian roads. Journal of Social Sciences, 31, 19-25.

  • Berkson, J. (1980). Minimum Chi-Square, not maximum likelihood. Annals of Statistics, 8(3) , 457-487.

  • Brunsdon, C., & Corcoran, J. (2006). Using circular statistics to analyse time patterns in crime incidence. Computers, Environment and Urban Systems, 30, 300-319.

  • Chen, J., & Gupta, A. K. (1997). Testing and locating variance changepoints with application to stock prices. Journal of the American Statistical Association, 92, (438), 739-747.

  • Cools, M., Moons, E., & Wets, G. (2010). Assessing the impact of weather on traffic intensity. American Meteorological Society, Weather, Climate and Society, 2, 60-68.

  • Corcoran, J. , Chhetri, P., & Stimson, R. (2009). Using circular statistics to explore the geography of the journey to work, Progress in Regional Science, 88(1), 119-132.

  • David, M. B, & Hyder, A. A. (2006). Modelling the cost effectiveness of injury interventions in lower and middle income countries: Opportunities and challenges. Cost Effectiveness and Resource Allocation, 4, 2, 1-11.

  • Faggian, A., Corcoran, J., & McCann, P. (2013). Modelling geographical graduate job search using circular statistics, Papers in regional Science, 92(2), 329-343.

  • Ghosh, K., Jammalamadaka, S. R., & Vasudaven, M. (1999). Change-point problems for the von-Mises distribution. Journal of applied statistics, 26(4), 423-434.

  • Gill, J., & Hangartner, D. (2010). Circular data in Political Science and how to handle It, Political Analysis, 18 (3), 316-336.

  • Grabovsky, I., & Horvath, L. (2001). Change-point detection in angular data. Annals of the Institute of Statistical Mathematics, 53(3), 552-556.

  • Jammalamadaka, S. R., & SenGupta, A. (2001). Topics in circular statistics. Singapore: World scientific.

  • Jiang, Q. (2009). On fitting a mixture of two von-Mises distributions, with applications. M.Sc Project, Department of Statistics and Actuarial Science, Simon Fraser University, Canada.

  • Kato, S., & Jones, M. C. (2010). A family of distributions on the circle with links to, and applications arising from mobius transformation. Journal of American Statistical Association, 105 (489), 249-262.

  • Kong, L. B., Lekawa,M., Navarro, R. A,. McGrath, J., Cohen, M., Margulies, D. R., &Hiatt, J. R. (1996). Pedestrian-motor vehicle trauma: an analysis of in jury profiles by age. Journal of the American College of Surgeons, 182, 17-23.

  • Lombard, F. (1986). The change-point problem for angular data: A nonparametric approach. Technometrics, 28, 391-397.

  • Mardia, K. V., & Jupp, P. E. (2000). Directional statistics. Chichester,Wiley.

  • Mooney, J. A., Helms, P. J., & Jollife, I. T. (2003). Fitting mixtures of von-Mises distributions: A case study involving sudden infant death syndrome. Computational Statistics & Data Analysis, 41, 505-513.

  • Mullen, K. M., Ardia, D., Gil,D. L., Windover, D., & Cline, J. (2011) . DEoptim: An R package for global optimization by differential evolution. Journal of Statistical Software, 40(6), 1-26.

  • Owens, D. A., & Sivak , M. (1993). The role of reduced visibilityin night time road fatalities. Report no. UMTRI-93-33, University of Michigan. Transportation Research Institute, U.S.A.

  • Page, E. S. (1955). A test for a change in a parameter occurring at an unknown point. Biometrika, 42, 523-527.

  • Plainis, S, Murray, I. J., & Pallikaris, I. G. (2006). Injury prevention, 12, 125-128. doi: 10.1136/ ip.2005.011056.

  • Roy, A., Parui, S. K., & Roy, U. (2012). A mixture model of circular-linear distributions for color image segmentation. International Journal of Computer Applications (0975 - 8887), 58(9), 6-11.

  • Schwarz, G. (1978). Estimating the dimension of a model. The Annals of Statistics, 6, 461-464.

  • Sengupta, A., & Laha, A. K. (2008a). A likelihood integrated method for exploratory graphical analysis of change point problem with directional data. Communications in statistics. Theory and methods, 37(11-12), 1783-1791.

  • Sengupta, A., & Laha, A. K. (2008b). A Bayesian analysis of the change-point problem for directional data. Journal of Applied Statistics, 35(6), 693-700.

  • von Mises, R. (1918). UJber die ‘Ganzzahligheit’ der Atomgewichte und vermandteFragen. Phys. Z. 19, 490-500.

  • Zhang, H., & Huang, Y. (2015). Finite mixture models and their applications: A review. Austin Biometrics and Biostatistics, 2(1), 1013, 1-6.


Abstract Views: 319

PDF Views: 0




  • Distribution of Traffic Accident Times in India - Some Insights Using Circular Data Analysis

Abstract Views: 319  |  PDF Views: 0

Authors

Arnab Kumar Laha
Indian Institute of Management, Ahmedabad, Gujarat, India
A. C. Pravida Raja
Indian Institute of Management, Ahmedabad, Gujarat, India
Dilip Kumar Ghosh
Saurashtra University, Rajkot, Gujarat, India

Abstract


Traffic accidents are a major hazard for travellers on Indian roads. These are caused by a variety of reasons including the bad condition of roads, traffic density, lack of proper training of drivers, slack in enforcement of traffic rules, poor road lighting etc. It is further known that certain times of the day are more prone to traffic accidents than others. In this paper we investigate the distribution of traffic accident times using the data published annually by the National Crime Records Bureau (NCRB) over the period 2001-2014 using the tools of circular data analysis. It is seen that the observed distribution of the traffic accident times in most years is bimodal. Thus, several modelling strategies for bimodal distributions are tried which include fitting of mixture of von-Mises distributions and mixture of Kato-Jones distribution. It is seen from this analysis that the distribution of the traffic accident times are changing over the years. Notably, the proportion of accidents happening in late night has reduced over the years while the same has increased for late evening hours. Some more insights obtained from this analysis are also discussed.

Keywords


Circular Statistics, Kato-Jones Distribution, Mixture Distribution, Traffic Accidents, Von-Mises Distribution.

References