Logo PTI
Polish Information Processing Society
Logo FedCSIS

Annals of Computer Science and Information Systems, Volume 8

Proceedings of the 2016 Federated Conference on Computer Science and Information Systems

Evaluating model of traffic accident rate on urban data


DOI: http://dx.doi.org/10.15439/2016F195

Citation: Proceedings of the 2016 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 8, pages 181186 ()

Full text

Abstract. Public safety, especially the daily traffic accident is concerned by the public. Previous studies have already discussed accident reasons associated with accidents statistically. There is method called Innovators Marketplace on Data Jackets created by Professor Ohsawa. This method is used to externalize the value of data via stakeholders' requirement communication. This paper applied the solution from an IMDJ workshop to reseach this topic creatively. This novel solution suggested to do analysis on the combination of urban data and traffic accident rate to find the impact factors to the traffic accident rate in the urban system. This paper used factor analysis, structure equation modeling and data mining to construct a theoretical frame for traffic accident rate analysis for urban data. Different accident indexes, such as total number of accident, fatality rate, injury rate, and casualty rate are combined to construct a traffic accident risk evaluation model. This paper chosen the urban data as the solution from IMDJ workshop, such as population structure information, vehicle information, road characters, public traffic system information, and the other kinds of data to explore factor meaning, and to identify relationships between different factors. It segmented these urban data based on their categories, and determined accident risk for each section. By doing analysis on not only the original data but also the changing rate of these data each year, the result analytical results showed that traffic accident rate on urban data could be described by the combination of population structure, road characters, public traffic system and public facilities. These four sections affects traffic accident rate significantly during the development of urban; however, the vehicle factor does not have influence on traffic accident rate. And it proofs the solution from IMDJ workshop is not only novel but also practical strongly. Making some solution from IMDJ into reality, we will find another new way to affect the world.


  1. Al-Ghamdi, A. S., Analysis of traffic accidents at urban intersections in Riyadh Accident Analysis and Prevention, 35(5), pp. 717-724, 2003, http://dx.doi.org/10.1016/S0001-4575(02)00050-7
  2. K. Ivan, I. Haidu, J. Benedek, and S. M. CiobanuIdentification of traffic accident risk-prone areas under low-light conditions Nat. Hazards Earth Syst. Sci., 15, 2059-2068, 2015. http://dx.doi.org/10.5194/nhessd-3-1453-2015
  3. Ariana Vorko-Jovic, Josipa Kern, Zrinka Biloglav, Risk factors in urban road traffic accidents Journal of Safety Research, 37(1), pp. 93-98, 2006. http://dx.doi.org/10.1136/ip.2010.029215.429
  4. John C. Milton, Venky N. Shankar, Fred L. Mannering, Highway accident severities and the mixed logit model: An exploratory empirical analysi Analysis and Prevention, Volume: 40, Issue: 1, January, pp. 260-266, 2008. http://dx.doi.org/10.1016/j.aap.2007.06.006
  5. Kim, K., Nitz, L., Richardson, J., Li, L., Personal and behavioral predictors of automobile crash and injury severity Accident Analysis and Prevention, 27(4), pp.469-481,1995. http://dx.doi.org/10.1016/0001-4575(95)00001-G
  6. Y. Ohsawa, H.Kido,T.Hayashi,C.Liu,Innovators Marketplace on Data Jackets for Externalizing the Value of Data via Stakeholders Requirement Communication Procedia Computer Science,pp.709-716,2013. http://dx.doi.org/10.1007/978-3-319-13545-8_6
  7. Kuhnert, P. M., Do, K. A., McClure, R., Combining non-parametric models with logistic regression: an application to motor vehicle in- jury data Statistics and Data Analysis, 34(3), pp. 371-386,2000. http://dx.doi.org/10.1016/S0167-9473(99)00099-7
  8. Archer,J., Vogel,K.,The Traffic Safety Problem in urban areas Royal Institute of Technology Publication,2000. http://dx.doi.org/10.1016/j.aap.2016.03.017
  9. Kathleen,L. Wolf and Nicholas Bratton,Urban Trees and Traffic Safety: Considering U.S. Roadside Policy and Crash Data International Society of Arboriculture,pp.170-179,2006. http://dx.doi.org/10.1061/(ASCE)0733-947X(1990)116:1(90)
  10. K. Ivan, I. Haidu, J. Benedek, and S. M.Ciobanu,Identification of traffic accident risk-prone areas under low-light conditions Nat. Hazards Earth Syst. Sci.,pp. 20592068, 2015. http://dx.doi.org/10.5194/nhess-15-2059-2015
  11. Zajac, S. S., Ivan, J. N., Factors influencing injury severity of motor vehicle-crossing pedestrian crashes in rural Connecticut Accident Analysis and Prevention, 35(3), pp. 369-379,2003. http://dx.doi.org/10.1016/S0001-4575(02)00013-1
  12. Milton, John C., Shankar, Venky N., Mannering, Fred L., Highway accident severities and the mixed logit model: An exploratory empirical analysis Accident Analysis and Prevention, Volume: 40, Issue: 1, pp. 260-266, 2008. http://dx.doi.org/10.1016/j.aap.2007.06.006
  13. Cass D. T., Ross F., Lam L., School Bus Related Deaths And Injuries In New South Wales Med J Austr;166(2), pp.07-108,1997. PMID: 8709875
  14. Darrell. S, Dana. H, Gender, structural disadvantage, and urban crime:do macro-social variables also explan female offending rates Criminology, volume38, number 2, pp. 403-438, 2000. http://dx.doi.org/10.1111/j.1745-9125.2000.tb00895.x
  15. Judith R. Blau, Peter M. Blau, The cost of inequality: metropolitan structure and violent crime American sociological review, 1982, Vol. 47, pp:114-129,1982. http://dx.doi.org/10.2307/2095046
  16. Adam Krasuski, A framework for Dynamic Analytical Risk Management at the emergency scene. From tribal to top down in the risk management maturity model Proceedings of the 2014 Federated Conference on Computer Science and Information Systems, ACSIS, Vol. 2, pp. 323–330., 2014. http://dx.doi.org/10.15439/2014F371
  17. Yau, K. K. W., Risk factors affecting the severity of single vehicle traffic accidents in Hong Kong Accident analysis and prevention,36(3), pp. 333-340,2004. http://dx.doi.org/10.1016/S0001-4575(03)00012-5
  18. O'Donnell, C. J., Connor, D.H., Predicting the severity of motor vehicle accident injuries using models of ordered multiple choic Accident Analysis and Prevention, 28(6), pp. 739-753.,1996. http://dx.doi.org/10.1016/S0001-4575(96)00050-4