ROAD ACCIDENT ESTIMATION MODEL IN URBAN AREAS

Publications

Share / Export Citation / Email / Print / Text size:

Transport Problems

Silesian University of Technology

Subject: Economics, Transportation, Transportation Science & Technology

GET ALERTS

eISSN: 2300-861X

DESCRIPTION

34
Reader(s)
68
Visit(s)
0
Comment(s)
0
Share(s)

SEARCH WITHIN CONTENT

FIND ARTICLE

Volume / Issue / page

Related articles

VOLUME 11 , ISSUE 3 (September 2016) > List of articles

ROAD ACCIDENT ESTIMATION MODEL IN URBAN AREAS

Serban RAICU / Dorinela COSTESCU * / Stefan BURCIU / Florin RUSCA / Mircea ROSCA

Keywords : road accidents, land use features, traffic analysis zone, spatial analysis, accidents estimation model

Citation Information : Transport Problems. Volume 11, Issue 3, Pages 33-42, DOI: https://doi.org/10.20858/tp.2016.11.3.4

License : (CC BY-SA 4.0)

Received Date : 17-January-2015 / Accepted: 23-August-2016 / Published Online: 27-February-2017

ARTICLE

ABSTRACT

Summary. Urban areas are significantly different in terms of traffic risk. In a decisive manner, they are the result of urban development policies. The shape, size and configuration of an entire urban area, the facilities to satisfy people and the need for mobility of goods, as well as behavioural attitudes of the population, are essential for a traffic pattern and its associated risks. In this framework, the purpose of this paper is to identify the effects of urban area characteristics on road accidents. Using specific spatial analysis, a model of accident estimation in the urban areas of Bucharest is developed. The study aims to provide useful tools for urban decision makers for a-priori analysis of the consequences of urban outline changes on traffic risks.

Content not available PDF Share

FIGURES & TABLES

REFERENCES

  1. Raicu, S. & Costescu, D. On estimate of risk associated with urban road traffic. Advances in Automatic Control. Proceedings of the 16th International Conference on Automatic Control, Modelling & Simulation (ACMOS 2014). Brașov – Romania. June 26-28. 2014. Mastorakis, N. & Udriste, C. & et al. (Eds.). WSEAS Press, Recent Advances in Electrical Engineering Series – 35. 2014. P. 92-97.
  2. Pulugurtha, S.S. & Duddu, V. D. & Kotagiri, Y. Traffic analysis zone level crash estimation models based on land use characteristics. Accident Analysis and Prevention. 2013. Vol. 50. P. 678– 687.
  3. Ladron de Guevara, F. & Washington, S.P. & Oh, J. Forecasting Crashes at the Planning Level: Simultaneous Negative Binomial Crash Model Applied in Tucson, Arizona. Transportation Research Record. 2004. Vol. 1897. P. 191–100.
  4. Li, L. & Zhu, L. & Sui, D.Z. A GIS-based Bayesian approach for analyzing spatial–temporal patterns of intra-city motor vehicle crashes. Journal of Transport Geography. 2007. Vol. 15. P. 274–285.
  5. Lord, D. & Mannering, F. The statistical analysis of crash-frequency data: A review and assessment of methodological alternatives. Transportation Research Part A. 2010. Vol. 44. P. 291–305.
  6. Lord, D. Issues related to the application of accident prediction models for the computation of accident risk on transportation networks. Transportation Research Record. 2002. Vol. 1784. P. 17–26.
  7. Lord, D. & Persaud, B.N. Estimating the safety performance of urban road transportation networks. Accident Analysis and Prevention. 2004. Vol. 36 (4). P. 609–620.
  8. Miaou, S.P & Lord, D. Modeling Traffic Crash-Flow Relationships for Intersections: Dispersion Parameter, Functional Form, and Bayes Versus Empirical Bayes Methods. Transportation Research Record. 2003. Vol. 1840. P. 31-40.
  9. Hauer, E. Overdispersion in modelling accidents on road sections and in Empirical Bayes estimation. Accident Analysis and Prevention. 2001. Vol. 33 (6). P. 799–808.
  10. Lord, D. & Persaud, B. Accident Prediction Models With and Without Trend: Application of the Generalized Estimating Equations Procedure. Transportation Research Record. 2000. Vol. 1717. P. 102 – 108.
  11. Hauer, E. & Harwood, D. W. & Council, F. M. & Griffith, M., S. Estimating Safety by the Empirical Bayes Method: A Tutorial. Transportation Research Record. 2002. Vol. 1784. P. 126-131.
  12. Fernandes, A. & Neves J. An approach to accidents modeling based on compounds road environments. Accident Analysis and Prevention. 2013. Vol. 53. P. 39-45.
  13. Marshall, W. E. & Garrick, N. Does Street Network Design Affect Traffic Safety? Accident Analysis and Prevention. 2011. Vol. 43 (3). P. 769-781.
  14. Fleury, D. (Coord.) Projets urbains de cohérence fonctions/réseaux. 2011. PREDIT Groupe Opérationnel N° 2: Qualité des systèmes de transport. 08 MT S 027 et 08 MT S 028. IFSTTAR-France.
  15. Lord, D. Modeling motor vehicle crashes using Poisson-gamma models: examining the effects of low sample mean values and small sample size on the Estimation of the fixed dispersion parameter. Accident Analysis and Prevention. 2006. Vol. 38 (4). P. 751–766.
  16. Cameron, A. C. & Trivedi, P. K. Regression analysis of count data. Cambridge University Press. 1998.
  17. Mitchell, A. The ESRI Guide to GIS Analysis. Vol. 2. ESRI Press. 2005.
  18. EuropeAid/123579/D/SER/RO. Bucharest General Master Plan for Urban Transport. Final Rapport. 2008. [In Romanian: Master Plan General pentru Transport Urban – Bucuresti, Sibiu si Ploiesti. Raport Final Bucuresti. EuropeAid/123579/D/SER/RO].
  19. Zhang, H. & Liu, Y. & Li, B. Notes on discrete compound Poisson model with applications to risk theory. Insurance: Mathematics and Economics. 2014. Vol. 9. P. 325–336.

EXTRA FILES

COMMENTS