OJCE  Vol.4 No.3 , September 2014
Pedestrian Crash Prediction Models and Validation of Effective Factors on Their Safety (Case Study: Tehran Signalized Intersections)
ABSTRACT
The quantity and severity of traffic accidents have increased with the development of machinery life and traffic growth in cities and roads in the past 50 years. Among the road users, pedestrians are the most vulnerable groups to be exposed to high risks. Vehicle crashes with pedestrian are almost inevitable and cause injury or death to pedestrian. Crash investigation and statistical studies indicate that percentage of pedestrian deaths caused by vehicle accidents are much more than all deaths. A considerable amount of accidents occur at signalized and urban intersections which are the intensive crash places. Therefore in this paper appropriate models that could specify safety indicators have been indicated with existing information by characterized parametric and nonparametric variables for twenty signalized intersections. Categories and correlations of variables also have been investigated. Three models including Regression, Poisson, and Negative binomial with defined variables have been determined. T and chi square tests, calibration and comparison of variables have been done by curve fitting. The role of each parameter was specified in pedestrian crashes. Validating models had the following outcomes: Pedestrian crash prediction models were based on none linear relations at intersections. Predictable variables, developing extended linear models and also pedestrian crash prediction are on the basis of Negative binomial distribution which is used due to more data dispersion. As observed, the Negative binomial regression because of its more R2 correlation factor has more validity among other regression models such as linear regression and Poisson. Calibrated models are put into sensitivity analysis to study the effect of each previously mentioned parameter in overall performance. Hence much better perception of future transportation plans can be achieved by development of safety models at planning levels.

Cite this paper
Haghighatpour, P. and Moayedfar, R. (2014) Pedestrian Crash Prediction Models and Validation of Effective Factors on Their Safety (Case Study: Tehran Signalized Intersections). Open Journal of Civil Engineering, 4, 240-254. doi: 10.4236/ojce.2014.43021.
References
[1]   Pulugurthaa, S.S. and Sambharab, V.R. (2011) Pedestrian Crash Estimation Models for Signalized Intersections. Accident Analysis and Prevention, 43, 439-446.
http://dx.doi.org/10.1016/j.aap.2010.09.014

[2]   Joksch, H.C. and Kostyniuk, L.P. (1997) Modeling Intersection Crash Counts and Traffic Volume.
http://deepblue.lib.umich.edu/bitstream/2027.42/1213/2/90762.0001.001.pdf

[3]   Brude, U. and Larsson, J. (1993) Models for Predicting Accidents at Junctions Where Pedestrians and Cyclists Are Involved. How Well DO they Fit? Accident Analysis and Prevention Journal, 25, 449-509.
http://dx.doi.org/10.1016/0001-4575(93)90001-D

[4]   Huang, H., Chin, H.C. and Haque, M.M. (2008) Bayesian Hierarchical Analysis of Crash Prediction Models. Transportation Research Board 87th Annual Meeting Compendium of Papers DVD, Washington, DC.

[5]   Lyon, C. and Persaud, B.N. (2002) Pedestrian Collision Prediction Models for Urban Intersections. Transportation Research Record # 1818, 102-107.
http://dx.doi.org/10.3141/1818-16

[6]   Wier, M., Weintraub, J., Humphreys, E.H., Seto, E. and Bhatia, R. (2009) An Area-Level Model of Vehicle-Pedestrian Collisions with Implications for Land Use and Transportation Planning. Accident Analysis & Prevention Journal, 41, 137-145.
http://dx.doi.org/10.1016/j.aap.2008.10.001

[7]   Torbic, D.J., Harwood, D.W., Bokenkroger, C.D., Srinivasan, R., Carter, D.L., Zegeer, C.V. and Lyon, C. (2010) Pedestrian Safety Prediction Methodology for Urban Signalized Intersections. Transportation Research Board 89th Annual Meeting Compendium of Papers DVD, Washington DC.

[8]   Lee, C. and Abdel-Aty, M.A. (2005) Comprehensive Analysis of Vehicle-Pedestrian Crashes at Intersections in Florida. Accident Analysis & Prevention Journal, 37, 775-786.
http://dx.doi.org/10.1016/j.aap.2005.03.019

[9]   Elvik, R. (2009) The Non-Linearity of Risk and the Promotion of Environmentally Sustainable Transport. Accident Analysis & Prevention Journal, 41, 849-855.
http://dx.doi.org/10.1016/j.aap.2009.04.009

[10]   Harwood, D.W., Torbic, D.J., Gilmore, D.K., Bokenkroger, C.D., Dunn, J.M., Zegeer, C.V., Srinivasan, R., Carter, D., Raborn, C., Lyon, C. and Persaud, B. (2008) Pedestrian Safety Prediction Methodology. NCHRPWeb-Only Document 129: Phase. III. Transportation Research Board, Washington DC.

[11]   FHWA How to Develop a Pedestrian Safety Action Plan Traffic Safety Basic Facts (2005).

[12]   Pickering, D., Hall, R.D. and Grimmer, M. (1986) Estimation of Safety at Two-Way STOP-Controlled Intersections on Rural Highways. Transportation Research Record, 1401, 83-89.

[13]   PASW. Statistics18 lnk. Statistics Software SPSS 18.

[14]   Microsoft Office Excel ( 2007). Lnk.

 
 
Top