Development Of Crash Prediction Model Of Mid Size City And Accident Analysis

G. Sivaprasad, P. Gopi


In most cities and towns, the majority of crash black-spots occur at major intersections. Given this, crash reduction studies often focus on the major signalised intersections. However, there is limited information that links the phasing configuration, degree of saturation and overall cycle time to crashes. While a number of analysis tools are available for assessing the efficiency of intersections, there are very few tools that can assist engineers in assessing the safety effects of intersection upgrades and new intersections. Separate models were built for peak periods and for motor vehicles and pedestrians. The key crash types that were analysed were right-angle, right-turning, lost-control and rear-end type crashes.Considerable research has been carried out in recent years to establish relationships between crashes and traffic flow, geometric infrastructure characteristics and environmental factors for two-lane rural roads. Crash-prediction models focused on multilane rural roads, however, have rarely been investigated. In addition, most research has paid but little attention to the safety effects of variables such as stopping sight distance and pavement surface characteristics. Moreover, the statistical approaches have generally included Poisson and Negative Binomial regression models, whilst Negative Multinomial regression model has been used to a lesser extent. Finally, as far as the authors are aware, prediction models involving all the above-mentioned factors have still not been developed in Italy for multilane roads, such as motorways. Thus, in this paper crash-prediction models for a four-lane median-divided Italian motorway were set up on the basis of accident data observed during a 5-year monitoring period extending between 1999 and 2003. The Poisson, Negative Binomial and Negative Multinomial regression models, applied separately to tangents and curves, were used to model the frequency of accident occurrence. Model parameters were estimated by the Maximum Likelihood Method, and the Generalized Likelihood Ratio Test was applied to detect the significant variables to be included in the model equation.The developed methodology and results can be used to incorporate safety into long range transportation plans and land use decisions so as to minimize anticipated crashes in the future. The models developed using the methodology can also be used to examine the effect of changes in

land use characteristics (new development or re-zoning) on safety. Road safety is the main concern in many developing countries including India. Road accidents are influenced by many factors and the factors that influence road accidents interact in obscure ways which are not easily identified. Hence, factor analysis is used to classify observed variables into several groups and also to reduce the number of observed variables to a smaller set of factors. In this study, factor analysis is used to analyze the correlation among observed variables in order to estimate and describe the number of fundamental dimensions that underlie the observed data. The model suggests that road factors and traffic factors, both exhibit strong correlations among themselves and are also strongly related to traffic accidents.Most research has been carried out about crash modeling but there is little attention to the urban highways. The candidate’s set of explanatory parameters were: traffic flow parameters, geometric infrastructure characteristics and pavement conditions. Statistical analysis is done by SPSS on the basis of nonlinear regression modeling and during the analysis, principal components are identified to assist the principal component analysis method and more important variables recognized that could indicate the best description of crash occurrence on the basis of available logics. The presented models show that the crashes occurrence increase with the increase in each of section length, peak hour volume and longitudinal slope variables whereas it is decreased with the decrease of curvature. The remarkable result in this study was the effect of longitudinal slope variable on the crash occurrence.

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