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.
Highlights
Depending on accidents, they may have different causes
Scatter plots and preliminary statistical tests indicated that the relationship between pedestrian crashes and predictor variables are non-linear in nature
Non-linear relationships based on Poisson distribution, negative binomial distribution, and lognormal distributions as well as zero-inflated model were tested to identify the best model that can explain the relationship between pedestrian crashes and the selected predictor variables
Summary
They may have different causes. Eliminating the risk of each cause requires separate solutions. Pedestrian crashes and activities depend on demographic, land use, road network, geometric and transit characteristics. An increase in demographic and socio-economic characteristics such as population, household units, and total employment within walking distance of an intersection may increase pedestrian volume and the number of pedestrian crashes at intersection. While an increase in mean income level within the same area may result in a decrease (or sometimes increase) in pedestrian volume and the number of pedestrian crashes at the intersection. Pedestrian volume and pedestrian crashes could depend on land use characteristics within the walking distance of an intersection. An increase in street width (or the number of lanes); speed limit, traffic volume, and the number of transit stops within the vicinity of an intersection may increase pedestrian exposure to risk and crashes at the intersection. Data pertaining to these characteristics are required for developing and assessing pedestrian crash estimation models [1]
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