Abstract
Pedestrian crashes account for 11% of all reported traffic crashes in Melbourne metropolitan area between 2004 and 2013. There are very limited studies on pedestrian accidents at mid-blocks. Mid-block crashes account for about 46% of the total pedestrian crashes in Melbourne metropolitan area. Meanwhile, about 50% of all pedestrian fatalities occur at mid-blocks. In this research, Partial Proportional Odds (PPO) model is applied to examine vehicle-pedestrian crash severity at mid-blocks in Melbourne metropolitan area. The PPO model is a logistic regression model that allows the covariates that meet the proportional odds assumption to affect different crash severity levels with the same magnitude; whereas the covariates that do not meet the proportional odds assumption can have different effects on different severity levels. In this research vehicle-pedestrian crashes at mid-blocks are analysed for first time. In addition, some factors such as distance of crashes to public transport stops, average road slope and some social characteristics are considered to develop the model in this research for first time. Results of PPO model show that speed limit, light condition, pedestrian age and gender, and vehicle type are the most significant factors that influence vehicle-pedestrian crash severity at mid-blocks.
Highlights
1.1 BackgroundIn order to reduce air pollution and obtain better public health outcomes, efforts to encourage non-motorized transport modes have increased in recent years (Wey & Chiu 2013)
Mid-block crashes account for 46% of total pedestrian crashes in Melbourne metropolitan area and 49% of pedestrian fatalities occur at mid-blocks
The chi-square test statistics and associated p-values shown in the table indicate that Speed, Light Condition, Pedestrian Gender, pedestrian age, vehicle type, traffic volume, per-cent of Islamic affiliation in suburbs, distance of public transport stops to location of accidents, and suburb’s population density in the model significantly improved the model fit
Summary
In order to reduce air pollution and obtain better public health outcomes, efforts to encourage non-motorized transport modes have increased in recent years (Wey & Chiu 2013). 34 pedestrians are killed in traffic crashes in Melbourne metropolitan area, representing 24% of the total traffic fatalities. Mid-block crashes account for 46% of total pedestrian crashes in Melbourne metropolitan area and 49% of pedestrian fatalities occur at mid-blocks. Since the factors contributing to vehicle crashes at intersections and mid-blocks are significantly different [8,9,10,11,12,13], more research needs to be done to develop a model for vehicle-pedestrian crashes at mid-blocks. Identifying and predicting trends in crash severity is one of the main steps to improve pedestrian safety. In this approach crash severity modelling assist in distinguish influencing variables on pedestrian crashes. The proportional odds assumption will be relaxed for independents variables that have different effects on different level of injuries
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