Pedestrian safety models for urban environments with high roadside activities
Section snippets
Introduction and background
Pedestrians are considered as the most vulnerable road users because of their fragility and slow movement. They have a higher risk of road crash potential than motorised vehicle occupants (Zhang et al., 2014). In addition, pedestrian fatalities accounting for about 23% of total traffic deaths globally (WHO, 2018). In 2016 for instance, 5320 pedestrians’ fatalities were reported in Europe (excluding Lithuania and Slovakia) accounting for 2% of total traffic deaths (European Commission, 2018).
Objective
The above factors albeit frequently considered in statistical analyses, there have been comparatively fewer attempts to include them in appropriate models. Therefore, it was felt necessary to examine the safety of pedestrians moving in urban road environments with high roadside activities through the development of a modelling approach which considers both the infrastructure, and traffic attributes and seeks to capture the effect of roadside activities on pedestrian safety.
Methodology
The research concerns the interaction between the pedestrians and the drivers of motorised vehicles in urban roads. It aims to determine the relationship between the characteristics of the road environment and the incidence of conflicts between pedestrians and the drivers of motorised vehicles which result in injury or death.
In order to investigate the significant contributing factors influencing pedestrian safety, the study will develop crash models for pedestrians moving in urban areas with
Data description and collection
The second part involves selecting study sites and variables to perform the study process. Based on literature review, it was felt that a range of independent factors that may influence the potential of crash events and could be considered in the model developed in this work. These included:
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Traffic volume measured as annual average daily traffic (AADT),
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Traffic mean speed (V),
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Coefficient of speed variation (CV),
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Pedestrian crossing violations (PCV),
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Number of bus stoppings (BS),
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Pedestrian walking
Model building
To provide a more suitable model, the natural log transformation was applied to both traffic volume and intersecting traffic volume (El-Basyouny and Sayed, 2009; Wier et al., 2009; Miranda-Moreno et al., 2011).
In addition, the factors of intersecting traffic volume and number of side roads intersecting the analysed segment were included separately in the model estimation. This is due to the fact that intersecting volume and side roads are related, since in the event of no side road intersecting
Conclusion and recommendations
This study developed a Poisson regression model to correlate pedestrian crash risk with parameters of road environments with high roadside activities. The study concluded that the number of bus stoppings per unit of time, parking, pedestrian crossing and violations’ volume, the traffic speed variation, the number of intersecting side roads, in addition to through and intersecting traffic volume, were among the significant risk factors related to the pedestrian crash risk. The model provided
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