Accident; analysis and prevention
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This paper presents the design, analysis and results of a driving simulator experiment conducted to study the interaction between drivers and pedestrians in a mixed-street environment. Ninety-six students of the American University of Beirut (AUB) participated in the experiment that took place in the Transportation and Infrastructure Laboratory of AUB. The study looked at the driver-pedestrian interaction from the driver's perspective, by quantifying the effects of different scenario variables on the driving behavior of the participants. ⋯ The model was then used to evaluate the effect of policy variables on the yielding probabilities of the drivers. The results of this study enrich current knowledge and understanding of drivers' behavior and their interaction with pedestrians, especially with studying the effects of scenario variables that were not addressed before; this would help planners propose and evaluate safety measures and traffic calming techniques to reduce the risks on pedestrians. The study also confirms the effectiveness of driving simulators in studying driver-pedestrian interactions.
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Pedestrian safety is one of the most challenging issues in road networks. Understanding how pedestrians maneuver across an intersection is the key to applying countermeasures against traffic crashes. It is known that the behaviors of pedestrians at signalized crosswalks are significantly different from those in ordinary walking spaces, and they are highly influenced by signal indication, potential conflicts with vehicles, and intersection geometries. ⋯ The amount of speed change before and after the event is modeled using regression analysis. A Monte Carlo simulation is applied for the entire speed profile of the pedestrians. The results show that the model can represent the pedestrian travel time distribution more accurately than the constant speed model.
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Quantitative analysis of pedestrian safety at uncontrolled multi-lane mid-block crosswalks in China.
A lot of pedestrian-vehicle crashes at mid-block crosswalks severely threaten pedestrian's safety around the world. The situations are even worse in China due to low yielding rate of vehicles at crosswalks. In order to quantitatively analyze pedestrian's safety at multi-lane mid-block crosswalks, the number of pedestrian-vehicle conflicts was utilized to evaluate pedestrian's accident risk. ⋯ The results show that: pedestrian refuge have positive effects on pedestrian safety; on the other hand, high vehicle speed, high traffic volume, rolling gap crossing pattern, and larger pedestrian platoon have negative effects on pedestrian safety. Based on our field observation and PVCA model, the number of conflicts will rise by 2% while the traffic volume increases 200 pcu/h; similarly, if the vehicle speed increases 5km/h, the number of conflicts will rise by 12% accordingly. The research results could be used to evaluate pedestrian safety at multi-lane mid-block crosswalks, and useful to improve pedestrian safety by means of pedestrian safety education, pedestrian refuge setting, vehicle speed limiting, and so on.
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With the increasing demand for sustainability, walking is being encouraged as one of the main active modes of transportation. However, pedestrians are vulnerable to severe injuries when involved in crashes which can discourage road users from walking. Therefore, studying factors that affect the safety of pedestrians is important. ⋯ However, network connectivity was associated with higher crash occurrence. The spatial effects were accounted for in the full Bayes models and were found significant. The models provide insights about the factors that influence pedestrian safety and the spatial variability of pedestrian crashes within a city, which can be useful for the planning of pedestrian networks.
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This study aims at contributing to the literature on pedestrian and bicyclist safety by building on the conventional count regression models to explore exogenous factors affecting pedestrian and bicyclist crashes at the macroscopic level. In the traditional count models, effects of exogenous factors on non-motorist crashes were investigated directly. However, the vulnerable road users' crashes are collisions between vehicles and non-motorists. ⋯ Subsequently, a novel joint screening method is suggested based on the proposed model to identify hot zones for non-motorist crashes. The hot zones of non-motorist crashes are identified and divided into three types: hot zones with more dangerous driving environment only, hot zones with more hazardous walking and cycling conditions only, and hot zones with both. It is expected that the joint model and screening method can help decision makers, transportation officials, and community planners to make more efficient treatments to proactively improve pedestrian and bicyclist safety.