Road crashes continue to be a leading cause of death globally, with most of these crashes reportedly occurring due to human factors. Traditional road safety assessment utilises geometric and traffic parameters that consider road design inadequacies and identify traffic conflicts. However, previous studies do not represent risky driving behavior and its influence on crash occurrence. Incorporating human factors into safety evaluation is crucial to enhance the prediction and subsequent prevention of unsafe events. This research establishes a methodology to identify risky driving behavior using driving performance measures. These measures are computed based on continuous driving profiles collected using instrumented vehicles from a sample set of drivers on an expressway and are compared with historical crash data. The results indicate the significance of driving performance measures in evaluating road safety. The performance measures find application in collision avoidance systems, assessing the road design quality, testing safety countermeasures and guide for policymakers..
The study primarily seeks to investigate the behavioral attribution of distraction in pedestrian road crossing. A distraction-themed questionnaire survey was conducted across Kolkata city, India, to understand the contribution of distraction to near-misses and injuries. The survey response showed that among all reported respondents, 13.7% (61) encountered at least one near-miss and 4.5% (20) experienced at least one accident in the past. The video-based observational field study of 2,360 pedestrians revealed that 28.7% of the pedestrians were distracted while crossing the road. Pedestrians who text walked relatively slowly and 7.9% more likely to violate signal. Additionally, mobile phone talkers were observed to be 4.5% more likely to nearly hit/bump into another oncoming pedestrian. The present study constitutes vital information for planners and policymakers and plays a pivotal role in identifying critical intersections and developing countermeasures to minimize the impact or occurrence of pedestrian distraction and unsafe behavior.
Tourism is a major source of income for many regions; however, its impacts on residents’ daily lives are significant, especially regarding their travel behavior. Local transportation infrastructure is challenged by large tourist flows, and thus residents need to adapt to the new environment by changing their usual habits, such as travel mode, frequency, destination etc. In this study, a multinomial logistic regression mode choice model is developed to capture tourism impacts on residents’ travel behavior, using data from the island of Rhodes, Greece. According to study findings, tourism has in fact an impact on residents’ travel mode preferences, as they tend to opt for more agile modes such as motorcycles, instead of cars, and adopt defensive driving during tourist seasons. This study can facilitate public transport operators, planners, and municipalities in tourist regions, to apply effective policies to mitigate negative impacts of tourism in local traffic conditions.