Exploring weather-related factors affecting the delay caused by traffic incidents: Mitigating the negative effect of traffic incidents

Incident duration prediction through integration of uncertainty and risk factor evaluation: A San Francisco incidents case study

Predicting incident duration and understanding incident types are essential in traffic management for resource optimization and disruption minimization. Precise predictions enable the efficient deployment of response teams and strategic traffic rerouting, leading to reduced congestion and enhanced safety. Furthermore, an in-depth understanding of incident types helps in implementing preventive measures and formulating strategies to alleviate their influence on road networks. In this paper, we present a comprehensive framework for accurately predicting incident duration, with a particular emphasis on the critical role of street conditions and locations as major incident triggers. To demonstrate the effectiveness of our framework, we performed an in-depth case study using a dataset from San Francisco. We introduce a novel feature called "Risk" derived from the Risk Priority Number (RPN) concept, highlighting the significance of the incident location in both incident occurrence and prediction. Additionally, we propose a refined incident categorization through fuzzy clustering methods, delineating a unique policy for identifying boundary clusters that necessitate further modeling and testing under varying scenarios. Each cluster undergoes a Multiple Criteria Decision-Making (MCDM) process to gain deeper insights into their distinctions and provide valuable managerial insights. Finally, we employ both traditional Machine Learning (ML) and Deep Learning (DL) models to perform classification and regression tasks. Specifically, incidents residing in boundary clusters are predicted utilizing the scenarios outlined in this study. Through a rigorous analysis of feature importance using top-performing predictive models, we identify the "Risk" factor as a critical determinant of incident duration. Moreover, variables such as distance, humidity, and hour demonstrate significant influence, further enhancing the predictive power of the proposed model.

Good weather for a ride (or not?): how weather conditions impact road accidents - a case study from Wielkopolska (Poland)

This study offers a likely assessment of extreme meteorological events' impact on human perceptivity, frame of mind or even health during driving which might have had a consequence as a car accident. Research covered an analysis of car accidents during period 2010-2019 in the Wielkopolska (Poland) and four indices like maximum daily temperature, maximum value of humidex, difference between maximum temperatures observed from day to day and also difference between mean atmospheric pressure at the sea level observed from day to day. A distributed lag nonlinear model (DLNM) approach was used to obtain the relationship between these indices and car accidents. Our finding evidence that the "good weather for a ride" conditions are actually generating an increased risk of accidents. For indices related to high temperature, i.e., maximum temperature and humidex, it was possible to identify the critical values by which the risks of car accidents were the highest.