Driver Route Planning Method Based on Accident Risk Cost Prediction

Abstract

The number of cars on roadways around the world continues to increase year over year. However, the imbalance between traffic supply and demand has not only brought traffic congestion but also caused serious safety problems. To reduce travel risk, this study proposes a driver route planning method based on accident risk cost prediction for connected and automated vehicles. According to the entropy weight method and an improved algorithm of K shortest paths, a route planning model with accident risk as the main optimization objective was established. Firstly, an accident risk evaluation system was built based on traffic accident data, and a quantitative prediction model of accident risk cost based on driver-, vehicle-, road-, and environment-related factors was constructed. Secondly, the entropy weight method was used to calculate the weights of each indicator to determine accident risk considering the aforementioned factors. Then, the route planning model was established, and the solution algorithm based on K shortest paths was designed to solve the optimal route by comprehensively considering accident risk cost and travel time. The accident risk index of each road section in the example road network was assigned, and the risk of the road section was quantified according to the accident risk cost model. Three candidate paths were calculated by using the path planning algorithm proposed in this study; the total risk cost is 6.19, 6.26, and 6.39, respectively; and the total travel time is 29, 29, and 31, respectively. After comparison, the optimal path and two alternative paths are obtained. The results show that the accident risk cost prediction model based on historical accident data can be used to quantify driving risk. The proposed method can help drivers in the connected and automated environment choose the optimal travel route with the lowest risk and shortest travel time and improve overall traffic safety and efficiency.

Route Planning under Mobility Restrictions in the Palestinian Territories

“…The index used to evaluate the severity of violent action against travellers on the route includes the number of previous violent acts [28] and the time the violence occurred [18]. The indices used to evaluate the physical road characteristics are the physical road condition and the road segment's lighting condition [9], as well as the built environment (presented in the category of the adjacent built-up area to the road section) [30]. The number of mobility restrictions on the road section was used as an index for the mobility restriction criteria.…”

“…Other scholars have used objective methods to avoid the interference of human factors; for example, Liao et al [9] used the entropy weight method (EWM) for traffic safety criteria evaluation, leveraging its capacity to objectively assess criteria weights. Similarly, Xiao et al [36] used principal component analysis (PCA) for a comprehensive road ranking of road safety conditions, harnessing PCA's ability in reducing data dimensionality and identifying key factors for driving safety.…”

“…This study adopted an objective weighting method, the entropy weight method (EWM), to calculate the weights of risk indicators. The EWM has been commonly used in multi-criteria decision-making practices [9]. It measures the dispersion value; the greater the degree of dispersion, the greater the degree of differentiation, and thus the more information that can be derived.…”

“…In addition, the EWM provided higher accuracy than the PCA method. Liao et al [9] showed that the EWM has a higher reliability and accuracy than subjective weighting. Consequently, the EWM is more suitable for describing the impact of abnormal values in restrictions, violence, and physical environment indicators on the severity of the risk on the road section.…”

“…In this method, m indexes and n samples of road sections are used in the evaluation and the measured value of the j th index if the i th sample is recorded as X ij . See the following steps [9]:…”