scholarly journals Estimating Rear-End Accident Probabilities at Signalized Intersections: Occurrence-Mechanism Approach

2003 ◽  
Vol 129 (4) ◽  
pp. 377-384 ◽  
Author(s):  
Yinhai Wang ◽  
Hitoshi Ieda ◽  
Fred Mannering
2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Weijie Wang ◽  
Yingshuai Li ◽  
Jian Lu ◽  
Yaping Li ◽  
Qian Wan

Rear-end accidents are the most common accident type at signalized intersections because of the different driving tendencies in the dilemma zone (DZ), where drivers are faced with indecisiveness of making “stop or go” decisions at yellow onset. In various researches, the number of vehicles in the DZ has been used as a safety indicator—the more the vehicles in the DZ, the higher the probability of rear-end accidents. However, the DZ-associated rear-end accident potential varies depending on drivers’ driving tendencies and the situations (position and speed) at the yellow onset. This study’s primary objective is to explore how the driving tendency impacts the DZ distribution and the probability of rear-end accidents. To achieve this, three types of driving tendencies were classified using K-means clustering analysis based on driving variables. Further, the boundary of the DZ is determined by logistic regression model of drivers’ stop/go decision. Then, we proposed the conditional probability model of rear-end accidents and developed a Monte Carlo simulation framework to calculate the model. The results indicate that the rear-end accident probability is dependent on the driving tendency even at the same position with the same speed in the DZ. The aggressive type has the highest risk probability followed by conservative and then the normal types. The quantitative results of the study can provide the basis for rear-end accident assessments.


2019 ◽  
Vol 11 (4) ◽  
pp. 168781401984183 ◽  
Author(s):  
Zhuping Zhou ◽  
Sixian Liu ◽  
Wenxin Xu ◽  
Ziyuan Pu ◽  
Shuichao Zhang ◽  
...  

Author(s):  
Zihang Wei ◽  
Yunlong Zhang ◽  
Xiaoyu Guo ◽  
Xin Zhang

Through movement capacity is an essential factor used to reflect intersection performance, especially for signalized intersections, where a large proportion of vehicle demand is making through movements. Generally, left-turn spillback is considered a key contributor to affect through movement capacity, and blockage to the left-turn bay is known to decrease left-turn capacity. Previous studies have focused primarily on estimating the through movement capacity under a lagging protected only left-turn (lagging POLT) signal setting, as a left-turn spillback is more likely to happen under such a condition. However, previous studies contained assumptions (e.g., omit spillback), or were dedicated to one specific signal setting. Therefore, in this study, through movement capacity models based on probabilistic modeling of spillback and blockage scenarios are established under four different signal settings (i.e., leading protected only left-turn [leading POLT], lagging left-turn, protected plus permitted left-turn, and permitted plus protected left-turn). Through microscopic simulations, the proposed models are validated, and compared with existing capacity models and the one in the Highway Capacity Manual (HCM). The results of the comparisons demonstrate that the proposed models achieved significant advantages over all the other models and obtained high accuracies in all signal settings. Each proposed model for a given signal setting maintains consistent accuracy across various left-turn bay lengths. The proposed models of this study have the potential to serve as useful tools, for practicing transportation engineers, when determining the appropriate length of a left-turn bay with the consideration of spillback and blockage, and the adequate cycle length with a given bay length.


2020 ◽  
Vol 11 (1) ◽  
pp. 216-226
Author(s):  
Bara’ W. Al-Mistarehi ◽  
Ahmad H. Alomari ◽  
Mohamad S. Al Zoubi

AbstractThis study aimed to investigate a potential list of variables that may have an impact on the saturation flow rate (SFR) associated with different turning movements at signalized intersections in Jordan. Direct visits to locations were conducted, and a video camera was used. Highway capacity manual standard procedure was followed to collect the necessary traffic data. Multiple linear regression was performed to classify the factors that impact the SFR and to find the optimal model to foretell the SFR. Results showed that turning radius, presence of camera enforcement, and the speed limit are the significant factors that influence SFR for shared left- and U-turning movements (LUTM) with R2 = 76.9%. Furthermore, the presence of camera enforcement, number of lanes, speed limit, city, traffic volume, and area type are the factors that impact SFR for through movements only (THMO) with R2 = 69.6%. Also, it was found that the SFR for LUTM is 1611 vehicles per hour per lane (VPHPL),which is less than the SFR for THMO that equals to 1840 VPHPL. Calibration and validation of SFR based on local conditions can improve the efficiency of infrastructure operation and planning activities because vehicles’ characteristics and drivers’ behavior change over time.


2020 ◽  
Vol 14 (2) ◽  
Author(s):  
Yoichiro Fujii ◽  
Michiko Ogaku ◽  
Mahito Okura ◽  
Yusuke Osaki

AbstractSome people have optimistic expectations regarding their accident probability, and thus, refrain from purchasing adequate insurance. This study investigates how insurance firms use advertisements to lower the ratio of optimistic individuals in the market. The main results are as follows: first, the optimal level of advertisements is maximized when the insurance premium is moderate. Second, the maximum level of advertisement varies according to the degree of optimism, which is measured by the difference between accurate and optimistic accident probabilities. Third, the advertisement decision is affected by the free-rider problem, and the equilibrium number of insurance firms with advertisement is always larger than that of firms without advertisement in a competitive insurance market.


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