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Predicting Traffic Conflicts for Expressway Diverging Areas Using Vehicle Trajectory Data
Traffic crashes occur frequently in expressway diverging areas. The influential factors for traffic crashes in expressway diverging areas were investigated in this study using vehicle trajectory data. An hourly conflict risk index (HCRI) was introduced to develop a traffic conflict prediction model for expressway diverging areas. The data collectors were trained to identify conflict severity, and Tracker 5.0 was used to calculate the time to collision (TTC) for rear-end and lane-change conflicts, respectively. Based on the value of direct economic losses, the traffic risk index for traffic conflicts of different types and severities is established, and the severity of traffic conflict was characterized by the HCRI. A multivariate linear regression model was applied to analyze the relationship between HCRI and various influential factors. A comparison between an hourly conflict ratio (HCR) model and the HCRI model showed that the HCRI model performed better. Finally, it was found that the mainline traffic volume and the ramp traffic volume were positively associated with HCRI, while the deceleration lane length and mainline speed were negatively associated with the HCRI.
Predicting Traffic Conflicts for Expressway Diverging Areas Using Vehicle Trajectory Data
Traffic crashes occur frequently in expressway diverging areas. The influential factors for traffic crashes in expressway diverging areas were investigated in this study using vehicle trajectory data. An hourly conflict risk index (HCRI) was introduced to develop a traffic conflict prediction model for expressway diverging areas. The data collectors were trained to identify conflict severity, and Tracker 5.0 was used to calculate the time to collision (TTC) for rear-end and lane-change conflicts, respectively. Based on the value of direct economic losses, the traffic risk index for traffic conflicts of different types and severities is established, and the severity of traffic conflict was characterized by the HCRI. A multivariate linear regression model was applied to analyze the relationship between HCRI and various influential factors. A comparison between an hourly conflict ratio (HCR) model and the HCRI model showed that the HCRI model performed better. Finally, it was found that the mainline traffic volume and the ramp traffic volume were positively associated with HCRI, while the deceleration lane length and mainline speed were negatively associated with the HCRI.
Predicting Traffic Conflicts for Expressway Diverging Areas Using Vehicle Trajectory Data
Ma, Yongfeng (author) / Meng, Hongcheng (author) / Chen, Shuyan (author) / Zhao, Jiguang (author) / Li, Shen (author) / Xiang, Qiaojun (author)
2020-01-11
Article (Journal)
Electronic Resource
Unknown
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