Quantifying the Severity of Traffic Conflict by Assuming Moving Elements as Rectangles at Intersection

Left-turn conflict identification at signal intersections based on vehicle trajectory reconstruction under real-time communication conditions

Connected vehicle (CV)technologies offer promising solutions to several problems in transportation systems. The trajectory data generated from CV technology can be used to identify real-time conflicts in intersections. To perform such identification, accurate vehicle localisation should be obtained to clearly recognise the conflicts between left-turning vehicles and straight-through vehicles in the opposite direction at the signal control intersection. This study presents a CV framework that uses the two-way time of arrival to locate the vehicles on the basis of the Intelligent Vehicle Infrastructure Cooperative Environment. Kalman Filter (KF) is used to improve the accuracy of the vehicle location, and the corresponding algorithm is used to estimate the vehicle trajectory to obtain the vehicle kinematics information via the on-board system. The traffic conflict areas of the left-turning vehicles and straight-through vehicles in the opposite direction are determined through vehicle trajectory extrapolation, and the left-turn collision at the signal intersection is identified using the post-encroachment time algorithm and vehicle movement information. In addition, Anderson-Darling and modified Kolmogorov-Smirnov tests are performed to verify the goodness of fit of the data. Results show that the vehicle speed and localisation errors of the proposed method decreased by 66.67 % and 83.33 % compared with the results before filtering, respectively. Moreover, the results of the conflict recognition method based on CV trajectory reconstruction is consistent for both goodness of fit tests under real-time communication conditions. This study can provide driving decision for drivers of left-turning vehicles under the Intelligent Vehicle Infrastructure Cooperative Environment and provide technical support for the research and development of left-turn anti-collision systems.

Developing a safety heatmap of uncontrolled intersections using both conflict probability and severity

This paper presents a method to assess the safety of uncontrolled intersections considering two major properties of traffic conflicts-conflict probability and severity. This method assesses both the safety level of the entire intersection in addition to the distribution of safety within it. Intersections are modeled by a two-dimensional Cartesian coordinate system and the internal space of intersections is divided into cells. First, the vehicle movement characteristics of an uncontrolled intersection are modeled. Second, the conflict probability of each cell within the intersection is estimated considering the approaching probability and lateral migration probability of vehicles. The quantification of conflict severity is based on kinetic energy loss of potential crashes. Cluster analysis is used to combine conflict probability and severity to model the safety assessment of each cell. Third, the application of the method is discussed, and an overall safety index of intersections is proposed which considers weighted safety level and relative value of areas of different safety levels. Finally, a case study, which includes three different designs, is presented along with safety heatmaps to demonstrate the results. The results not only demonstrate the validity of the model, but also indicate that the proposed method can be applied to: i) safety evaluation of build-up intersections; ii) dangerous position management within an intersection; iii) safety assessment of designed intersections, and iv) safety level comparison among different intersections or various designs for a single intersection. Using this method, engineers and planners can better evaluate and improve the safety of existing or future uncontrolled intersections.