This paper presents a method to assess the safety of uncontrolled intersections considering both conflict probability and severity, which are two major properties of traffic conflicts. This method provides not only the safety level of the entire intersection but also the distribution of safety within intersections. Intersections are modelled by a two-dimensional Cartesian coordinate system and the internal space of intersections is divided into cells. Firstly, vehicle movement characteristics of at uncontrolled intersections are modelled. Secondly, conflict probability of each cell within the intersection is estimated considering approaching probability and lateral migration probability of vehicles. The quantification of conflict severity is based on kinetic energy loss of potential crashes. Grey cluster analysis is used to combine conflict probability and severity to model the safety assessment of each cell. Thirdly, the application of this method discussed, and an overall safety index of intersections is proposed considering weighted safety level and relative value of area with different safety levels. Finally, a case study, which includes three different designs, is presented along with visualization technique to demonstrate the results. The results not only show the validity of the model, but also indicate that the proposed method can be applied for: i) safety evaluation of existing or designed intersections; ii) dangerous position management within intersections; iii) safety assessment of designed intersections and iv) safety level comparison among different intersections or various designs for the same intersection. Using this method, engineers and planners can better evaluate and improve the safety of existing or the future uncontrolled intersections. The future work will use this approach to develop the models of other types of intersections and different road users.
The objective of this work is to identify better metrics of exposure when comparing traffic crash risk across modes or across locations. We propose that total time travelled should be used for road user exposure to crash risk. The idea behind this is that travel time reflects the differences in speeds across different modes and hence should be used as the basic exposure metric from which crash risk based on other metrics, such as travel distance, can easily be derived. We also propose that when comparing crash risk of different modes across different locations the time-based mode share should be used as an explanatory variable. By using mode share we are generalizing the safety in numbers concept which focuses on absolute numbers. This work presents a discussion on why these two metrics were chosen and how they are different from the commonly used metrics. Quantitative evidence for the choice of time-based metrics is also presented using travel survey data to compare crash risk across modes and locations.
Rear-end collisions would occur if vehicle speeds decrease abruptly when encountering kinematic waves (KWs) emanating from active bottlenecks. This study aims to develop a control strategy in variable speed limits (VSL) to reduce rear-end collision risks near recurrent bottlenecks. Using the crash prediction model developed for rear-end collisions related to risky KWs, the effectiveness of VSL control strategies were evaluated in the cell transmission model (CTM). Several strategies were tested in sequence to determine the best case for risk reduction. Results of this study show that the collision risk can be effectively reduced if the speed limit is lowered in an opportune occasion. The VSL needs to response quickly to the fast changing traffic condition while should simultaneously avoid the potential disturbance to the traffic. According to the simulation results, the following recommendations are made when implementing the VSL to improve safety situation near recurrent bottlenecks: moderate threshold of collision likelihood to active VSL, moderate design speed limit in VSL, fast speed change over time, and small speed difference between consecutive VSLs.
