Journal of Intelligent Transportation Systems最新文献

Ephemeral incidents, or events in traffic or on the roadside that have only local and short-term impact on road safety and road capacity, are noteworthy for vehicles nearby—especially those approaching and planning to pass by. We study ways to communicate detected ephemeral incidents between connected vehicles, comparing various decentralized (vehicle-to-vehicle) communication strategies and weighing with established centralized mechanisms with regard to efficiency and broadcasting redundancy. The strategies are implemented in a simulation using realistic road networks, travel routes and traffic. We identify the strategy that achieves up to 100% success rate in transmitting incident messages to the affected vehicles under each scenario, while minimizing broadcast redundancy. In general, decentralized vehicle-to-vehicle communication strategies show strong potential to transmit incident messages efficiently and effectively.

This work is about the generation of driving styles for autonomous cars. Here, we propose a definition of driving style based on the partition of controller parameters for self-driving vehicles. The main contributions of this work are the following. 1) A methodology based on the controllers’ parameters for creating comfortable driving styles that can be used as autonomous cars’ operation modes. 2) A proposal to use virtual reality as a testbed for the evaluation of driving styles by users. 3) As an illustration of our methodology, we determine and evaluate distinguishable driving styles by partitioning the time-to-collision parameter of the Intelligent Driver Model (IDM) controller using the Just Noticeable Difference (JND). 4) A proposal of four driving styles that are equally preferable among passengers.

Real-time prediction of crash risk can support traffic incident management by generating critical information for practitioners to allocate resources for responding to anticipated traffic crashes proactively. Unlike previous studies using archived traffic data covering a limited highway environment such as a segment or corridor, this study uses a statewide live traffic database from HERE to develop real-time traffic crash prediction models. This database provides crowdsourced probe vehicle data that are high-resolution real-time traffic speed for the entire freeway network (nearly 2,000 miles) in Alabama. This study aims to use machine learning models to predict crash risk on freeways according to pre-crash traffic dynamics (e.g., mean speed, speed reduction) along with static freeway attributes. Traffic speed characteristics were extracted from the HERE database for both pre-crash and crash-free traffic conditions. Random Forest (RF), Support Vector Machine (SVM) and Extreme Gradient Boosting (XGBoost) were developed and compared. Separate models were estimated for three major crash types: single-vehicle, rear-end, and sideswipe crashes. The model prediction accuracy indicated that the RF models outperform other models. Models for rear-end crashes are found to have greater accuracy than other models, which implies that rear-end crashes have a significant relationship with pre-crash traffic dynamics and are more predictable. The traffic speed factors that are ranked high in terms of feature importance are the speed variance and speed reduction prior to crashes. According to partial dependence plots, the rear-end crash risk is positively related to the speed variance and speed reductions. More results are discussed in the paper.