Despite the advancements in the technologies of autonomous driving, it is still challenging to study the safety of a self-driving vehicle. Trajectory prediction is one core function of an autonomous vehicle. This study proposes an Attention-based Interaction-aware Trajectory Prediction (AI-TP) for traffic agents around the autonomous vehicle. With an encoder-decoder architecture, the AI-TP model uses Graph Attention Networks (GAT) to describe the interactions of traffic agents and Convolutional Gated Recurrent Units (ConvGRU) to carry out predictions. Based on the attention mechanism, the AI-TP model con-structs graphs from various traffic scenes to predict trajectories of different types of traffic agents. Traffic data from both the high-way (i.e., NGSIM) and urban road areas (i.e., ApolloScape and Argoverse) are used to evaluate the performance of the AI-TP model. Numerical results demonstrate that the AI-TP model requires less inference time and achieves better prediction accuracy than state-of-the-art methods. Specifically, the AI-TP model improves the performance by 36% on average with much less inference time on the NGSIM dataset, which shows the promise of predicting trajectories under various scenarios. The code of the AI-TP model will be available at https://github.com/KP-Zhang/AI-TP.

Accurate short-term passenger demand prediction contributes to the coordination of traffic supply and demand. This paper proposes an end-to-end multi-task learning temporal convolutional neural network (MTL-TCNN) to predict the short-term passenger demand in a multi-zone level. Along with a feature selector named spatiotemporal dynamic time warping (ST-DTW) algorithm, this proposed MTL-TCNN is quite qualified for the multi-task prediction problem with the consideration of spatiotemporal correlations. Then, based on the car-calling demand data from Didi Chuxing, Chengdu, China, and taxi demand data from the New York City, the numerical results show that the MTL-TCNN outperforms both classic methods (i.e., historical average (HA), v-support vector machine (v-SVM), and XGBoost) and the state-of-the-art deep learning approaches [e.g., long short-term memory (LSTM) and convolutional LSTM (ConvLSTM)] in both the single task learning (STL) and multi-task learning (MTL) scenarios. In summary, the proposed MTL-TCNN with the ST-DTW algorithm is a promising method for short-term passenger demand prediction in a multi-zone level.

In the era of big data, mining data instead of collecting data are a new challenge for researchers and engineers. In the field of transportation, extracting traffic dynamics from widely existing probe vehicle data is meaningful both in theory and practice. Therefore, this article proposes a simple mapping-to-cells method to construct a spatiotemporal traffic diagram for a freeway network. The method partitions a network region into small square cells and represents a real network inside the region by using the cells. After determining the traffic flow direction pertaining to each cell, the spatiotemporal traffic diagram colored according to traffic speed can be well constructed. By taking the urban freeway in Beijing, China, as a case study, the mapping-to-cells method is validated, and the advantages of the method are demonstrated. The method is simple because it is completely based on the data themselves and without the aid of any additional tool such as Geographic Information System software or a digital map. The method is efficient because it is based on discrete space-space and time-space homogeneous cells that allow us to match the probe data through basic operations of arithmetic. The method helps us understand more about traffic congestion from the probe data, and then aids in carrying out various transportation researches and applications.

Imagine in the future that autonomous vehicles coordinated and guided by signal-free autonomous intersections are able to pass through the intersections immediately after the vehicles in the conflicting direction leave. Meanwhile, with the coordination of the autonomous intersections, autonomous vehicles on any approaching lane are able to turn onto any downstream lane, expecting that driving in such a road system, autonomous vehicles can reach their destinations without any on-road lane changes, and high traffic efficiency will be achieved as well as great traffic safety. To draw the picture in detail, this paper designs a signal-free autonomous intersection with all-direction turn lanes (ADTL) under the environment of autonomous vehicles, and proposes a conflict-avoidance-based approach to coordinate all approaching vehicles in different directions. Communicating with the approaching autonomous vehicles and utilizing the approach, the autonomous ADTL intersection is able to coordinate the approaching vehicles in all directions and guide them to safely and efficiently pass through the intersection. Two simulation scenarios are conducted in a road network with an isolated intersection composed of four three-lane arms. One scenario validates the collision-free design of the system, and the other shows that the designed ADTL intersection outperforms the conventional signal controlled intersection in terms of traffic efficiency, and is potentially better than the autonomous intersection with specific-direction turn lanes. The autonomous ADTL intersection can be an important basis for designing a future autonomous urban road traffic system.