Traffic flow prediction is one of the challenges in the development of an Intelligent Transportation System (ITS). Accurate traffic flow prediction helps to alleviate urban traffic congestion and improve urban traffic efficiency, which is crucial for promoting the synergistic development of smart transportation and smart cities. With the development of deep learning, many deep neural networks have been proposed to address this problem. However, due to the complexity of traffic maps and external factors, such as sports events, these models cannot perform well in long-term prediction. In order to enhance the accuracy and robustness of the model on long-term time series prediction, a Graph Attention Informer (GAT-Informer) structure is proposed by combining the graph attention layer and informer layer to capture the intrinsic features and external factors in spatial–temporal correlation. The external factors are represented as sports events impact factors. The GAT-Informer model was tested on real-world data collected in London, and the experimental results showed that our model has better performance in long-term traffic flow prediction compared to other baseline models.

Electric Vehicle (EV) charging demand and charging station availability forecasting is one of the challenges in the intelligent transportation system. With accurate EV station availability prediction, suitable charging behaviors can be scheduled in advance to relieve range anxiety. Many existing deep learning methods have been proposed to address this issue; however, due to the complex road network structure and complex external factors, such as points of interest (POIs) and weather effects, many commonly used algorithms can only extract the historical usage information and do not consider the comprehensive influence of external factors. To enhance the prediction accuracy and interpretability, the Attribute-Augmented Spatiotemporal Graph Informer (AST-GIN) structure is proposed in this study by combining the Graph Convolutional Network (GCN) layer and the Informer layer to extract both the external and internal spatiotemporal dependence of relevant transportation data. The external factors are modeled as dynamic attributes by the attributeaugmented encoder for training. The AST-GIN model was tested on the data collected in Dundee City, and the experimental results showed the effectiveness of our model considering external factors’ influence on various horizon settings compared with other baselines.

Although the existing autonomous driving systems (ADS) can implement lane-change behaviors without human operation, they still rely on the lane-change decisions made by human drivers in a complex traffic environment. The reason is that human drivers can reasonably estimate the driving intentions of surrounding vehicles in advance, and decide their own driving trajectory according to their driving experiences. Therefore, the future ADS needs to learn the ability of human beings to predict the driving intentions of surrounding vehicles. Alternatively, it needs to make safer lane-change decisions than human beings without sacrificing the possibility as much as possible. This paper develops a Lane-Change Responsibility-Sensitive Safety (LC-RSS) model to improve the safety of lane-change decisions and solve the above research gap. A novel lane-change trajectory planning method is proposed, which considers multiple interactions between the ego vehicle and surrounding vehicles with realistic position constraints and fuel consumption optimization. Specifically, it combines the lane-change prediction of surrounding vehicles to provide a more reasonable recommended lane-change trajectory for the ego vehicle, therefore, could enhance the safety constraints from the Responsibility-Sensitive Safety (RSS) model introduced in the latest published 2846-2022-IEEE Standard. The simulation results have shown that by considering lane-changing prediction in the trajectory planning module, LC-RSS not only enhances the safety of a single vehicle's recommended trajectory but also increases the traffic flow speed of the urban transportation system.

Accurate vehicle type classification plays a significant role in intelligent transportation systems. It is critical to understand the road conditions and usually contributive for the traffic light control system to respond correspondingly to alleviate traffic congestion. New technologies and comprehensive data sources, such as aerial photos and remote sensing data, provide richer and higher-dimensional information. In addition, due to the rapid development of deep neural network technology, image-based vehicle classification methods can better extract underlying objective features when processing data. Recently, several deep learning models have been proposed to solve this problem. However, traditional purely convolution-based approaches have constraints on global information extraction, and complex environments such as bad weather seriously limit their recognition capability. To improve vehicle type classification capability under complex environments, this study proposes a novel Densely Connected Convolutional Transformer-in-Transformer Neural Network (Dense-TNT) framework for vehicle type classification by stacking Densely Connected Convolutional Network (DenseNet) and Transformer-in-Transformer (TNT) layers. Vehicle data for three regions under four different weather conditions were deployed to evaluate the recognition capability. Our experimental findings validate the recognition ability of the proposed vehicle classification model, showing little decay even under heavy fog.

—With the development of vehicles and traffic system in the early 21st century, the need for a monitored traffic system and vehicle classification is enlarging. Together with the development of deep learning, computer vision realm has emerged versatile models that is able to fulfill the need of classification. Those popular models include CNN, Vision Trans- former, Metaformer and so on. However, these models handle the problem based on different data processing techniques, they either lacks efficiency or effectiveness. In particular, CNN is shortcoming in global data while ViT is lack of extraction of local information. Therefore, based on this research gap, we proposed a model called MetaCNN, which combines CNN and Poolformer – a specific metaformer structure, which takes the strength of the two models and compensate for both models’ deficiencies. Finally, in order to verify the feasibility of our model, we tested our model on a real-world remote sensing datasets of vehicle images in six different regions with different weather conditions. Our model MetaCNN has demonstrated better recognition performance compared to other baseline models. The results further prove that our model MetaCNN is adept at vehicle classification of remote sensing images though under complex scenarios

In modern cities, the number of Electric vehicles (EV) is increasing rapidly for their low emission and better dynamic performance, leading to increasing demand for EV charging. However, due to the limited number of EV charging facilities, catering to the huge demand for time-consuming EV charging becomes a critical problem. It is quite a challenge to dispatch EVs in the dynamic traffic environment and coordinate interaction among agents. To better serve further research on various related Deep Reinforcment Learning (DRL) EV dispatching algorithms, an efficient simulation environment is necessary to ensure success. As simulator Simulation Urban Mobility (SUMO) is one of the most widely used open-source simulators, it has great significance in creating an environment that satisfies research requirements on SUMO. We aim to improve the efficiency of EV charging station usage and save time for EV users in further work. As a result, we design an EV navigation system on the basis of the traffic simulator SUMO using Jurong Area, Singapore in this paper. Various state-of-the-art DRL algorithms are deployed on the designed testbed to validate the feasibility of the framework in terms of EV charging dispatching problems. Besides EV dispatching problems, the environment can also serve for other reinforcement learning (RL) traffic control problems