Management of connected vehicles at unsignalised intersections is a large‐scale complex problem with safety constraints and time‐varying unsolved variables, which is crucial but hard to solve online. A faster coordination system, however, not only benefits from smaller time granularity to find optimum, but also has more robustness towards a scenario with fast‐moving vehicle nodes. This paper proposes a real‐time coordination scheme consisting of three stages. (a) Target velocity optimisation: collision‐free passage is formulated as a mixed integer linear programming problem, each approaching lane corresponding to an independent variable; (b) vehicle subgraph extraction: a directed graph is built and pruned based on the optimisation result, determining a subgraph wherein vehicle nodes pass without redundant time slot; (c) velocity profile synchronisation: velocity profile of the selected vehicles is planned synchronously, respecting inter‐subgraph constraints. The main contribution of this study is to propose a fast hierarchical optimization‐based coordination method, of which the complexity is invariant with the traffic density. Simulation has verified the effectiveness of the scheme from both microscopic behaviour and statistical characteristics, reducing single‐step computation time to 0.02 s, and saving average driving delay by 59.83% compared to the benchmark method.

Expressway section speed can visually reflect the section operation condition, and accurate short time section speed prediction has a wide range of applications in path planning and traffic guidance. However, existing expressway speed prediction data have defects, such as sparse density and incomplete object challenges. Thus, this paper proposes a framework for a combined expressway traffic speed prediction model based on wavelet transform and spatial-temporal graph convolutional network (WSTGCN) of the Electronic Toll Collection (ETC) gantry transaction data. First, the framework pre-processes the ETC gantry transaction data to construct the section speeds. Then wavelet decomposition and single-branch reconstruction are performed on the section speed sequences, and the spatial features are captured by graph convolutional network (GCN) for each reconstructed single-branch sequence, and the temporal features are extracted by connecting the gated recurrent unit (GRU). The experiments use the ETC gantry transaction data of the expressway from Quanzhou to Xiamen. The results indicate that the WSTGCN model makes notable improvements compared to the model of the baseline for different prediction ranges.

Using deep reinforcement learning to solve traffic signal control problems is a research hotspot in the intelligent transportation field. Researchers have recently proposed various solutions based on deep reinforcement learning methods for intelligent transportation problems. However, most signal control optimization takes the maximization of traffic capacity as the optimization goal, ignoring the concerns of pedestrians at intersections. To address this issue, we propose a pedestrian-considered deep reinforcement learning traffic signal control method. The method combines a reinforcement learning network and traffic signal control strategy with traffic efficiency and safety aspects. At the same time, the waiting time of pedestrians and vehicles passing through the intersection is considered, and the Discrete Traffic State Encoding (DTSE) method is applied and improved to define the more comprehensive states and rewards. In the training of the neural network, the multi-process operation method is adopted, and multiple environments are run for training simultaneously to improve the model’s training efficiency. Finally, extensive simulation experiments are conducted on actual intersection scenarios using the simulation software Simulation of Urban Mobility (SUMO). The results show that compared to Dueling DQN, the waiting time due to our method decreased by 58.76% and the number of people waiting decreased by 51.54%. The proposed method can reduce both the number of people waiting and the waiting time at intersections.

Object detection in autonomous driving scenarios has become a popular task in recent years. Due to the high-speed movement of vehicles and the complex changes in the surrounding environment, objects of different scales need to be detected, which places high demands on the performance of the network model. Additionally, different driving devices have varying performance capabilities, and a lightweight model is needed to ensure the stable operation of devices with limited computing power. To address these challenges, we propose a lightweight network called BiGA-YOLO based on YOLOv5. We design the Ghost-Hardswish Conv module to simplify the convolution operations and incorporate spatial coordinate information into feature maps using Coordinate Attention. We also replace the PANet structure with the BiFPN structure to enhance the expression ability of features through different weights during the process of fusing multi-scale feature maps. Finally, we conducted extensive experiments on the KITTI dataset, and our BiGA-YOLO achieved a mAP@0.5 of 92.2% and a mAP@0.5:0.95 of 68.3%. Compared to the baseline model YOLOv5, our proposed model achieved improvements of 1.9% and 4.7% in mAP@0.5 and mAP@0.5:0.95, respectively, while reducing the model size by 15.7% and the computational cost by 16%. The detection speed was also increased by 6.3 FPS. Through analysis and discussion of the experimental results, we demonstrate that our proposed model is superior, achieving a balance between detection accuracy, model size, and detection speed.

Short-term load forecasting is a key digital technology to support urban sustainable development. It can further contribute to the efficient management of the power system. Due to strong volatility of the electricity load in the different stages, the existing models cannot efficiently extract the vital features capturing the change trend of the load series. The above problem limits the forecasting performance and creates the challenge for the sustainability of urban development. As a result, this paper designs the novel ResNet-based model to forecast the loads of the next 24 h. Specifically, the proposed method is composed of a feature extraction module, a base network, a residual network, and an ensemble structure. We first extract the multi-scale features from raw data to feed them into the single snapshot model, which is modeled with a base network and a residual network. The networks are concatenated to obtain preliminary and snapshot labels for each input, successively. Also, the residual blocks avoid the probable gradient disappearance and over-fitting with the network deepening. We introduce ensemble thinking for selectively concatenating the snapshots to improve model generalization. Our experiment demonstrates that the proposed model outperforms exiting ones, and the maximum performance improvement is up to 4.9% in MAPE.