As a key technology to alleviate urban congestion, Cooperative Adaptive Cruise Control (CACC) requires testing and evaluation before being commercialized. To evaluate CACC, simulation plays a key role. However, conventional simulation platforms focus more on highway scenes but less on urban scenes. Hence, this paper proposes a CACC simulation platform for urban scenes. The platform bears the following features: i) able to plug in production Connected and Automated Vehicle (CAV) control systems; ii) able to capture the interaction between Human-driven Vehicles (HVs) and CAVs; iii) able to evaluate CACC with regard to various traffic control; iv) capable of quantifying CACC safety in urban scenes; v) capable of assessing the impact of CACC on mobility in urban scenes. The platform is validated by comparing against actual field tests and previously published theoretical studies. Its credibility is confirmed in terms of free flow CACC, interrupted CACC flow and free flow Partially Connected and Automated Traffic (PCAT). Through the proposed platform, evaluations on CACC safety and mobility are conducted. The results reveal that current CACC technology requires significant enhancement, since CACC technology may interrupt the traffic in urban areas due to platoon splitting, interaction with lane-changing vehicles, etc. Rushing the deployment of current CACC technology in urban areas is probably not a good idea.

Lane-Change (LC) maneuvers are deemed to jeopardize traffic safety, mobility, and sustainability. Cooperative Lane-Change (CLC) solves this problem by accelerating the LC process. This research proposes an optimal control based CLC controller. It bears the following features: i) with the faster completion of a CLC maneuver by making space and changing lane at the same time; ii) with less speed oscillation perturbations on traffic. To make space and change lane at the same time, a formulation method in the relative spatial domain is proposed. In this method, the conventional longitudinal and lateral coupled nonlinear avoidance constraints are linearized into a relative longitudinal distance fixed lateral-only avoidance constraint. Evaluations indicate that the proposed method enhances the completion efficiency of CLC by 15.67%, reduce traffic emission by 0.24%, reduce fuel consumption by 0.36%, and improve traffic stability by 47.9%.

For urban traffic, traffic accidents are the most direct and serious risk to people’s lives, and rapid recognition and warning of traffic accidents is an important remedy to reduce their harmful effects. However, research scholars are often confronted with the problem of scarce and difficult-to-collect accident data resources for traffic accident scenarios. Therefore, in this paper, a traffic data generation model based on Generative Adversarial Networks (GAN) is developed. To make GAN applicable to non-graphical data, we improve the generator network structure of the model and used the generated model to resample the original data to obtain new traffic accident data. By constructing an adversarial neural network model, we generate a large number of data samples that are similar to the original traffic accident data. Results of the statistical test indicate that the generated samples are not significantly different from the original data. Furthermore, the experiments of traffic accident recognition with several representative classifiers demonstrate that the augmented data can effectively enhance the performance of accident recognition, with a maximum increase in accuracy of 3.05% and a maximum decrease in the false positive rate of 2.95%. Experimental results verify that the proposed method can provide reliable mass data support for the recognition of traffic accidents and road traffic safety.