Traffic flow detection methods tend to be diversified and intelligent, and the data anomalies become a prominent problem due to the influence of equipment, network and environment. Recovering abnormal data to obtain complete and accurate traffic flow data is crucial for traffic flow research work. To address this issue, traditional and mainstream traffic flow data recovery methods require a large amount of complete historical traffic flow data as a priori information for learning the trend of traffic flow data. Large amount of complete historical traffic flow data is hard to obtain in real traffic scenarios because traffic flow data collection is always accompanied by data anomalies. In this paper, we analyze the causes of abnormal data, classify the types of abnormal data, and propose identification methods applicable to different types of abnormal data based on the collection principles of freeway multi-source traffic data. We propose an auxiliary discrimination mechanism-oriented generative adversarial network (ADM-GAN) model to overcome the difficulties of traffic flow data recovery. Different from previous studies, we develop an auxiliary discrimination matrix to increase the utilization of original data, enhance the recovery accuracy, and improve the computational efficiency. We evaluate our model under different data missing rates (10%, 20%, 30%, 40% and 50%) by manually introducing missingness to the actual traffic flow data on the G50 freeway near Suzhou, China. The experimental results show that the proposed model outperforms other comparative methods. Under different missing rates, the recovery results obtained by ADM-GAN method are better than those obtained by comparative methods, and the lower the missing rate, the more obvious the advantages.

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The application of connected and automated vehicles (CAVs) technology has changed the operation characteristics of vehicles. Investigating the traffic capacity of bus stops under a CAVs environment can allocate traffic flow more reasonably, which is effective in alleviating traffic congestion. Therefore, this paper proposes a method that can be used to evaluate the traffic capacity of bus stops under a CAVs environment. First, two evaluation indexes, failure duration time (FD) and forced lane-changing rate (FLR) are proposed. Second, the simulation scheme with ten scenarios is determined, and simulation experiments are conducted. Then, the relationships between FD, FLR, and traffic flow under different penetration rates of CAVs are analyzed. Finally, the relationship models between FD, FLR, and traffic capacity are fitted to verify their validity for traffic capacity analysis. Additionally, a predictive model is proposed for estimating capacity under a CAVs environment using indicators from HV traffic flow. Results indicate that: (i) FD and FLR both positively correlate with capacity, and perform well in capacity evaluation of bus stops; (ii) FD and FLR can be utilized to predict the capacity under a CAVs environment; (iii) the higher the penetration rate of CAVs, the smaller the impact of the bus failure phenomenon and forced lane change on traffic flow.