With the advancement of artificial intelligence, automobiles are progressively transitioning from traditional mechanization to Connected Autonomous Vehicles (CAVs), significantly enhancing driving comfort and safety. As the standard communication protocol in CAVs, the Controller Area Network (CAN) remains vulnerable to attacks due to the lack of robust security mechanisms. While existing deep learning-based vehicle network intrusion detection systems can effectively identify known attacks, their ability to detect unknown attacks is limited due to the same data distribution in the source and target domain. To address this issue, we propose a domain adaptation and time-series imaging-based intrusion detection system (DATI-IDS) to detect known and unknown attacks, where the deep domain adaptation method is used to solve the source and target domain data distribution difference problem by optimizing the multiple kernel maximum mean discrepancy (MK-MMD) between the source domain and target domain images and the classification loss, and the time-series imaging method is used to capture temporal dependencies and improve efficiency by transforming the CAN ID sequence into a two-dimensional gramian angular summation field (GASF) image. The effectiveness of the proposed model is evaluated across nine distinct unknown attack scenarios using the Car-Hacking dataset and the survival analysis dataset. Comparative analysis with previous studies demonstrates superior performance, faster inference times, and reduced model complexity.

Existing studies on traffic flow stability primarily focused on local stability, with little attention given to its extension to the network level, known as network stability. In this paper, a reliability-based equilibrium model in a mixed traffic network including human-driven vehicles and connected and autonomous vehicles is developed to analyze the impact of connected and autonomous vehicles on traffic flow stability. The basic characteristics of the model are first examined on a small network, demonstrating the non-uniqueness of the link flow in the user equilibrium pattern. Then, the model is extended to the case of a general network with Variational Inequality (VI) equations. In addition, a two-level optimization strategy is developed by incorporating the pricing and quantity control strategies to the reliability model. Numerical examples are conducted based on Sioux Falls networks to examine the performance of the proposed models.

The development and applications of mobile communication technologies in intelligent autonomous transportation systems have led to an extraordinary rise in the mount of connected and autonomous vehicles (CAVs). Ensuring the security of in-vehicle communication data is the basis for the safety of cooperative transportation systems. An in-vehicle controller area network (CAN) bus is an important issue in in-vehicle security, and some hackers have mastered remote vehicle control methods through the CAN bus network. This paper proposes an improved isolation forest method with data mass (MS-iForest) for data tampering attack detection, in which we use data mass instead of the number of divisions and give an anomaly score ranking to quantify the degree of anomalies. This method is promising to be used as part of the intrusion detection system, like a security component in the onboard gateway, which can effectively avoid the data tampering attacks. We compare the proposed method with other anomaly detection schemes based on the data collected from an in-vehicle simulated dataset and two standard datasets. The experiment results show that the proposed method performs better than the other anomaly detection schemes in terms of the area under the receiver operating curve (AUC).

Cooperative Adaptive Cruise Control (CACC) systems can significantly improve traffic safety and roadway capacity utilizing short following gaps of vehicles enabled by inter-vehicle communications. However, due to merging processes occurring at freeway merging areas, existing CACC operation approaches are generally not applicable and the operation will have to revert back to Adaptive Cruise Control (ACC) or human-driven mode, which in turn will result in a capacity drop. This paper proposes an optimal trajectory optimization strategy for Connected and Automated Vehicles (CAVs) to cooperatively carry out mainline platooning and on-ramp merging. Firstly, a control framework of the CACC is adopted for a longitudinal control of CAVs, which helps individual CAVs to join platoons and to maintain platoon operations. Secondly, to ensure smooth lane-changing executions while achieving stable platoons, an optimal controller that considers lane-changing motivation of merging vehicles and impact of merging on platoons, is proposed. Third, a Legendre pseudo-spectral algorithm is applied to transform the controller into a simpler nonlinear programming problem and to efficiently solve it. Simulation assessments of the proposed method are conducted at both individual vehicle level and traffic-flow level. At the individual vehicle level, the proposed method has the potential to improve the traffic safety without compromising fuel consumption and emissions compared with unoptimized feasible schemes. At a traffic-flow level, an online evaluation platform is implemented, and a typical freeway on-ramp area is studied. The simulation results have demonstrated that the proposed controller provides significant improvements in terms of efficiencies in traffic operations.

The string stability of cooperative adaptive cruise control (CACC) platoons is largely affected by complex driving environment and abnormal driving behaviors. Fast and repetitive driving-state changes always occur during the period of changing driving states (such as leaving a platoon or lane-change), due to errors made in driver decisions or automatic driving system. This research proposes a framework which combines recognition of driving states with platoon operations and risk-prediction in order to reduce disturbance and unnecessary platoon operations resulting from driving-state jitters. First of all, long short-term memory (LSTM) neural networks were used in this research combined with a time-window in order to recognize driving states. Based on this research, the LSTM mode with an added time-window was found to be able to effectively reduce comparatively the jitters of recognition results. After that, an integrated mode which incorporates a recognition mode with danger probabilities was demonstrated to present better platoon operations. Monte Carlo simulation and importance sampling method will be given to predict platoons’ and vehicles’ trajectories and compute danger probabilities. In addition, an innovative strategy is implemented to identify an additional leader and execute a platoon splitting in order to improve driving smoothness, if a vehicle is recognized in an abnormal car-following state with a high danger-probability. In summary, this research has conducted extensive numerical tests to evaluate performances of the proposed system and the analysis results show that the proposed strategies will effectively increase smoothness and safety for a multi-platooning system.

Modern complexities associated with an arterial traffic makes existing safety prediction methods insufficient to meet desired standards required by recent developmental needs. This paper proposes an enhanced active safety prediction method based on big-data approach and Stacked AutoEncoder-Gated Recurrent Unit. Firstly, the big-data technology is used to construct a dynamic identification model to recognize real-time operation state and risk state. Secondly, the Stacked AutoEncoder-Gated Recurrent Unit is used to predict a level of safety based on associated recognition results. This paper uses data from working days of Sunset Boulevard, California, from January$1^{\mathrm{st}}$, 2020, to February$28^{\mathrm{th}}$, 2020. The results of analysis show that the accuracy of the proposed dynamic recognition model reaches 98.92%, which is better than existing models such as random forest, K-nearest neighbor, and naïve Bayes models. In addition, it is found that the Stacked AutoEncoder-Gated Recurrent Unit can achieve a prediction accuracy of 95.157% and has significant advantages in terms of efficiency. The proposed methods will provide feasible solutions for actively monitoring safety levels.

This paper comprehensively discusses the cooperative communication and computation of vehicular system. Based on the cooperative transmission, an stochastic model of vehicle-to-vehicle (V2V) communication reliability is established using probability theory. Furthermore, the computation reliability is defined as a new metric for computation offloading, and a vehicle computational performance evaluation model is also established. In order to effectively compute the required data, we combine V2V communication and vehicle computing to further characterize the coupling reliability of cooperative communications and computation systems. In addition, we propose a virtual queue model that combines queue length and vehicle privacy entropy to optimize partitioning. Finally, considering the amount of processing data and cut-off time of vehicle applications, we establish the optimal partition model of vehicle computing with the goal of maximizing the coupling reliability, and propose the coupling-oriented reliability calculation for vehicle collaboration using dynamic programming methods. Simulations show that the proposed scheme outperforms traditional approaches in terms of coupling reliability and completion rate. In addition, the allocation between local computing and data offloading is controlled by the server’s privacy perception of collaboration events.

Reasonable bus timetable can reduce the operating costs of bus company and improve the quality of bus services. A data-driven method is proposed to optimize bus timetable in this study. Firstly, a bi-objective optimization model is constructed considering minimize the total waiting time of passengers and the departure times of bus company. Then, Global Positioning System (GPS) trajectories of buses and passenger information collected from Smart Card are fused and applied to calculate the key parameters or variables in optimization model, including time-dependent travel time, bus dwell time and passenger volume. Finally, by adopting a specific coding scheme, an improved Non-dominated Sorting Genetic Algorithm-II (NSGA-II) is designed to quickly search Pareto optimal solutions. Furthermore, an experiment is conducted in Beijing city from one bus line to validate the effectiveness of the proposed method. Comparing with empirical scheduling method and traditional single-objective optimization base on GA, the results show that the proposed model could quickly provide high-quality and reasonable timetable schemes for the administrator in urban transit system.

As an emerging urban public transport mode, responsive feeder transit system is flexible and can offer door-to-door services between new districts at margins with low urban transit coverage and trunk bus station. In this study, a joint optimization of running route and scheduling for responsive feeder transit under mixed demand (i.e., reservation and real-time demands) of the time-dependent road network was investigated. A two-stage optimization method was designed together with considering the mixed demands. At the first stage, the initial running route and scheduling were determined according to all reservation demands. At the second stage, the running route and scheduling were continuously optimized based on the real-time demands. The real-time demand responsive strategy, which is built up by using quantitative batch treatment rather than immediate treatment and dynamic route updating strategy for global optimization, were designed by utilizing the submission order of real-time demands. A joint optimization model of running route and scheduling was constructed based on the quantitative batch decision points in the time-dependent road network together with combination of the actual road network. In this model, the minimum total system cost was used, which is composed of the vehicle running costs and passengers' traveling time costs with constraints including vehicle capacity, passengers' time window, and vehicle running time. A solving algorithm based on the adaptive genetic algorithm was designed by considering the characteristics of the joint optimization model.

Unstable vehicle-to-vehicle (V2V) communication connections are a vital phenomenon in connected vehicle (CV) environments which lead to the change of communication topologies and delays among electric connected vehicles (ECVs). This paper aims to evaluate the effects of the switching period of communication topology and delay on the dynamic performance and energy consumption of an ECV traffic stream considering the characteristics of car-following (CF) theory. To this end, a communication topology characterization method is developed by using the beacon transmission mechanism, graph theory, and probability theory. Then, a new CF model incorporating the effects of the communication topologies and delays is proposed to capture the interactions under a CV environment. The stability of the proposed model is analyzed by using the perturbation method. Finally, extensive simulations are implemented to be separately discussed by considering the effects of different switching periods of communication topologies and delays.

This paper introduces a model to analyze route choice behavior of taxi drivers for finding next passenger in urban road network. Considering the situation of path overlapping between selected routes in the process of customer-searching, a mixed path size logit (MPSL) model is proposed to analyze route choice behaviors through considering spatio-temporal features of route including customer generation rate, path travel time, cumulative intersection delay, path distance, and path size. Specially, customer generation rate is defined as attraction strength based on historical pick-up records in the route, the intersection travel delay and path travel time are estimated based on large scaled taxi global positioning system (GPS) trajectories. In the experiment, the GPS data were collected from about 36000 taxi vehicles in Beijing at 30-s interval during six months. In the model application, an area of approximately 10 square kilometers in the center of Beijing is selected to demonstrate the effectiveness of the proposed model. The results indicated that the MPSL model could effectively analyze the route choice behavior in customer-searching process and express higher accuracy than traditional multinomial logit model and basic PSL model.

Traffic signals as part of intelligent transportation systems can play a significant role in making cities smart. Conventionally, most traffic lights are designed with fixed-time control, which induces a lot of slack time (unused green time). Actuated traffic lights control traffic flow in real time and are more responsive to the variation of traffic demands. For an isolated signal, a family of time series models, such as autoregressive integrated moving average (ARIMA) models, can be beneficial for predicting the next cycle length. However, when there are multiple signals placed along a corridor with different spacing and configurations, the cycle length variation of such signals is not just related to each signal’s values, but it is also affected by the platoon of vehicles coming from neighboring intersections. In this paper, a multivariate time series model is developed to analyze the behavior of signal cycle lengths of multiple intersections placed along a corridor in a fully actuated setup. Five signalized intersections have been modeled along a corridor, with different spacing among them, together with multiple levels of traffic demand. To tackle the high-dimensional nature of the problem, a penalized least-squares method is utilized in the estimation procedure to output sparse models. Two proposed sparse time series methods captured the signal data reasonably well and outperformed the conventional vector autoregressive model—in some cases up to 17%—as well as being more powerful than univariate models, such as ARIMA.