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1. Data-Driven City Traffic Planning Simulation

   https://doi.org/10.1109/ISMAR-Adjunct57072.2022.00185  

   Nguyen, Tam V.; Tran, Thanh Ngoc-Dat; Huynh, Viet-Tham; Truong, Bao; Le, Minh-Quan; Kumavat, Mohit; Patel, Vatsa S.; Tran, Mai-Khiem; Tran, Minh-Triet (October 2022, ISMAR 2022)

   Big cities are well-known for their traffic congestion and high density of vehicles such as cars, buses, trucks, and even a swarm of motorbikes that overwhelm city streets. Large-scale development projects have exacerbated urban conditions, making traffic congestion more severe. In this paper, we proposed a data-driven city traffic planning simulator. In particular, we make use of the city camera system for traffic analysis. It seeks to recognize the traffic vehicles and traffic flows, with reduced intervention from monitoring staff. Then, we develop a city traffic planning simulator upon the analyzed traffic data. The simulator is used to support metropolitan transportation planning. Our experimental findings address traffic planning challenges and the innovative technical solutions needed to solve them in big cities. 

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2. Probabilistic Traffic State Prediction Based on Vehicle Trajectory Data

   https://doi.org/10.1007/s42421-023-00085-8  

   Khajeh_Hosseini, Mohammadreza; Talebpour, Alireza (December 2023, Data Science for Transportation)

   Accurate prediction of traffic flow dynamics is a key step towards effective congestion mitigation strategies. The dynamic nature of traffic flow and lack of comprehensive data coverage (e.g., availability of data at loop detector locations), however, have historically prevented accurate traffic state prediction, leading to the widespread utilization of reactive congestion mitigation strategies. The introduction of connected automated vehicles provides an opportunity to address this challenge. These vehicles rely on trajectory-level prediction of their surrounding traffic environment to plan a safe and efficient path. This study proposes a methodology to utilize the outcome of such predictions to estimate the future traffic state. Moreover, the same approach can be applied to data from connected vehicles for traffic state prediction. Since in many driving scenarios, more than one maneuver is feasible, it is more logical to predict the location of the vehicles in a probabilistic manner based on the probability of different maneuvers. The key contribution of this study is to introduce a methodology to convert such probabilistic trajectory predictions to aggregate traffic state predictions (i.e., flow, space–mean speed, and density). The key advantage of this approach (over directly predicting traffic state based on aggregated traffic data) is its ability to capture the interactions among vehicles to increase the accuracy of the prediction. The down side of this approach, on the other hand, is that any increase in the prediction horizon reduces the accuracy of prediction (due to the uncertainty in the vehicles’ interactions and the increase in the possibility of different maneuvers). At the microscopic level, this study proposes a probability based version of the time–space diagram, and at the macroscopic level, this study proposes probabilistic estimates of flow, density, and space–mean speed using the trajectory-level predictions. To evaluate the effectiveness of the proposed approach in predicting traffic state, the mean absolute percentage error for each probabilistic macroscopic estimate is evaluated on multiple subsamples of the NGSIM US-101 and I-80 data sets. Moreover, while introducing this novel traffic state prediction approach, this study shows that the fundamental relation among the average traffic flow, density, and space–mean speed is still valid under the probabilistic formulations of this study. 

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3. DxNAT - Deep Neural Networks for Explaining Non-Recurring Traffic Congestion

   sun, Fangzhou; Dubey, Abhishek; White, Jules (December 2017, IEEE Big Data (Special Session on Intelligent Data Mining))

   Non-recurring traffic congestion is caused by temporary disruptions, such as accidents, sports games, adverse weather, etc. We use data related to real-time traffic speed, jam factors (a traffic congestion indicator), and events collected over a year from Nashville, TN to train a multi-layered deep neural network. The traffic dataset contains over 900 million data records. The network is thereafter used to classify the real-time data and identify anomalous operations. Compared with traditional approaches of using statistical or machine learning techniques, our model reaches an accuracy of 98.73 percent when identifying traffic congestion caused by football games. Our approach first encodes the traffic across a region as a scaled image. After that the image data from different timestamps is fused with event- and time-related data. Then a crossover operator is used as a data augmentation method to generate training datasets with more balanced classes. Finally, we use the receiver operating characteristic (ROC) analysis to tune the sensitivity of the classifier. We present the analysis of the training time and the inference time separately. 

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4. Legal Framework for Rear-End Crashes in Mixed-Traffic Platooning: A Matrix Game Approach

   https://doi.org/10.3390/futuretransp3020025  

   Chen, Xu; Di, Xuan (June 2023, Future Transportation)

   Autonomous vehicles (AV) hold great potential to increase road safety, reduce traffic congestion, and improve mobility systems. However, the deployment of AVs introduces new liability challenges when they are involved in car accidents. A new legal framework should be developed to tackle such a challenge. This paper proposes a legal framework, incorporating liability rules to rear-end crashes in mixed-traffic platoons with AVs and human-propelled vehicles (HV). We leverage a matrix game approach to understand interactions among players whose utility captures crash loss for drivers according to liability rules. We investigate how liability rules may impact the game equilibrium between vehicles and whether human drivers’ moral hazards arise if liability is not designed properly. We find that compared to the no-fault liability rule, contributory and comparative rules make road users have incentives to execute a smaller reaction time to improve road safety. There exists moral hazards for human drivers when risk-averse AV players are in the car platoon. 

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5. Digital Twin Based Asynchronous Federated Learning Enabled IDS for False Data Injection Attacks in Vehicular CPS

   https://doi.org/10.1109/ICCSM63823.2024.00012  

   Safavat, Sunitha; Rawat, Danda B (July 2024, IEEE)

   Cyber Physical Systems (CPS) consist of integration of cyber and physical spaces through computing, communication, and control operations. In vehicular CPS, modern vehicles with multiple Electronic Control Units (ECUs) and networking with other vehicles help autonomous driving. Vehicular CPS is vulner-able to multitude of cyber attacks, including false data injection attacks. This paper presents an Asynchronous Federated Learning (AFL) with a Gated Recurrent Unit (GRU) model for identifying False Data Injection (FDI) attacks in a VCPS. The AFL model continuously monitors the network and constructs a digital twin using the data obtained from a VCPS for intrusion detection. The proposed model is evaluated using different evaluation metrics. Numerical results show that the AFL model outperforms other existing models. 

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