Human mobility plays a crucial role in transportation, urban planning, and public health, but current approaches face important limitations. Existing deep learning models tend to overlook the semantic interdependencies among activities and households and rely on restricted GPS data, while activity-based models depend on rigid assumptions and extensive data, making them costly and difficult to adapt to new regions, especially those with limited conventional travel data. We propose a generative Transformer model that uses socio-demographic and household attributes to synthesize daily activity chains, with a location module assigning spatial zones to produce complete daily trajectories. To address these limitations, we propose a generative Transformer model that uses socio-demographic and household attributes to synthesize daily activity chains, with a location module assigning spatial zones to produce complete daily trajectories. Trained on open-source and widely available household travel survey data and then fine-tuned with local data, the model captures national activity patterns and transfers effectively to California, Washington, and Mexico City. This approach offers significant potential for advancing synthetic human mobility modeling and provides urban planners and policymakers with improved tools for simulating transportation systems and supporting decisions in urban development and public health. Its practical utility is demonstrated through large-scale traffic simulations in Los Angeles County. Compared to the SCAG Activity-Based Model (ABM), the proposed method produces consistent spatial demand patterns, achieving an activity location cosine similarity of 0.997 and a network-level vehicle-miles-traveled Mean Absolute Percentage Error (MAPE) of 4.97%. Compared to real-world observations from Caltrans PeMS, the simulated traffic achieves MAPE of 5.85% for traffic volume and 4.36% for speed on California’s I-405 corridor. This demonstrates the practicality of learning-based synthetic mobility for regional simulation and planning.

Understanding the behavior of human drivers and how they interact with other drivers is crucial to develop and improve the decision-making capabilities of connected and automated vehicles (CAVs). This allows CAVs to anticipate and proactively respond to the actions of other road users in a safe and efficient manner, especially in mixed-traffic environments. Most existing studies rely on neural networks to model such interaction implicitly, and very few studies attempt to interpret the interaction. Considering its interpretability and flexibility, the Granger causality (GC) framework is widely used to understand the relationships between different agents, as well as how these relationships change over time. In this paper, we integrate the knowledge of traffic and vehicle dynamics into the neural network to learn the Granger causality and explore vehicular interaction from multi-vehicle trajectories. The proposed algorithm has been validated using both the INTERACTION dataset and field data collected in Riverside, California. The results show that our algorithm is able to address three key questions regarding vehicular interaction: 1) whether the interactions exist between/among the vehicles; 2) when the interactions occur and terminate; and 3) how strong the interactions between/among vehicles are.

Advanced Driver Assistance Systems (ADAS) are increasingly important in improving driving safety and comfort, with Adaptive Cruise Control (ACC) being one of the most widely used. However, pre-defined ACC settings may not always align with driver's preferences and habits, leading to discomfort and potential safety issues. Personalized ACC (P-ACC) has been proposed to address this problem, but most existing research uses historical driving data to imitate behaviors that conform to driver preferences, neglecting real-time driver feedback. To bridge this gap, we propose a cloud-vehicle collaborative P-ACC framework that incorporates driver feedback adaptation in real time. The framework is divided into offline and online parts. The offline component records the driver's naturalistic car-following trajectory and uses inverse reinforcement learning (IRL) to train the model on the cloud. In the online component, driver feedback is used to update the driving gap preference in real time. The model is then retrained on the cloud with driver's takeover trajectories, achieving incremental learning to better match driver's preference. Human-in-the-loop (HuiL) simulation experiments demonstrate that our proposed method significantly reduces driver intervention in automatic control systems by up to 62.8%. By incorporating real-time driver feedback, our approach enhances the comfort and safety of P-ACC, providing a personalized and adaptable driving experience.

Connected and automated vehicles (CAVs) are supposed to share the road with human-driven vehicles (HDVs) in a foreseeable future. Therefore, considering the mixed traffic environment is more pragmatic, as the well-planned operation of CAVs may be interrupted by HDVs. In the circumstance that human behaviors have significant impacts, CAVs need to understand HDV behaviors to make safe actions. In this study, we develop a driver digital twin (DDT) for the online prediction of personalized lane-change behavior, allowing CAVs to predict surrounding vehicles’ behaviors with the help of the digital twin technology. DDT is deployed on a vehicle-edge–cloud architecture, where the cloud server models the driver behavior for each HDV based on the historical naturalistic driving data, while the edge server processes the real-time data from each driver with his/her digital twin on the cloud to predict the personalized lane-change maneuver. The proposed system is first evaluated on a human-in-the-loop co-simulation platform, and then in a field implementation with three passenger vehicles driving along an on/off ramp segment connecting to the edge server and cloud through the 4G/LTE cellular network. The lane-change intention can be recognized in 6 s on average before the vehicle crosses the lane separation line, and the Mean Euclidean Distance between the predicted trajectory and GPS ground truth is 1.03 m within a 4-s prediction window. Compared to the general model, using a personalized model can improve prediction accuracy by 27.8%. The demonstration video of the proposed system can be watched athttps://youtu.be/5cbsabgIOdM.

In the foreseeable future, connected and auto-mated vehicles (CAVs) and human-driven vehicles will share the road networks together. In such a mixed traffic environment, CAVs need to understand and predict maneuvers of surrounding vehicles for safer and more efficient interactions, especially when human drivers bring in a wide range of uncertainties. In this paper, we propose a learning-based lane-change prediction algorithm that considers the driving behaviors of the target human driver. To provide accurate maneuver prediction, we adopt a hierarchical structure that seamlessly seals both the lane-change decision prediction and the vehicle trajectory pre-diction together. Specifically, we propose a lane-change decision prediction method based on a Long-Short Term Memory (LSTM) network, and a trajectories prediction considering driver preference and vehicular interactions based on Inverse Reinforcement Learning (IRL). To validate the performance of the proposed methodology, a case study of an on-ramp merging scenario is conducted on a uniquely built human-in-the-loop simulation platform that can provide an immersive driving environment, collect data of lane-change behaviors, and test drivers' reactions to the prediction results in real time. It is shown in the simulation results that we can predict the lane-change decision 3 seconds before the vehicle crosses the line to another lane, and the Mean Euclidean Distance between the predicted trajectory and ground truth is 0.39 meters within a 4-second prediction window.

Ramp merging is considered as one of the most difficult driving scenarios due to the chaotic nature in both longitudinal and lateral driver behaviors (namely lack of effective coordination) in the merging area. In this study, we have designed a cooperative ramp merging system for connected vehicles, allowing merging vehicles to cooperate with others prior to arriving at the merging zone. Different from most of the existing studies that utilize dedicated short-range communication, we adopt a Digital Twin approach based on vehicle-to-cloud communication. On-board devices upload the data to the cloud server through the 4G/LTE cellular network. The server creates Digital Twins of vehicles and drivers whose parameters are synchronized in real time with their counterparts in the physical world, processes the data with the proposed models in the digital world, and sends advisory information back to the vehicles and drivers in the physical world. A real-world field implementation has been conducted in Riverside, California, with three passenger vehicles. The results show the proposed system addresses the issues of safety and environmental sustainability with an acceptable communication delay, compared to the baseline scenario where no advisory information is provided during the merging process.

Ramp merging is considered to be one of the major causes of traffic accidents and congestion due to its inherent chaotic nature. With the development of the connected and automated vehicle (CAV) technology, CAVs can conduct cooperative merging using communication, and can also handle complicated situations even with legacy vehicles. In this article, a game theory-based ramp merging strategy has been developed for the optimal merging coordination of CAVs in mixed traffic, which can determine the dynamic merging sequence and corresponding longitudinal/lateral control. This strategy improves the safety and efficiency of the merging process by ensuring a safe intervehicle distance and harmonizing the speeds of CAVs in the traffic stream. To verify the proposed strategy, mixed traffic simulation runs under different penetration rates and different congestion levels have been carried out on an innovative Unity-SUMO integrated platform, which connects a game engine-based driving simulator with a state-of-the-art microscopic traffic simulator. The results show that the average speed of traffic flow can be increased up to 210%, while the fuel consumption can be reduced up to 53.9%. In addition, the driving volatility can be stabilized to a level with 0% extreme values.

Digital twin, an emerging representation of cyberphysical systems, has attracted increasing attentions very recently. It opens the way to real-time monitoring and synchronization of real-world activities with the virtual counterparts. In this study, we develop a digital twin paradigm using an advanced driver assistance system (ADAS) for connected vehicles. By leveraging vehicle-to-cloud (V2C) communication, on-board devices can upload the data to the server through cellular network. The server creates a virtual world based on the received data, processes them with the proposed models, and sends them back to the connected vehicles. Drivers can benefit from this V2C based ADAS, even if all computations are conducted on the cloud. The cooperative ramp merging case study is conducted, and the field implementation results show the proposed digital twin framework can benefit the transportation systems regarding mobility and environmental sustainability with acceptable communication delays and packet losses.