An Attention-Based Multi-Context Convolutional Encoder-Decoder Neural Network for Work Zone Traffic Impact Prediction

Work zone is one of the major causes of non-recurrent traffic congestion and road incidents. Despite the significance of its impact, studies on predicting the traffic impact of work zones remain scarce. In this paper, we propose a data integration pipeline that enhances the utilization of work zone and traffic data from diversified platforms, and introduce a novel deep learning model to predict the traffic speed and incident likelihood during planned work zone events. The proposed model transforms traffic patterns into 2D space-time images for both model input and output and employs an attention-based multi-context convolutional encoder-decoder architecture to capture the spatial-temporal dependencies between work zone events and traffic variations. Trained and validated on four years of archived work zone traffic data from Maryland, USA, the model demonstrates superior performance over baseline models in predicting traffic speed, incident likelihood, and inferred traffic attributes such as queue length and congestion timings (i.e., start time and duration). Specifically, the proposed model outperforms the baseline models by reducing the prediction error of traffic speed by 5% to 34%, queue length by 11% to 29%, congestion timing by 6% to 17%, and increasing the accuracy of incident predictions by 5% to 7%. Consequently, this model offers substantial promise for enhancing the planning and traffic management of work zones.

Deep Activity Model: A Generative Approach for Human Mobility Pattern Synthesis

Human mobility significantly impacts various aspects of society, including transportation, urban planning, and public health. The increasing availability of diverse mobility data and advancements in deep learning have revolutionized mobility modeling. Existing deep learning models, however, mainly study spatio-temporal patterns using trajectories and often fall short in capturing the underlying semantic interdependency among activities. Moreover, they are also constrained by the data source. These two factors thereby limit their realism and adaptability, respectively. Meanwhile, traditional activity-based models (ABMs) in transportation modeling rely on rigid assumptions and are costly and time-consuming to calibrate, making them difficult to adapt and scale to new regions, especially those regions with limited amount of required conventional travel data. To address these limitations, we develop a novel generative deep learning approach for human mobility modeling and synthesis, using ubiquitous and open-source data. Additionally, the model can be fine-tuned with local data, enabling adaptable and accurate representations of mobility patterns across different regions. The model is evaluated on a nationwide dataset of the United States, where it demonstrates superior performance in generating activity chains that closely follow ground truth distributions. Further tests using state- or city-specific datasets from California, Washington, and Mexico City confirm its transferability. This innovative approach offers substantial potential to advance mobility modeling research, especially in generating human activity chains as input for downstream activity-based mobility simulation models and providing enhanced tools for urban planners and policymakers.

Semantic Trajectory Data Mining with LLM-Informed POI Classification

Human travel trajectory mining is crucial for transportation systems, enhancing route optimization, traffic management, and the study of human travel patterns. Previous rule-based approaches without the integration of semantic information show a limitation in both efficiency and accuracy. Semantic information, such as activity types inferred from Points of Interest (POI) data, can significantly enhance the quality of trajectory mining. However, integrating these insights is challenging, as many POIs have incomplete feature information, and current learning-based POI algorithms require the integrity of datasets to do the classification. In this paper, we introduce a novel pipeline for human travel trajectory mining. Our approach first leverages the strong inferential and comprehension capabilities of large language models (LLMs) to annotate POI with activity types and then uses a Bayesian-based algorithm to infer activity for each stay point in a trajectory. In our evaluation using the OpenStreetMap (OSM) POI dataset, our approach achieves a 93.4% accuracy and a 96.1% F-1 score in POI classification, and a 91.7% accuracy with a 92.3% F-1 score in activity inference.

Evaluating Cybersecurity Risks of Cooperative Ramp Merging in Mixed Traffic Environments

Connected and Automated Vehicle (CAV) technology has the potential to greatly improve transportation mobility, safety, and energy efficiency. However, ubiquitous vehicular connectivity also opens up the door for cyber-attacks. In this study, we investigate cybersecurity risks of a representative cooperative traffic management application, i.e., highway on-ramp merging, in a mixed traffic environment. We develop threat models with two trajectory spoofing strategies on CAVs to create traffic congestion, and we also devise an attack-resilient strategy for system defense. Furthermore, we leverage VENTOS, a Veins extension simulator made for CAV applications, to evaluate cybersecurity risks of the attacks and performance of the proposed defense strategy. A comprehensive case study is conducted across different traffic congestion levels, penetration rates of CAVs, and attack ratios. As expected, the results show that the performance of mobility decreases up to 55.19% at the worst case when the attack ratio increases, as does safety and energy. With our proposed mitigation defense algorithm, the system's cyber-attack resiliency is greatly improved.

End-to-End Vision-Based Adaptive Cruise Control Using Deep Reinforcement Learning

This paper presented a deep reinforcement learning method named Double Deep Q-networks to design an end-to-end vision-based adaptive cruise control (ACC) system. A simulation environment of a highway scene was set up in Unity, which is a game engine that provided both physical models of vehicles and feature data for training and testing. Well-designed reward functions associated with the following distance and throttle/brake force were implemented in the reinforcement learning model for both internal combustion engine (ICE) vehicles and electric vehicles (EV) to perform adaptive cruise control. The gap statistics and total energy consumption are evaluated for different vehicle types to explore the relationship between reward functions and powertrain characteristics. Compared with the traditional radar-based ACC systems or human-in-the-loop simulation, the proposed vision-based ACC system can generate either a better gap regulated trajectory or a smoother speed trajectory depending on the preset reward function. The proposed system can be well adaptive to different speed trajectories of the preceding vehicle and operated in real-time.