Jewel Rana Palit

Jewel Rana Palit, Vijayalakshmi K Kumarasamy, Osama A. Osman

Automated Vehicles (AV) hold potential to reduce or eliminate human driving errors, enhance traffic safety, and support sustainable mobility. Recently, crash data has increasingly revealed that AV behavior can deviate from expected safety outcomes, raising concerns about the technology's safety and operational reliability in mixed traffic environments. While past research has investigated AV crash, most studies rely on small-size California-centered datasets, with a limited focus on understanding crash trends across various SAE Levels of automation. This study analyzes over 2,500 AV crash records from the United States National Highway Traffic Safety Administration (NHTSA), covering SAE Levels 2 and 4, to uncover underlying crash dynamics. A two-stage data mining framework is developed. K-means clustering is first applied to segment crash records into 4 distinct behavioral clusters based on temporal, spatial, and environmental factors. Then, Association Rule Mining (ARM) is used to extract interpretable multivariate relationships between crash patterns and crash contributors including lighting conditions, surface condition, vehicle dynamics, and environmental conditions within each cluster. These insights provide actionable guidance for AV developers, safety regulators, and policymakers in formulating AV deployment strategies and minimizing crash risks.

Jewel Rana Palit, Osama A Osman

Traffic flow forecasting is a crucial first step in intelligent and proactive traffic management. Traffic flow parameters are volatile and uncertain, making traffic flow forecasting a difficult task if the appropriate forecasting model is not used. Additionally, the non-Euclidean data structure of traffic flow parameters is challenging to analyze from both spatial and temporal perspectives. State-of-the-art deep learning approaches use pure convolution, recurrent neural networks, and hybrid methods to achieve this objective efficiently. However, many of the approaches in the literature rely on complex architectures that can be difficult to train. This complexity also adds to the black-box nature of deep learning. This study introduces a novel deep learning architecture, referred to as the multigraph convolution neural network (MGCNN), for turning movement prediction at intersections. The proposed architecture combines a multigraph structure, built to model temporal variations in traffic data, with a spectral convolution operation to support modeling the spatial variations in traffic data over the graphs. The proposed model was tested using twenty days of flow and traffic control data collected from an arterial in downtown Chattanooga, TN, with ten signalized intersections. The model's ability to perform short-term predictions over 1, 2, 3, 4, and 5 minutes into the future was evaluated against four baseline state-of-the-art models. The results showed that our proposed model is superior to the other baseline models in predicting turning movements with a mean squared error (MSE) of 0.9