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Understanding the impact of various factors on bus deceleration behavior atintersections has important implications for bus operations control, management, andsafety. This paper introduces a multiple linear regression model to analyze the factorsinfluencing bus driver deceleration (a proxy of safe driving state) at intersections usingdata from multiple sources, including the on-board closed-circuit television (CCTV), the advanced driver assistance system (ADAS), the bus controller area network (CAN), the bus operation data, and the driver profile data. We develop a comprehensive modeldata extraction framework and corresponding methods to effectively estimate/calculatethe bus deceleration rate (dependent variable) and its influencing factors (independent variables). We explored the factors impact on bus deceleration behavior at intersections using data from a typical bus route in China. The results highlight significant factors, including driver characteristics (age), en-route and intersection approaching driving states (trip delay, turnaround time, driving direction, andapproaching speed), traffic states (surrounding vehicles), and intersection characteristics (types, number of lanes, zebra crossing, divider, bus lane, right turnlane, location). Generally, drivers with younger ages (short reaction time) and driving with psychological anticipation of complex situations(surrounding vehicles and pedestrians, unsignalized intersections) tend to decelerate more smoothly. The agencies may enhance safe bus driving behavior by allowing enough turnaround time in timetabling, recommending intersection approaching speed, and providing tailored ADAS system (rather than flooding all alerts). Also, the planning of bus infrastructures(e.g., dedicated lanes and stop locations) should be properly evaluated considering their soft contribution to safe driving behaviors., QC 20240318

Bus delays pose significant challenges to urban public transportation systems, impacting operational efficiency and incurring substantial costs. Understanding the causative factors behind bus delays is crucial for developing effective strategies to mitigate them. However, existing research predominantly relies on correlational analysis, which falls short of revealing the underlying causal relationships among factors. This study introduces a novel data-driven causality-based modeling approach, combining data-driven causal discovery and structure equation models (SEM), to investigate the causal relationships among various operational factors influencing bus delays. It can automatically generate a causal delay graph that elucidates the interconnectedness and influence of operational factors on bus delays, providing a deeper understanding of the causal mechanism of bus delays. We explored and evaluated the performance of different causal discovery algorithms in generating causal graph from both aspects of the data fitting and causality discovery performance. The SEM model is used to quantify the direct causal effects among factors in the causal graph. The case study is conducted to validate the causal discovery model performance using Google Transit Feed Specification (GTFS) data from high-frequency bus routes in Stockholm, Sweden. The validation results highlight potential of the data-driven causal discovery models in discovering causality relationships and automating the knowledge discovery process, particularly combining with domain knowledge. The empirical findings show the complexity of factors contributing to bus delays and emphasize the importance of integrating causality into the bus delay factor analysis. For example, a high correlation between origin delay and current arrival delay (coefficient = 0.63) doesn't necessarily indicate causation, and a strong causal link from dwell time and arrival delay also does not reflect a high correlation (coefficient = 0.12), QC 20240108

Pedestrian crossing intention prediction is an important task for intelligent vehicles to enhance safety and reduce the risk of accidents. The high prediction accuracy and fast execution speed are essential requirements for this task. Existing studies on pedestrian crossing intention prediction have achieved good performance by using complex models and multiple modalities of input data. However, these approaches are limited in practical implications due to their high computational complexity and resource requirements. To address these, the paper propose the Spatial-Temporal Graph Convolution Network (GCN) with multiple attentions for fast and robust Pedestrian Crossing Intention Prediction (STMAGCN_PedCross). It utilizes easily accessible yet robust keypoints as input for predicting pedestrian crossing intention, making it both accurate and practical. To evaluate the effectiveness of the proposed model, we compared it with state-of-the-art models on a large-scale public Joint Attention in Autonomous Driving (JAAD) dataset. The results demonstrate that the STMA-GCN_PedCross model achieves comparable accuracy perfmance to the state-of-the-art models while having a higher robustness (recall rate) and requiring significantly less execution time. The ablation analysis further confirms the effectiveness of attention mechanisms in capturing spatial-temporal features from the skeleton sequence data by giving different attentions. Moreover, the analysis also reveals the significance of different keypoints in the prediction results., QC 20240314Part of ISBN 979-835039946-2