| 1. |
- Ling, Yancheng, et al.
(författare)
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Analyzing factors contributing to bus driver deceleration behavior at intersections using multi-source naturalistic driving data
- 2024
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Konferensbidrag (refereegranskat)abstract
- 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.
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| 2. |
- Ling, Yancheng, et al.
(författare)
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STMA-GCN_PedCross: Skeleton Based Spatial-Temporal Graph Convolution Networks with Multiple Attentions for Fast Pedestrian Crossing Intention Prediction
- 2023
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Ingår i: 2023 IEEE 26th International Conference on Intelligent Transportation Systems, ITSC 2023. - : Institute of Electrical and Electronics Engineers Inc.. ; , s. 500-506
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Konferensbidrag (refereegranskat)abstract
- 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.
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| 3. |
- Liu, Yongxin, et al.
(författare)
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Exploring data validity in transportation systems for smart cities
- 2017
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Ingår i: IEEE Communications Magazine. - : Institute of Electrical and Electronics Engineers (IEEE). - 0163-6804 .- 1558-1896. ; 55:5, s. 26-33
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Tidskriftsartikel (refereegranskat)abstract
- Efficient urban transportation systems are widely accepted as essential infrastructure for smart cities, and they can highly increase a city°s vitality and convenience for residents. The three core pillars of smart cities can be considered to be data mining technology, IoT, and mobile wireless networks. Enormous data from IoT is stimulating our cities to become smarter than ever before. In transportation systems, data-driven management can dramatically enhance the operating efficiency by providing a clear and insightful image of passengers° transportation behavior. In this article, we focus on the data validity problem in a cellular network based transportation data collection system from two aspects: Internal time discrepancy and data loss. First, the essence of time discrepancy was analyzed for both automated fare collection (AFC) and automated vehicular location (AVL) systems, and it was found that time discrepancies can be identified and rectified by analyzing passenger origin inference success rate using different time shift values and evolutionary algorithms. Second, the algorithmic framework to handle location data loss and time discrepancy was provided. Third, the spatial distribution characteristics of location data loss events were analyzed, and we discovered that they have a strong and positive relationship with both high passenger volume and shadowing effects in urbanized areas, which can cause severe biases on passenger traffic analysis. Our research has proposed some data-driven methodologies to increase data validity and provided some insights into the influence of IoT level data loss on public transportation systems for smart cities.
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