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学者姓名：郭建钢
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Abstract ：
为提高城市过江桥梁连续合流区路段的通行效率，完善相关设计规范和细则，基于统计学规律和交通流理论，分析了驾驶员在连续合流区路段的驾驶特征，以此建立了过江桥梁连续合流区的最小间距模型。以福建省福州市3座过江桥梁的连续合流区为研究对象，采用双无人机航拍视频，获取自然行驶状态下的交通流运行特征与车辆行驶轨迹。从交通安全的角度，对最小间距模型中的关键参数进行研究和标定，计算得到了过江桥梁连续合流区的最小间距建议值。
Keyword ：
交通安全 交通安全 最小间距 最小间距 过江桥梁 过江桥梁 连续合流区 连续合流区 驾驶特征 驾驶特征
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| GB/T 7714 | 汪嘉淇 , 黄洁玲 , 郭建钢 et al. 城市过江桥梁连续合流区最小间距研究 [J]. | 大连交通大学学报 , 2024 , 45 (03) : 31-37 . |
| MLA | 汪嘉淇 et al. "城市过江桥梁连续合流区最小间距研究" . | 大连交通大学学报 45 . 03 (2024) : 31-37 . |
| APA | 汪嘉淇 , 黄洁玲 , 郭建钢 , 高圣涵 . 城市过江桥梁连续合流区最小间距研究 . | 大连交通大学学报 , 2024 , 45 (03) , 31-37 . |
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ObjectivesTo conduct an in-depth study on the spatial distribution of traffic conflicts in the continuous merging areas of cross-river bridges and ensure public transportation safety.MethodsFirst, we utilized drone aerial photography to collect videos of vehicle movements. Using the YOLOv7 object detection algorithm and the Strong SORT multi-object tracking algorithm, we extracted high-precision vehicle trajectory time-series data. Next, based on the motion characteristics of traffic entities, we proposed using Deceleration Rate (DR) to describe rear-end conflicts and Lane Change Speed (LCS) to describe lane-changing conflicts. Additionally, we employed the K-means clustering method to determine the threshold values for minor, moderate, and severe levels of rear-end and lane-changing conflicts. Finally, based on the obtained trajectory data, the values of traffic conflicts are calculated and their severity is classified. A heat map of the spatial distribution of vehicle conflicts in continuous merging zones is then created to study the spatial distribution patterns of traffic conflicts.ResultsThe threshold values for minor, moderate, and severe levels of rear-end conflicts are determined to be 3.06 m/s2, 5.36 m/s2, and 8.04 m/s2, respectively. For lane-changing conflicts, the thresholds are 1.13 m/s, 2.07 m/s, and 3.45 m/s. The spatial distribution of traffic conflicts exhibits a "first increase, then decrease, and then increase again" trend.ConclusionsThe study identifies the critical areas of traffic conflicts in the continuous merging zones of cross-river bridges. The research results provide a novel approach for acquiring traffic data in these areas and offer a reliable quantitative method for assessing safety risks on these road segments. This provides a theoretical basis for proposing targeted traffic safety management strategies.
Keyword ：
continuous confluence area continuous confluence area traffic conflict traffic conflict Traffic safety Traffic safety vehicle trajectory vehicle trajectory
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| GB/T 7714 | Wang, Jiaqi , Ye, Zhiyi , Lin, Yushun et al. Traffic conflict analysis in continuous confluence area of cross-river bridge driven by vehicle trajectory data [J]. | TRAFFIC INJURY PREVENTION , 2024 , 26 (1) : 102-110 . |
| MLA | Wang, Jiaqi et al. "Traffic conflict analysis in continuous confluence area of cross-river bridge driven by vehicle trajectory data" . | TRAFFIC INJURY PREVENTION 26 . 1 (2024) : 102-110 . |
| APA | Wang, Jiaqi , Ye, Zhiyi , Lin, Yushun , Wang, Zhanyong , Guo, Jiangang . Traffic conflict analysis in continuous confluence area of cross-river bridge driven by vehicle trajectory data . | TRAFFIC INJURY PREVENTION , 2024 , 26 (1) , 102-110 . |
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To address the challenges of vehicle target detection in aerial images from Unmanned Aerial Vehicle (UAV) perspectives, this paper proposes a novel vehicle detection model named UAV-YOLO, based on the YOLOv7 object detection framework. First, Omni-dimensional Dynamic Convolution (ODConv) is introduced into the backbone of the feature extraction network to enrich the acquisition of small target information, enhancing the network's capability to extract features of small objects. Second, the Mixed Local Channel Attention (MLCA) module is added to the ELAN and feature pyramid structures within the backbone feature extraction module to integrate both local and global information, further improving the model's perception and information fusion for small targets. Lastly, a joint loss function, Normalized Wasserstein Complete IoU Loss (NWC-IoU Loss), is designed to enhance the model's detection performance and stability for small targets. Experimental results show that the improved detection algorithm increases the mean Average Precision (mAP@0.5) by 4.8%, reduces the FLOPs by 27.3%, and achieves a detection speed of 62.2 FPS, meeting real-time requirements. The proposed method demonstrates superior detection performance in various complex scenarios of the VisDrone2019 dataset. The UAV-YOLO model exhibits excellent performance in vehicle target detection in aerial images from UAV perspectives. © 2024 IEEE.
Keyword ：
Aerial photography Aerial photography Aircraft detection Aircraft detection Data fusion Data fusion Image enhancement Image enhancement Information fusion Information fusion Unmanned aerial vehicles (UAV) Unmanned aerial vehicles (UAV)
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| GB/T 7714 | Wang, Jiaqi , Wang, Yuhang , Ye, Zhiyi et al. Detection Model for Small Vehicle Targets from Unmanned Aerial Vehicle Perspectives [C] . 2024 : 207-212 . |
| MLA | Wang, Jiaqi et al. "Detection Model for Small Vehicle Targets from Unmanned Aerial Vehicle Perspectives" . (2024) : 207-212 . |
| APA | Wang, Jiaqi , Wang, Yuhang , Ye, Zhiyi , Lin, Yushun , Wang, Zhanyong , Guo, Jiangang . Detection Model for Small Vehicle Targets from Unmanned Aerial Vehicle Perspectives . (2024) : 207-212 . |
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当前高铁越来越成为人们中长距离出行的第一选择，而福建省高速铁路仍然处于发展中阶段，线路连通性还具有较大提升空间.以福建省高速铁路建设线路、站点分布和列车运行情况为基础，构建了福建省高速铁路地理网络和运营网络，利用复杂网络理论进行拓扑特征分析，包括度与度分布、聚类系数、平均路径长度以及非直线系数等参数.通过分析可知，福建省高速铁路地理网络具有随机网络的特点，运营网络具有无标度性质和小世界网络的特点.引入非直线系数，通过聚类分析指出当前福建省高速铁路网络连通性上存在的不足，提出优化方案，为福建省高速铁路的规划与发展提供支持.
Keyword ：
复杂网络 复杂网络 拓扑特征 拓扑特征 福建省高速铁路 福建省高速铁路 聚类分析 聚类分析 连通性 连通性 铁路网络 铁路网络 非直线系数 非直线系数
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| GB/T 7714 | 汪嘉淇 , 郭建钢 , 王占永 et al. 基于复杂网络的福建省高铁网络连通性分析 [J]. | 哈尔滨商业大学学报(自然科学版) , 2023 , 39 (05) : 628-633 . |
| MLA | 汪嘉淇 et al. "基于复杂网络的福建省高铁网络连通性分析" . | 哈尔滨商业大学学报(自然科学版) 39 . 05 (2023) : 628-633 . |
| APA | 汪嘉淇 , 郭建钢 , 王占永 , 徐锦强 . 基于复杂网络的福建省高铁网络连通性分析 . | 哈尔滨商业大学学报(自然科学版) , 2023 , 39 (05) , 628-633 . |
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平面交叉口的拥堵极易造成城市道路交通系统的瘫痪，合理的信号配时能有效提高交叉口的通行能力.在路口车流量不均衡的情况下，使用传统的韦伯斯特配时法将会造成某些相位的“空放”现象，造成绿灯时间的浪费.为解决这个问题，在韦伯斯特配时进行优化的基础上，引入搭接相位的配时方法，并利用VISSIM仿真软件对实际平面交叉口进行仿真.试验结果表明，利用搭接相位配时方法针对早高峰拥堵严重的进口道效果显著，机动车、非机动车通过车辆数分别提升7.91%、2.81%,能够有效的缓解早高峰进口道拥堵严重的问题，同时对其余三进口道皆有一定的优化效果，控制效果优于现状的四相位配时.
Keyword ：
信号配时 信号配时 拥堵 拥堵 搭接相位 搭接相位 潮汐交通流 潮汐交通流 通行能力 通行能力 韦伯斯特配时法 韦伯斯特配时法
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| GB/T 7714 | 苏灿航 , 高圣涵 , 郭建钢 et al. 基于潮汐交通流特性的交叉口信号配时研究 [J]. | 哈尔滨商业大学学报(自然科学版) , 2022 , 38 (02) : 186-191 . |
| MLA | 苏灿航 et al. "基于潮汐交通流特性的交叉口信号配时研究" . | 哈尔滨商业大学学报(自然科学版) 38 . 02 (2022) : 186-191 . |
| APA | 苏灿航 , 高圣涵 , 郭建钢 , 廖飞宇 . 基于潮汐交通流特性的交叉口信号配时研究 . | 哈尔滨商业大学学报(自然科学版) , 2022 , 38 (02) , 186-191 . |
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为研究非机动车密度对公交车进站的影响，选取福州市区2处典型的直线式公交停靠站点，通过无人机视频获取路段车辆信息；将公交车进站强制换道分为保守型和激进型，在考虑车辆延误损失和风险损失的基础上，量化公交车与非机动车博弈双方的收益，建立保守型强制换道决策模型；考虑相邻车辆最小安全距离，建立公交车激进型强制换道模型，提出一种强制换道元胞自动机仿真模型。仿真结果表明，公交车激进型强制换道概率随着非机动车密度和公交车密度的增大而增大。
Keyword ：
保守型 保守型 元胞自动机 元胞自动机 公交车 公交车 城市交通 城市交通 换道行为 换道行为 激进型 激进型
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| GB/T 7714 | 苏灿航 , 高圣涵 , 郭建钢 et al. 基于元胞自动机的公交车靠站换道行为研究 [J]. | 公路与汽运 , 2022 , (02) : 27-31 . |
| MLA | 苏灿航 et al. "基于元胞自动机的公交车靠站换道行为研究" . | 公路与汽运 02 (2022) : 27-31 . |
| APA | 苏灿航 , 高圣涵 , 郭建钢 , 廖飞宇 . 基于元胞自动机的公交车靠站换道行为研究 . | 公路与汽运 , 2022 , (02) , 27-31 . |
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为了研究直线式公交站点及上游区域内公交车与非机动车的冲突关系，选取福州市区两个典型的直线式公交站点，利用无人机获取公交车辆进站和非机动车的行驶轨迹，提取公交车换道点的实际位置。将完全信息静态非合作博弈与元胞自动机仿真模型相结合，探求冲突双方的纳什均衡值，并改进元胞自动机换道规则，提出了一种基于完全信息静态非合作博弈的改进型元胞自动机模型，用于模拟公交车占用非机动车道停靠情况下交通流运行状态。以换道点空间分布比例作为校验指标，校验结果表明，仿真值与实际值的误差均低于8%，说明改进型元胞自动机仿真模型能较好反映交通流实际运行情况；并以公交车近距离换道概率为指标，仿真结果表明，公交车近距离换道概率与非机动车交通量正相关。
Keyword ：
元胞自动机 元胞自动机 公交车辆 公交车辆 强制换道 强制换道 纳什均衡 纳什均衡 非合作博弈 非合作博弈
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| GB/T 7714 | 苏灿航 , 高圣涵 , 郭建钢 et al. 考虑非机动车影响的公交车进站决策模型研究 [J]. | 华东交通大学学报 , 2022 , 39 (01) : 82-88 . |
| MLA | 苏灿航 et al. "考虑非机动车影响的公交车进站决策模型研究" . | 华东交通大学学报 39 . 01 (2022) : 82-88 . |
| APA | 苏灿航 , 高圣涵 , 郭建钢 , 廖飞宇 . 考虑非机动车影响的公交车进站决策模型研究 . | 华东交通大学学报 , 2022 , 39 (01) , 82-88 . |
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为识别山区公路弯道车辆轮迹集中分布区,实现对轮迹集中分布区内的交通标线和道路结构的建设与养护,基于实地行车状态数据,采用BP神经网络建立了双车道公路弯道左、右转车辆轮迹模型;采用重心聚类法对驶经弯道的车辆进行分类,根据道路参数和占比最大车辆入弯时行驶状态的临界值,建立轮迹模型,计算出弯道左转车辆、右转车辆和左、右转车辆叠加轮迹集中分布区.结果表明:弯道的左、右转车辆叠加轮迹集中分布区内的交通标线范围为-28.54～-24.38 m和-13.20～18.67 m;叠加轮迹集中分布区在道路横断面方向上的分布是-150～50 cm.
Keyword ：
BP神经网络 BP神经网络 公路弯道 公路弯道 聚类分析 聚类分析 轮迹 轮迹
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| GB/T 7714 | 孟欣强 , 郭建钢 . 山区公路弯道车辆轮迹集中分布区的识别 [J]. | 福建农林大学学报(自然科学版) , 2021 , 50 (01) : 140-144 . |
| MLA | 孟欣强 et al. "山区公路弯道车辆轮迹集中分布区的识别" . | 福建农林大学学报(自然科学版) 50 . 01 (2021) : 140-144 . |
| APA | 孟欣强 , 郭建钢 . 山区公路弯道车辆轮迹集中分布区的识别 . | 福建农林大学学报(自然科学版) , 2021 , 50 (01) , 140-144 . |
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Active transit signal priority (TSP) is used more conveniently and widely than the other strategies for real-world signal controllers. However, the active TSP strategies of real-world signal controllers use the first-come-first-served rule to respond to any active TSP request and are not effective at responding to the number of bus arrivals. With or without the green extension strategy, the active TSP has little impact on the final green time of priority phase, even in the case where more buses arrive during the priority phase. The reduced green time of early green strategy is relatively large when a bus arrives, and it would be worse when more buses arrive, the active TSP has a big adverse impact on the final green time of the non-priority phase. Therefore, the active TSP strategies of real-world signal controllers cannot handle the downtown intersection where many bus lines converge or where many buses arrive in a signal cycle during the evening rush hour. Traffic engineers need to do much work to optimize the TSP parameters before field application. Consequently, it is necessary to improve the TSP strategy of the real-world signal controllers for the intersections with a lot of bus arrivals. In order to achieve that objective, the authors present the CNOB (cumulative number of buses) TSP strategy based on the Siemens 2070 signal controller. The TSP strategy extends the max call time according to the number of buses in the arrival section when priority phases are active. The TSP strategy truncates the green time according to the number of buses in the storage section when non-priority phases are active. The experiment's result shows that the CNOB TSP strategy can not only significantly reduce the average delay per person without using TSP optimization but can also reduce the adverse impact on the general vehicles of non-bus-priority approaches for the intersections with a lot of bus arrivals. Additionally, because the system dynamically adjusts, traffic engineers do not need to do much optimization work before the TSP implementation.
Keyword ：
active priority active priority CNOB TSP strategy CNOB TSP strategy early green strategy early green strategy green extension strategy green extension strategy signal controller signal controller transit signal priority transit signal priority TSP optimization TSP optimization
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| GB/T 7714 | Lian, Peikun , Wu, Yiyuan , Li, Zhenlong et al. An Improved Transit Signal Priority Strategy for Real-World Signal Controllers that Considers the Number of Bus Arrivals [J]. | SUSTAINABILITY , 2020 , 12 (1) . |
| MLA | Lian, Peikun et al. "An Improved Transit Signal Priority Strategy for Real-World Signal Controllers that Considers the Number of Bus Arrivals" . | SUSTAINABILITY 12 . 1 (2020) . |
| APA | Lian, Peikun , Wu, Yiyuan , Li, Zhenlong , Keel, Jack , Guo, Jiangang , Kang, Yaling . An Improved Transit Signal Priority Strategy for Real-World Signal Controllers that Considers the Number of Bus Arrivals . | SUSTAINABILITY , 2020 , 12 (1) . |
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Automated lane marking detection is essential for advanced driver assistance system (ADAS) and pavement management work. However, prior research has mostly detected lane marking segments from a front-view image, which easily suffers from occlusion or noise disturbance. In this paper, we aim at accurate and robust lane marking detection from a top-view perspective, and propose a deep learning-based detector with adaptive anchor scheme, referred to as A(2)-LMDet. On the one hand, it is an end-to-end framework that fuses feature extraction and object detection into a single deep convolutional neural network. On the other hand, the adaptive anchor scheme is designed by formulating a bilinear interpolation algorithm, and is used to guide specific-anchor box generation and informative feature extraction. To validate the proposed method, a newly built lane marking dataset contained 24,000 high-resolution laser imaging data is further developed for case study. Quantitative and qualitative results demonstrate that A(2)-LMDet achieves highly accurate performance with 0.9927 precision, 0.9612 recall, and a 0.9767 F-1 score, which outperforms other advanced methods by a considerable margin. Moreover, ablation analysis illustrates the effectiveness of the adaptive anchor scheme for enhancing feature representation and performance improvement. We expect our work will help the development of related research.
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| GB/T 7714 | Lin, Chunmian , Li, Lin , Cai, Zhixing et al. Deep Learning-Based Lane Marking Detection using A2-LMDet [J]. | TRANSPORTATION RESEARCH RECORD , 2020 , 2674 (11) : 625-635 . |
| MLA | Lin, Chunmian et al. "Deep Learning-Based Lane Marking Detection using A2-LMDet" . | TRANSPORTATION RESEARCH RECORD 2674 . 11 (2020) : 625-635 . |
| APA | Lin, Chunmian , Li, Lin , Cai, Zhixing , Wang, Kelvin C. P. , Xiao, Danny , Luo, Wenting et al. Deep Learning-Based Lane Marking Detection using A2-LMDet . | TRANSPORTATION RESEARCH RECORD , 2020 , 2674 (11) , 625-635 . |
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