An Analysis of Trajectory Streamline and Curvature Characteristics of Right-turn Vehicles at Urban Arterial Road Intersections
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摘要: 为明确城市干路交叉口汽车右转的轨迹特性和轨迹曲率模式,使用无人机在重庆市4个城市道路交叉口上方进行高空拍摄。利用图像分析方法采集了右转车辆的轨迹数据,包括时间、行驶速度和轨迹坐标等,通过对相邻轨迹点外接圆半径的计算得到轨迹曲率。运用轨迹线-车道边缘线的间距值分析了右转车辆轨迹通过位置分布与交叉口几何布局之间的关系,明确了交叉口右转车辆轨迹的曲率特性。运用聚类方法识别了右转车辆的6种轨迹曲率形态,确定了不同轨迹曲率形态下的常见驾驶行为,并研究了车辆行驶速度与轨迹曲率的相关关系。研究结果表明:①交叉口几何布局(包括路缘半径、车道宽度和出口车道数)对右转轨迹通过位置分布存在影响;②带渠化设计的右转专用道可以限制轨迹分布范围,减少右转交通的冲突和延误;③在右转过程中公交车辆较小型汽车所需侧向空间更大,轨迹分布的离散程度更低;④轨迹曲率的关键点与圆曲线设计中的主要点变化趋势不一致;⑤车辆加速度与轨迹曲率变化率呈负相关关系,相关系数为-0.843 5;⑥行驶速度与等效半径存在正相关关系,车辆行驶速度越快,圆曲线内轨迹的等效半径越大。Abstract: A drone was used to take high-altitude images of four urban road intersections in Chongqing to study the trajectory characteristics and curvature patterns of right-turn vehicles at urban arterial road intersections. The trajectory data of right-turn vehicles, including time, driving speed, and trajectory coordinates, are collected by using image analysis methods, based on which the trajectory curvature of vehicles is obtained by calculating the radius of circumcircle of adjacent trajectory points. The relationship between the distribution of passing positions of right-turn vehicles and the geometric layout of intersections is analyzed by using the distance between the trajectory line and the curb line, and the trajectory characteristics of right-turn vehicles at the intersections are further studied. Six types of curvature patterns of the trajectories of right-turn vehicles are identified based on a clustering method, and common driving behaviors under different curvature patterns are determined, and the relationship between vehicle speed and trajectory curvature is finally investigated. The results reveal the following conclusions. ① The geometric layout (including the curb radius, lane width, and the number of exit lanes) has impacts on the distribution of trajectories of right-turn vehicles. ②Right-turn lanes with channelized design can limit the distribution of trajectories and reduce conflicts and delays in right-turn traffic. ③ During a process of right-turning, more lateral space is required by buses than by cars, and the distribution of trajectories is less discrete. ④The key points of trajectory curvature are inconsistent with the trends of the main points in the circular curve design. ⑤ The acceleration of a vehicle has a negative correlation with the change rate of the trajectory curvature, and the correlation coefficient is -0.8435. ⑥ The driving speed has a positive correlation with the equivalent radius: the faster the vehicle moves, the larger the equivalent radius of the trajectory in the circular curve.
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表 1 交叉口右转角主要技术参数信息
Table 1. Main technical parameters of the right corner of the intersection
交叉口名称 右转角编号 进口道 路缘半径R/m 转角度数θ/(°) 进口道车道数 出口道车道数 样本数量 丹回路-兰花路 1 东进口道 25 90 2 4 188 2 西进口道 35 68 2 2 80 3 南进口道 35 112 1 2 186 金开大道-斑竹路 4 东进口道 15 99 1 3 102 5 南进口道 20 81 1 3 58 汇宾一路-融汇大道 6 东进口道 25 90 1 2 107 7 北进口道 30 90 1 3 131 二塘路-汇龙路 8 北进口道 20 90 1 2 38 9 东进口道 15 90 1 3 127 10 西进口道 15 90 1 3 86 11 南进口道 25 90 1 2 167 表 2 轨迹曲率形态特征表
Table 2. Trajectory curvature morphological feature table
类型序号 曲线形态 特点描述 常见道路 Ⅰ. 缓坡状 轨迹曲率上升和下降的趋势较为缓慢,在峰值处斜率保持不变且低于转角设计曲率,此种形态常见于车辆选择最内侧的车道路径通过 Ⅱ. 脉冲状 此种形态是理想的行驶轨迹,轨迹曲率整体变化较为缓和,峰值与路缘设计曲率值较为相近,贴合圆曲线线形,是最常见的曲率模式,常见于外侧和中间出口车道路径中 Ⅲ. 尖峰状 直线路段内轨迹曲率上升速度较慢,通过直圆点后加快上升速度达到峰值。通常是右转车辆受直行和对向左转车流合流干扰,减速观察缓慢通过导致 Ⅳ. 峭壁状 轨迹曲率值进入圆曲线后仍保持在0上下波动,通过停止线后以垂直的斜率上升到达峰值。此种形态常见于右转车辆遇到非机动车或过街行人干扰停止让行,机非冲突严重 Ⅴ. 斜坡状 右转专用道常见形式,轨迹曲率贴合转角线形设计曲率,对轨迹起到一定约束作用,同时减少和侧向直行车流带来的干扰。与Ⅱ型的区别在于进出圆曲线时轨迹曲率曲线接近于1条直线 Ⅵ. 2段式组合 轨迹曲率存在2个波峰,即车辆先以相对小的轨迹半径进入圆曲线,转向过程中车头部分回正,最后在转向完成时全部回正 -
[1] 曲昭伟, 罗瑞琪, 陈永恒, 等. 信号交叉口右转机动车轨迹特性[J]. 浙江大学学报(工学版), 2018, 52(2): 341-351. https://www.cnki.com.cn/Article/CJFDTOTAL-ZDZC201802017.htmQU Z W, LUO R Q, CHEN Y H, et al. Characteristics of right-turning vehicles trajectories at signalized intersections[J]. Journal of Zhejiang University (Engineering Science), 2018, 52(2): 341-351. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-ZDZC201802017.htm [2] 郭延永. 基于交通冲突理论的信号交叉口安全评价技术[D]. 南京: 东南大学, 2016.GUO Y Y. Signalized intersection safety evaluation techniques based on traffic conflict theory[D]. Nanjing: Southeast University, 2016. (in Chinese) [3] 任耀, 张锐, 贾倩楠. 考虑礼让行人的信号交叉口配时优化方法[J]. 交通信息与安全, 2022, 40(1): 45-53. doi: 10.3963/j.jssn.1674-4861.2022.01.006REN Y, ZHANG R, JIA Q N. A timing optimization method for signalized intersections considering the courtesy rules to pedestrians[J]. Journal of Transport Information and Safety, 2022, 40(1): 45-53(. in Chinese doi: 10.3963/j.jssn.1674-4861.2022.01.006 [4] ASANO M, ALHAJYASEEN W K M, SUZUKI K, et al. Modeling the variation in the trajectory of left turning vehicles considering intersection geometry[C]. 90th Transportation Research Board Annual Meeting. Washington, D. C., USA: TRB, 2011. [5] CHEN P, ZENG W L, YU G Z. Assessing right-turning vehicle-pedestrian conflicts at intersections using an integrated microscopic simulation model[J]. Accident Analysis & Prevention, 2019, 129: 211-224. [6] 王雪松, 谢琨, 陈小鸿, 等. 考虑空间相关性的信控交叉口安全分析[J]. 同济大学学报(自然科学版), 2012, 40(12): 1814-1820. doi: 10.3969/j.issn.0253-374x.2012.12.012WANG X S, X K, CHEN X H, etal. Risk factor analysis for signalized intersections along corridors with a consideration of spatial correlation[J]. Journal of Tongji University(Natural Science), 2012, 40(12): 1814-1820(. in Chinese doi: 10.3969/j.issn.0253-374x.2012.12.012 [7] 赵晓华, 姚莹, 丁阳, 等. 基于导航数据的交叉口进口道安全风险评估及诊断方法[J]. 同济大学学报(自然科学版), 2020, 48(12): 1733-1741. https://www.cnki.com.cn/Article/CJFDTOTAL-TJDZ202012006.htmZHAO X H, YAO Y, DING Y, etal. Navigation-data-based risk evaluation method at intersection entrance[J]. Journal of Tongji University (Natural Science), 2020, 48(12): 1733-1741. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-TJDZ202012006.htm [8] CHEN Y H, LIU F H, BAI Q W, et al. Analysis of conflict factors between pedestrians and right-turning vehicles at signalized intersections[J]. Journal of Southeast University(English Edition), 2019, 35(1): 118-124. [9] 郭延永, 刘攀, 徐铖铖, 等. 基于交通冲突模型的信号交叉口右转设施安全分析[J]. 中国公路学报, 2016, 29(11): 139-146. doi: 10.3969/j.issn.1001-7372.2016.11.018GUO Y Y, LIU P, XU C C, et al. Safety analysis of right-turn facility at signalized intersections using traffic conflict model[J]. Journal of China Highway and Transportation, 2016, 29 (11): 139-146. (in Chinese) doi: 10.3969/j.issn.1001-7372.2016.11.018 [10] 邱锐. 信号交叉口右转车道渠化对于行人过街安全的影响研究[D]. 成都: 西南交通大学, 2020.QIU R. Impact of right-turn channelization on pedestrian safety at signalized intersections[D]. Chengdu: Southwest Jiaotong University, 2020. (in Chinese) [11] ALHAJYASEEN W K M, ASANO M, NAKAMURA H, et al. Stochastic approach for modeling the effects of intersection geometry on turning vehicle paths[J]. Transportation Research Part C: Emerging Technologies, 2013, 32: 179-192. doi: 10.1016/j.trc.2012.09.006 [12] YAMAMOTO M, YAMAMOTO T, NISHIYAMA S. Investigation of trajectory planning of a travel route while turning at an intersection[J]. Journal of Advanced Mechanical Design, Systems, and Manufacturing, 2018, 12(7): 204-222. [13] 徐进, 陈莹, 张晓波, 等. 回头曲线路段的轨迹曲率特性和汽车过弯方式[J]. 西南交通大学学报, 2021, 56(6): 1143-1152. https://www.cnki.com.cn/Article/CJFDTOTAL-XNJT202106002.htmXU J, CHEN Y, ZHANG X B, et al. Track curvature behavior and vehicle cornering patterns on hairpin curves[J]. Journal of Southwest Jiaotong University, 2021, 56(6): 1143-1152. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-XNJT202106002.htm [14] ASANO M, ALHAJYASEEN W K M, SUZUKI K, et al. Modeling the variation in the trajectory of left turning vehicles considering intersection geometry[C]. 90th Transportation Research Board Annual Meeting. Washington D C, USA: TRB, 2011. [15] 江昕炜, 陈龙, 华一丁, 等. 基于改进型ELM的熟练驾驶员行车轨迹拟合方法研究[J]. 汽车工程, 2021, 43(11): 1620-1630. https://www.cnki.com.cn/Article/CJFDTOTAL-QCGC202111007.htmJIANG X W, CHEN L, HUA Y D, et al. Research on skilled driver's trajectory fitting based on improved ELM[J]. Automotive Engineering, 2021, 43(11): 1620-1630. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-QCGC202111007.htm [16] KAWASAKI A, TASAKI T. Trajectory prediction of turning vehicles based on intersection geometry and observed velocities[C]. IEEE Intelligent Vehicles Symposium, Suzhou, China: IEEE, 2018. [17] 吴昊, 江剑英, 谢林华. 城市道路交叉口右转交通空间设计要素相关关系分析[J]. 中外公路, 2021, 41(5): 327-332. https://www.cnki.com.cn/Article/CJFDTOTAL-GWGL202105070.htmWU H, JIANG J Y, XIE L H. Analysis of the correlation between the design elements of right-turn traffic space at urban road intersections[J]. Zhongwai Highway, 2021, 41(5): 327-332. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-GWGL202105070.htm [18] 李铁洪, 吴华金. 长直线接小半径曲线公路交通事故成因及预防对策[J]. 中国公路学报, 2007(20): 35-40. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGGL200701006.htmLI T H, WU H J. Causes and countermeasures of highway traffic accidents in long straight line combined with sharp curve[J]. China Journal of Highway and Transport, 2007 (20): 35-40. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-ZGGL200701006.htm [19] 李维东, 乔建刚, 郭蕊. 弯道路段集重型大型货物运输车辆通过宽度研究[J]. 中国安全生产科学技术, 2020, 16(10): 172-177. https://www.cnki.com.cn/Article/CJFDTOTAL-LDBK202010034.htmLI W D, QIAO J G, GUO R. Research on passage width of weight concentrated large cargo transport vehicles in curved section of highway[J]. Journal of Safety Science and Technology, 2020, 16(10): 172-177. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-LDBK202010034.htm [20] 徐令选. 大型车右转盲区形成机理及防治措施研究[D]. 天津: 河北工业大学, 2017.XU L X. Study on formation mechanism and prevention measures of right-turn blind area of large vehicles[D]. Tianjin: Hebei University of Technology. 2017. (in Chinese) [21] 李英帅, 闫琦若, 赵聪. 基于公交车右转内轮差效应的范围研究[J]. 重庆交通大学学报(自然科学版), 2021, 40(9): 43-48. https://www.cnki.com.cn/Article/CJFDTOTAL-CQJT202109007.htmLI Y S, YAN Q R, ZHAO C. Range of inner wheel deviation effect in right turn of buses[J]. Journal of Chongqing Jiaotong University(Natural Science Edition), 2021, 40(9): 43-48. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-CQJT202109007.htm [22] 刘小明. 基于高空视频图像的山地城市道路交叉口车辆运行特性研究[D]. 重庆: 重庆交通大学, 2021.LIU X M. Study on vehicle operation characteristics of road intersections in mountainous cities based on aerial videos[D]. Chongqing: Chongqing Jiaotong University, 2021. (in Chinese)