基于浮动车数据的城市道路通行能力时空特征分析

doi:10.3724/SP.J.1047.2015.00336

地球信息科学学报 ›› 2015, Vol. 17 ›› Issue (3): 336-343.doi: 10.3724/SP.J.1047.2015.00336

基于浮动车数据的城市道路通行能力时空特征分析

张希瑞¹(), 方志祥^1,^*(), 李清泉^1,², 鲁仕维¹

1. 武汉大学测绘遥感信息工程国家重点实验室,武汉 430079
2. 深圳大学空间信息智能感知与服务深圳市重点实验室,深圳 518060

收稿日期:2014-08-04 修回日期:2014-10-24 出版日期:2015-03-10 发布日期:2015-03-10
通讯作者: 方志祥 E-mail:xrzhangchn@gmail.com;zxfang@whu.edu.cn
作者简介:
作者简介：张希瑞(1990-),女,硕士生,研究方向为时空数据挖掘、交通地理信息系统。E-mail:xrzhangchn@gmail.com
基金资助:
国家自然科学基金项目(41231171、41371420)

A Spatio-temporal Analysis on the Heterogeneous Distribution of Urban Road Network Capacity Based on Floating Car Data

ZHANG Xirui¹(), FANG Zhixiang^1,^*(), LI Qingquan^1,², LU Shiwei¹

1. State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
2. Shenzhen Key Laboratory of Spatial Smart Sensing and Services, Shenzhen University, Shenzhen 538060, China

Received:2014-08-04 Revised:2014-10-24 Online:2015-03-10 Published:2015-03-10
Contact: FANG Zhixiang E-mail:xrzhangchn@gmail.com;zxfang@whu.edu.cn
About author:
*The author: SHEN Jingwei, E-mail:jingweigis@163.com

摘要/Abstract

摘要：

针对城市道路通行能力定量度量指标,提出基于浮动车数据的道路基本通行能力评估方法;以道路通行能力指标来分析深圳市道路网通行能力的时空分异规律,结论如下：城市道路通行能力具有明显的时变特征和空间分布的不均衡性;各行政区域通行能力在工作日变化平缓,在休息日出现午高峰;通行能力余量无论在工作日和休息日都浮动较大,并在休息日中午显著下降;道路通行能力具有明显区域分类现象,人口密度和发展程度较高区域的道路通行能力整体较低,且一天内的变化较小;道路等级对通行能力和通行能力余量影响显著,较高等级道路一天内变化较大,低等级道路则较平稳。

关键词: 交通地理信息系统, 浮动车数据, 道路通行能力, 时空分布

Abstract:

The spatio-temporal distribution of road network transportation capacity in Shenzhen City was analyzed based on floating car data. The raw floating car data was pre-processed before it was matched to the exact road. Then, the trajectory of each floating car was rebuilt. This paper calculated the travel speed of all different types of road in different time periods. Meanwhile, a new urban road capacity indicator was proposed, by which the road capacity was directly derived from the travel speed. Taking consideration of various traffic conditions and interactions between vehicles, the indices used to describe lane reduction and lane width reduction were applied to adjust the road capacity. The influence of intersections on road capacity can be ignored when taking the traffic flow as a continuous flow. Time-varying characteristics and heterogeneous spatial distribution was found in the urban road capacity. The trend of hourly road capacity was significantly different between weekdays and weekends. Road capacity kept stable in weekdays until it revealed a peak that appeared at noon in weekends. Reserve capacity changed greatly in both weekdays and weekends. And it decreased significantly at noon in weekends. The road capacity varied dramatically within different districts of the city: the road capacity in Yantian District and Nanshan District were both high, and they shared similar trends, while the road capacity in Longgang District and Bao'an District, which also shared similar trends, were in the average level of the capacity within the city, and slightly lower than the former two districts. Additionally, the road capacity of Futian District and Luohu District were in the lower level of the city. We also found that the urban road capacity was overall evidently low in the areas with higher population density and higher development level, and didn’t change much during the day. Road grade had significant influence on the road capacity and reserve capacity. For higher grade of the urban roads in Shenzhen, the capacity and the reserve capacity varied obviously with time, however there was little change in reserve capacity for lower level of the urban roads.

Key words: GIS-T, floating car data, road capacity, spatio-temporal distribution

张希瑞, 方志祥, 李清泉, 鲁仕维. 基于浮动车数据的城市道路通行能力时空特征分析[J]. 地球信息科学学报, 2015, 17(3): 336-343.DOI:10.3724/SP.J.1047.2015.00336

ZHANG Xirui,FANG Zhixiang,LI Qingquan,LU Shiwei. A Spatio-temporal Analysis on the Heterogeneous Distribution of Urban Road Network Capacity Based on Floating Car Data[J]. Journal of Geo-information Science, 2015, 17(3): 336-343.DOI:10.3724/SP.J.1047.2015.00336

图/表 12

表1

表2

图1

表3

表4

车道宽度折减系数取值"

车道宽度（m）	$≥$ 3.50	3.25	3.00	2.75
α_车道宽	1.00	0.94	0.85	0.77

表4

表5

图2

图3

表6

图4

图5

图6

参考文献 24

[1]	李作敏. 交通工程学[M].北京:人民教育出版社,2000.
[2]	张亚平,裴玉龙.道路通行能力研究现状及发展综述[J].交通运输工程学报,2002,2(2):91-97.
[3]	Transportation Research Board.Highway capacity manual[M]. Washington, D C: National Research council, 2000.
[4]	Hoban C J.Evaluating traffic capacity and improvements to road geometry[M]. Washington, D C: World Bank, 1987.
[5]	Chandra S, Kumar U.Effect of lane width on capacity under mixed traffic conditions in India[J]. Journal of Transportation Engineering, 2003,129(2):155-160.
[6]	Kwon T J, Fu L, Jiang C.Effect of winter weather and road surface conditions on macroscopic traffic parameters[J]. Transportation Research Record: Journal of the Transportation Research Board, 2013,2329(1):54-62.
[7]	Ben-Akiva M E, Gao S, Wei Z, et al. A dynamic traffic assignment model for highly congested urban networks[J]. Transportation Research Part C: Emerging Technologies, 2012,24:62-82.
[8]	Wu N.A universal procedure for capacity determination at unsignalized (priority-controlled) intersections[J]. Transportation Research Part B: Methodological, 2001,35(6):593-623.
[9]	杨晓光,赵靖,马万经,等.信号控制交叉口通行能力计算方法研究综述[J].中国公路学报,2014,27(5):148-157.
[10]	孙锋,金茂菁,王殿海等.公交站点对路段通行能力的影响研究[J].北京理工大学学报,2013,33(12):1284-1288.
[11]	Yang H, Bell M G H, Meng Q. Modeling the capacity and level of service of urban transportation networks[J]. Transportation Research Part B: Methodological, 2000,34(4):255-275.
[12]	杜攀峰,金双泉,李嘉.高速公路路段通行能力仿真研究[J].湖南大学学报(自然科学版),2009,36(6):7-12.
[13]	杨小宝,王文凯.道路通行能力研究现状及展望[J].中外公路,2006,26(4):217-220.
[14]	Schäfer R P, Thiessenhusen K U, Wagner P A.Traffic information system by means of real-time floating-car data[C]. ITS world congress, Chicago, 2002.
[15]	Vinagre Diaz J J, Fernandez Llorca D, Rodríguez González A B, et al. Extended floating car data system: Experimental results and application for a hybrid route level of service[J]. IEEE Transactions on Intelligent Transportation Systems, 2012,13(1):25-35.
[16]	Wang X, Liu H, Yu R, et al.Exploring operating speeds on urban arterials using floating car data: Case study in Shanghai[J]. Journal of Transportation Engineering, 2014,140(9): 04014044.
[17]	Fang Z, Shaw S L, Tu W, et al.Spatiotemporal analysis of critical transportation links based on time geographic concepts: A case study of critical bridges in Wuhan, China[J]. Journal of Transport Geography, 2012,23:44-59.
[18]	马健霄,吕志英,王大明.城市道路通行能力分析与改善技术[J].南京林业大学学报,2001,25(2):71-74.
[19]	钟连德,李秀文,荣建.城市快速路基本路段通行能力的确定[J].北京工业大学学报,2006,32(7):605-609.
[20]	JTGB01-2003,公路工程技术标准[S].北京:中华人民共和国交通部,2003.
[21]	CJJ37-2012,城市道路交通规范设计规范[S].北京:中华人民共和国住房和城乡建设部,2012.
[22]	童小华,陈建阳,吴淑琴.基于GIS和GPS的交通状态参数估计仿真[J].同济大学学报(自然科学版),2006,34(1):47-52.
[23]	Chen B Y, Yuan H, Li Q, et al.Map-matching algorithm for large-scale low-frequency floating car data[J]. International Journal of Geographical Information Science, 2014, 28(1):22-38.
[24]	Fouladvand M E, Darooneh A H.Statistical analysis of floating-car data: An empirical study[J]. The European Physical Journal B-Condensed Matter and Complex Systems, 2005,47(2):319-328.

城市道路等级	快速路	主干路	次干路			支路
城市道路等级	1	2	3	4	5	6	7
6:00-22:00	58.3	52.0	36.8	32.0	24.8	16.6	12.8
22:00-6:00	61.9	50.7	35.9	30.1	23.6	16.2	12.5

	宝安区	南山区	福田区	龙岗区	盐田区	罗湖区
快速路（%）	2.44	0.29	0.37	0.91	0.00	0.00
主干路（%）	1.09	0.65	0.49	2.42	0.36	0.59
次干路（%）	15.75	5.94	4.05	9.96	1.15	2.30
支路（%）	22.19	3.98	3.91	17.37	1.01	2.78

基于浮动车数据的城市道路通行能力时空特征分析

A Spatio-temporal Analysis on the Heterogeneous Distribution of Urban Road Network Capacity Based on Floating Car Data

RichHTML

PDF (PC)

赞

可视化

被引次数

摘要/Abstract

引用本文

使用本文

图/表 12

参考文献 24

相关文章 15

Metrics

本文评价

推荐阅读 0

[1]	高菠阳, 罗会琳, 黄志基, 徐凡雅, 刘柏宏. 中国工业用地出让价格空间格局及影响因素[J]. 地球信息科学学报, 2020, 22(6): 1189-1201.
[2]	柳林, 梁斯毅, 宋广文. 基于潜在受害者动态时空分布的街面接触型犯罪研究[J]. 地球信息科学学报, 2020, 22(4): 887-897.
[3]	刘艳霞, 冯莉, 田慧慧, 阳少奇. 中国气候舒适度时空分布特征分析[J]. 地球信息科学学报, 2020, 22(12): 2338-2347.
[4]	宋海慧, 余卓渊, 丁晓彤, 谢云鹏, 吕可晶. 森林脑炎时空分布特征和环境影响因素分析[J]. 地球信息科学学报, 2020, 22(12): 2371-2382.
[5]	刘卫华, 王思远, 马元旭, 申明, 游永发, 海凯, 吴林霖. 一种湿地河流叶绿素a遥感反演方法[J]. 地球信息科学学报, 2020, 22(10): 2062-2077.
[6]	庞海洋, 孔祥生, 贺正洋, 苏晓强. 山东半岛降雪时空分布特征[J]. 地球信息科学学报, 2019, 21(11): 1721-1734.
[7]	熊俊楠,李进,朱吉龙,程维明,郭良,王楠,张晓蕾. 重庆市山洪灾害时空格局及影响因素研究[J]. 地球信息科学学报, 2019, 21(10): 1550-1564.
[8]	李智, 李卫红. 点模式条件下的犯罪嫌疑人时空同现模式挖掘与分析[J]. 地球信息科学学报, 2018, 20(6): 827-836.
[9]	阿迪来·乌甫, 玉素甫江·如素力, 热伊莱·卡得尔, 姜红. 伊犁河谷蒸散量时空分布特征及变化趋势[J]. 地球信息科学学报, 2018, 20(2): 217-227.
[10]	隋琦, 王瑛, 李婷, 刘庆爱, 俞海洋. 多源信息结合的雪灾交通风险评估研究[J]. 地球信息科学学报, 2018, 20(11): 1571-1578.
[11]	周洋, 祝善友, 华俊玮, 刘祎, 向嘉敏, 丁文. 南京市高温热浪时空分布研究[J]. 地球信息科学学报, 2018, 20(11): 1613-1621.
[12]	熊俊楠, 赵云亮, 程维明, 郭良, 王楠, 李伟. 四川省山洪灾害时空分布规律及其影响因素研究[J]. 地球信息科学学报, 2018, 20(10): 1443-1456.
[13]	彭超, 廖一兰, 张宁旭. 中国城市群臭氧污染时空分布研究[J]. 地球信息科学学报, 2018, 20(1): 57-67.
[14]	除多, 达珍, 拉巴卓玛. 西藏高原积雪覆盖空间分布及地形影响[J]. 地球信息科学学报, 2017, 19(5): 635-645.
[15]	唐炉亮, 段倩, 阚子涵, 李清泉. 出租车交接班行为识别与时空分布研究[J]. 地球信息科学学报, 2017, 19(2): 167-175.