基于浮动车数据的城市旅游景点周边路网交通状态评价
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摘要
旅游业迅速发展、人口基数大的特点造成我国重要旅游景点的交通需求在节假日呈现爆炸性增长现象,而此问题在城市旅游景点上尤为突出。文章基于厦门市浮动车数据,首先通过对比多种空间插值法确定以克里金插值法划定旅游景点对周边路网交通状态的影响范围;其次引入双流模型对旅游景点周边路网交通状态进行评估;最后以厦门大学—南普陀寺景点为例,通过克里金插值法划定该景点的交通影响范围,并对比旅游日、工作日和周末该景点周边路网的交通状态,实证研究旅游景点对其周边路网交通状态的影响。研究城市旅游景点周边路网交通状态有助于挖掘城市旅游景点周边道路的供需矛盾,为城市旅游景区和城市道路交通的规划管理提供依据。
The rapid development of China's tourism industry and the large population base lead to the explosive growth of the traffic demand during holidays, and this problem is particularly prominent in famous tourist attractions. Based on the floating car data in Xiamen, this paper firstly compared various spatial interpolation methods and applied Kriging interpolation method to determine the impact scope of road traffic status in the surrounding of tourist attractions;Secondly, the two-fluid model is introduced to evaluate the traffic status of the tourist attractions; Finally, taking Xiamen University-Nanputuo Temple tourist attractions as a research object, this paper delimited the traffic impact scope of the tourist attractions applying Kriging interpolation method, compared the road traffic status on tourism day, weekday and weekend, and empirically analyzed the impact of tourist attractions on the surrounding road network traffic state.Studying the road traffic state around the urban tourist attractions is helpful to understand the contradiction between the supply and demand, and provide basis for the planning and management of the urban tourist attractions and urban road traffic.
The rapid development of China's tourism industry and the large population base lead to the explosive growth of the traffic demand during holidays, and this problem is particularly prominent in famous tourist attractions. Based on the floating car data in Xiamen, this paper firstly compared various spatial interpolation methods and applied Kriging interpolation method to determine the impact scope of road traffic status in the surrounding of tourist attractions;Secondly, the two-fluid model is introduced to evaluate the traffic status of the tourist attractions; Finally, taking Xiamen University-Nanputuo Temple tourist attractions as a research object, this paper delimited the traffic impact scope of the tourist attractions applying Kriging interpolation method, compared the road traffic status on tourism day, weekday and weekend, and empirically analyzed the impact of tourist attractions on the surrounding road network traffic state.Studying the road traffic state around the urban tourist attractions is helpful to understand the contradiction between the supply and demand, and provide basis for the planning and management of the urban tourist attractions and urban road traffic.
引文
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[16]吴立新,史文中.地理信息系统原理与算法[M].北京:科学出版社,2003:184-196.
[17]李欣,孟德友.基于路网相关性的分布式增量交通流大数据预测方法[J].地理科学,37(2):209-216.
[18]李新,程国栋,卢玲.空间内插方法比较[J].地球科学进展,2000(3):260-265.
[19]朱求安,张万昌,余钧辉.基于GIS的空间插值方法研究[J].江西师范大学学报:自然科学版,2004,28(2):183-188.
[20]邹文杰,翁剑成,周翔,等.基于浮动车数据的宏观路网运行状态评价研究[J].公路交通科技,2009,26(S1):35-38,57.
[2]孟娟.非常态事件下道路交通状态评价指标体系研究[D].长春:吉林大学,2012.
[3]陈小鸿,王翔,钱昕午.城市大型活动车队行驶特征与交通影响分析[J].同济大学学报:自然科学版,2013,41(3):415-421.
[4]刘文婷.大型活动影响下区域交通特性及控制策略研究[D].北京:北方工业大学,2013.
[5]张伟.大型公共场所行人交通状态评价及其应急疏散方法研究[D].长春:吉林大学,2014.
[6] LEI F S,WANG Y P. A Travel Time Reliability Model of Urban Expressways With Varying Levels of Service[J]. Transportation Research Part C,2014(48):453-467.
[7] Vanlommel M,Houbraken M,Audenaert P,et al. An Evaluation of Section Control on Floating Car Data[J]. Transportation Research Part C,2015(58):617-627.
[8]岳园圆,于雷,朱琳,等.基于速度里程分布的快速路宏观交通状态评价模型[J].交通运输系统工程与信息,2014,14(4):85-92.
[9] Jenelius E,Koutsopoulos H N. Travel Time Estimation for Urban Road Networks Using Low Frequency Probe Vehicle Data[J]. Transportation Research Part B,2013(53):64-81.
[10] Canepa E S,Claudel C G. Networked Traffic State Estimation Involving Mixed Fixed-Mobile Sensor Data Using HamiltonJacobi Equations[J]. Transportation Research Part B,2017(104):686-709.
[11]王勇,庄大方,刘好德.浮动车车速空间分布特性研究[J].交通运输系统工程与信息,2012,12(1):36-41.
[12] Herman R. Prigogine I. A Two-fluid Approach to Town Traffic[J]. Science,1979,204:148-151.
[13]刘伟铭,田世艳.基于地图匹配的城市道路实时交通状态模糊综合判别方法[J].公路交通科技,2011,28(10):86-91.
[14]唐克双,梅雨,李克平.基于浮动车数据的交通状态估计精度仿真评价[J].同济大学学报:自然科学版,2014,42(9):1 347-1 351,1 407.
[15] Seku?a P,Markovi?N,Laan Z V,et al. Estimating Historical Hourly Traffic Volumes Via Machine Learning and Vehicle Probe Data:A Maryland Case Study[J]. Transportation Research Part C,2018(97):147-158.
[16]吴立新,史文中.地理信息系统原理与算法[M].北京:科学出版社,2003:184-196.
[17]李欣,孟德友.基于路网相关性的分布式增量交通流大数据预测方法[J].地理科学,37(2):209-216.
[18]李新,程国栋,卢玲.空间内插方法比较[J].地球科学进展,2000(3):260-265.
[19]朱求安,张万昌,余钧辉.基于GIS的空间插值方法研究[J].江西师范大学学报:自然科学版,2004,28(2):183-188.
[20]邹文杰,翁剑成,周翔,等.基于浮动车数据的宏观路网运行状态评价研究[J].公路交通科技,2009,26(S1):35-38,57.