基于手机基站数据的城市交通流量模拟
Traffic Flow Simulation Based on Call Detail Records
查看参考文献32篇
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文摘
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基于移动定位数据的城市内社会经济活动特征分析是人类移动性的重要研究内容,而交通流量更是这些特征的基本反映。为还原城市道路网络的使用情况并分析其分布特征,本文从产生交通流量的个体出发,对包含基站位置的手机话单数据进行系统抽样,利用蒙特卡洛方法产生个体的出行起止点,并结合当地道路交通网络求得最短路径,最后估算出一天内道路交通网络上的流量分布。通过分析发现:城市内大部分道路的流量小,使用率低,大部分交通流量集中在小部分主干道路;进一步统计分析可知,当地道路交通流量符合20/80规律,即大约20%的道路承担着80%的交通流量;而对不同类型的道路,流量分布也反映出其在城市道路网络中的地位和作用。此研究对于历史交通流量分布的重现、城市道路交通模式的研究以及基于此的道路网络规划情景模拟都有着重要意义。 |
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其他语种文摘
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Urban social and economic activity analysis based on mobile location data is a magnificent context for human mobility research and the traffic flow is one of the most basic activities. In order to restore the use of urban transportation network and examine its distribution, we apply a novel approach to draw a traffic flow distribution map of local road network based on a large number of individual cellphone detailed records. We reconstruct details of individual user's mobility and generate its traffic flow step by step: 1. Sampling cellphone records from local operator; 2. simulating the random start point and end point for each individual by Monte Carlo; 3. working out its route through the shortest path. After sampling and simulating thousands of records in one day, we finally draw a traffic flow distribution map of local road network, in which we uncover that a large portion of roads contains a small portion of flows and vice versa. In further statistical analysis, we reach the 20/80 principle of traffic flow: 20% of the top roads accommodate 80% of traffic flow. And flow distribution of different road types reflects the function of urban transportation networks. This research make a contribution to the reconstructed historical traffic flow distribution, studies on urban road network pattern and scenario simulation of transportation planning. |
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来源
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地理学报
,2012,67(12):1657-1665 【核心库】
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关键词
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人类移动性
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手机基站数据
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城市交通流量模拟
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地址
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北京大学深圳研究生院, 城市人居环境科学与技术国家级重点实验室, 深圳, 518055
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北京大学遥感与地理信息系统研究所, 北京, 100871
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北京大学城市与环境学院, 北京, 100871
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香港大学城市规划与设计系, 香港
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语种
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中文 |
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ISSN
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0375-5444 |
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学科
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公路运输 |
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基金
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国家自然科学基金项目
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文献收藏号
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CSCD:4735084
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参考文献 共
32
共2页
|
|
1.
路紫. 旅游网站访问者行为的时间分布及导引分析.
地理学报,2007,62(6):621-630
|
被引
25
次
|
|
|
|
|
2.
毛夏. 深圳市人口分布的细网格动态特征.
地理学报,2010,65(4):443-453
|
被引
25
次
|
|
|
|
|
3.
刘瑜. 基于位置感知设备的人类移动研究综述.
地理与地理信息科学,2011,27(4):8-13
|
被引
45
次
|
|
|
|
|
4.
柴彦威. 基于移动定位的行为数据采集与地理应用研究.
地域研究与开发,2010,29(6):1-7
|
被引
15
次
|
|
|
|
|
5.
Kwan M. The interaction between ICT and human activity-travel behavior.
Transportation Research Part A: Policy and Practice,2007,41(2):121-124
|
被引
6
次
|
|
|
|
|
6.
Brockmann D. The scaling laws of human travel.
Nature,2006,439(7075):462-465
|
被引
75
次
|
|
|
|
|
7.
Brockmann D. Money circulation, trackable items, and the emergence of universal human mobility patterns.
Pervasive Computing, IEEE,2008,7(4):28-35
|
被引
2
次
|
|
|
|
|
8.
Kang C. Analyzing and geo-visualizing individual human mobility patterns using mobile call records.
18th International Conference on Geoinformatics,2010
|
被引
1
次
|
|
|
|
|
9.
Ahas R. Evaluating passive mobile positioning data for tourism surveys: An Estonian case study.
Tourism Management,2008,29(3):469-486
|
被引
17
次
|
|
|
|
|
10.
Ahas R. Seasonal tourism spaces in Estonia: Case study with mobile positioning data.
Tourism Management,2007,28(3):898-910
|
被引
15
次
|
|
|
|
|
11.
Nobis C. Communication and mobility behaviour: A trend and panel analysis of the correlation between mobile phone use and mobility.
Journal of Transport Geography,2009,17(2):93-103
|
被引
8
次
|
|
|
|
|
12.
Schwanen T. The Internet, mobile phone and space-time constraints.
Geoforum,2008,39(3):1362-1377
|
被引
22
次
|
|
|
|
|
13.
Phithakkitnukoon S. Activity-aware map: Identifying human daily activity pattern using mobile phone data.
Human Behavior Understanding, Lecture Notes in Computer Science,2010,6219:14-25
|
被引
4
次
|
|
|
|
|
14.
Ahas R. Daily rhythms of suburban commuters' movements in the Tallinn metropolitan area: Case study with mobile positioning data.
Transportation Research Part C: Emerging Technologies,2010,18(1):45-54
|
被引
15
次
|
|
|
|
|
15.
Gonzalez M C. Understanding individual human mobility patterns.
Nature,2008,453(7196):779-782
|
被引
200
次
|
|
|
|
|
16.
Piorkowski M. Sampling urban mobility through on-line repositories of GPS.tracks.
Proceedings of the 1st ACM International Workshop on Hot Topics of Planet-Scale Mobility Measurements,2009
|
被引
1
次
|
|
|
|
|
17.
Candia J. Uncovering individual and collective human dynamics from mobile phone records.
Journal of Physics A: Mathematical and Theoretical,2008,41(22):224015
|
被引
15
次
|
|
|
|
|
18.
Sevtsuk A. Does urban mobility have a daily routine? Learning from the aggregate data of mobile networks.
Journal of Urban Technology,2010,17(1):41-60
|
被引
13
次
|
|
|
|
|
19.
Yuan Y. Correlating mobile phone usage and travel behavior: A case study of Harbin, China.
Computers, Environment and Urban Systems,2012,36(2):118-130
|
被引
16
次
|
|
|
|
|
20.
Song C. Limits of predictability in human mobility.
Science,2010,327(5968):1018-1021
|
被引
100
次
|
|
|
|
|
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