Sparse Link Travel Time Estimation Using Big Data of Floating Car

ZHANG Faming; ZHU Xinyan; GUO Wei; HU Tao

doi:10.13203/j.whugis20150425

Volume 42 Issue 1

Jan. 2017

Turn off MathJax

Article Contents

Abstract

References

Geomatics and Information Science of Wuhan University > 2017 > 42(1): 56-62. > DOI: 10.13203/j.whugis20150425

ZHANG Faming, ZHU Xinyan, GUO Wei, HU Tao. Sparse Link Travel Time Estimation Using Big Data of Floating Car[J]. Geomatics and Information Science of Wuhan University, 2017, 42(1): 56-62. DOI: 10.13203/j.whugis20150425

Citation:

PDF (1611 KB)

Sparse Link Travel Time Estimation Using Big Data of Floating Car

ZHANG Faming^1,,
ZHU Xinyan^{1, 2},
GUO Wei^{1, 2, ,},
HU Tao^{1, 2}

1.
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
2.
Collaborative Innovation Center of Geospatial Technology, 129 Luoyu Road, Wuhan 430079, China

Funds:

The National 863 Program of China 2013AA122301

National Science and Technology Support Program 2012BAH35B03

More Information

Received Date: January 20, 2016
Published Date: January 04, 2017

Graphical Abstract

Abstract

Abstract

Although there exists quantities of GPS data of floating car, partial links lack real data during some certain period of time. Therefore, we can't estimate target link travel time. Considering the problem of sparse data when using floating car data estimating link travel time, we put forward a kind of inferred method based on big data of floating car. We designed a three-layer artificial neural network model, whose input information and output information are the feature relationship and the travel time ratio between target link and adjacent link respectively. We obtained traffic spatiotemporal association relationship using historical big data of floating car and then inferred link travel time. The model was verified by historical big data of Wuhan's floating car from March to July, 2014 and the MAPE of estimated value of link travel time is less than 25% which proved the effectiveness of the proposed method.

FullText(HTML)

References (17)

References

[1]	杨兆升.关于智能运输系统的关键理论:综合路段行程时间预测的研究[J].交通运输工程学报, 2005, 1(1):65-67 http://www.cnki.com.cn/Article/CJFDTOTAL-JYGC200101016.htm Yang Zhaosheng. Study on the Synthetic Link Travel Time Prediction Model of Key Theory of ITS[J]. Journal of Traffic and Transportation Engineering,2005, 1(1):65-67 http://www.cnki.com.cn/Article/CJFDTOTAL-JYGC200101016.htm
[2]	袁晶.大规模轨迹数据的检索、挖掘和应用[D].合肥:中国科技大学, 2012 http://cdmd.cnki.com.cn/Article/CDMD-10358-1012503318.htm Yuan Jing. Querying, Mining with Applications on Large-Scale Trajectory Data[D].Hefei:University of Science and Technology of China, 2012 http://cdmd.cnki.com.cn/Article/CDMD-10358-1012503318.htm
[3]	Zheng Yu, Liu Furui, Hsie H P. U-Air:When Urban Air Quality Inference Meets Big Data[C]. The 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Chicago, 2013
[4]	Jula H, Dessouky M, Ioannou P A. Real-Time Estimation of Travel Times Along the Arcs and Arrival Times at the Nodes of Dynamic Stochastic Networks[J].IEEE Transactions on Intelligent Transportation Systems,2008, 9(1):97-110 doi: 10.1109/TITS.2007.908571
[5]	Zheng F, van Zuylen H. Urban Link Travel Time Estimation Based on Sparse Probe Vehicle Data[J]. Transportation Research Part C:Emerging Technologies,2013, 31:145-157 doi: 10.1016/j.trc.2012.04.007
[6]	Jenelius E, Koutsopoulos H N. Travel Time Estimation for Urban Road Networks Using Low Frequency Probe Vehicle Data[J]. Transportation Research Part B:Methodological,2013, 53:64-81 doi: 10.1016/j.trb.2013.03.008
[7]	Zhang Fuzhen, Wilkie D, Zheng Yu, et al. Sensing the Pulse of Urban Refueling Behavior[C]. The ACM International Joint Conference on Pervasive and Ubiquitous Computing, Switzerland, 2013
[8]	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 doi: 10.1080/13658816.2013.816427
[9]	Zhang Yunfei, Yang Bisheng, Luan Xuechen. Automated Matching Urban Road Networks Using Probabilistic Relaxation[J]. Acta Geodaetica et Catographica Sinica,2312, 41(6):933-939
[10]	李清泉, 胡波, 乐阳.一种基于约束的最短路径低频浮动车数据地图匹配算法[J].武汉大学学报·信息科学版, 2013, 38(7):805-808 http://ch.whu.edu.cn/CN/abstract/abstract2697.shtml Li Qingquan, Hu Bo, Yue Yang. Flowing Car Data Map-Matching Based on Constrained Shortest Path Algorithm[J]. Geomatics and Information Science of Wuhan University,2013, 38(7):805-808 http://ch.whu.edu.cn/CN/abstract/abstract2697.shtml
[11]	李宇光, 李清泉.利用地图栅格化的海量浮动车数据道路匹配快速算法[J].武汉大学学报·信息科学版, 2014, 39(6):724-728 http://ch.whu.edu.cn/CN/abstract/abstract3007.shtml Li Yuguang, Li Qingquan.A Fast Algorithm for Huge Volume Floating Car Data Map-Matching:A Vector to Raster Map Conversion Approach[J]. Geomatics and Information Science of Wuhan University, 2014, 39(6):724-728 http://ch.whu.edu.cn/CN/abstract/abstract3007.shtml
[12]	于德新, 高学英, 杨兆升.基于GPS数据及车辆运行特性分析的单车路段行程时间估计[J].吉林大学学报(工学版), 2010(4):965-970 http://www.cnki.com.cn/Article/CJFDTOTAL-JLGY201004016.htm Yu Dexin, Gao Xueying, Yang Zhaosheng. Individual Vehicle Travel-Time Estimation Based on GPS Data and Analysis of Vehicle Running Characteristics[J].Journal of Jilin University (Engineering and Technology Edition), 2010(4):965-970 http://www.cnki.com.cn/Article/CJFDTOTAL-JLGY201004016.htm
[13]	唐炉亮, 阚子涵, 黄方贞, 等.利用低频时空GPS轨迹进行交叉口通行时间探测[J].武汉大学学报·信息科学版, 2016, 41(1):136-142 http://ch.whu.edu.cn/CN/abstract/abstract3446.shtml Tang Luliang, Kan Zihan, Huang Fangzhen, et al. Travel Time Detection at Intersections from Taxis' Trace Data[J]. Geomatics and Information Science of Wuhan University, 2016, 41(1):136-142 http://ch.whu.edu.cn/CN/abstract/abstract3446.shtml
[14]	张和生, 张毅, 温慧敏.利用GPS数据估计路段的平均行程时间[J].吉林大学学报(工学版), 2007, 37(3):533-537 http://www.cnki.com.cn/Article/CJFDTOTAL-JLGY200703010.htm Zhang Hesheng, Zhang Yi, Wen Huimin. Estimation Approaches of Average Link Travel Time Using GPS Data[J]. Journal of Jilin University (Engineering and Technology Edition, 2007, 37(3):533-537 http://www.cnki.com.cn/Article/CJFDTOTAL-JLGY200703010.htm
[15]	Fei X, Lu C C, Liu K.A Bayesian Dynamic Linear Model Approach for Real-Time Short-Term Freeway Travel Time Prediction[J]. Transportation Research Part C:Emerging Technologies, 2011, 19(6):1306-1318 doi: 10.1016/j.trc.2010.10.005
[16]	Liu X, Gong L, Gong Y, et al. Revealing Daily Travel Patterns and City Structure with Taxi Trip Data[J]. Journal of Transport Geography,2015, 43:78-90 doi: 10.1016/j.jtrangeo.2015.01.016
[17]	李清泉, 李汉武, 谢智颖, 等.面向动态路径选择的路段行程时间的分析研究[J].武汉大学学报·信息科学版, 2006, 31(6):519-522 http://ch.whu.edu.cn/CN/abstract/abstract2482.shtml Li Qingquan, Li Hanwu, Xie Zhiying. On the Road Travel Time for Dynamic Route Choice[J]. Geomatics and Information Science of Wuhan University, 2006, 31(6):519-522 http://ch.whu.edu.cn/CN/abstract/abstract2482.shtml

[1]	CAI Xianhua, LIU Kaili, HU Zhuoliang, ZHANG Yuan. An Algorithm for Constructing Road Network Using Block Polygon Topology[J]. Geomatics and Information Science of Wuhan University, 2021, 46(8): 1170-1177. DOI: 10.13203/j.whugis20190348
[2]	YANG Wei, AI Tinghua. Extracting Arterial Road Polygon from OpenStreetMap Data Based on Delaunay Triangulation[J]. Geomatics and Information Science of Wuhan University, 2018, 43(11): 1725-1731. DOI: 10.13203/j.whugis20160294
[3]	ZHANG Hao, WU Fang, GONG Xianyong, XU Junkui, ZHANG Juntao. A Parallel Factor-Based Method of Arterial Two-Lane Roads Recognition[J]. Geomatics and Information Science of Wuhan University, 2017, 42(8): 1123-1130. DOI: 10.13203/j.whugis20150122
[4]	WANG Xiao, QIAN Haizhong, LIU Hailong, HE Haiwei, CHEN Jingnan. A Hierarchical and Iterative Road Network Matching Method by Using Road Classification[J]. Geomatics and Information Science of Wuhan University, 2016, 41(8): 1072-1078. DOI: 10.13203/j.whugis20140441
[5]	LIU Hailong, QIAN Haizhong, WANG Xiao, HE Haiwei. Road Networks Global Matching Method Using Analytical Hierarchy Process[J]. Geomatics and Information Science of Wuhan University, 2015, 40(5): 644-651. DOI: 10.13203/j.whugis20130350
[6]	LI Fei, LUAN Xuechen, YANG Bisheng, LI Qiuping. Automatic Topology Maintenance Approach for High-level Road Networks[J]. Geomatics and Information Science of Wuhan University, 2014, 39(6): 729-733. DOI: 10.13203/j.whugis20140115
[7]	LUAN Xuechen, YANG Bisheng, ZHANG Yunfei. Structural Hierarchy Analysis of Streets Based on Complex Network Theory[J]. Geomatics and Information Science of Wuhan University, 2012, 37(6): 728-732.
[8]	LI Qingquan, ZENG Zhe, YANG Bisheng, LIBijun. Betweenness Centrality Analysis for Urban Road Networks[J]. Geomatics and Information Science of Wuhan University, 2010, 35(1): 37-41.
[9]	ZHU Qing, LI Yuan. Review of Road Network Models[J]. Geomatics and Information Science of Wuhan University, 2007, 32(6): 471-476.
[10]	DENG Hongyan, WU Fang, ZHAI Renjian. A Generalization Model of Road Networks Based on Genetic Algorithm[J]. Geomatics and Information Science of Wuhan University, 2006, 31(2): 164-167.

Cited By

Cited by

Periodical cited type(5)

1.	庞琪沛，吴云龙，徐景田，史绪国，张毅. 甘肃积石山Ms 6.2地震深部构造特征和动力学过程. 武汉大学学报(信息科学版). 2025(02): 356-367 .
2.	段国勇，马如梦. 三峡库区灰岩浸泡损伤-孔隙水压耦合模型研究. 水利水电技术(中英文). 2024(09): 178-186 .
3.	胡锦涛，谢军，危自根，金超. 三峡库区秭归段浅层速度结构和孕震环境. 地震学报. 2023(02): 223-233 .
4.	李路顺，汪泽成，肖安成，胡安平，陈友智，王芊芊. 扬子板块北缘新元古代盆地结构与马槽园群归属研究. 地学前缘. 2022(06): 291-304 .
5.	韩飞，苏艳华，王泽. 三峡地区流动重力差分变化及其特征分析. 测绘科学. 2021(04): 1-5+19 .

Other cited types(1)

Get Citation

PDF

XML

Article views (1496) PDF downloads (514) Cited by(6)

Sparse Link Travel Time Estimation Using Big Data of Floating Car

Abstract

References

Related Articles

Cited by

Periodical cited type(5)

Other cited types(1)

Catalog

Related

Sparse Link Travel Time Estimation Using Big Data of Floating Car

Abstract

References

Related Articles

Cited by

Periodical cited type(5)

Other cited types(1)

Catalog

Related

Export File

Citation

Format

Content