Fine-grained analysis of traffic congestions at the turning level using GPS traces

被引：0

作者：

Tang L. ^{[1
]}

Kan Z. ^{[1
]}

Ren C. ^{[1
]}

Zhang X. ^{[2
]}

Li Q. ^{[1
,3
]}

机构：

[1] State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan

[2] School of Urban Design, Wuhan University, Wuhan

[3] Shenzhen Key Laboratory of Spatial Smart Sensing and Services, College of Civil Engineering, Shenzhen University, Shenzhen

来源：

Cehui Xuebao/Acta Geodaetica et Cartographica Sinica | 2019年 / 48卷 / 01期

基金：

中国国家自然科学基金;

关键词：

Big data; GPS trace; Space time analysis; Traffic congestions; Turning-level;

D O I：

10.11947/j.AGCS.2019.20170448

中图分类号：

学科分类号：

摘要：

For the issue that existing approaches on studying traffic conditions using GPS traces lack of detailed analysis of traffic congestion, this paper puts forward an approach for detecting traffic congestion events based on taxis' GPS traces at turning level. Firstly, this approach analyzed taxis' operating patterns and filtered valid traces. Then this approach detected traffic congestion traces of three different intensities: mild congestion, moderate congestion and serious congestion, based on analyzing traffic conditions from the filtered valid trace segments. Finally, traffic flow speed, congestion time and congestion distance of each turning direction at an intersection were explored at a fine-grained level. The experimental results show that the proposed approach is able to detect congestions of different intensities and analyze congestion events at turning level. © 2019, Surveying and Mapping Press. All right reserved.

引用

页码：75 / 85

页数：10

共 27 条

[1] Traffic congestion has led to economic losses: the tendecy of ITS's growth is deffinite
[2] Liu Y., Tian C., Analysis of dynamic characteristics of urban traffic congestion based on specificity of network configuration, China Journal of Highway and Transport, 26, 1, (2013)
[3] Tsekeris T., Geroliminis N., City size, network structure and traffic congestion, Journal of Urban Economics, 76, 1, pp. 1-14, (2013)
[4] Krause B., Von Altrock C., Pozybill M., Intelligent highway by fuzzy logic: congestion detection and traffic control on multi-lane roads with variable road signs, Proceedings of IEEE 5th International Conference on Fuzzy Systems, pp. 1832-1837, (1996)
[5] Coifman B., Identifying the onset of congestion rapidly with existing traffic detectors, Transportation Research Part A: Policy and Practice, 37, 3, pp. 277-291, (2003)
[6] Cho Y., Rice J., Estimating velocity fields on a freeway from low-resolution videos, IEEE Transactions on Intelligent Transportation Systems, 7, 4, pp. 463-469, (2006)
[7] Bauza R., Gozalvez J., Sanchez-Soriano J., Et al., Road traffic congestion detection through cooperative vehicle-to-vehicle communications, Proceedings of IEEE Local Computer Networks Conference, pp. 606-612, (2010)
[8] Qiu P., Zhang H., Yu L., Et al., Automatic event labeling for traffic information extraction from microblogs, Journal of Chinese Information Processing, 31, 2, pp. 107-116, (2017)
[9] Wang X., Peng L., Chi T., A method of urban traffic flow speed estimation using sparse floating car data, Acta Geodaetica et Cartographica Sinica, 45, 7, pp. 866-873, (2016)
[10] Xu L., Yue Y., Li Q., Identifying urban traffic congestion pattern from historical floating car data, Procedia-Social and Behavioral Sciences, 96, 11, pp. 2084-2095, (2013)

← 1 2 3 →