How many probe vehicles are enough for identifying traffic congestion?—a study from a streaming data perspective

doi:10.1007/s11707-012-0343-x

Front Earth Sci

2013, Vol. 7

Issue (1) : 34-42 https://doi.org/10.1007/s11707-012-0343-x

RESEARCH ARTICLE

How many probe vehicles are enough for identifying traffic congestion?—a study from a streaming data perspective

Handong WANG^1,3, Yang YUE^1,2(

), Qingquan LI^1,2

1. State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China; 2. Engineering Research Center for Smart Acquisition and Applications of Spatiotemporal Data, Ministry of Education, Wuhan 430079, China; 3. Changjiang Institute of Survey, Planning, Design and Research, Changjiang Water Resources Commission, Wuhan 430010, China

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Abstract

Many studies have been carried out using vehicle trajectory to analyze traffic conditions, for instance, identifying traffic congestion. However, there is a lack of a systematic study on the appropriate number of probe vehicles and their sampling interval in order to identify traffic congestion accurately. Moreover, most of related studies ignore the streaming feature of trajectory data. This paper first represents a novel method of identifying traffic congestion considering the stream feature of vehicle trajectories. Instead of processing the whole data stream, a series of snapshots are extracted. Congested road segments can be identified by analyzing the clusters’ evolution among a series of adjacent snapshots. We then calculated a series of parameters and their corresponding congestion identification accuracy. The results have implications for related probe vehicle deployment and traffic analysis; for example, when 5% of probe vehicles are available, 85% identification accuracy can be reached if the sampling time interval is 10 s.

Keywords vehicle trajectory data floating car data streaming data traffic congestion

Corresponding Author(s): YUE Yang,Email:yueyang@whu.edu.cn

Issue Date: 05 March 2013

Cite this article:

Handong WANG,Yang YUE,Qingquan LI. How many probe vehicles are enough for identifying traffic congestion?—a study from a streaming data perspective[J]. Front Earth Sci, 2013, 7(1): 34-42.

URL:

https://academic.hep.com.cn/fesci/EN/10.1007/s11707-012-0343-x
https://academic.hep.com.cn/fesci/EN/Y2013/V7/I1/34

Fig.1 An example of a cluster snapshot

Fig.2 Flowchart of our proposed method

Fig.3 The sketch of simulated urban road network

Tab.1 Vehicle speed and traffic state ()

Tab.2 DBSCAN parameters

Fig.4 Identified congestions under different values of cluster overlap and snapshot time interval. (a) Number of identified traffic congested areas, (b) Queue length, (c) Traffic congestion duration

Fig.5 The impact of different cluster overlap. Snapshot time interval= 10 s. (a) Overlap value= 0.5; (b) overlap value= 0.6; (c) overlap value= 0.7; (d) overlap value= 0.8; (e) overlap value= 0.9

Fig.6 The impact of different snapshot time interval. Cluster overlap value= 0.7. (a) Time interval= 5 s; (b) time interval= 10 s; (c) time interval= 15 s; (d) time interval= 20 s

Fig.7 Identification accuracy under different parameter settings

Tab.3 Relationship between percentage of probe vehicle and sampling interval with identification rate

Fig.8 The impact of DBSCAN parameters (Minpt) to identification accuracy

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