Spatial and temporal characterization of travel patterns in a traffic network using vehicle trajectories

Kim, Jiwon and Mahmassani, Hani S. (2015). Spatial and temporal characterization of travel patterns in a traffic network using vehicle trajectories. In: Masao Kuwahara, Hideyuki Kita and Yasuo Asakura, The 21st International Symposium on Transportation and Traffic Theory. International Symposium on Transportation and Traffic Theory, Kobe, Japan, (164-184). 5-7 August 2015. doi:10.1016/j.trpro.2015.07.010

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Author Kim, Jiwon
Mahmassani, Hani S.
Title of paper Spatial and temporal characterization of travel patterns in a traffic network using vehicle trajectories
Conference name International Symposium on Transportation and Traffic Theory
Conference location Kobe, Japan
Conference dates 5-7 August 2015
Proceedings title The 21st International Symposium on Transportation and Traffic Theory   Check publisher's open access policy
Journal name Transportation Research Procedia   Check publisher's open access policy
Place of Publication Amsterdam, Netherlands
Publisher Elsevier
Publication Year 2015
Sub-type Fully published paper
DOI 10.1016/j.trpro.2015.07.010
Open Access Status DOI
ISSN 2352-1465
Editor Masao Kuwahara
Hideyuki Kita
Yasuo Asakura
Volume 9
Start page 164
End page 184
Total pages 21
Collection year 2016
Language eng
Abstract/Summary This paper presents a trajectory clustering method to discover spatial and temporal travel patterns in a traffic network. The study focuses on identifying spatially distinct traffic flow groups using trajectory clustering and investigating temporal traffic patterns of each spatial group. The main contribution of this paper is the development of a systematic framework for clustering and classifying vehicle trajectory data, which does not require a pre-processing step known as map-matching and directly applies to trajectory data without requiring the information on the underlying road network. The framework consists of four steps: similarity measurement, trajectory clustering, generation of cluster representative subsequences, and trajectory classification. First, we propose the use of the Longest Common Subsequence (LCS) between two vehicle trajectories as their similarity measure, assuming that the extent to which vehicles’ routes overlap indicates the level of closeness and relatedness as well as potential interactions between these vehicles. We then extend a density-based clustering algorithm, DBSCAN, to incorporate the LCS-based distance in our trajectory clustering problem. The output of the proposed clustering approach is a few spatially distinct traffic stream clusters, which together provide an informative and succinct representation of major network traffic streams. Next, we introduce the notion of cluster representative subsequence (CRS), which reflects dense road segments shared by trajectories belonging to a given traffic stream cluster, and present the procedure of generating a set of CRSs by merging the pairwise LCSs via hierarchical agglomerative clustering. The CRSs are then used in the trajectory classification step to measure the similarity between a new trajectory and a cluster. The proposed framework is demonstrated using actual vehicle trajectory data collected from New York City, USA. A simple experiment was performed to illustrate the use of the proposed spatial traffic stream clustering in application areas such as network-level traffic flow pattern analysis and travel time reliability analysis.
Keyword Trajectory clustering
Traffic flow
Spatial and temporal traffic patterns
Longest common subsequence
Q-Index Code EX
Q-Index Status Provisional Code
Institutional Status UQ

Document type: Conference Paper
Collections: School of Civil Engineering Publications
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Created: Wed, 02 Mar 2016, 23:47:08 EST by Jiwon Kim on behalf of School of Civil Engineering