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

Abstract

a b s t r a c tThis 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.

Space–Time Sequential Similarity for Identifying Factors of Activity‐Travel Pattern Segmentation: A Measure of Sequence Alignment and Path Similarity

“…Existing path similarity techniques concern the difference in shape between trajectories (Adrienko, Adrienko, and Wrobel 2007;Ossama, Mokhtar, and El-Sharkawi 2011;Shaw and Gopalan 2014;Kim and Mahmassani 2015). The measures typically compute the average shape of lines to compare.…”

“…Summation of duration differences however seems not capturing the sequential characteristics of activity implementation. Alternatively, one may consider the path similarity technique that measures the shape of a trajectory (Ossama, Mokhtar, and El-Sharkawi 2011;Shaw and Gopalan 2014;Ranacher and Tzavella 2014 for a review; Adrienko, Adrienko, and Wrobel 2007;Kim and Mahmassani 2015). Rather than assigning the duration weight to the sequential similarity, this may better capture the space-time choreography of an individual's movements along the day.…”

Spatial-Temporal Similarity Correlation between Public Transit Passengers Using Smart Card Data

“…The ones above are the major measures introduced, studied, and compared in the literature. Each measure has its pros and cons that make them suitable for specific applications and networks [22][23][24][25][26][27][28].…”

Detecting Anomalous Trajectories and Behavior Patterns Using Hierarchical Clustering from Taxi GPS Data

“…The data used in these relevant works include GPS data, social data, video data, etc. [13][14][15][16]. The relevant methods can be classified as the statistical method, distance-based method, clustering-based method, or classification-based method [12,17].…”