Travel Frequent-Route Identification Based on the Snake Algorithm Using License Plate Recognition Data

Feiyang Liu, Jie Zeng, Jinjun Tang and TianJian Yu ()
Additional contact information
Feiyang Liu: School of Traffic and Transportation Engineering, Central South University, Changsha 410075, China
Jie Zeng: School of Traffic and Transportation Engineering, Central South University, Changsha 410075, China
Jinjun Tang: School of Traffic and Transportation Engineering, Central South University, Changsha 410075, China
TianJian Yu: School of Traffic and Transportation Engineering, Central South University, Changsha 410075, China

Mathematics, 2025, vol. 13, issue 15, 1-30

Abstract: Path flow always plays a critical role in extracting vehicle travel patterns and reflecting network-scale traffic features. However, the comprehensive topological structure of urban road networks induces massive route choices, so frequent travel routes have been gradually regarded as an ideal countermeasure to represent traffic states. Widely used license plate recognition (LPR) devices can collect the abundant traffic features of all vehicles, but their sparse spatial distributions restrict the conventional models in frequent travel identification. Therefore, this study develops a network reconstruction method to construct a topological network from the LPR dataset, avoiding the adverse effects caused by the sparse distribution of detectors on the road network and further uses the Snake algorithm to fully utilize the road network structure and traffic attributes for clustering to obtain various travel patterns, with frequent routes under different travel patterns finally identified based on Steiner trees and frequent item recognition. To address the sparse spatial distribution of LPR devices, we utilize the word2vec model to extract spatial correlations among intersections. A threshold-based method is then applied to transform the correlation matrix into a reconstructed network, connecting intersections with strong vehicle transition relationships. This community structure can be interpreted as representing different travel patterns. Consequently, the Snake algorithm is employed to cluster intersections into distinct categories, reflecting these varied travel patterns. By leveraging the word2vec model, the detector installation rate requirement for Snake is significantly reduced, ensuring that the clustering results accurately represent the intrinsic relevance of traffic roads. Subsequently, frequent routes are identified from both macro- and micro-perspectives using the Steiner tree and Frequent Pattern Growth (FP Growth) algorithm, respectively. Validated on the LPR dataset in Changsha, China, the experiment results demonstrate that the proposed method can effectively identify travel patterns and extract frequent routes in the sparsely installed LPR devices.

Keywords: Snake algorithm; frequent routes; license plate recognition data; word2vec; travel pattern analysis (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/13/15/2536/pdf (application/pdf)
https://www.mdpi.com/2227-7390/13/15/2536/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:13:y:2025:i:15:p:2536-:d:1719502

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().