Exploring the Individual Travel Patterns Utilizing Large-Scale Highway Transaction Dataset

Jianmin Jia (), Mingyu Shao, Rong Cao, Xuehui Chen, Hui Zhang, Baiying Shi and Xiaohan Wang ()
Additional contact information
Jianmin Jia: Department of Transportation Engineering, Shandong Jianzhu University, Jinan 250101, China
Mingyu Shao: Department of Computer Science, Shandong Jianzhu University, Jinan 250101, China
Rong Cao: Department of Transportation Engineering, Shandong Jianzhu University, Jinan 250101, China
Xuehui Chen: Shandong Hi-Speed Company Limited, Jinan 250014, China
Hui Zhang: Department of Transportation Engineering, Shandong Jianzhu University, Jinan 250101, China
Baiying Shi: Department of Transportation Engineering, Shandong Jianzhu University, Jinan 250101, China
Xiaohan Wang: Department of Transportation Engineering, Shandong Jianzhu University, Jinan 250101, China

Sustainability, 2022, vol. 14, issue 21, 1-13

Abstract: With the spread of electronic toll collection (ETC) and electronic payment, it is still a challenging issue to develop a systematic approach to investigate highway travel patterns. This paper proposed to explore spatial–temporal travel patterns to support traffic management. Travel patterns were extracted from the highway transaction dataset, which provides a wealth of individual information. Additionally, this paper constructed the analysis framework, involving individual, and temporal and spatial attributes, on the basis of the RFM (Recency, Frequency, Monetary) model. In addition to the traditional factors, the weekday trip and repeated rate were introduced in the study. Subsequently, various models, involving K-means, Fuzzy C-means and SOM (Self-organizing Map) models, were employed to investigate travel patterns. According to the performance evaluation, the SOM model presented better performance and was utilized in the final analysis. The results indicated that six groups were categorized with a significant difference. Through further investigation, we found that the random traveler occupied over 40% of the samples, while the commuting traveler and long-range freight traveler presented relatively fixed spatial and temporal patterns. The results were also meaningful for highway authority management. The discussion and implication of travel patterns to be integrated with the dynamic pricing strategy were also discussed.

Keywords: highway transaction dataset; sustainable transportation; travel pattern analysis; clustering algorithm (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/2071-1050/14/21/14196/pdf (application/pdf)
https://www.mdpi.com/2071-1050/14/21/14196/ (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:jsusta:v:14:y:2022:i:21:p:14196-:d:958759

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

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