Next Article in Journal
A Text Analytics-Based Importance Performance Analysis and Its Application to Airline Service
Next Article in Special Issue
Location-Based Tracking Data and Customer Movement Pattern Analysis for Sustainable Fashion Business
Previous Article in Journal
Changing the Accounting System to Foster Universities’ Financial Sustainability: First Evidence from Italy
Previous Article in Special Issue
Exploring the Relationship between Urban Vitality and Street Centrality Based on Social Network Review Data in Wuhan, China
Open AccessArticle

Exploring the Weekly Travel Patterns of Private Vehicles Using Automatic Vehicle Identification Data: A Case Study of Wuhan, China

1
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
2
Institute for Social Research, University of Michigan, Ann Arbor, MI 48109, USA
3
Institute of Space Science and Technology, Nanchang University, Nanchang 330031, China
*
Author to whom correspondence should be addressed.
Sustainability 2019, 11(21), 6152; https://doi.org/10.3390/su11216152
Received: 15 October 2019 / Revised: 28 October 2019 / Accepted: 1 November 2019 / Published: 4 November 2019
(This article belongs to the Special Issue Spatial Analysis and Geographic Information Systems)
Automatic vehicle identification (AVI) systems collect 24 h vehicle travel data for the efficient management of traffic flows. The automatic vehicle identification data collected by an overhead traffic monitoring system provides a means for understanding urban traffic flows and human mobility. This article explores the weekly travel patterns of private vehicles based on AVI data in Wuhan, a megacity in Central China. We extracted origin–destination information and applied the K-Means clustering algorithm to classify spatial traffic hot spots by camera locations. Subsequently, the Latent Dirichlet Allocation algorithm was used to mine the temporal travel patterns of individual vehicles. The cluster results are summarized in nine travel probability matrixes. The effectiveness of this approach is illustrated by a case study using a large set of AVI data collected from 19 to 24 November 2018, in Wuhan, China. The results revealed six variations of the travel demand on weekdays and weekends—the commuting behaviors of private drivers triggered a tidal change in traffic flows. This study also exposed nine weekly travel patterns for private cars, reflecting temporal similarities of human mobility patterns. We identified four types of commuters. These results can help city managers understand daily changes in urban travel demands. View Full-Text
Keywords: license plate recognition; travel pattern; data mining; human mobility license plate recognition; travel pattern; data mining; human mobility
Show Figures

Figure 1

MDPI and ACS Style

Zhao, Y.; Zhu, X.; Guo, W.; She, B.; Yue, H.; Li, M. Exploring the Weekly Travel Patterns of Private Vehicles Using Automatic Vehicle Identification Data: A Case Study of Wuhan, China. Sustainability 2019, 11, 6152.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop