Abstract
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.
Highlights
With the rapid economic growth in China, the purchase of private vehicles has seen a pronounced increase
Using the origin and destination extraction algorithm proposed in Section 3.3.1, we extract a total of 4,89,9260 private vehicle trips from 18 to 23 November 2018
It can be inferred that the tertiary industry will bring more cross-regional interaction than the primary and secoTnadbalrey4i.nTdhuesnturimesb.eTr hoferperfiovarete, uverbhaicnlettrraipffiscinmsaixndagayesm. ent departments need to formuNlautemmbeore targeDteidstrmicatnagemTeontatlpTorlicpieNsufomrbtehre areaIsnwtrha/eTreottahleTtreirptiary iInndteurs/tTroytaglatThreiprs
Summary
With the rapid economic growth in China, the purchase of private vehicles has seen a pronounced increase. The small load of private vehicles leads to low transport efficiency and low road utilization. This sustained growth has been accompanied by severe traffic congestion, traffic accidents, and social security problems. With the development of Location Base Service, most of the human mobility studies have used big data with location information, such as the Global Positioning System (GPS) records, subway card records, mobile phone Call Detail Records (CDRs), and social media check-in data [4,5,6,7,8,9,10,11,12]. Call Detail Records from mobile phones have a low sampling rate, making it difficult to distinguish the travel tools (private vehicle, public transportation, and taxi). For the big data in transportation planning for investment and policy-related decisions, the issue of representativeness must be addressed [15]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
