Travel Behavior Analysis Using 2016 Qingdao’s Household Traffic Surveys and Baidu Electric Map API Data
Joint Authors
Wang, Wei
Gao, Ge
Wang, Zhen
Liu, Xinmin
Zhang, Junyou
Li, Qing
Source
Journal of Advanced Transportation
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-18, 18 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-03-11
Country of Publication
Egypt
No. of Pages
18
Main Subjects
Abstract EN
Household traffic surveys are widely used in travel behavior analysis, especially in travel time and distance analysis.
Unfortunately, any one kind of household traffic surveys has its own problems.
Even all household traffic survey data is accurate, it is difficult to get the trip routes information.
To our delight, electric map API (e.g., Google Maps, Apple Maps, Baidu Maps, and Auto Navi Maps) could provide the trip route and time information, which remedies the traditional traffic survey’s defect.
Thus, we can take advantage of the two kinds of data and integrate them into travel behavior analysis.
In order to test the validity of the Baidu electric map API data, a field study on 300 taxi OD pairs is carried out.
According to statistical analysis, the average matching rate of total OD pairs is 90.74%, which reflects high accuracy of electric map API data.
Based on the fused data of household traffic survey and electric map API, travel behavior on trip time and distance is analyzed.
Results show that most purposes’ trip distances distributions are concentrated, which are no more than 10 kilometers.
It is worth noting that students have the shortest travel distance and company business’s travel distance distribution is dispersed, which has the longest travel distance.
Compared to travel distance, the standard deviations of all purposes’ travel time are greater than the travel distance.
Car users have longer travel distance than bus travelers, and their average travel distance is 8.58km.
American Psychological Association (APA)
Gao, Ge& Wang, Zhen& Liu, Xinmin& Li, Qing& Wang, Wei& Zhang, Junyou. 2019. Travel Behavior Analysis Using 2016 Qingdao’s Household Traffic Surveys and Baidu Electric Map API Data. Journal of Advanced Transportation،Vol. 2019, no. 2019, pp.1-18.
https://search.emarefa.net/detail/BIM-1170027
Modern Language Association (MLA)
Gao, Ge…[et al.]. Travel Behavior Analysis Using 2016 Qingdao’s Household Traffic Surveys and Baidu Electric Map API Data. Journal of Advanced Transportation No. 2019 (2019), pp.1-18.
https://search.emarefa.net/detail/BIM-1170027
American Medical Association (AMA)
Gao, Ge& Wang, Zhen& Liu, Xinmin& Li, Qing& Wang, Wei& Zhang, Junyou. Travel Behavior Analysis Using 2016 Qingdao’s Household Traffic Surveys and Baidu Electric Map API Data. Journal of Advanced Transportation. 2019. Vol. 2019, no. 2019, pp.1-18.
https://search.emarefa.net/detail/BIM-1170027
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1170027