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Open AccessArticle

Finding Causes of Irregular Headways Integrating Data Mining and AHP

by , * and
School of Transportation Science and Engineering, Harbin Institute of Technology, Harbin 150090, China
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Academic Editors: Emmanuel Stefanakis, Yaolin Liu, Phaedon Kyriakidis and Wolfgang Kainz
ISPRS Int. J. Geo-Inf. 2015, 4(4), 2604-2618; https://doi.org/10.3390/ijgi4042604
Received: 29 September 2015 / Accepted: 18 November 2015 / Published: 24 November 2015
(This article belongs to the Special Issue Advances in Spatio-Temporal Data Analysis and Mining)
Irregular headways could reduce the public transit service level heavily. Finding out the exact causes of irregular headways will greatly help to develop efficient strategies aiming to improve transit service quality. This paper utilizes bus GPS data of Harbin to evaluate the headway performance and proposes a statistical method to identify the abnormal headways. Association mining is used to dig deeper and recognize six causes of bus bunching. The AHP, embedded data analysis, is applied to determine the weight of each cause in the case of that these causes are combined with each other constantly. Results show that the front bus has a greater effect on bus bunching than the following bus, and the traffic condition is the most critical factor affecting bus headway. View Full-Text
Keywords: public transit; spatio-temporal data analysis; association mining; analytic hierarchy process; bus GPS data; bus bunching public transit; spatio-temporal data analysis; association mining; analytic hierarchy process; bus GPS data; bus bunching
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An, S.; Zhang, X.; Wang, J. Finding Causes of Irregular Headways Integrating Data Mining and AHP. ISPRS Int. J. Geo-Inf. 2015, 4, 2604-2618.

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