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Information 2017, 8(2), 67; doi:10.3390/info8020067

Understanding the Impact of Human Mobility Patterns on Taxi Drivers’ Profitability Using Clustering Techniques: A Case Study in Wuhan, China

1,2,3
,
1,2,3,* and 1,2,3
1
Intelligent Transportation Systems Research Center, Wuhan University of Technology, Wuhan 430063, China
2
Engineering Research Center of Transportation Safety, Ministry of Education, Wuhan 430063, China
3
National Engineering Research Center for Water Transportation Safety, Heping Road, Wuchang District, Wuhan 430063, Hubei, China
*
Author to whom correspondence should be addressed.
Academic Editor: Willy Susilo
Received: 17 April 2017 / Revised: 15 June 2017 / Accepted: 15 June 2017 / Published: 19 June 2017
View Full-Text   |   Download PDF [3289 KB, uploaded 20 June 2017]   |  

Abstract

Taxi trajectories reflect human mobility over the urban roads’ network. Although taxi drivers cruise the same city streets, there is an observed variation in their daily profit. To reveal the reasons behind this issue, this study introduces a novel approach for investigating and understanding the impact of human mobility patterns (taxi drivers’ behavior) on daily drivers’ profit. Firstly, a K-means clustering method is adopted to group taxi drivers into three profitability groups according to their driving duration, driving distance and income. Secondly, the cruising trips and stopping spots for each profitability group are extracted. Thirdly, a comparison among the profitability groups in terms of spatial and temporal patterns on cruising trips and stopping spots is carried out. The comparison applied various methods including the mash map matching method and DBSCAN clustering method. Finally, an overall analysis of the results is discussed in detail. The results show that there is a significant relationship between human mobility patterns and taxi drivers’ profitability. High profitability drivers based on their experience earn more compared to other driver groups, as they know which places are more active to cruise and to stop and at what times. This study provides suggestions and insights for taxi companies and taxi drivers in order to increase their daily income and to enhance the efficiency of the taxi industry. View Full-Text
Keywords: human mobility patterns; K-means; driving profitability; cruising trips; stopping spots; DBSCAN human mobility patterns; K-means; driving profitability; cruising trips; stopping spots; DBSCAN
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Naji, H.A.H.; Wu, C.; Zhang, H. Understanding the Impact of Human Mobility Patterns on Taxi Drivers’ Profitability Using Clustering Techniques: A Case Study in Wuhan, China. Information 2017, 8, 67.

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