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Article

Estimating Potential Demand of Bicycle Trips from Mobile Phone Data—An Anchor-Point Based Approach

by 1,2, 1,3,4,*, 3,4 and 5
1
Department of Geography, University of Tennessee, Knoxville, TN 37996, USA
2
Senseable City Laboratory, SMART Centre, Singapore 138602, Singapore
3
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
4
Collaborative Innovation Center of Geospatial Technology, 129 Luoyu Road, Wuhan 430079, China
5
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
*
Author to whom correspondence should be addressed.
Academic Editor: Wolfgang Kainz
ISPRS Int. J. Geo-Inf. 2016, 5(8), 131; https://doi.org/10.3390/ijgi5080131
Received: 22 June 2016 / Revised: 18 July 2016 / Accepted: 21 July 2016 / Published: 26 July 2016
(This article belongs to the Special Issue Intelligent Spatial Decision Support)
This study uses a large-scale mobile phone dataset to estimate potential demand of bicycle trips in a city. By identifying two important anchor points (night-time anchor point and day-time anchor point) from individual cellphone trajectories, this study proposes an anchor-point based trajectory segmentation method to partition cellphone trajectories into trip chain segments. By selecting trip chain segments that can potentially be served by bicycles, two indicators (inflow and outflow) are generated at the cellphone tower level to estimate the potential demand of incoming and outgoing bicycle trips at different places in the city and different times of a day. A maximum coverage location-allocation model is used to suggest locations of bike sharing stations based on the total demand generated at each cellphone tower. Two measures are introduced to further understand characteristics of the suggested bike station locations: (1) accessibility; and (2) dynamic relationships between incoming and outgoing trips. The accessibility measure quantifies how well the stations could serve bicycle users to reach other potential activity destinations. The dynamic relationships reflect the asymmetry of human travel patterns at different times of a day. The study indicates the value of mobile phone data to intelligent spatial decision support in public transportation planning. View Full-Text
Keywords: mobile phone data; anchor point; trajectory segmentation; bike sharing; trip chain; location-allocation; travel demand mobile phone data; anchor point; trajectory segmentation; bike sharing; trip chain; location-allocation; travel demand
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MDPI and ACS Style

Xu, Y.; Shaw, S.-L.; Fang, Z.; Yin, L. Estimating Potential Demand of Bicycle Trips from Mobile Phone Data—An Anchor-Point Based Approach. ISPRS Int. J. Geo-Inf. 2016, 5, 131. https://doi.org/10.3390/ijgi5080131

AMA Style

Xu Y, Shaw S-L, Fang Z, Yin L. Estimating Potential Demand of Bicycle Trips from Mobile Phone Data—An Anchor-Point Based Approach. ISPRS International Journal of Geo-Information. 2016; 5(8):131. https://doi.org/10.3390/ijgi5080131

Chicago/Turabian Style

Xu, Yang, Shih-Lung Shaw, Zhixiang Fang, and Ling Yin. 2016. "Estimating Potential Demand of Bicycle Trips from Mobile Phone Data—An Anchor-Point Based Approach" ISPRS International Journal of Geo-Information 5, no. 8: 131. https://doi.org/10.3390/ijgi5080131

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