Cited By
View all- Hsieh HLin FJiang JKuo TChang Y(2021)Inferring Long-Term Demand of Newly Established Stations for Expansion Areas in Bike Sharing SystemApplied Sciences10.3390/app1115674811:15(6748)Online publication date: 22-Jul-2021
Analysis of Public Transportation Patterns in a Densely Populated City with Station-based Shared Bikes
Authors: 
Di Wang, Evan Wu, Ah-Hwee TanAuthors Info & Claims
Di Wang, Evan Wu, Ah-Hwee TanAuthors Info & ClaimsArticle No.: 8, Pages 1 - 8
Abstract
Densely populated cities face great challenges of high transportation demand and limited physical space. Thus, in these cities, the public transportation system is heavily relied on. Conventional public transportation modes such as bus, taxi and subway have been globally deployed over the past century. In the last decade, a new type of public transportation mode, shared bike, emerged in many cities. These shared bikes are deployed by either government-regulated or profit-driven companies and are either station-based or station-less. Nonetheless, all of them are designed to better solve the last-mile problem in densely populated cities as complements to the conventional public transportation system. In this paper, we analyse the public transportation patterns in a densely populated city, Chicago, USA, using comprehensive datasets covering the transportation records on shared bikes, buses, taxis and subways collected over one year's time. Specifically, we apply self-regulated clustering methods to reveal both the majority transportation patterns and the irregular ones. Other than reporting the autonomously discovered transportation patterns, we also show that our method achieves better clustering performance than the benchmarking methods.
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Index Terms
- Analysis of Public Transportation Patterns in a Densely Populated City with Station-based Shared Bikes
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July 2018
220 pages
ISBN:9781450365871
DOI:10.1145/3265689
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Published: 28 July 2018
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ICCSE'18
ICCSE'18: The 3rd International Conference on Crowd Science and Engineering
July 28 - 31, 2018
Singapore, Singapore
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ICCSE'18 Paper Acceptance Rate 33 of 89 submissions, 37%;
Overall Acceptance Rate 92 of 247 submissions, 37%
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View all- Hsieh HLin FJiang JKuo TChang Y(2021)Inferring Long-Term Demand of Newly Established Stations for Expansion Areas in Bike Sharing SystemApplied Sciences10.3390/app1115674811:15(6748)Online publication date: 22-Jul-2021
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