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Functional Zone Based Hierarchical Demand Prediction For Bike System Expansion

Published: 13 August 2017 Publication History

Abstract

Bike sharing systems, aiming at providing the missing links in public transportation systems, are becoming popular in urban cities. Many providers of bike sharing systems are ready to expand their bike stations from the existing service area to surrounding regions. A key to success for a bike sharing systems expansion is the bike demand prediction for expansion areas. There are two major challenges in this demand prediction problem: First. the bike transition records are not available for the expansion area and second. station level bike demand have big variances across the urban city. Previous research efforts mainly focus on discovering global features, assuming the station bike demands react equally to the global features, which brings large prediction error when the urban area is large and highly diversified. To address these challenges, in this paper, we develop a hierarchical station bike demand predictor which analyzes bike demands from functional zone level to station level. Specifically, we first divide the studied bike stations into functional zones by a novel Bi-clustering algorithm which is designed to cluster bike stations with similar POI characteristics and close geographical distances together. Then, the hourly bike check-ins and check-outs of functional zones are predicted by integrating three influential factors: distance preference, zone-to-zone preference, and zone characteristics. The station demand is estimated by studying the demand distributions among the stations within the same functional zone. Finally, the extensive experimental results on the NYC Citi Bike system with two expansion stages show the advantages of our approach on station demand and balance prediction for bike sharing system expansions.

References

[1]
Ramon Alvarez-Valdes, Jose M. Belenguer, Enrique Benavent, Jose D. Bermudez, Facundo Muñoz, Enriqueta Vercher, and Francisco Verdejo 2016. Optimizing the level of service quality of a bike-sharing system. Omega Vol. 62 (2016), 163 -- 175. nfopersonZhenjiang Shen 2015. Discovering functional zones using bus smart card data and points of interest in Beijing. Geospatial analysis to support urban planning in Beijing. Springer, 193--217.
[2]
Ulrike Luxburg. 2007. A tutorial on spectral clustering. Statistics and Computing Vol. 17, 4 (2007), 395--416.
[3]
Yutaka Motoaki and Ricardo A. Daziano 2015. A hybrid-choice latent-class model for the analysis of the effects of weather on cycling demand. Transportation Research Part A: Policy and Practice Vol. 75 (2015), 217 -- 230. showISSN0965--8564
[4]
Oliver O'Brien, James Cheshire, and Michael Batty. 2014. Mining bicycle sharing data for generating insights into sustainable transport systems. Journal of Transport Geography Vol. 34 (2014), 262 -- 273. showISSN0966--6923
[5]
Alex Rodriguez and Alessandro Laio 2014. Clustering by fast search and find of density peaks. Science, Vol. 344, 6191 (2014), 1492--1496.
[6]
Jasper Schuijbroek, Robert Hampshire, and Willem-Jan van Hoeve. 2013. Inventory rebalancing and vehicle routing in bike sharing systems. (2013).
[7]
Susan A Shaheen, Stacey Guzman, and Hua Zhang 2010. Bikesharing in Europe, the Americas, and Asia. Transportation Research Record: Journal of the Transportation Research Board, Vol. 2143, 1 (2010), 159--167.
[8]
Nguyen Xuan Vinh, Julien Epps, and James Bailey. 2009. Information Theoretic Measures for Clusterings Comparison: Is a Correction for Chance Necessary. In Proceedings of the 26th ICML. 1073--1080.
[9]
Patrick Vogel, Torsten Greiser, and Dirk Christian Mattfeld. 2011. Understanding bike-sharing systems using data mining: Exploring activity patterns. Procedia-Social and Behavioral Sciences Vol. 20 (2011), 514--523.
[10]
Wen Wang 2016. Forecasting Bike Rental Demand Using New York Citi Bike Data. (2016).
[11]
Rui Xu and D. Wunsch, II 2005. Survey of Clustering Algorithms. IEEE Transactions on Neural Networks Vol. 16, 3 (2005), 645--678.
[12]
Nicholas Jing Yuan, Yu Zheng, Xing Xie, Yingzi Wang, Kai Zheng, and Hui Xiong. 2015. Discovering urban functional zones using latent activity trajectories. IEEE Transactions on Knowledge and Data Engineering, Vol. 27, 3 (2015), 712--725.
[13]
Ming Zeng, Tong Yu, Xiao Wang, Vincent Su, Le T Nguyen, and Ole J Mengshoel. 2016. Improving Demand Prediction in Bike Sharing System by Learning Global Features. Machine Learning for Large Scale Transportation Systems (LSTS)@ KDD-16 (2016).
[14]
Xiaolu Zhou. 2015. Understanding Spatiotemporal Patterns of Biking Behavior by Analyzing Massive Bike Sharing Data in Chicago. PLOS ONE, Vol. 10, 10 (10 2015), 1--20.

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cover image ACM Conferences
KDD '17: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
August 2017
2240 pages
ISBN:9781450348874
DOI:10.1145/3097983
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 13 August 2017

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Author Tags

  1. bike sharing system
  2. clustering
  3. demand prediction

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KDD '17 Paper Acceptance Rate 64 of 748 submissions, 9%;
Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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  • (2024)MulSTE: A Multi-view Spatio-temporal Learning Framework with Heterogeneous Event Fusion for Demand-supply PredictionProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3672030(1781-1792)Online publication date: 25-Aug-2024
  • (2024)Human Mobility Prediction Based on Trend Iteration of Spectral ClusteringIEEE Transactions on Mobile Computing10.1109/TMC.2023.3288132(1-16)Online publication date: 2024
  • (2024)An Adaptive Spatial-Temporal Method Capturing for Short-Term Bike-Sharing PredictionIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2024.340668225:11(16761-16774)Online publication date: Nov-2024
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