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Machine Learning Approaches to Bike-Sharing Systems: A Systematic Literature Review

Vitória Albuquerque, Miguel Sales Dias, Fernando Bacao
2021 ISPRS International Journal of Geo-Information  
modes, such as bike-sharing systems.  ...  The preferred reporting items for systematic reviews and meta-analyses (PRISMA) method was adopted to identify specific factors that influence bike-sharing systems, resulting in an analysis of 35 papers  ...  Acknowledgments: We wish to thank Vitor Duarte Santos and Maria Anastasiadou for their help in the PRISMA methodology.  ... 
doi:10.3390/ijgi10020062 fatcat:uagkcbtxe5gitb4cyx5n64nnhq

Long-term Joint Scheduling for Urban Traffic [article]

Xianfeng Liang, Likang Wu, Joya Chen, Yang Liu, Runlong Yu, Min Hou, Han Wu, Yuyang Ye, Qi Liu, Enhong Chen
2019 arXiv   pre-print
Previous works have demonstrated that by reasonable scheduling, e.g, rebalancing bike-sharing systems and optimized bus transportation, the traffic efficiency could be significantly improved with little  ...  Recently, the traffic congestion in modern cities has become a growing worry for the residents.  ...  For bike-sharing systems, the cluster represents the similar stations, which are close to each other a er clustering shown in section 3.1.2. De nition 3.  ... 
arXiv:1910.12283v1 fatcat:o6l5fvow3benjf62qnkp2lw44y

Traffic prediction in a bike-sharing system

Yexin Li, Yu Zheng, Huichu Zhang, Lei Chen
2015 Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems - GIS '15  
Bike-sharing systems are widely deployed in many major cities, providing a convenient transportation mode for citizens' commutes.  ...  In this paper, we propose a hierarchical prediction model to predict the number of bikes that will be rent from/returned to each station cluster in a future period so that reallocation can be executed  ...  For the first time, Geo-clustering is conducted on all stations in a sharing system.  ... 
doi:10.1145/2820783.2820837 dblp:conf/gis/LiZZC15 fatcat:ps6z7usmvfht5ncovw7eebjcsm

Rebalancing Strategy for Bike-Sharing Systems Based on the Model of Level of Detail

Zhenghua Hu, Kejie Huang, Enyou Zhang, Qi'ang Ge, Xiaoxue Yang, Chi-Hua Chen
2021 Journal of Advanced Transportation  
Compared with the traditional method, this algorithm helps reduce the effective time for rebalancing bike-sharing systems by 28.3%. Therefore, it is an effective rebalancing scheme.  ...  A rebalancing strategy based on the model of level of detail in combination with genetic algorithm was proposed. Data were collected from the bike-sharing system in Ningbo.  ...  LQ18D010008), Natural Science Foundation of Ningbo (no. 2018A610132), and a project supported by Scientific Research Fund of Zhejiang Provincial Education Department (Y201736984).  ... 
doi:10.1155/2021/3790888 fatcat:axqhconinnc77h3itmkg6ielvq

Study on Clustering of Free-Floating Bike-Sharing Parking Time Series in Beijing Subway Stations

Xu, Bain, Rong, Wang, Yin
2019 Sustainability  
In recent years, the free-floating bike-sharing (FFBS) system has become a significant mode of travel to satisfy urban residents' travel demands.  ...  Second, a hierarchical clustering method based on dynamic time warping (DTW) was proposed to cluster the FFBS parking time series.  ...  There are mainly three types of bike-sharing programs, including the public bike-sharing system (PBS), free-floating bike-sharing system (FFBS) and closed campus bike-sharing system (CBS) [4] .  ... 
doi:10.3390/su11195439 fatcat:mvenfutcezhu5ezkqtrlgcxufu

Understanding and Visualizing the District of Columbia Capital Bikeshare System Using Data Analysis for Balancing Purposes [article]

Kiana Roshan Zamir, Ali Shafahi, Ali Haghani
2017 arXiv   pre-print
We provide the temporal profile of the center of each cluster which can be used as a simple and practical approach for approximating the number of pickups and drop-offs of the stations.  ...  Bike sharing systems' popularity has consistently been rising during the past years. Managing and maintaining these emerging systems are indispensable parts of these systems.  ...  (22) proposed a hierarchical prediction model for predicting the rented and returned bikes from and to each station.  ... 
arXiv:1708.04196v1 fatcat:opr63p6oonaoflgobcrm6angt4

A Spatiotemporal Prediction Model for Regional Scheduling of Shared Bicycles Based on the INLA Method

Zhuoran Yu, Yimeng Duan, Shen Zhang, Xin Liu, Kui Li, Xinqiang Chen
2021 Journal of Advanced Transportation  
In this study, we make an effort to model a POI-level cycling demand with a Bayesian hierarchical method.  ...  Most of the existing demand prediction models for shared bikes take regions as research objects; therefore, a POI-based method can be a beneficial complement to existing research, including zone-level,  ...  A reliable explanation for this is that the study area has a large demand for the use of shared bikes, along with an intersecting travel demands through different types of POIs.  ... 
doi:10.1155/2021/4959504 fatcat:yo6mqzo6ancdzlfxjiybj72oqy

A low dimensional model for bike sharing demand forecasting

Cantelmo Guido, Kucharski Rafal, Antoniou Constantinos
2019 2019 6th International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS)  
This paper proposes a low dimensional approach to combine these data sources with weather data in order to forecast the daily demand for Bike Sharing Systems (BSS).  ...  This allows identifying recursive mobility patterns that - when combined with weather data - provide accurate predictions of the demand. The method is tested with real-world data from New York City.  ...  Finally, many works focus on predicting demand patterns of bike sharing systems. Demand is, in fact, a fundamental input for the rebalancing problem.  ... 
doi:10.1109/mtits.2019.8883283 dblp:conf/mtits/CantelmoKA19 fatcat:co45m7kv4neb5ghqbilpzlkeqy

Inferring Long-Term Demand of Newly Established Stations for Expansion Areas in Bike Sharing System

Hsun-Ping Hsieh, Fandel Lin, Jiawei Jiang, Tzu-Ying Kuo, Yu-En Chang
2021 Applied Sciences  
Our work is the first to address the long-term demand of new stations, providing the government with a tool to pre-evaluate the bike flow of new stations before deployment; this can avoid wasting resources  ...  Research on flourishing public bike-sharing systems has been widely discussed in recent years.  ...  areas. • A G-clustering algorithm, a hierarchical POI clustering method to cluster POI categories, is proposed in this work, and it is shown to be effective.  ... 
doi:10.3390/app11156748 fatcat:tfnsbxmxo5dt7kwtjhdkn4otnm

Spatial Cluster-Based Model for Static Rebalancing Bike Sharing Problem

Bahman Lahoorpoor, Hamed Faroqi, Abolghasem Sadeghi-Niaraki, Soo-Mi Choi
2019 Sustainability  
Second, a similarity measure based on the trips between stations is defined to discover groups of correlated stations, using a hierarchical agglomerative clustering method.  ...  The paper proposes a bottom-up cluster-based model to solve the static rebalancing problem in bike sharing systems.  ...  in bike sharing systems.  ... 
doi:10.3390/su11113205 fatcat:33zynstgv5dmjajovunsgbfelu

Comparing cities' cycling patterns using online shared bicycle maps

Advait Sarkar, Neal Lathia, Cecilia Mascolo
2015 Transportation  
In this work, we analyse 4.5 months of online bike-sharing map data from 10 cities which, combined, have 996 stations.  ...  We further show how these similarities are reflected in the predictability of stations' occupancy data via a cross-city comparison of the error that a variety of approaches achieve when forecasting the  ...  The reasons for membership to this large cluster may be unique to each station: there may be a demand for the station to be a source, but it cannot act as one because it runs out of bikes too quickly;  ... 
doi:10.1007/s11116-015-9599-9 fatcat:mmbxzibqznbglpvpzgcaquvldi

Statistical patterns of human mobility in emerging Bicycle Sharing Systems

Xiangyu Chang, Jingzhou Shen, Xiaoling Lu, Shuai Huang, Kewei Chen
2018 PLoS ONE  
Therefore, a deep understanding of the statistical patterns embedded in the bike flow data is an urgent and overriding issue to inform decision-makings for a variety of problems including traffic prediction  ...  The emerging Bicycle Sharing System (BSS) provides a new social microscope that allows us to "photograph" the main aspects of the society and to create a comprehensive picture of human mobility behavior  ...  Discussion The emerging bike sharing systems provide a new data source for us to understand human mobility.  ... 
doi:10.1371/journal.pone.0193795 pmid:29543832 pmcid:PMC5854355 fatcat:nyuxjubo4zdzfncsi5ixfwllgq

Learning Heterogeneous Spatial-Temporal Representation for Bike-Sharing Demand Prediction

Youru Li, Zhenfeng Zhu, Deqiang Kong, Meixiang Xu, Yao Zhao
2019 PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE  
With an accurate demand prediction model, shared bikes, though with a limited amount, can be effectively utilized whenever and wherever there are travel demands.  ...  Bike-sharing systems, aiming at meeting the public's need for "last mile" transportation, are becoming popular in recent years.  ...  sponsored by the National Key Research and Development of China (No.2016YFB0800404) and the National Natural Science Foundation of China (No.61572068, No.61532005) and the Fundamental Research Funds for  ... 
doi:10.1609/aaai.v33i01.33011004 fatcat:4xo7b35m75hmxny7tob7vbbici

A Rebalancing Strategy for the Imbalance Problem in Bike-Sharing Systems

Peiyu Yi, Feihu Huang, Jian Peng
2019 Energies  
Shared bikes have become popular traveling tools in our daily life. The successful operation of bike sharing systems (BSS) can greatly promote energy saving in a city.  ...  In addition, a simulation system of shared bikes based on the historical records of Bay Area Bikeshare is built to evaluate the performance of our proposed rebalancing strategy.  ...  [31] proposed a dynamic cluster-based framework for over-demand bike prediction. Recently, neural networks have been adopted to predict bike demand. Liu et al.  ... 
doi:10.3390/en12132578 fatcat:e2solcqxvbbu3jkmsdfon6kuhq

BRAVO

Shuai Wang, Tian He, Desheng Zhang, Yuanchao Shu, Yunhuai Liu, Yu Gu, Cong Liu, Haengju Lee, Sang H. Son
2018 Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies  
Bike sharing systems, which provide a convenient commute choice for short trips, have emerged rapidly in many cities.  ...  While bike sharing has greatly facilitated people's commutes, those systems are facing a costly maintenance issue -rebalancing bikes among stations.  ...  [12] propose a hierarchical prediction model to predict the number of bikes in each station cluster. They first cluster bike stations into groups using a bipartite clustering algorithm.  ... 
doi:10.1145/3191776 fatcat:asyibempjzgjdpsh52gfkmhc4i
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