Hierarchical Prediction Based on Network-Representation-Learning-Enhanced Clustering for Bike-Sharing System in Smart City

Jingjing Yang · Benzhen Guo · Zhihui Wang · Yanli Ma

28 citations0/0 files storedIEEE Internet of Things Journal

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Urban Transport and Accessibility Traffic Prediction and Management Techniques Sharing Economy and Platforms

Abstract

The sharing bike system is emerging as a new type of transportation with the advance of the smart city in recent years. More and more people will choose to ride a sharing bicycle for short-distance travel. While sharing bikes provide convenient services to customers, there are also many unfavorable factors in the sharing bike system, which have a certain impact on the riding experience of customers. One of the obvious disadvantages is the imbalance of usage because of the abnormal distribution of sharing bike stations in different areas. Despite some prediction work have been done, most of them merely consider the geographical factors between station whereas the user preferences and global network information are not fully considered. In this work, we propose a hierarchical model for sharing bike prediction, which can predict the number of rents/returns of each sharing bike station in the future to achieve resource redistribution. The proposed model is composed of two steps, including the sharing bike station clustering via network representation learning by considering the migration trends and geographic location information of bike-sharing between station. In the hierarchical prediction step, the total number of all bike-sharing station is predicted with an inference model based on multiple similarities. Finally, the number of rents/returns at each station can be derived. Our method is evaluated based on two publicly available sharing data sets by comparison with several baseline methods. Extensive experimental results on two open sharing bike data sets demonstrate that our proposed hierarchical can achieve the best prediction results. Our proposed method has a 17.34% improvement in root-mean-squared logarithmic error and a 10.4% improvement in return prediction.

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References

2010Urban cycles and mobility patterns: Exploring and predicting trends in a bicycle-based public transport system 2015Traffic prediction in a bike-sharing system 2009Sensing and predicting the pulse of the city through shared bicycling 2014Model-Based Count Series Clustering for Bike Sharing System Usage Mining: A Case Study with the Vélib’ System of Paris 2019Citywide Bike Usage Prediction in a Bike-Sharing System 2019Hierarchical prediction based on two-level Gaussian mixture model clustering for bike-sharing system 2020A Hybrid Machine Learning Model for Demand Prediction of Edge-Computing-Based Bike-Sharing System Using Internet of Things 2019Mobility Modeling and Data-Driven Closed-Loop Prediction in Bike-Sharing Systems

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2023Multi-objective optimal scheduling model for shared bikes based on spatiotemporal big data 2022Quantifying the comprehensive benefit of the bike-sharing system under cycling behavior differences 2023Is digital finance environmentally friendly in China? Evidence from shared-bike trips 2022A Meta-Learning Algorithm for Rebalancing the Bike-Sharing System in IoT Smart City

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