Smart City Gnosys
Smart city article details
| Title | Hierarchical Prediction Based On Network-Representation-Learning-Enhanced Clustering For Bike-Sharing System In Smart City |
|---|---|
| ID_Doc | 29019 |
| Authors | Yang, JJ; Guo, BZ; Wang, ZH; Ma, YL |
| Year | 2021 |
| Published | IEEE INTERNET OF THINGS JOURNAL, 8, 8 |
| DOI | http://dx.doi.org/10.1109/JIOT.2020.3034991 |
| 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. |
| Author Keywords | Bicycles; Public transportation; Predictive models; Smart cities; Roads; Market research; Internet of Things; Clustering; network representation learning; sharing bike prediction; smart city |
Similar Articles
| Id | Similarity | Authors | Title | Published |
|---|---|---|---|---|
33156  | 0.921 | Onen S.; Ggaliwango M.; Mugabi S.; Nabende J. | Interpretable Machine Learning For Intelligent Transportation In Bike-Sharing | 2023 2nd International Conference on Smart Technologies and Systems for Next Generation Computing, ICSTSN 2023 (2023) |
| 18245 | 0.916 | Yang X.; Xu Y.; Zhou Y.; Song S.; Wu Y. | Demand-Aware Mobile Bike-Sharing Service Using Collaborative Computing And Information Fusion In 5G Iot Environment | Digital Communications and Networks, 8, 6 (2022) |
| 26705 | 0.909 | Avignone A.; Napolitano D.; Cagliero L.; Chiusano S. | Flowcasting: A Dynamic Machine Learning Based Dashboard For Bike-Sharing System Management | 18th IEEE International Conference on Application of Information and Communication Technologies, AICT 2024 (2024) |
| 52829 | 0.909 | Chai J.; Song J.; Fan H.; Xu Y.; Zhang L.; Guo B.; Xu Y. | St-Bikes: Predicting Travel-Behaviors Of Sharing-Bikes Exploiting Urban Big Data | IEEE Transactions on Intelligent Transportation Systems, 24, 7 (2023) |
| 37370 | 0.903 | Shir B.; Prakash Verma J.; Bhattacharya P. | Mobility Prediction For Uneven Distribution Of Bikes In Bike Sharing Systems | Concurrency and Computation: Practice and Experience, 35, 2 (2023) |
| 35054 | 0.898 | Manai M.; Sellami B.; Yahia S.B. | Leveraging Data For Better Bike Sharing: A Methodology For Terminal Availability Prediction | Procedia Computer Science, 246, C (2024) |
| 2536 | 0.897 | Zhang C.; Wu F.; Wang H.; Tang B.; Fan W.; Liu Y. | A Meta-Learning Algorithm For Rebalancing The Bike-Sharing System In Iot Smart City | IEEE Internet of Things Journal, 9, 21 (2022) |
| 20781 | 0.897 | Ali A.; Salah A.; Bekhit M.; Fathalla A. | Divide-And-Train: A New Approach To Improve The Predictive Tasks Of Bike-Sharing Systems | Mathematical Biosciences and Engineering, 21, 7 (2024) |
| 12181 | 0.896 | Guo R.; Jiang Z.; Huang J.; Tao J.; Wang C.; Li J.; Chen L. | Bikenet: Accurate Bike Demand Prediction Using Graph Neural Networks For Station Rebalancing | Proceedings - 2019 IEEE SmartWorld, Ubiquitous Intelligence and Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Internet of People and Smart City Innovation, SmartWorld/UIC/ATC/SCALCOM/IOP/SCI 2019 (2019) |
| 42618 | 0.895 | Lee C.-H.; Lee J.-W.; Jung Y. | Practical Method To Improve Usage Efficiency Of Bike-Sharing Systems | ETRI Journal, 44, 2 (2022) |