Smart City Gnosys

Smart city article details

Title	St-Bikes: Predicting Travel-Behaviors Of Sharing-Bikes Exploiting Urban Big Data
ID_Doc	52829
Authors	Chai J.; Song J.; Fan H.; Xu Y.; Zhang L.; Guo B.; Xu Y.
Year	2023
Published	IEEE Transactions on Intelligent Transportation Systems, 24, 7
DOI	http://dx.doi.org/10.1109/TITS.2022.3197778
Abstract	With the development of the modern smart city, sharing-bikes require behaviors prediction for grid-level areas which is essential for intelligent transportation systems. A model which can predict bike sharing demand behaviours accurately can allocate sharing-bikes in advance to satisfy travel demands alongside saving energy, reducing traffic, cutting down waste for those sharing-bikes companies putting excessive sharing-bikes in unsaturated demand areas. In this paper, we abandon the traditional time series prediction method and use a more efficient deep learning method to solve the traffic forecasting problem. Moreover, instead of considering spatial relation and temporal relation relatively, we produced a deep multi-view spatial-temporal network to combine them into one prediction model framework. In the experimental section, we investigate in the experiment on enormous amount of real sharing-bikes application use data in the core region of Beijing to test the performance of the model framework with a 1 km × 1 km grid-level scale and compare it with other existing machine learning approaches and prediction models. And the 4G/5G/6G communication technology facilitate the real-time control of the space-time locations of sharing bikes dynamically. Thus, it provides the basis for high-frequency analysis of space-time patterns, especially supported by the 6G large-scale application in the future. © 2000-2011 IEEE.
Author Keywords	deep learning; ITS; multi-view; Sharing-bikes prediction; spatial-temporal feature; travel-behaviors 4G/5G/6G communication

Similar Articles

Id	Similarity	Authors	Title	Published
12181	0.911	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)
58150	0.909	He S.; Shin K.G.	Towards Fine-Grained Flow Forecasting: A Graph Attention Approach For Bike Sharing Systems	The Web Conference 2020 - Proceedings of the World Wide Web Conference, WWW 2020 (2020)
29019	0.909	Yang, JJ; Guo, BZ; Wang, ZH; Ma, YL	Hierarchical Prediction Based On Network-Representation-Learning-Enhanced Clustering For Bike-Sharing System In Smart City	IEEE INTERNET OF THINGS JOURNAL, 8, 8 (2021)
20781	0.889	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)
26705	0.888	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)
33156	0.886	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)
35584	0.886	Cenni D.; Collini E.; Nesi P.; Pantaleo G.; Paoli I.	Long-Term Prediction Of Bikes Availability On Bike-Sharing Stations	Proceedings - DMSVIVA 2021: 27th International DMS Conference on Visualization and Visual Languages (2021)
18245	0.885	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)
24000	0.883	Subramanian M.; Cho J.; Veerappampalayam Easwaramoorthy S.; Murugesan A.; Chinnasamy R.	Enhancing Sustainable Transportation: Ai-Driven Bike Demand Forecasting In Smart Cities	Sustainability (Switzerland), 15, 18 (2023)
9519	0.882	Cuong D.V.; Ngo V.M.; Cappellari P.; Roantree M.	Analyzing Shared Bike Usage Through Graph-Based Spatio-Temporal Modeling	IEEE Open Journal of Intelligent Transportation Systems, 5 (2024)