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Smart city article details

Title	Mobility Prediction For Uneven Distribution Of Bikes In Bike Sharing Systems
ID_Doc	37370
Authors	Shir B.; Prakash Verma J.; Bhattacharya P.
Year	2023
Published	Concurrency and Computation: Practice and Experience, 35, 2
DOI	http://dx.doi.org/10.1002/cpe.7465
Abstract	User mobility represents the movement of either individual or group. In smart cities, detection and prediction of mobility patterns are required for numerous applications like resource distribution, traffic management, and user behavioral analysis. With the increase in the number of smart vehicles, urban mobility detection and prediction have become a critical problem for study. Bike-sharing ecosystems (BSS) form an integral part of such ecosystems, as it supports the green revolution, ease of access, and solves traffic problems. However, recent schemes have suggested that BSS are challenged by issues of high density, mobility complexity of bikes (stations), large commute cost, uneven distribution, and route imbalances. To address the critical issues, the article proposes a hybrid scheme that combines rebalancing using clustering that addresses the mobility complexity. Once rebalancing is done, we address the uneven distribution among clusters using prediction models. This article is presented a comparative analysis of algorithms like fuzzy C-means clustering, linear regression, decision tree, and random forest classifiers for predictive analysis performed on weather data and nonweather data. The presented results indicate the viability of the proposed model in real-world scenarios. © 2022 John Wiley & Sons, Ltd.
Author Keywords	bike sharing system; clustering; machine learning; mobility detection; mobility prediction; user mobility

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