Prediction of Shared Bicycle Entry and Exit Flow based on LSTM Model

Authors

  • Aobo Zhang

DOI:

https://doi.org/10.54097/d98ww109

Keywords:

Shared bike; LSTM; usage prediction.

Abstract

As the use of shared bicycles becomes increasingly prevalent in urban areas, the issue of short-distance travel for residents has been significantly alleviated. Moreover, within the 30-minute radius of daily life, bicycles serve as a convenient solution for short-distance travel. However, the deployment strategy employed by shared bike operators lacks scientific rationality. The placement of bicycles in inappropriate locations compromises their convenience. This research aims to establish a predictive model for forecasting the entry and exit flow of shared bicycles in each region of the city. The shared bicycle data for January 2024 were collected from the Divvy online platform in Chicago. Python was utilized to extract and process the trip data, and time features were handled by minute-wise aggregation of entry and exit flows. By selecting data from the past 30 minutes as input features, normalizing the data, and constructing time series data suitable for LSTM input, the predictive model was developed. Following model training, traffic prediction was conducted using the test set, and the model's performance was evaluated. Ultimately, the effectiveness of the model was intuitively understood by plotting comparative graphs between actual and predicted values. This research aims to provide real-time decision support for urban traffic management and shared bike operations by offering insights into the predictive trends.

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References

Kuang Jiaheng, Wu Qunyong. Spatial-temporal Equilibrium Analysis and Attraction Area Optimization of Dockless Sharing Bicycles Connected to Subway Stations. Journal of Geo information Science, 2022, 24(7): 1337-1348

Zhao B, Deng Y, Luo L, et al. Preferred streets: assessing the impact of the street environment on cycling behaviors using the geographically weighted regression. Transportation, 2024, 1-27.

Zhou Chuan. Optimization of sharing bicycle density distribution based on improved salp swarm algorithm. Computer Science, 2021.

Jiang Xiao, Bai Lubin, Lou Xiayin, et al. Usage Patterns Identification and Flow Prediction of Bikesharing System Based on Multiscale Spatiotemporal Clustering. Journal of Geo-information Science, 2022, 24(6): 1047-1060.

Bao H, Zhou X, Hamann C, et al. Understanding children's cycling route selection through spatial trajectory data mining. Transportation research interdisciplinary perspectives, 2023.

Collini E, Nesi P, Pantaleo G. Deep learning for short-term prediction of available bikes on bike-sharing stations. IEEE Access, 2021, 9.

Liu, M.; Shi, J. A cellular automata traffic flow model combined with a BP neural network based microscopic lane changing decision model. J. Intell. Transp. Syst., 2019, 23, 309-318.

Ma X, Yin Y, Jin Y, et al. Short-term prediction of bike-sharing demand using multi-source data: a spatial-temporal graph attentional LSTM approach. Applied Sciences, 2022, 12(3): 1161.

Aqib M, Mehmood R, Alzahrani A, et al. Rapid transit systems: smarter urban planning using big data, in-memory computing, deep learning, and GPUs. Sustainability, 2019, 11(10): 2736.

Chen J, et al. Dynamic planning of bicycle stations in dockless public bicycle-sharing system using gated graph neural network. ACM Transactions on Intelligent Systems and Technology (TIST), 2021, 12(2).

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Published

30-06-2024

How to Cite

Zhang, A. (2024). Prediction of Shared Bicycle Entry and Exit Flow based on LSTM Model. Highlights in Science, Engineering and Technology, 105, 30-36. https://doi.org/10.54097/d98ww109