Predicting bike sharing demand using recurrent neural networks

Yan Pan · Ray Chen Zheng · Xin Yao

90 citations0/0 files storedProcedia Computer Science

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Urban Transport and Accessibility Hydrological Forecasting Using AI Traffic Prediction and Management Techniques

Abstract

Predicting bike sharing demand can help bike sharing companies to allocate bikes better and ensure a more sufficient circulation of bikes for customers. This paper proposes a real-time method for predicting bike renting and returning in different areas of a city during a future period based on historical data, weather data, and time data. We construct a network of bike trips from the data, use a community detection method on the network, and find two communities with the most demand for shared bikes. We use data of stations in the two communities as our dataset, and train an deep LSTM model with two layers to predict bike renting and returning, making use of the gating mechanism of long short term memory and the ability to process sequence data of recurrent neural network. We evaluate the model with the Root Mean Squared Error of data and show that the prediction of proposed model outperforms that of other deep learning models by comparing their RMSEs.

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References

2016Factors influencing the choice of shared bicycles and shared electric bikes in Beijing 2010Urban cycles and mobility patterns: Exploring and predicting trends in a bicycle-based public transport system 2011Understanding Bike-Sharing Systems using Data Mining: Exploring Activity Patterns 2016Mobility Modeling and Prediction in Bike-Sharing Systems

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