Prediction for Origin-Destination Distribution of Dockless Shared Bicycles: A Case Study in Nanjing City

Abstract

Shared bicycles are currently widely welcomed by the public due to their flexibility and convenience; they also help reduce chemical emissions and improve public health by encouraging people to engage in physical activities. However, during their development process, the imbalance between the supply and demand of shared bicycles has restricted the public's willingness to use them. Thus, it is necessary to forecast the demand for shared bicycles in different urban regions. This article presents a prediction model called QPSO-LSTM for the origin and destination (OD) distribution of shared bicycles by combining long short-term memory (LSTM) and quantum particle swarm optimization (QPSO). LSTM is a special type of recurrent neural network (RNN) that solves the long-term dependence problem existing in the general RNN, and is suitable for processing and predicting important events with very long intervals and delays in time series. QPSO is an important swarm intelligence algorithm that solves the optimization problem by simulating the process of birds searching for food. In the QPSO-LSTM model, LSTM is applied to predict the OD numbers. QPSO is used to optimize the LSTM for a problem involving a large number of hyperparameters, and the optimal combination of hyperparameters is quickly determined. Taking Nanjing as an example, the prediction model is applied to two typical areas, and the number of bicycles needed per hour in a future day is predicted. QPSO-LSTM can effectively learn the cycle regularity of the change in bicycle OD quantity. Finally, the QPSO-LSTM model is compared with the autoregressive integrated moving average model (ARIMA), back propagation (BP), and recurrent neural networks (RNNs). This shows that the QPSO-LSTM prediction result is more accurate.

A Vehicle Velocity Prediction Method with Kinematic Segment Recognition

“…In terms of the PM2.5 concentration prediction, the prediction model of QPSO combined with LSTM can accurately predict the PM2.5 concentration after training [37]. In terms of the usage prediction of shared bicycles, the prediction model of QPSO combined with LSTM could predict the number of bicycles needed per hour in the future day [38]. The forecast model of QPSO combined with LSTM could predict the freight volume in the future time (hour, day, or month), [39] and each of the above models showed good predictive power when verified.…”

3PG-MT-LSTM: A Hybrid Model under Biomass Compatibility Constraints for the Prediction of Long-Term Forest Growth to Support Sustainable Management

“…This is difficult to explain and quantify using known mechanisms [64,65]. Because of its gating mechanism, LSTM is better suited to processing and predicting a long-time series with relatively large intervals and delays [66,67]. LSTM can accurately capture the effect of events on forest biomass, whether it be forest thinning with extended gap intervals or climatic change with lag effects.…”