Key Laboratory of Virtual Geographic Environment (Ministry of Education of PRC), Nanjing Normal University, Nanjing, China.
State Key Laboratory Cultivation Base of Geographical Environment Evolution (Jiangsu Province), Nanjing Normal University, Nanjing, China.
Front Public Health. 2022 Apr 8;10:849766. doi: 10.3389/fpubh.2022.849766. eCollection 2022.
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.
共享单车因其灵活性和便利性而受到公众广泛欢迎；它们还通过鼓励人们进行体育活动来帮助减少化学排放物并改善公众健康。然而，在发展过程中，共享单车的供需不平衡限制了公众使用它们的意愿。因此，有必要预测不同城市地区对共享单车的需求。本文提出了一种名为 QPSO-LSTM 的预测模型，通过结合长短期记忆（LSTM）和量子粒子群优化（QPSO）来预测共享单车的起讫点（OD）分布。LSTM 是一种特殊类型的递归神经网络（RNN），它解决了一般 RNN 中存在的长期依赖问题，适合处理和预测时间序列中具有非常长间隔和延迟的重要事件。QPSO 是一种重要的群体智能算法，通过模拟鸟类寻找食物的过程来解决优化问题。在 QPSO-LSTM 模型中，LSTM 用于预测 OD 数量。QPSO 用于优化 LSTM，以解决涉及大量超参数的问题，并快速确定超参数的最佳组合。以南京为例，将预测模型应用于两个典型区域，并预测未来一天每小时所需的自行车数量。QPSO-LSTM 可以有效地学习自行车 OD 数量变化的周期规律。最后，将 QPSO-LSTM 模型与自回归综合移动平均模型（ARIMA）、反向传播（BP）和递归神经网络（RNNs）进行比较。结果表明，QPSO-LSTM 的预测结果更准确。
