Forecasting usage and bike distribution of dockless bike‐sharing using journey data

Hua, Mingzhuang; Chen, Jingxu; Chen, Xuewu; Gan, Zuoxian; Wang, Pengfei; Zhao, De

doi:10.1049/iet-its.2020.0305

Cited by 23 publications

(10 citation statements)

References 31 publications

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“…Ma et al [39] used the long short-term memory (LSTM) neural network [40] to fit the nonlinear timevarying characteristics of traffic flow for the first time. After that, many LSTM-based models emerge [41]. For instance, Gu et al [7] proposed a hybrid model that combined the LSTM and the gated recurrent unit (GRU) to improve the prediction accuracy.…”

Section: Deep Learning Based Methodsmentioning

confidence: 99%

Efficient deep learning based method for multi‐lane speed forecasting: a case study in Beijing

Wan

et al. 2020

IET intell. transp. syst

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Real-time and accurate multi-lane traffic condition forecasting is of great importance to the connected and automated vehicle highway system. However, the majority of existing deep learning based traffic prediction methods focus on pursuing the precision of the methods while neglect to improve the efficiency of the methods. To achieve the high accuracy and high efficiency of multi-lane traffic flow prediction simultaneously, this study proposes a novel combination method via the integration of the clockwork recurrent neural network (CWRNN) and random forest (RF) method, which is RF-CWRNN. To the best of the authors' knowledge, this is the first time that the CWRNN is introduced to capture the temporal feature of lane-level traffic flow and make traffic speed prediction. Meanwhile, the RF method is employed to measure the temporal relevance of the traffic flow and determine the optimal input time window. To verify the performance of the RF-CWRNN method, the ground-truth data of the expressways in Beijing were utilised to carry out experiments. The results indicate that the RF-CWRNN method is superior to the baseline models in terms of accuracy and robustness. Besides, the proposed method can save plenty of training time compared with the classical long short-term memory neural network.

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Section: Deep Learning Based Methodsmentioning

confidence: 99%

Efficient deep learning based method for multi‐lane speed forecasting: a case study in Beijing

Wan

et al. 2020

IET intell. transp. syst

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“…An analogous problem that has received much more attention in the past is the prediction of the availability of bicycles at public bicycle sharing stations [26][27][28][29][30][31][32][33][34][35]. The problems are comparable since there are a number of spots that can either be occupied or not and it is relevant to determine when stations are out of bicycles to rent.…”

Section: Literature Reviewmentioning

confidence: 99%

“…In the context of this paper, the aggregation unit is the individual CS, with the corresponding high noise making such tools unsuitable. A selection of employed machine learning models includes random forest models [26], deep learning models [27], k-means clustering [31,32], or, less frequently, models such as the averaged one dependence estimators with subsumption resolution model [33]. Most of these models are in fact quite useful for our purpose as well and have been investigated.…”

Section: Literature Reviewmentioning

confidence: 99%

Predicting Electric Vehicle Charging Station Availability Using Ensemble Machine Learning

2021

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Electric vehicles may reduce greenhouse gas emissions from individual mobility. Due to the long charging times, accurate planning is necessary, for which the availability of charging infrastructure must be known. In this paper, we show how the occupation status of charging infrastructure can be predicted for the next day using machine learning models— Gradient Boosting Classifier and Random Forest Classifier. Since both are ensemble models, binary training data (occupied vs. available) can be used to provide a certainty measure for predictions. The prediction may be used to adapt prices in a high-load scenario, predict grid stress, or forecast available power for smart or bidirectional charging. The models were chosen based on an evaluation of 13 different, typically used machine learning models. We show that it is necessary to know past charging station usage in order to predict future usage. Other features such as traffic density or weather have a limited effect. We show that a Gradient Boosting Classifier achieves 94.8% accuracy and a Matthews correlation coefficient of 0.838, making ensemble models a suitable tool. We further demonstrate how a model trained on binary data can perform non-binary predictions to give predictions in the categories “low likelihood” to “high likelihood”.

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“…Dockless shared bicycles provide residents with more convenient services due to their stop-on-ride, flexibility, ease of use and low price. However, there are also many factors that are not conducive to the development of urban transportation, such as disorderly parking and the imbalance between supply and demand (16)(17)(18). These problems might decrease the opportunity and willingness of the public to use shared bicycles, but they have occurred in almost every city where shared bicycles are deployed in Asia, Europe and the Americas, including Beijing, Shanghai and Nanjing in China (17,19,20).…”

Section: Introductionmentioning

confidence: 99%

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

Cao

Liang

Zhu

et al. 2022

Front. Public Health

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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.

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Forecasting usage and bike distribution of dockless bike‐sharing using journey data

Cited by 23 publications

References 31 publications

Efficient deep learning based method for multi‐lane speed forecasting: a case study in Beijing

Efficient deep learning based method for multi‐lane speed forecasting: a case study in Beijing

Predicting Electric Vehicle Charging Station Availability Using Ensemble Machine Learning

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

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