Rental Prediction in Bicycle-Sharing System Using Recurrent Neural Network

Lu, Erl-Huei; Lin, Zhan-Qing

doi:10.1109/access.2020.2994588

Cited by 9 publications

(4 citation statements)

References 27 publications

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“…Long short-term memory (LSTM) [ 26 ] is based on the recurrent neural network (RNN) [ 27 ] architecture, which aims to solve the problem of long-term dependence of RNN. It can be better captured the complex nonlinear relationship in time-series data [ 28 ]. Gated recurrent unit (GRU) [ 29 ] is a variant of LSTM which composes of an update gate z t and a reset gate r t .…”

Section: The Proposed Methodsmentioning

confidence: 99%

[Retracted] CNN‐GRU‐AM for Shared Bicycles Demand Forecasting

Peng

Liang

Hao

et al. 2021

Computational Intelligence and Neuroscience

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The demand forecast of shared bicycles directly determines the utilization rate of vehicles and projects operation benefits. Accurate prediction based on the existing operating data can reduce unnecessary delivery. Since the use of shared bicycles is susceptible to time dependence and external factors, most of the existing works only consider some of the attributes of shared bicycles, resulting in insufficient modeling and unsatisfactory prediction performance. In order to address the aforementioned limitations, this paper establishes a novelty prediction model based on convolutional recurrent neural network with the attention mechanism named as CNN-GRU-AM. There are four parts in the proposed CNN-GRU-AM model. First, a convolutional neural network (CNN) with two layers is used to extract local features from the multiple sources data. Second, the gated recurrent unit (GRU) is employed to capture the time-series relationships of the output data of CNN. Third, the attention mechanism (AM) is introduced to mining the potential relationships of the series features, in which different weights will be assigned to the corresponding features according to their importance. At last, a fully connected layer with three layers is added to learn features and output the prediction results. To evaluate the performance of the proposed method, we conducted massive experiments on two datasets including a real mobile bicycle data and a public shared bicycle data. The experimental results show that the prediction performance of the proposed model is better than other prediction models, indicating the significance of the social benefits.

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Section: The Proposed Methodsmentioning

confidence: 99%

[Retracted] CNN‐GRU‐AM for Shared Bicycles Demand Forecasting

Peng

Liang

Hao

et al. 2021

Computational Intelligence and Neuroscience

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“…Taking hours as an example, temporal features are usually represented by 1 to 24, although using hours to represent the temporal features can reflect the periodicity, it may not be able to fully express the period of time. Therefore, Lu et al considered the cyclic features of time, and designed the coordinates on a unit circle using the sine and cosine functions to represent the cyclic features of time [20]. Kazemi et al proposed Time2vec.…”

Section: Feature Extractionmentioning

confidence: 99%

“…The first method uses the numbers 1-24 (hours) to represent temporal features. The second method is sin and cos, proposed by Lu et al [20], whereby the authors had designed the coordinates on a unit circle using the sine and cosine functions to represent the cyclic features of time. In this experiment, using hour or sin and cos does not perform well.…”

Section: Location Featurementioning

confidence: 99%

A Self-Attention Model for Next Location Prediction Based on Semantic Mining

Lu,

Lin

2023

IJGI

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With the rise in the Internet of Things (IOT), mobile devices and Location-Based Social Network (LBSN), abundant trajectory data have made research on location prediction more popular. The check-in data shared through LBSN hide information related to life patterns, and obtaining this information is helpful for location prediction. However, the trajectory data recorded by mobile devices are different from check-in data that have semantic information. In order to obtain the user’s semantic, relevant studies match the stay point to the nearest Point of Interest (POI), but location error may lead to wrong semantic matching. Therefore, we propose a Self-Attention model for next location prediction based on semantic mining to predict the next location. When calculating the semantic feature of a stay point, the first step is to search for the k-nearest POI, and then use the reciprocal of the distance from the stay point to the k-nearest POI and the number of categories as weights. Finally, we use the probability to express the semantic without losing other important semantic information. Furthermore, this research, combined with sequential pattern mining, can result in richer semantic features. In order to better perceive the trajectory, temporal features learn the periodicity of time series by the sine function. In terms of location features, we build a directed weighted graph and regard the frequency of users visiting locations as the weight, so the location features are rich in contextual information. We then adopt the Self-Attention model to capture long-term dependencies in long trajectory sequences. Experiments in Geolife show that the semantic matching of this study improved by 45.78% in TOP@1 compared with the closest distance search for POI. Compared with the baseline, the model proposed in this study improved by 2.5% in TOP@1.

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“…In the bikesharing research area, some researchers have applied deep learning to forecast short-term travel demand. Lu and Lin [31] input the rental records of the past time into Recurrent Neural Network (RNN) to predict the bicycle rental in the coming day. Pan et al [32] used the deep long short-term memory (LSTM) sequence learning model to predict the rentals and returns at a single station based on historical trip data, weather data, and time data.…”

Section: A Short-term Bike-sharing Demand Predictionmentioning

confidence: 99%

Station Importance Evaluation in Dynamic Bike-Sharing Rebalancing Optimization Using an Entropy-Based TOPSIS Approach

Jin

2021

IEEE Access

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As an eco-friendly travel mode, bike-sharing has prevailed around the world. However, the systems are imbalanced due to the asymmetric spatial and temporal distribution of user demand. Station prioritization strategies are needed to rebalance more shared bikes for more important stations. This paper proposes an evaluation method of station importance in dynamic bike-sharing rebalancing. Firstly, a shortterm demand prediction model is applied to capture the temporal and spatial characteristics of bike-sharing trip data and predict bike-sharing demand at the station level. Based on the prediction results, the method of determining rebalancing quantity is proposed with consideration of bike-sharing usage throughout the rebalancing period. Then, three criteria are employed to evaluate the importance of bike-sharing stations, including rebalancing quantity, closeness to inventory threshold, and distance from the key station. An entropy-based Technique for Order of Preference by Similarity to the Ideal Solution (TOPSIS) approach is proposed to weigh different criteria and evaluate station importance. Furthermore, the experiments on bikesharing data from Nanjing City demonstrate the effectiveness of the proposed methods. This research is helpful for operators and managers to dynamically rebalance shared bikes with high efficiency and improve the service quality of bike-sharing systems.

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Rental Prediction in Bicycle-Sharing System Using Recurrent Neural Network

Cited by 9 publications

References 27 publications

[Retracted] CNN‐GRU‐AM for Shared Bicycles Demand Forecasting

[Retracted] CNN‐GRU‐AM for Shared Bicycles Demand Forecasting

A Self-Attention Model for Next Location Prediction Based on Semantic Mining

Station Importance Evaluation in Dynamic Bike-Sharing Rebalancing Optimization Using an Entropy-Based TOPSIS Approach

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