Dynamic linear models to predict bike availability in a bike sharing system

Almannaa, Mohammed; Elhenawy, Mohammed; Rakha, Hesham A.

doi:10.1080/15568318.2019.1611976

Cited by 30 publications

(16 citation statements)

References 24 publications

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“…In the same time interval, it uses the average value of historical inflows and outflows to make predictions. ARIMA [ 37 ]: ARIMA is a popular time-series forecasting model. It is simple and does not require other exogenous variables.…”

Section: Experimental Analysismentioning

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: Experimental Analysismentioning

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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“…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%

“…The employed tools are quite similar to the ones used for CSs and include statistical models or machine learning models. Examples for the former are a generalised extreme value count model [28], dynamic linear models [29], generalised additive models [30], autoregressive, or autoregressive, integrating, moving average models (ARIMA) [26]. These models can typically be employed when usage patterns are fairly regular, particularly for the latter two.…”

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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“…In mobility, structural decomposition models have also been used for prediction purposes, as in Almannaa, Elhenawy, and Rakha (2020). The authors developed structural models to predict the rate of a bike sharing system usage.…”

Section: Mobility Analysis and Structural Models For Time Series Decompositionmentioning

confidence: 99%

What can we learn from 9 years of ticketing data at a major transport hub? A structural time series decomposition

Nailly

Côme

Samé

et al. 2021

Transportmetrica A: Transport Science

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Mobility demand analysis is increasingly based on smart card data, that are generally aggregated into time series describing the volume of riders along time. These series present patterns resulting from multiple external factors. This paper investigates the problem of decomposing daily ridership data collected at a multimodal transportation hub. The analysis is based on structural time series models that decompose the series into unobserved components. The aim of the decomposition is to highlight the impact of long-term factors, such as trend or seasonality, and exogenous factors such as maintenance work or unanticipated events such as strikes or the COVID-19 health crisis. We focus our analysis on incoming flows of passengers to two transport lines known to be complementary in the Parisian public transport network. The available ridership data allows analysis over both long-term and short-term time horizons including significant events that have impacted people's mobility in the Paris region.

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Dynamic linear models to predict bike availability in a bike sharing system

Cited by 30 publications

References 24 publications

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

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

Predicting Electric Vehicle Charging Station Availability Using Ensemble Machine Learning

What can we learn from 9 years of ticketing data at a major transport hub? A structural time series decomposition

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