Automated Box–Jenkins forecasting tool with an application for passenger demand in urban rail systems

Anvari, Saeedeh; Tuna, Selçuk; Çancı, Metin; Türkay, Metin

doi:10.1002/atr.1332

Cited by 46 publications

(30 citation statements)

References 56 publications

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“…forecast transportation demand, such as the Box-Jenkins [5], smoothing techniques [6], autoregressive integrated moving average (ARIMA) [4], grey forecasting [7], and state space models [8]. Of these models, the ARIMA model, which is a linear function of time-lagged variables and error terms, has been commonly used as early as 1970s [9].…”

Section: For Parametric Techniques Several Methods Have Been Used Tomentioning

confidence: 99%

Forecasting Bus Passenger Flows by Using a Clustering-Based Support Vector Regression Approach

Wang

Cheng³

et al. 2020

IEEE Access

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As a significant component of the intelligent transportation system, forecasting bus passenger flows plays a key role in resource allocation, network planning, and frequency setting. However, it remains challenging to recognize high fluctuations, nonlinearity, and periodicity of bus passenger flows due to varied destinations and departure times. For this reason, a novel forecasting model named as affinity propagation-based support vector regression (AP-SVR) is proposed based on clustering and nonlinear simulation. For the addressed approach, a clustering algorithm is first used to generate clustering-based intervals. A support vector regression (SVR) is then exploited to forecast the passenger flow for each cluster, with the use of particle swarm optimization (PSO) for obtaining the optimized parameters. Finally, the prediction results of the SVR are rearranged by chronological order rearrangement. The proposed model is tested using real bus passenger data from a bus line over four months. Experimental results demonstrate that the proposed model performs better than other peer models in terms of absolute percentage error and mean absolute percentage error. It is recommended that the deterministic clustering technique with stable cluster results (AP) can improve the forecasting performance significantly.

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Section: For Parametric Techniques Several Methods Have Been Used Tomentioning

confidence: 99%

Forecasting Bus Passenger Flows by Using a Clustering-Based Support Vector Regression Approach

Wang

Cheng³

et al. 2020

IEEE Access

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“…In the case of time-series with linear behavior, the previous techniques generate a near-exact approximation of future demands. Thus, we can find applications of these methods in forecasting exchange rate [17], demand flowmeters [14], aircraft failure rates [18], general prices [19], and transport demand [20] among others.…”

Section: B Classical Time-series Forecasting Literaturementioning

confidence: 99%

Sales Prediction through Neural Networks for a Small Dataset

Croda¹,

Gibaja-Romero²,

Morales³

2019

IJIMAI

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Sales forecasting allows firms to plan their production outputs, which contributes to optimizing firms' inventory management via a cost reduction. However, not all firms have the same capacity to store all the necessary information through time. So, time-series with a short length are common within industries, and problems arise due to small time series does not fully capture sales' behavior. In this paper, we show the applicability of neural networks in a case where a company reports a short time-series given the changes in its warehouse structure. Given the neural networks independence form statistical assumptions, we use a multilayer-perceptron to get the sales forecasting of this enterprise. We find that learning rates variations do not significantly increase the computing time, and the validation fails with an error minor to five percent.

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“…Li et al [5] utilized the MSRBF model with the data of Beijing IC card and predicted the rail transit passenger flow under the circumstance of sudden events. Anvari et al [6] put forward the Box-Jenkins method based on timing characteristics of passenger flow and utilized this method to predict passenger flow of Istanbul Rail Transit in individual time frames. The mixed EMD-BPN prediction model put forward by Wei and Chen [7] predicted the shortterm passenger flow of rail transit in three phases including EMD phase, element identification phase, and BPN phase.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Daily Entrance and Exit Passenger Flow of Rail Transit Stations by Deep Learning Method

Zhu

Yang

Wang

2018

Journal of Advanced Transportation

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The prediction of entrance and exit passenger flow of rail transit stations is one of key research focuses in the area of intelligent transportation. Based on the big data of rail transit IC card (Public Transportation Card), this paper analyzes the data of major dynamic factors having effect on entrance passenger flow and exit passenger flow of rail transit stations: weather data, atmospheric temperature data, holiday and festival data, ground index data, and elevated road data and calculates the daily entrance passenger flow and daily exit passenger flow of individual rail transit stations with data reduction. Furthermore, based on the history data of passenger flow of rail transit stations and relevant influence factors, it applies the deep learning method to choose the relatively optimal hidden layer node by means of the cut-and-try method, set up input data and labeled data, select the activation function and loss function, and use the Adam Gradient Descent Optimization Algorithm for iterative global convergence. The results verify that this method accurately predicts the daily entrance passenger flow and daily exit passenger flow of rail transit stations with the prediction error of less than 4.1%. Finally, the proposed model is compared with the linear regression model.

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Automated Box–Jenkins forecasting tool with an application for passenger demand in urban rail systems

Cited by 46 publications

References 56 publications

Forecasting Bus Passenger Flows by Using a Clustering-Based Support Vector Regression Approach

Forecasting Bus Passenger Flows by Using a Clustering-Based Support Vector Regression Approach

Sales Prediction through Neural Networks for a Small Dataset

Prediction of Daily Entrance and Exit Passenger Flow of Rail Transit Stations by Deep Learning Method

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