ARIMA-FSVR Hybrid Method for High-Speed Railway Passenger Traffic Forecasting

Ge, Meng; Zhang, Junfeng; Wu, Jinfei; Han, Houde; Shan, Xinghua; Hongye, Wang

doi:10.1155/2021/9961324

Cited by 6 publications

(4 citation statements)

References 18 publications

(15 reference statements)

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“…Jackson et al (2021) benefited from various Bayesian Network (BN) models for predicting bus schedule time. Ge et al (2021) implemented a combination of differentially ARIMA and SVM to achieve a highly predictive model for passenger flow in Shanghai-Guangzhou railway station. Kamandanipour et al (2022) presented a multi-layer ANN system to forecast the strength of demand caused by seasonal conditions using train ticket service data.…”

Section: Literature Reviewmentioning

confidence: 99%

Multi-layer perceptron based transfer passenger flow prediction in Istanbul transportation system

Utku

Kaya

2022

Decis. Mak. Appl. Manag. Eng.

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Estimating passenger movement in transportation networks is a critical aspect of public transportation systems. It allows for a greater understanding of traffic patterns, as well as efficient system evaluation and monitoring. It could also help with precise timing to emergencies or important events, as well as the improvement of urban transport system weaknesses and service quality. The number of transfer passengers demand in Istanbul, Turkey's biggest and most developed metropolis, was used to construct a real-world forecasting model in this study. The number of transfer passengers has been forecasted using popular machine learning methods such as kNN (k-Nearest Neighbours), LR (Linear Regression), RF (Random Forest), SVM (Support Vector Machine), XGBoost and MLP. The dataset utilized is made up of hourly passenger transfer counts gathered at two public transportation transfer stations in Istanbul in January 2020. Using MSE, RMSE, MAE and R2 parameters, each model's experimental data have been thoroughly evaluated. MLP has more successfully other machine learning algorithms in the majority of transportation lines, according to the experimental results.

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Section: Literature Reviewmentioning

confidence: 99%

Multi-layer perceptron based transfer passenger flow prediction in Istanbul transportation system

Utku

Kaya

2022

Decis. Mak. Appl. Manag. Eng.

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“…Ma et al (2020) concatenate a fundamental neural network model with the ARIMA model for network-wide traffic forecasting. Ge et al (2021) propose a hybrid ARIMA model and fuzzy SVR for high-speed railway passenger traffic forecasting. However, these learningbased models may lack interpretability, and some statistical assumptions made in these models are hard to justify in practice.…”

Section: Related Workmentioning

confidence: 99%

A Novel Markov Model for Near-Term Railway Delay Prediction

Xu¹,

Wang²,

Gao³

et al. 2022

Preprint

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Predicting the near-future delay with accuracy for trains is momentous for railway operations and passengers' traveling experience. This work aims to design prediction models for train delays based on Netherlands Railway data. We first develop a chi-square test to show that the delay evolution over stations follows a first-order Markov chain. We then propose a delay prediction model based on non-homogeneous Markov chains. To deal with the sparsity of the transition matrices of the Markov chains, we propose a novel matrix recovery approach that relies on Gaussian kernel density estimation. Our numerical tests show that this recovery

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“…Combining ARIMA with additional techniques could increase the forecasting precision of hybrid models. [17] DBNs were used by Koesdwiady et al [18] combining traffic flow and weather data at the decision level can lead to more accurate predictions based on traffic and weather data. However, achieving good performance in decisionlevel data fusion can be difficult since some studies have only focused on traffic prediction based on weather data, without considering other weather factors.…”

Section: Introductionmentioning

confidence: 99%

Experimental Analysis of Time Series Models for Traffic Flow Prediction

Tiwari,

Prasad

2023

SMSJ

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Traffic flow prediction is a research topic signified by several researchers in a league span of disciplines. In this context, one of the most in-demand techniques of Machine Learning, especially time series-based techniques, helps in predicting traffic flow forecasting and increases the accuracy of the prediction model. In order to deliver extremely precise traffic forecasts, it is crucial that we put the prediction system into practice in the actual world. We aim to perform computations related to traffic on the traffic datasets and determine the accuracy of each model. For this purpose, we are using three distinct time series models: Long Short Term Memory (LSTM), the Autoregressive Integrated Moving Average (ARIMA), and the Seasonal Autoregressive Integrated Moving Average (SARIMA). From the results obtained, it is concluded that the proposed model achieves the highest prediction accuracy with the lowest root mean squared error.

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ARIMA-FSVR Hybrid Method for High-Speed Railway Passenger Traffic Forecasting

Cited by 6 publications

References 18 publications

Multi-layer perceptron based transfer passenger flow prediction in Istanbul transportation system

Multi-layer perceptron based transfer passenger flow prediction in Istanbul transportation system

A Novel Markov Model for Near-Term Railway Delay Prediction

Experimental Analysis of Time Series Models for Traffic Flow Prediction

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