Three Revised Kalman Filtering Models for Short-Term Rail Transit Passenger Flow Prediction

Jiao, Pengpeng; Li, Ruimin; Sun, Tuo; Hou, Zenghao; Ibrahim, Amir M. H.

doi:10.1155/2016/9717582

Cited by 71 publications

(38 citation statements)

References 30 publications

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“…This has been the focus of [16] for forecasting passenger flows using Artificial Neural Networks (ANN) on a single metro line in Naples with a simulated dataset. Short-term forecasting on urban metros has also been studied along with other methods, such as Kalman filter in [17] and ARIMA (autoregressive integrated moving average) models in [18]. Li et al [19] proposed a Multi-Scale Radial Basis Function (MSRBF) for forecasting short-term metro passenger flows on special occasions, such as sporting events and concerts.…”

Section: State Of the Artmentioning

confidence: 99%

“…In [30] two models are computed: (i) efficiency, using the number of cars-kilometers produced as output, and (ii) effectiveness, considering the number of transported passengers. The large number of variables and the limited number of analyzed URT networks (17) ends up with most of the evaluated systems considered highly efficient (here most URT networks excel in some, disjoint parameters, increasing its efficiency). The impact (elasticity) of the variables has also been considered, but the work fails in selecting the most representative ones.…”

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confidence: 99%

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Exploratory Data Analysis and Data Envelopment Analysis of Urban Rail Transit

Taboada

Han

2020

Electronics

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This paper deals with the efficiency and sustainability of urban rail transit (URT) using exploratory data analytics (EDA) and data envelopment analysis (DEA). The first stage of the proposed methodology is EDA with already available indicators (e.g., the number of stations and passengers), and suggested indicators (e.g., weekly frequencies, link occupancy rates, and CO2 footprint per journey) to directly characterize the efficiency and sustainability of this transport mode. The second stage is to assess the efficiency of URT with two original models, based on a thorough selection of input and output variables, which is one of the key contributions of EDA to this methodology. The first model compares URT against other urban transport modes, applicable to route personalization, and the second scores the efficiency of URT lines. The main outcome of this paper is the proposed methodology, which has been experimentally validated using open data from the Transport for London (TfL) URT network and additional sources.

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Section: State Of the Artmentioning

confidence: 99%

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confidence: 99%

Exploratory Data Analysis and Data Envelopment Analysis of Urban Rail Transit

Taboada

Han

2020

Electronics

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“…), the AFC data has been used in the researches of transportation engineering. These studies are mainly focused on four fields: prediction of passenger flow [ 2 , 7 , 10 , 11 , 12 ], analysis of passenger flow patterns [ 13 ], investigation of passenger behaviors [ 14 , 15 ], and evaluation of metro networks [ 3 , 6 ].…”

Section: Introductionmentioning

confidence: 99%

Passenger Flow Forecasting in Metro Transfer Station Based on the Combination of Singular Spectrum Analysis and AdaBoost-Weighted Extreme Learning Machine

Zhou

Wang

Zhao

2020

Sensors

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The metro system plays an important role in urban public transit, and the passenger flow forecasting is fundamental to assisting operators establishing an intelligent transport system (ITS). The forecasting results can provide necessary information for travelling decision of travelers and metro operations of managers. In order to investigate the inner characteristics of passenger flow and make a more accurate prediction with less training time, a novel model (i.e., SSA-AWELM), a combination of singular spectrum analysis (SSA) and AdaBoost-weighted extreme learning machine (AWELM), is proposed in this paper. SSA is developed to decompose the original data into three components of trend, periodicity, and residue. AWELM is developed to forecast each component desperately. The three predicted results are summed as the final outcomes. In the experiments, the dataset is collected from the automatic fare collection (AFC) system of Hangzhou metro in China. We extracted three weeks of passenger flow to carry out multistep prediction tests and a comparison analysis. The results indicate that the proposed SSA-AWELM model can reduce both predicted errors and training time. In particular, compared with the prevalent deep-learning model long short-term memory (LSTM) neural network, SSA-AWELM has reduced the testing errors by 22% and saved time by 84%, on average. It demonstrates that SSA-AWELM is a promising approach for passenger flow forecasting.

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“…And WILLIAMS, et al [4] proposed a SARIMA model by incorporating the influence of seasonal factors into the ARIMA model. On the other hand, Karman filtering model 2 of 19 is applied to traffic flow prediction because it is not affected by its own data noise [5]. The variance invariance of the traditional Kalman filtering model process is improved, an adaptive Karman filter approach is provided, and the feasibility prediction is carried out by 15 min time granularity [6].…”

Section: Introductionmentioning

confidence: 99%

Analysis of Prediction Accuracy under the Selection of Optimum Time Granularity in Different Metro Stations

Jing

et al. 2019

Sustainability

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The improvement of accuracy of short-term passenger flow prediction plays a key role in the efficient and sustainable development of metro operation. The primary objective of this study is to explore the factors that influence prediction accuracy from time granularity and station class. An important aim of the study was also in presenting the proposition of change in a forecasting method. Passenger flow data from 87 Metro stations in Xi’an was collected and analyzed. A framework of short-term passenger flow based on the Empirical Mode Decomposition-Support Vector Regression (EMD-SVR) was proposed to predict passenger flow for different types of stations. Also, the relationship between the generation of passenger flow prediction error and passenger flow data was investigated. First, the metro network was classified into four categories by using eight clustering factors based on the characteristics of inbound passenger flow. Second, Pearson correlation coefficient was utilized to explore the time interval and time granularity for short-term passenger flow prediction. Third, the EMD-SVR was used to predict the passenger flow in the optimal time interval for each station. Results showed that the proposed approach has a significant improvement compared to the traditional passenger flow forecast approach. Lookback Volatility (LVB) was applied to reflect the fluctuation difference of passenger flow data, and the linear fitting of prediction error was conducted. The goodness-of-fit (R2) was found to be 0.768, indicating a good fitting of the data. Furthermore, it revealed that there are obvious differences in the prediction error of the four kinds of stations.

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Three Revised Kalman Filtering Models for Short-Term Rail Transit Passenger Flow Prediction

Cited by 71 publications

References 30 publications

Exploratory Data Analysis and Data Envelopment Analysis of Urban Rail Transit

Exploratory Data Analysis and Data Envelopment Analysis of Urban Rail Transit

Passenger Flow Forecasting in Metro Transfer Station Based on the Combination of Singular Spectrum Analysis and AdaBoost-Weighted Extreme Learning Machine

Analysis of Prediction Accuracy under the Selection of Optimum Time Granularity in Different Metro Stations

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