Short-Term Forecasting of Passenger Flow on the Metro Platform Using an Improved Kalman Filtering Method

CICTP 2019 ◽  
2019 ◽  
Author(s):  
Zhiyong Zhang ◽  
Tianwen Liang
Keyword(s):  
Kalman Filtering ◽  
Short Term ◽  
Filtering Method ◽  
Passenger Flow ◽  
Short Term Forecasting

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

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Pengpeng Jiao ◽  
Ruimin Li ◽  
Tuo Sun ◽  
Zenghao Hou ◽  
Amir Ibrahim
Keyword(s):  
Real Time ◽  
Nonparametric Regression ◽  
Kalman Filtering ◽  
Rail Transit ◽  
Correction Coefficient ◽  
Short Term ◽  
Transit System ◽  
Filtering Method ◽  
Passenger Flow ◽  
Model Based

Short-term prediction of passenger flow is very important for the operation and management of a rail transit system. Based on the traditional Kalman filtering method, this paper puts forward three revised models for real-time passenger flow forecasting. First, the paper introduces the historical prediction error into the measurement equation and formulates a revised Kalman filtering model based on error correction coefficient (KF-ECC). Second, this paper employs the deviation between real-time passenger flow and corresponding historical data as state variable and presents a revised Kalman filtering model based on Historical Deviation (KF-HD). Third, the paper integrates nonparametric regression forecast into the traditional Kalman filtering method using a Bayesian combined technique and puts forward a revised Kalman filtering model based on Bayesian combination and nonparametric regression (KF-BCNR). A case study is implemented using statistical passenger flow data of rail transit line 13 in Beijing during a one-month period. The reported prediction results show that KF-ECC improves the applicability to historical trend, KF-HD achieves excellent accuracy and stability, and KF-BCNR yields the best performances. Comparisons among different periods further indicate that results during peak periods outperform those during nonpeak periods. All three revised models are accurate and stable enough for on-line predictions, especially during the peak periods.

scholarly journals A Hybrid Short-Term Forecasting Model of Passenger Flow on High-Speed Rail considering the Impact of Train Service Frequency

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Qingying Lai ◽  
Jun Liu ◽  
Yongji Luo ◽  
Minshu Ma
Keyword(s):  
Hybrid Model ◽  
Traffic Management ◽  
High Speed ◽  
Short Term ◽  
High Speed Rail ◽  
Passenger Flow ◽  
Proposed Model ◽  
Service Frequency ◽  
Short Term Forecasting ◽  
The Impact

Short-term forecasting of OD (origin to destination) passenger flow on high-speed rail (HSR) is one of the critical tasks in rail traffic management. This paper proposes a hybrid model to explore the impact of the train service frequency (TSF) of the HSR on the passenger flow. The model is composed of two parts. One is the Holt-Winters model, which takes advantage of time series characteristics of passenger flow. The other part considers the changes of TSF for the OD in different time during a day. The two models are integrated by the minimum absolute value method to generate the final hybrid model. The operational data of Beijing-Shanghai high-speed railway from 2012 to 2016 are used to verify the effectiveness of the model. In addition to the forecasting ability, with a definite formation, the proposed model can be further used to forecast the effects of the TSF.

scholarly journals Short-Term Passenger Flow Forecast of Rail Transit Station Based on MIC Feature Selection and ST-LightGBM considering Transfer Passenger Flow

2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Zhe Zhang ◽  
Cheng Wang ◽  
Yueer Gao ◽  
Jianwei Chen ◽  
Yiwen Zhang
Keyword(s):  
Feature Selection ◽  
Gradient Boosting ◽  
Short Term ◽  
Bp Network ◽  
Metro Station ◽  
Passenger Flow ◽  
Light Gradient ◽  
Time Space ◽  
Short Term Forecasting ◽  
Maximal Information Coefficient

To solve the problems of current short-term forecasting methods for metro passenger flow, such as unclear influencing factors, low accuracy, and high time-space complexity, a method for metro passenger flow based on ST-LightGBM after considering transfer passenger flow is proposed. Firstly, using historical data as the training set to transform the problem into a data-driven multi-input single-output regression prediction problem, the problem of the short-term prediction of metro passenger flow is formalized and the difficulties of the problem are identified. Secondly, we extract the candidate temporal and spatial features that may affect passenger flow at a metro station from passenger travel data based on the spatial transfer and spatial similarity of passenger flow. Thirdly, we use a maximal information coefficient (MIC) feature selection algorithm to select the significant impact features as the input. Finally, a short-term forecasting model for metro passenger flow based on the light gradient boosting machine (LightGBM) model is established. Taking transfer passenger flow into account, this method has a low space-time cost and high accuracy. The experimental results on the dataset of Lianban metro station in Xiamen city show that the proposed method obtains higher prediction accuracy than SARIMA, SVR, and BP network.

scholarly journals Short-Term Forecasting of Railway Passenger Flow Based on Clustering of Booking Curves

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Minshu Ma ◽  
Jun Liu ◽  
Jingjia Cao
Keyword(s):  
Empirical Study ◽  
Dynamic Pricing ◽  
Clustering Algorithm ◽  
Real Data ◽  
Data Sets ◽  
Short Term ◽  
Passenger Flow ◽  
Control Dynamic ◽  
Railway Passenger ◽  
Short Term Forecasting

For railway companies, the benefits from revenue management activities, like inventory control, dynamic pricing, and so forth, rely heavily on the accuracy of the short-term forecasting of the passenger flow. In this paper, based on the analysis of the relevance between final booking amounts and shapes of the booking curves, a novel short-term forecasting approach, which employs a specifically designed clustering algorithm and the data of both historical booking records and the bookings on hand, is proposed. The empirical study with real data sets from Chinese railway shows that the proposed approach outperforms the advanced pickup model (one of the most popular models in practice) during the early and middle stages of booking horizon when bookings are not concentrated in the final days before departure.

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