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 Comprehensive Comparative Analysis of the Basic Theory of the Short Term Bus Passenger Flow Prediction

Symmetry ◽  
2018 ◽  
Vol 10 (9) ◽  
pp. 369 ◽  
Author(s):  
Huawei Zhai ◽  
Licheng Cui ◽  
Yu Nie ◽  
Xiaowei Xu ◽  
Weishi Zhang
Keyword(s):  
Real Time ◽  
Public Transportation ◽  
Transportation Systems ◽  
Training Data ◽  
Time Data ◽  
Short Term ◽  
Passenger Flow ◽  
Modeling Process ◽  
Automatic Passenger Counters ◽  
Public Transportation Systems

In order to meet the real-time public travel demands, the bus operators need to adjust the timetables in time. Therefore, it is necessary to predict the variations of the short-term passenger flow. Under the help of the advanced public transportation systems, a large amount of real-time data about passenger flow is collected from the automatic passenger counters, automatic fare collection systems, etc. Using these data, different kinds of methods are proposed to predict future variations of the short-term bus passenger flow. Based on the properties and background knowledge, these methods are classified into three categories: linear, nonlinear and combined methods. Their performances are evaluated in detail in the major aspects of the prediction accuracy, the complexity of training data structure and modeling process. For comparison, some long-term prediction methods are also analyzed simply. At last, it points that, with the help of automatic technology, a large amount of data about passenger flow will be collected, and using the big data technology to speed up the data preprocessing and modeling process may be one of the directions worthy of study in the future.

scholarly journals Visual Analytic Method for Metro Anomaly Detection and Diffusion

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Yunhui Li ◽  
Yong Zhang ◽  
He Shi ◽  
Yun Wei ◽  
Baocai Yin
Keyword(s):  
Traffic Management ◽  
Rapid Development ◽  
Urban Rail Transit ◽  
Rail Transit ◽  
Analytic Method ◽  
Visual Metaphor ◽  
Transit System ◽  
Passenger Flow ◽  
Anomalous Propagation ◽  
And Diffusion

With the rapid development of urbanization in recent years, thousands of people have flooded into the city, which has brought tremendous pressure on the supervision and operation of relevant traffic management departments. In particular, the unexpected events in the urban rail transit system have caused great troubles for city managers. Aiming at the problem of abnormal passenger flow in the metro, this paper proposes a visual analytic method to support the abnormal passenger flow detection, verification, and diffusion analysis in the metro system. The method provides an intuitive visual metaphor and allows users to perform simple interactive operations to verify abnormal passenger flow. In addition, the method reveals the diffusion law of abnormal passenger flow in time and space in a two-dimensional diffusion view. The Beijing Rail Transit AFC data are used to validate the developed system, and two reliable analysis cases are presented. The system can help users quickly grasp the abnormal propagation rules and help them to develop different scheduling strategies for different anomalous propagation paths.

scholarly journals Passengers Flow Forecasting Model of Urban Rail Transit Based on the Macro-Factors

2012 ◽  
Vol 6-7 ◽  
pp. 688-693 ◽  
Author(s):  
Bin Shang ◽  
Xiao Ning Zhang
Keyword(s):  
Real Data ◽  
Urban Rail Transit ◽  
Rail Transit ◽  
Estimation Model ◽  
Population Total ◽  
Transit System ◽  
The Real ◽  
Passenger Flow ◽  
Urban Rail ◽  
Multiple Variables

In China, many cities are planning urban rail transit system, but a comprehensive passenger flow estimation model is still lacking. The total passenger flow of urban rail transit in a city depends on many factors, such as urban population, total length of rail lines, gross domestic production of the city etc. To estimate the total passenger flow of urban rail transit, a linear regression model with multiple variables is established in the paper, based on the real data collected in many cities with urban rail transit operating. The comparison of the estimated flow and the real flow in many cities shows that the model is very accurate in passenger flow forecasting.

scholarly journals Optimum Equilibrium Passenger Flow Control Strategies with Delay Penalty Functions under Oversaturated Condition on Urban Rail Transit

2021 ◽  
Vol 2021 ◽  
pp. 1-27
Author(s):  
Yonghao Yin ◽  
Dewei Li ◽  
Kai Zhao ◽  
Ruixia Yang
Keyword(s):  
Flow Control ◽  
Simulated Annealing Algorithm ◽  
Control Strategies ◽  
Penalty Functions ◽  
Urban Rail Transit ◽  
Rail Transit ◽  
Transit System ◽  
Passenger Flow ◽  
Urban Rail ◽  
Linear Delay

When passengers are oversaturated in the urban rail transit system and a further increase of train frequency is impossible, passenger flow control strategy is an indispensable approach to avoid congestion and ensure safety. To make the best use of train capacity and reduce the passenger waiting time, coordinative flow control is necessary at each station on a line. In most published studies, the equilibrium of passenger distributions among different stations and periods is not considered. As a result, two issues occur making it hard to implement in practical. First, a large number of passengers are held up outside a small number of stations for very long time. Second, there is a large variation of controlled flows for successive time intervals. To alleviate this problem, a single-line equilibrium passenger flow control model is constructed, which minimizes the total passenger delay. By applying different forms of the delay penalty function (constant and linear), flow control strategies such as independent flow control and equilibrium flow control can be reproduced. An improved simulated annealing algorithm is proposed to solve the model. A numerical case is studied to analyze the sensitivity of the functions, and the best parameter relationship in different functions could be confirmed. A real-world case from Batong Line corridor in Beijing subway is used to test the applicability of the model and algorithm, and the result shows that the solution with linear delay penalty functions can not only reduce the total passenger delay but also equilibrate the number of flow control passengers on spatial and temporal.

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