Accurate and real-time passenger flow prediction of rail transit is an important part of intelligent transportation systems(ITS).According to previous studies,it is found that the prediction effect of a single model i...Accurate and real-time passenger flow prediction of rail transit is an important part of intelligent transportation systems(ITS).According to previous studies,it is found that the prediction effect of a single model is not good for datasets with large changes in passenger flow characteristics and the deep learning model with added influencing factors has better prediction accuracy.In order to provide persuasive passenger flow forecast data for ITS,a deep learning model considering the influencing factors is proposed in this paper.In view of the lack of objective analysis on the selection of influencing factors by predecessors,this paper uses analytic hierarchy processes(AHP)and one-way ANOVA analysis to scientifically select the factor of time characteristics,which classifies and gives weight to the hourly passenger flow through Duncan test.Then,combining the time weight,BILSTM based model considering the hourly travel characteristics factors is proposed.The model performance is verified through the inbound passenger flow of Ningbo rail transit.The proposed model is compared with many current mainstream deep learning algorithms,the effectiveness of the BILSTM model considering influencing factors is validated.Through comparison and analysis with various evaluation indicators and other deep learning models,the results show that the R2 score of the BILSTM model considering influencing factors reaches 0.968,and the MAE value of the BILSTM model without adding influencing factors decreases by 45.61%.展开更多
This paper chooses passenger flow data of some stations in China from January 2015 to March 2016, and the time series prediction model of BP neural network for railway passenger flow is established. But because of its...This paper chooses passenger flow data of some stations in China from January 2015 to March 2016, and the time series prediction model of BP neural network for railway passenger flow is established. But because of its slow convergence speed and easily falling into local optimal solution of the problem, we propose to improve the time series model of BP neural network by genetic algorithm to predict railway passenger flow. Experimental results show that the improved method has higher prediction accuracy and better nonlinear fitting ability.展开更多
This paper studies the short-term prediction methods of sectional passenger flow, and selects BP neural network combined with the characteristics of sectional passenger flow itself. With a case study, we design three ...This paper studies the short-term prediction methods of sectional passenger flow, and selects BP neural network combined with the characteristics of sectional passenger flow itself. With a case study, we design three different schemes. We use Matlab to realize the prediction of the sectional passenger flow of the Beijing subway Line 2 and make comparative analysis. The empirical research shows that combining data characteristics of sectional passenger flow with the BP neural network have good prediction accuracy.展开更多
It is crucial to correctly predict the passenger flow of an air route for the construction and development of an airport.Based on the passenger flow data of Sanya Airport from 2008 to 2016,this paper respectively adop...It is crucial to correctly predict the passenger flow of an air route for the construction and development of an airport.Based on the passenger flow data of Sanya Airport from 2008 to 2016,this paper respectively adopted Holt-Winter Seasonal Model,ARMA and linear regression model to predict the passenger flow of Sanya Airport from 2017 to 2018.In order to reduce the prediction error and improve the prediction accuracy at meanwhile,the combinatorial weighted method is adopted to predict the data in a combined manner.Upon verification,this method has been proved to be an effective approach to predict the airport passenger flow.展开更多
Passenger flow plays an important role in the indoor environment and energy consumption of airport terminals.In this paper,field investigations were carried out in four typical airport terminals with different scales ...Passenger flow plays an important role in the indoor environment and energy consumption of airport terminals.In this paper,field investigations were carried out in four typical airport terminals with different scales and operation states to reveal the characteristics of passenger flow.A prediction model is established to forecast passengers’distribution in the main areas of an airport terminal based on its flight arrangement.The results indicate the dislocation peaks of passenger numbers in these areas,due to the airport’s departure process.The peak time interval is about 30 min between the check-in hall and the security check area,and 60-80 min between the check-in hall and the departure hall.RD value(i.e.,the ratio of the actual passenger number in a certain area to the design value)is used to describe this peak shifting feature.When the annual passenger throughput of an airport terminal reaches or even exceeds its design value,the total peak RD value is normally 0.6-0.8.For the airport affected by COVID-19,the peak RD is only 0.2,which reflects the decline in terminal passenger numbers during the pandemic.This research provides useful insight into the characteristics of passenger flow in airport terminals,and is beneficial for their design and operation.展开更多
In order to provide citizens with safe, convenient and comfortable services and infrastructure in a metropolis, the prediction of passenger flows in the metro-net of subway system has become more important than ever b...In order to provide citizens with safe, convenient and comfortable services and infrastructure in a metropolis, the prediction of passenger flows in the metro-net of subway system has become more important than ever before. Al- though a great number of prediction methods have been pre- sented in the field of transportation, all of them belong to the station oriented approach, which is not well suited to the Bei- jing subway system. This paper proposes a novel metro-net oriented method, called the probability tree based passenger flow model, which is also based on historic origin-destination (OD) information. First it learns and obtains the appearance probabilities for each kind of OD pair. For the real-time origin datum, the destination datum is calculated, and then several kinds of passenger flow in the metro-net can be pre- dicted by gathering all the contributions. The results of exper- iments, using the historical data of Beijing subway, show that although the proposed method has lower performance than existing prediction approaches for forecasting exit passenger flows, it is able to predict several additional kinds of passen- ger flow in stations and throughout the subway system; and it is a more feasible, suitable, and advanced passenger flow prediction model for Beijing subway system.展开更多
基金supported by the Program of Humanities and Social Science of Education Ministry of China(Grant No.20YJA630008)the Ningbo Natural Science Foundation of China(Grant No.202003N4142)+1 种基金the Natural Science Foundation of Zhejiang Province,China(Grant No.LY20G010004)the K.C.Wong Magna Fund in Ningbo University,China.
文摘Accurate and real-time passenger flow prediction of rail transit is an important part of intelligent transportation systems(ITS).According to previous studies,it is found that the prediction effect of a single model is not good for datasets with large changes in passenger flow characteristics and the deep learning model with added influencing factors has better prediction accuracy.In order to provide persuasive passenger flow forecast data for ITS,a deep learning model considering the influencing factors is proposed in this paper.In view of the lack of objective analysis on the selection of influencing factors by predecessors,this paper uses analytic hierarchy processes(AHP)and one-way ANOVA analysis to scientifically select the factor of time characteristics,which classifies and gives weight to the hourly passenger flow through Duncan test.Then,combining the time weight,BILSTM based model considering the hourly travel characteristics factors is proposed.The model performance is verified through the inbound passenger flow of Ningbo rail transit.The proposed model is compared with many current mainstream deep learning algorithms,the effectiveness of the BILSTM model considering influencing factors is validated.Through comparison and analysis with various evaluation indicators and other deep learning models,the results show that the R2 score of the BILSTM model considering influencing factors reaches 0.968,and the MAE value of the BILSTM model without adding influencing factors decreases by 45.61%.
文摘This paper chooses passenger flow data of some stations in China from January 2015 to March 2016, and the time series prediction model of BP neural network for railway passenger flow is established. But because of its slow convergence speed and easily falling into local optimal solution of the problem, we propose to improve the time series model of BP neural network by genetic algorithm to predict railway passenger flow. Experimental results show that the improved method has higher prediction accuracy and better nonlinear fitting ability.
文摘This paper studies the short-term prediction methods of sectional passenger flow, and selects BP neural network combined with the characteristics of sectional passenger flow itself. With a case study, we design three different schemes. We use Matlab to realize the prediction of the sectional passenger flow of the Beijing subway Line 2 and make comparative analysis. The empirical research shows that combining data characteristics of sectional passenger flow with the BP neural network have good prediction accuracy.
文摘It is crucial to correctly predict the passenger flow of an air route for the construction and development of an airport.Based on the passenger flow data of Sanya Airport from 2008 to 2016,this paper respectively adopted Holt-Winter Seasonal Model,ARMA and linear regression model to predict the passenger flow of Sanya Airport from 2017 to 2018.In order to reduce the prediction error and improve the prediction accuracy at meanwhile,the combinatorial weighted method is adopted to predict the data in a combined manner.Upon verification,this method has been proved to be an effective approach to predict the airport passenger flow.
基金The research described in this paper was supported by the National Natural Science Foundation of China(No.51878369)the National Key R&D Program of China(2018YFC0705001)+2 种基金Sichuan Science and Tech-nology Planning Project(2019YFSY0009)the China Postdoctoral Science Foundation(2021M701935),the Shuimu Tsinghua Scholar Pro-gram of Tsinghua University(2021SM001)Beijing Advanced Innovation Center For Future Urban Design,Beijing University Of Civil Engineering And Architecture.
文摘Passenger flow plays an important role in the indoor environment and energy consumption of airport terminals.In this paper,field investigations were carried out in four typical airport terminals with different scales and operation states to reveal the characteristics of passenger flow.A prediction model is established to forecast passengers’distribution in the main areas of an airport terminal based on its flight arrangement.The results indicate the dislocation peaks of passenger numbers in these areas,due to the airport’s departure process.The peak time interval is about 30 min between the check-in hall and the security check area,and 60-80 min between the check-in hall and the departure hall.RD value(i.e.,the ratio of the actual passenger number in a certain area to the design value)is used to describe this peak shifting feature.When the annual passenger throughput of an airport terminal reaches or even exceeds its design value,the total peak RD value is normally 0.6-0.8.For the airport affected by COVID-19,the peak RD is only 0.2,which reflects the decline in terminal passenger numbers during the pandemic.This research provides useful insight into the characteristics of passenger flow in airport terminals,and is beneficial for their design and operation.
基金This work was supported by the National High- Tech Research and Development Plan of China (863) (2011AA010502), the National Natural Science Foundation of China (Grant No. 61103093), the Doctoral Fund of Ministry of Education of China (20091102110017), the International Science & Technology Cooperation Program of China (2010DFB 13350), the Supported Project (SKLSDE-2012ZX-16) of the State Key Laboratory of Software Development Environment, and the Fundamen- tal Research Funds for the Central Universities. We are thankful to Bei- jing Municipal Committee of Transportation, Beijing Metro Network Con- trol Center, Beijing Mass Transit Railway Operation Corporation Limited, and Beijing MTR Corporation for their great help.
文摘In order to provide citizens with safe, convenient and comfortable services and infrastructure in a metropolis, the prediction of passenger flows in the metro-net of subway system has become more important than ever before. Al- though a great number of prediction methods have been pre- sented in the field of transportation, all of them belong to the station oriented approach, which is not well suited to the Bei- jing subway system. This paper proposes a novel metro-net oriented method, called the probability tree based passenger flow model, which is also based on historic origin-destination (OD) information. First it learns and obtains the appearance probabilities for each kind of OD pair. For the real-time origin datum, the destination datum is calculated, and then several kinds of passenger flow in the metro-net can be pre- dicted by gathering all the contributions. The results of exper- iments, using the historical data of Beijing subway, show that although the proposed method has lower performance than existing prediction approaches for forecasting exit passenger flows, it is able to predict several additional kinds of passen- ger flow in stations and throughout the subway system; and it is a more feasible, suitable, and advanced passenger flow prediction model for Beijing subway system.