Railway Passenger Flow Forecasting by Integrating Passenger Flow Relationship and Spatiotemporal Similarity

Railway passenger flow forecasting can help to develop sensible railway schedules,make full use of railway resources,and meet the travel demand of passengers.The structure of passenger flow in railway networks and the spatiotemporal relationship of passenger flow among stations are two distinctive features of railway passenger flow.Most of the previous studies used only a single feature for prediction and lacked correlations,resulting in suboptimal performance.To address the above-mentioned problem,we proposed the railway passenger flow prediction model called Flow-Similarity Attention Graph Convolutional Network(F-SAGCN).First,we constructed the passenger flow relations graph(RG)based on the Origin-Destination(OD).Second,the Passenger Flow Fluctuation Similarity(PFFS)algorithm is used to measure the similarity of passenger flow between stations,which helps construct the spatiotemporal similarity graph(SG).Then,we determine the weights of the mutual influence of different stations at different times through an attention mechanism and extract spatiotemporal features through graph convolution on the RG and SG.Finally,we fused the spatiotemporal features and the original temporal features of stations for prediction.The comparison experiments on a railway bureau’s accurate railway passenger flow data show that the proposed F-SAGCN method improved the prediction accuracy and reduced the mean absolute percentage error(MAPE)of 46 stations to 7.93%.

Dynamic train dwell time forecasting:a hybrid approach to address the influence of passenger flow fluctuations

Train timetables and operations are defined by the train running time in sections,dwell time at stations,and headways between trains.Accurate estimation of these factors is essential to decision-making for train delay reduction,train dispatching,and station capacity estimation.In the present study,we aim to propose a train dwell time model based on an averaging mechanism and dynamic updating to address the challenges in the train dwell time prediction problem(e.g.,dynamics over time,heavy-tailed distribution of data,and spatiotemporal relationships of factors)for real-time train dispatching.The averaging mechanism in the present study is based on multiple state-of-the-art base predictors,enabling the proposed model to integrate the advantages of the base predictors in addressing the challenges in terms of data attributes and data distributions.Then,considering the influence of passenger flow on train dwell time,we use a dynamic updating method based on exponential smoothing to improve the performance of the proposed method by considering the real-time passenger amount fluctuations(e.g.,passenger soars in peak hours or passenger plunges during regular periods).We conduct experiments with the train operation data and passenger flow data from the Chinese high-speed railway line.The results show that due to the advantages over the base predictors,the averaging mechanism can more accurately predict the dwell time at stations than its counterparts for different prediction horizons regarding predictive errors and variances.Further,the experimental results show that dynamic smoothing can significantly improve the accuracy of the proposed model during passenger amount changes,i.e.,15.4%and 15.5%corresponding to the mean absolute error and root mean square error,respectively.Based on the proposed predictor,a feature importance analysis shows that the planned dwell time and arrival delay are the two most important factors to dwell time.However,planned time has positive influences,whereas arrival delay has negative influences.

Hybrid Model for Short-Term Passenger Flow Prediction in Rail Transit

A precise and timely forecast of short-term rail transit passenger flow provides data support for traffic management and operation,assisting rail operators in efficiently allocating resources and timely relieving pressure on passenger safety and operation.First,the passenger flow sequence models in the study are broken down using VMD for noise reduction.The objective environment features are then added to the characteristic factors that affect the passenger flow.The target station serves as an additional spatial feature and is mined concurrently using the KNN algorithm.It is shown that the hybrid model VMD-CLSMT has a higher prediction accuracy,by setting BP,CNN,and LSTM reference experiments.All models’second order prediction effects are superior to their first order effects,showing that the residual network can significantly raise model prediction accuracy.Additionally,it confirms the efficacy of supplementary and objective environmental features.

Short-term inbound rail transit passenger flow prediction based on BILSTM model and influence factor analysis

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%.

A Distributionally Robust Optimization Method for Passenger Flow Control Strategy and Train Scheduling on an Urban Rail Transit Line

Regular coronavirus disease 2019(COVID-19)epidemic prevention and control have raised new require-ments that necessitate operation-strategy innovation in urban rail transit.To alleviate increasingly seri-ous congestion and further reduce the risk of cross-infection,a novel two-stage distributionally robust optimization(DRO)model is explicitly constructed,in which the probability distribution of stochastic scenarios is only partially known in advance.In the proposed model,the mean-conditional value-at-risk(CVaR)criterion is employed to obtain a tradeoff between the expected number of waiting passen-gers and the risk of congestion on an urban rail transit line.The relationship between the proposed DRO model and the traditional two-stage stochastic programming(SP)model is also depicted.Furthermore,to overcome the obstacle of model solvability resulting from imprecise probability distributions,a discrepancy-based ambiguity set is used to transform the robust counterpart into its computationally tractable form.A hybrid algorithm that combines a local search algorithm with a mixed-integer linear programming(MILP)solver is developed to improve the computational efficiency of large-scale instances.Finally,a series of numerical examples with real-world operation data are executed to validate the pro-posed approaches.

Optimization Scheme of Large Passenger Flow in Huoying Station,Line 13 of Beijing Subway System

This paper focuses on the distribution of passenger flow in Huoying Station,Line 13 of Beijing subway system.The transformation measures taken by Line 13 since operation are firstly summarized.Then the authors elaborate the facilities and equipment of this station,especially the node layout and passenger flow field.An optimization scheme is proposed to rapidly distribute the passenger flow in Huoying Station by adjusting the operation time of the escalator in the direction of Xizhimen.The authors adopt Queuing theory and Anylogic simulation software to simulate the original and the optimized schemes of Huoying Station to distribute the passenger flow.The results of the simulation indicate that the optimized scheme could effectively alleviate the traffic congestion in the hall of Huoying Station,and the pedestrian density in other places of the hall is lowered;passengers could move freely in the hall and no new congestion points would form.The rationality of the scheme is thus proved.

A prediction model to forecast passenger flow based on flight arrangement in airport terminals

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.

Real-Time Analysis and Prediction System for Rail Transit Passenger Flow Based on Deep Learning

With the rapid development of urban rail transit,rail transit plays an important role in alleviating city congestion.In recent years,with increasing pas-sengerflow,there has been huge pressure on passengerflow management.To address this problem,we propose a novel system to provide real-time statistics and predictions of passengerflow based on big data technology and deep learning technology.Moreover,the passengerflow is visualized efficiently in this system.It can provide refined passengerflow information so that people can make more rational decisions in terms of operation and planning,deploy contingency plans to avoid emergency situations,and integrate passengerflow analysis with train production,scheduling and operation to achieve cost reduction and efficiency enhancement.

Probability tree based passenger flow prediction and its application to the Beijing subway system

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.

Study on Optimization of Passenger Flow at a Metro Station Based on AnyLogic-Case Study of Youfangqiao Station of Nanjing Metro Line 2

In this study,simulation software AnyLogic was used to establish a station simulation model for a metro line.First,a basic model of the environment of the metro station was drawn,and accordingly,reasonable assumptions and simplifications were proposed.Then,a diagram of the passenger walking path was created and the simulation variables and functions for passenger flow management were designed.Considering Youfangqiao Station of Nanjing Metro Line 2 in China as an example,the real passenger flow data of this station were statistically analyzed.To simulate the station passenger flow management,input parameters such as the passenger space diameter,passenger flow generation rate,delay rate of automatic fare collection equipment and security check machine,and the number of gates were considered.Passenger flow management was optimized for the morning and evening peak periods,and reasonable suggestions were proposed based on the optimization results,providing a theoretical basis for the construction planning and pre-evaluation of station operation capacities of urban rail transit systems.

Forecast and Analysis of Passenger Flow at Sanya Airport Based on Gray System Theory

The forecast of airport passenger throughput can provide a scientific basis for airport construction and management and has important reference value.A Gray model GM(1,1)is established to predict the passenger flow of Sanya Phoenix International Airport in 2018 and 2019 by collecting yearly and monthly passenger flow data from 2012 to 2017.The results indicate that the predicted values are in good agreement with the actual values and that the relative errors are very close,which means that both the monthly forecast and the annual forecast can well reflect the actual situation of the airport passenger flow.

Medium-term forecast of daily passenger volume of high speed railway based on DLP-WNNMedium-term forecast of dailypassenger volume of high speedrailway based on DLP-WNN

Purpose – This paper aims to propose a medium-term forecast model for the daily passenger volume of HighSpeed Railway (HSR) systems to predict the daily the Origin-Destination (OD) daily volume formultiple consecutivedays (e.g. 120 days).Design/methodology/approach – By analyzing the characteristics of the historical data on daily passengervolume of HSR systems, the date and holiday labels were designed with determined value ranges.In accordance to the autoregressive characteristics of the daily passenger volume of HSR, the Double LayerParallel Wavelet Neural Network (DLP-WNN) model suitable for the medium-term (about 120 d) forecast of thedaily passenger volume of HSR was established. The DLP-WNN model obtains the daily forecast result byweighed summation of the daily output values of the two subnets. Subnet 1 reflects the overall trend of dailypassenger volumes in the recent period, and subnet 2 the daily fluctuation of the daily passenger volume toensure the accuracy of medium-term forecast.Findings – According to the example application, in which the DLP-WNN modelwas used for the medium-termforecast of the daily passenger volumes for 120 days for typical O-D pairs at 4 different distances, the averageabsolute percentage error is 7%-12%, obviously lower than the results measured by the Back Propagation (BP)neural network, the ELM (extreme learning machine), the ELMAN neural network, the GRNN (generalizedregression neural network) and the VMD-GA-BP. The DLP-WNN model was verified to be suitable for themedium-term forecast of the daily passenger volume of HSR.Originality/value – This study proposed a Double Layer Parallel structure forecast model for medium-termdaily passenger volume (about 120 days) of HSR systems by using the date and holiday labels and WaveletNeural Network. The predict results are important input data for supporting the line planning, scheduling andother decisions in operation and management in HSR systems.

On Distributary Model of Public Transportation in Metropolis

The expansion of population and the worsening of the city development pattern brought serious effects on public transportation in the metropolis. The traditional method is not suitable for the analysis of city public transportation. This paper presents the model of “frog jumping and permeation” of passage flow in city public transportation. In this model, the passages between the first class gradient centers are transported by the fastest ways, and transferred in the mode of permeation between the second class gradient centers. this model will improvc the situation in the metropolitan communications.

Analysis of the Correlation between Tourism and Climate in the Ancient City of Langzhong

Based on the meteorological data of Langzhong from 1981 to 2016,a comprehensive comfort index model of tourism climate suitable for Langzhong is established by calculating the meteorological and climatic factors affecting tourism in the ancient city of Langzhong.The model is used to evaluate the climate comprehensive comfort of Langzhong,and its grades and suitable tourism periods are divided.Based on the monthly index of passenger flow volume in the ancient city of Langzhong from 2013 to 2015,a mathematical model is established through OLS regression analysis to analyze the correlation between changes in monthly passenger flow volume in a year and the comprehensive comfort of tourism climate in the ancient city of Langzhong.The results show that the climate in Langzhong is suitable for tourism in spring and autumn.It is suitable for tourism from February to June and from September to December,of which it is most suitable for tourism from April to May and from September to October.It is less suitable for tourism in only January and from July to August,and there is no unsuitable period.The changes in monthly passenger flow volume in a year are mainly affected by the meteorology and climate.The changes of climate comprehensive comfort in various month have an extremely significant impact on passenger flow volume.The elastic coefficient of impact of climate comprehensive comfort index on the monthly index of passenger flow volume is 0.9614%.