Characteristics Of Passenger Flow Research Articles

Research on digital flow control model of urban rail transit under the situation of epidemic prevention and control

Purpose Beijing rail transit can actively control the density of rail transit passenger flow, ensure travel facilities and provide a safe and comfortable riding atmosphere for rail transit passengers during the epidemic. The purpose of this paper is to efficiently monitor the flow of rail passengers, the first method is to regulate the flow of passengers by means of a coordinated connection between the stations of the railway line; the second method is to objectively distribute the inbound traffic quotas between stations to achieve the aim of accurate and reasonable control according to the actual number of people entering the station. Design/methodology/approach This paper analyzes the rules of rail transit passenger flow and updates the passenger flow prediction model in time according to the characteristics of passenger flow during the epidemic to solve the above-mentioned problems. Big data system analysis restores and refines the time and space distribution of the finely expected passenger flow and the train service plan of each route. Get information on the passenger travel chain from arriving, boarding, transferring, getting off and leaving, as well as the full load rate of each train. Findings A series of digital flow control models, based on the time and space composition of passengers on trains with congested sections, has been designed and developed to scientifically calculate the number of passengers entering the station and provide an operational basis for operating companies to accurately control flow. Originality/value This study can analyze the section where the highest full load occurs, the composition of passengers in this section and when and where passengers board the train, based on the measured train full load rate data. Then, this paper combines the full load rate control index to perform reverse deduction to calculate the inbound volume time-sharing indicators of each station and redistribute the time-sharing indicators for each station according to the actual situation of the inbound volume of each line during the epidemic. Finally, form the specified full load rate index digital time-sharing passenger flow control scheme.

Prediction of Metro Short-term Entry Flow Based on Passenger Flow Characteristic Analysis

Prediction of short-term incoming passenger flow at metro stations is of great significance to the stable operation of Metro networks. Taking AFC data of Chengdu Metro as data source, based on Stochastic Forest model, support vector machine regression model and neural network model in machine learning method, this paper makes short-term prediction of metro entry flow and comparative analysis of three models. Metro stations are divided into four types according to the passenger flow characteristics: residential type, commercial type, business type and terminal type. Three models are trained in workdays and weekends conditions for different types of stations. The average absolute percentage error (MAPE) and root mean square error (RMSE) were used to evaluate the accuracy and stability of the prediction results. The results show that BP neural network has the best comprehensive performance and random forest has better prediction accuracy for stations with strong periodicity.

Research on Space-Time and Network Characteristics of Interprovincial Passenger Flow during the 2019 Spring Festival Transportation

To build the Spring Festival travel OD matrix of the Spring Festival of 2019 based on Baidu's inter-provincial migration data, China's inter-provincial passenger travel time and space features, the network passenger flow, and the characteristics of inter-provincial travel in different periods of Spring Festival travel in different provinces are analyzed by indicators, such as the volume of inter-provincial travel, inter-provincial migration, inter-provincial migration and net migration, the horizontal contrast of the characteristics of inter-provincial travel scale of 34 provincial administrative units across the country. The research conclusions are as follows. The inter-provincial travel of tourists has significant regular characteristics before and after the Spring Festival. The scale and time distribution characteristics of the rework flow before and after the Spring Festival can be used as an important basis for studying the social and economic activity levels based on the Spring Festival travel rush; 15 provinces with net inflow and 19 provinces with net outflow were identified. Among them, 10 provinces, such as Yunnan and Gansu, had average daily net inflows/outflows of less than 3000 people, which were relatively balanced. The 40-day Spring Festival travel period is divided into five periods according to the law of inter-provincial passenger flow. Different periods have different characteristics of passenger travel scale, providing a basis for the prediction of passenger flow and transportation capacity organization. Ten popular lines of inter-provincial travel before and after the festival were identified, indicating that the inter-provincial travel of Spring Festival passengers has an obvious spatial directivity, and the start and end of travel before and after the Spring Festival, the outflow city from the beginning or end of travel, and the inflow of the source city present a symmetric relationship. The analysis of Guangdong, Anhui, and other provinces with a large population mobility shows that they have the closest relationship with the travel of surrounding provinces, and the surrounding provinces with the top three inter-provincial trips account for more than 50% of the inter-provincial trips.

A cooperative evacuation strategy for mass passenger flow in urban rail transit transfer stations

In reviewing the evacuation problem of mass passenger flow in urban rail transit transfer stations, the cooperative evacuation strategy considering urban rail transit and emergency bus simultaneously is found to be an efficient way. In this work, firstly, the dynamic characteristics of mass passenger flow are analyzed based on the abstraction and simplification of major entities of urban rail transit, including passengers, stations and trains. Then, the operations of the urban rail transit system are modeled, including the boarding, landing and transferring processes of the passengers and the status updating flows of the trains and stations. To realize the cooperative evacuation, a multi-objective optimization model considering the evacuation speed, the number of passengers transferred, and the amount of emergency buses is proposed. The NSGA-II algorithm is adopted to solve the proposed model, which can balance the theoretical validity and computational convenience. Last, the proposed strategy is applied in a real-life case based on the Shanghai Metro line, and the results verify its effectiveness and efficiency.

Travel Characteristics Analysis and Passenger Flow Prediction of Intercity Shuttles in the Pearl River Delta on Holidays

As China’s urbanization process continues to accelerate, the demand for intercity residents’ transportation has increased dramatically. Holiday travel has different demand characteristics, causing serious shortage during peak periods. However, current research barely focuses on the passenger flow prediction along with travel characteristics of intercity shuttles. Accurately predicting passenger flow during the holidays helps to improve operational organization efficiency and residents’ satisfaction, and provides a basis for reasonable resource allocation by the management department. This paper analyzes the spatiotemporal characteristics of intercity shuttles passenger flow in the Pearl River Delta. Separate passenger flow prediction models on non-holiday and holiday are established using an improved genetic algorithm optimized back propagation neural network (IGA-BPNN) based on the characteristics of passenger flow, and the prediction models are validated based on panel data. The results of weekly flow show obvious holiday characteristics, and the hourly traffic flow of holidays is much larger than that of weekends and weekdays. There is a significant difference in the hourly flow between different holidays. The IGA-BPNN model used in this paper achieves lower prediction error relative to the benchmark BPNN approach (leads a two thirds reduction in MAPE, and an over 85% reduction in MSPE).

Analysis of passenger flow characteristics and their relationship with surrounding urban functional landscape pattern

AbstractThe passenger flow characteristics of rail transit stations and their relationship with surrounding urban space play an important role in urban planning and transit‐oriented development policies. However, passenger flow data are high‐dimensional and complex, and corresponding characteristics are difficult to extract. Moreover, the integration of natural, social and economic factors in urban space may satisfactorily explain passenger flow characteristics. In this study, we clustered stations with passenger flow by using a dynamic time warping (DTW) distance‐based affinity propagation (AP) algorithm. We introduced the urban functional landscape to reveal the urban space surrounding the station by landscape metrics. Then, we explored the relationship between passenger flow characteristics and urban functional landscape pattern. The DTW distance‐based AP algorithm method identified passenger flow characteristics under total, inbound and outbound conditions, with cluster numbers of 3, 5 and 5, respectively. With the distance from the station, the proportion of natural landscape increased (from 26.11 to 36.62%), whereas that of socioeconomic landscape decreased (from 59.87 to 51.57%). The multivariate regression analysis indicated that types of urban functional landscape had significant influence on the passenger flow characteristics in different accessible areas of the station. These findings highlighted the effect of urban functional landscape pattern on passenger flow.

Specific features of high-speed railway construction projects

The most important condition for the integration of the Russian transport infrastructure into the global logistics chain is the design and construction of high-speed railways. In the course of this research, the problems of implementation of high-speed highway “Moscow-Kazan” project by “High-speed Rail Lines” Joint stock Company were considered, and the necessity of large scale projects realization on high-speed railways construction was defined.Then, the aggregate demand values were determined, and the distribution of traffic between different transportation modes was predicted. Finally, the induced demand value was forecasted. Based on the predicted passenger flow characteristics, the indicators of train traffic were calculated.The passenger traffic on the road segment between Moscow and Kazan was studied. The methodology for passenger traffic calculation included the calculation of the induced demand, the possibility of passengers choosing the railway over other modes of transportation, and the evaluation of the demand fluctuations dynamics.The scientific novelty of this study is the application of an improved method of statistical analysis in the calculation of passenger traffic. Along with the theoretical results, the carried out research has an utmost practical importance-its results can be applied in the design of main railways.

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

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.

Analysis of Bus Line Passenger Flow Based on Big Data—A Case Study of Guangzhou Bus Line

The passenger flow analysis which is based on the public transport passenger travel data could provide an important reference for the urban transit network planning and layout optimization. 8,926,605 smart card records of Guangzhou bus lines from August to December in 2014 and weather data have already been collected. Ali cloud computing technology is used to process the big data and analyze passenger flow characteristics. The test between Ali cloud computing technology and traditional SQL database technology has been made. K-means algorithm is utilized to cluster the passenger travel frequency, and the structure of the passenger travel type is gained. The results show that the seasonal weather have some influence on passenger flow each month, and the group of older residents is higher than the group of students in travel frequency. The travel frequency of passengers in low level accounts for more than 80%. Cloud computing technology is used to deal with the such big data, which has a good adaptability.

基于多元数据的公路客流特征研究——以宁波市为例

基于多元数据的公路客流特征研究——以宁波市为例

Neural Network-Based Prediction Model for Passenger Flow in a Large Passenger Station: An Exploratory Study

As the hub and carrier to transfer the passengers, the railway station is an important factor that affects the rail passenger transportation because the normal operation of the station without load redundancy is determined by the moderate passenger flow. It means reasonable and accurate prediction of passengers entering and leaving the station can provide the basis and guarantee for the station security, the resources allocation and the personnel deployment. Since the neural network model is good at processing the common regular data changes through training the network and adjusting the weight value based on a large number of training samples, the neural network model is used in processing the short-term irregular data to predict the passenger flow at the railway station which is susceptible to the constantly changing external factors. In this paper, the neural network is used to predict the passenger flow. First, the key factors affecting the change of the passenger flow are selected and analyzed as the input of the neural network. Second, the learning and the rate updating of variable step size are adopted to estimate the number people entering the station during a certain time interval, which is then weighted with the historical data to derive the prediction of the passenger flow during the next time interval. The simulation results show that the experiment results show that the method proposed in this paper can better track and predict the sudden changes in the passenger flow caused by emergencies. Meanwhile, it can be found that in the process of forecasting abnormal passenger flow, the most critical link is to summarize and summarize the characteristics of railway station passenger flow, clarify the type and time distribution of passenger flow at each station, and analyze the factors that cause abnormal changes in passenger flow.

Analysis of Bus Trip Characteristic Analysis and Demand Forecasting Based on GA-NARX Neural Network Model

Passenger flow is the basis for bus operation scheduling. Huge advances are being made to develop smart city traffic using big data. Intelligent bus systems based on bus integrated circuit (IC) card systems are constantly developing and improving. Compared with traditional manual survey data, bus IC data is low-cost, real-time and accurate with a simple acquisition method. Bus IC data is an important basic data resource and data mining of bus IC cards can obtain dynamic information about urban bus passenger flow and help improve urban bus planning and service levels. The crucial factor in determining whether this data can be reasonably applied to the optimization of urban bus systems is whether spatial and temporal characteristics of the passenger bus trip can be obtained through bus IC data mining, and there is much current research interest into this topic. In this paper, the characteristics of one-day passenger flow and time-division passenger flow are analyzed based on data obtained from swiping IC cards for one week on a bus in Qingdao. Then, based on a GA-NARX neural network model, the passenger flow is forecast using the IC card swipe data for five working days of Qingdao No. 1 bus (using ten minutes as the time interval). The forecasting results show that the passenger flow can be successfully predicted using this method and thus this method can be used for short-term passenger flow forecasting using bus IC cards.

Optimization for train plan of full-length and short-turn routing under variable marshalling conditions

According to the imbalance characteristics of passenger flow in station section in urban railway transit, optimization for train plan of full-length and short-turn routing under variable marshalling conditions is developed. The cost composition and basic constraints of the two entities of passengers and enterprises in this context are analyzed in detail. The multi-objective mixed integer nonlinear programming of the train plan is developed. And the decision variables are train formation plans and positions of turn-back stations. Convert multi-objective models into single-target models by converting costs to total costs and solve the model with MATLAB. The validity of the model is verified by a case and the sensitivities of full-length and short-turn Routing trains’ numbers are also analyzed. The results show that the operation of full-length and short-turn routing mode during peak hours can improve the passenger flow service level of the central section; the optimization of full-length and short-turn routing can further reduce the operating costs of enterprises.

Passenger travel characteristics and bus operational states: a study based on IC card and GPS data in Yinchuan, China

ABSTRACTIn recent years, public transport has been developing rapidly and producing large amounts of traffic data. Emerging big data-mining techniques enable the application of these data in a variety of ways. This study uses bus intelligent card (IC card) data and global positioning system (GPS) data to estimate passenger boarding and alighting stations. First, an estimation model for boarding stations is introduced to determine passenger boarding stations. Then, the authors propose an innovative uplink and downlink information identification model (UDI) to generate information for estimating alighting stations. Subsequently, the estimation model for the alighting stations is introduced. In addition, a transfer station identification model is also developed to determine transfer stations. These models are applied to Yinchuan, China to analyze passenger flow characteristics and bus operations. The authors obtain passenger flows based on stations (stops), bus lines, and traffic analysis zones (TAZ) during weekdays and weekends. Moreover, average bus operational speeds are obtained. These findings can be used in bus network planning and optimization as well as bus operation scheduling.

Methods to calculate and evaluate the efficiency of combined traffic mode in the context of urban bus routes

Problem. Despite certain governmental support, the efficiency of urban passenger transport remains low under the current inflation. Improved economic feasibility of AOCs is possible if only combined operation mode for buses is implemented. Implementation of the measure results in the accelerated transportation, efficient use of road transport vehicles, and transportation services as well as significant reduction in combustible and lubrication materials, and current repair and maintenance of buses. However, lack of uniform methodology and reliable substantiation techniques, neglected basic economic and social factors of transportation process as well as high labour intensity of the available developments complicate seriously determination of rational parameters of the combined transportation forms implementation hindering their wide use. Goal is to develop methods as well as economic and mathematical model making it possible to study and determine both expediency and rational parameters of implementation of combined speedy transportation forms in terms of certain urban route using the selected efficiency criteria. To estimate the modeling results, it is also required to develop a structure of the efficiency complex index while implementing combined transportation forms which would involve basic economic, technologic, and social components of passenger transportation process. Methodology. The paper has applied calculation and experimental approaches. With the help of mathematical modeling, calculation technique has analyzed the effect of different ratios of buses, involved in customary and speedy transportation forms, on the estimation criterion of the combined transportation mode efficiency. Experimental technique studied characteristics of passenger flows within a route, determined average distances of passenger trips, turnover coefficient, and transportation velocity for various types of bus traffics. Results. The proposed nonequilibrum passenger flow coefficient made it possible to obtain zones of preliminary estimation of expediency to implement corresponding transportation forms within certain routes. Efficiency criteria to implement such combined transportation forms have been formulated. A structure of complex efficiency index has been proposed helping select the best variant of numerous ones while involving basic economic, technological, and social indices of passenger transportation process. Originality. To implement a procedure of passenger number redistribution between customary transportation type and speedy one (depending upon the number of buses of corresponding form), a technique has been proposed. The technique is based upon logic analysis of the two matrices: a matrix of interstop correspondence, and a standard time matrix for buses to travel between stopping points of their route. Practical value. Rational parameters to implement the combined transportation type in terms of urban route 70 (city of Dnipro) substantiating release of 7 buses from transportation process and making it possible to reduce unproductive transportation from 2 471.2 down to 216.7 pas. ∙km as well as transportation passenger time consuming from 385.6 down 378.4 hours (including transportation time reduce from 351.0 down to 296.9 hours).

Identifying critical nodes in metro network considering topological potential: A case study in Shenzhen city—China

How to identify critical nodes in metro networks is still an open and vital topic in complex networks, which has been a key issue in analyzing the structural organization of a network, especially in public transportation. Many effective measures have been developed to solve this problem in undirected or unweighted network. However, statistical indicators of static networks cannot reflect the spatial–temporal characteristics of passenger flow in the metro network. Furthermore, a single measure in critical nodes identification has its own shortcomings causing inaccurate estimation results. In this paper, a novel method for node significance on metro network based on Improved Topological Potential model considering Entropy (ITPE) is proposed. ITPE is utilized to aggregate the multi-measure by considering several different centrality measures to conduct the evaluation of node importance. In order to fully reflect the influence of nodes, topological entropy is adopted and applied to identify the weights of different centrality measures. In addition, invulnerability measurement is used to demonstrate the effectiveness of the proposed node identification method. Finally, the metro transit system in Shenzhen City, China was used as a case study to demonstrate the feasibility of the proposed method. It is found that ITPE method could effectively identify nodes or stations which are crucial both on network structure and passenger flow mobility while traditional undirected and unweighted network cannot completely identify. Accordingly, all the nodes estimated from ITPE method are ranked by the significance, and invulnerability is further used to test the rationality of the identification for critical nodes.

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

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.

Analysis of the use of public transport in Radom

An efficient transport system in cities requires rational separation of transport tasks. Determining the mode of travel and preferences of the choice of means of transport enables shaping commuting behaviours. Research on the mobility of public transport is of particular importance. Filling measurements are the basis of many analyzes, enabling the characteristics of passenger flows in the quantitative and qualitative aspect, performance of transport efficiency assessment of individual lines. The aim of the article is to present the results of a survey conducted among drivers of public transport buses. These tests included information on filling and passenger exchange in the means of transport of individual bus lines in Radom. The results of the surveys carried out in households were also presented, thanks to which the motivation behind source-destination journeys of the inhabitants of Radom with the use of public transport was analyzed.

Analysis of the Use of Public Transport in Radom

An efficient transport system in cities requires rational separation of transport tasks. Determining the mode of travel and preferences of the choice of means of transport enables shaping communication behaviours. Research on the mobility of public transport is of particular importance. Filling measurements are the basis of many analyzes, enabling the characteristics of passenger flows in the quantitative and qualitative aspect, performance of transport efficiency assessment of individual lines. The aim of the article is to present the results of a survey conducted among drivers of public transport buses. These tests included information on filling and passenger exchange in the means of transport of individual bus lines in Radom. The results of the surveys carried out in households were also presented, thanks to which the motivation of the source-destination journey of the inhabitants of Radom with the use of public transport was analyzed.

Vulnerability Analysis of Urban Rail Transit Network within Multi-Modal Public Transport Networks

In terms of urban rail transit network vulnerability, most studies have focused on the network topology characteristics and travel cost changes after network incidents and analyzed rail transit network independently. The neglects of passenger flow distributions on the network and alternative public transport modes under rail network disruptions would either underestimate or overestimate the vulnerability of rail transit network, and thus lead to inaccurate results and decisions. This study presents an accessibility-based measurement for urban rail transit network vulnerability analysis and explicitly accounts for rail passenger flow characteristics, travel cost changes, and alternative transit modes. It is shown that the proposed approach is capable of measuring the consequences on rail network, and the advantages of the accessibility method are demonstrated and compared. The methodology is applied to the urban rail transit network of Shenzhen, China within a multi-modal public transport network. Results reveal that the consequences of disruptions on network accessibility are obviously different for stations with different passenger flow characteristics, and some undisrupted stations are found to be vulnerable under surrounding station failures. The proposed methodology offers reliable measurements on rail transit network vulnerability and implications for decision-making under rail network disruptions.