Passenger Travel Behavior Research Articles

Passenger Travel Patterns and Behavior Analysis of Long-Term Staying in Subway System by Massive Smart Card Data

Due to the massive congestion in ground transportation in Beijing, underground rail transit has gradually become the main mode of travel for residents of large urban areas. Because the average daily traffic of the Beijing subway is over 12 million passengers, ensuring the safety of underground rail transit is particularly important. Big data shows that more than 4000 passengers participate in Long-term Stay in the Subway every day. However, the behaviors of these passengers have not been characterized. This paper proposes a method for identifying the Long-term Staying in Subway System (LSSS) in the subway based on the shortest path and analyze its travel mode. In combination with the past research of scholars, we try to quantify the suspected behavior with a database of assumed suspected behavior records. Finally, we extract the spatial-temporal travel characteristics of passengers and we propose a SAE-DNN algorithm to identify suspected anomalies; the accuracy of the training set can reach 95.7%, and the accuracy of the test set can also reach 93.5%, which provides a reference for the subway operators and the public security system.

A Review of Big Data Applications in Urban Transit Systems

Operations, management and planning of urban transit systems have evolved substantially since the application of transit data collection technologies, such as, automated fare collection (AFC), Global Position System (GPS), smartphones and face identification. A diversity of detailed sensor data in urban transit systems are being used as fundamental data sources to observe passenger travel behavior, reschedule operation plans and adjust policy decisions from the daily operations to the long-term network planning. This review aims to summarize and analyze those related challenges and data-driven applications. Firstly, we review the data collecting technologies since the late 1990s by classifying the various technologies into two groups: traditional technologies and advanced technologies. A vast body of literature has been developed in this area given the wide range of problems addressed under the transit data label. A summary diagram is proposed to demonstrate the transit data applications and research topics. The data applications are classified into three branches: passenger behavior, operation optimization, and policy application. For each branch, the hot research direction and dimension shown as sub-branches are represented by reviewing the highly cited and the latest literature. As a result, this article discussed the concept and characteristics of transit data and its collection technologies, and further summarized the methodology and potential for each transit data application and suggested a few promising implications for future efforts.

Passenger Travel Behavior in Public Transport Corridor After the Operation of Urban Rail Transit: A Random Forest Algorithm Approach

A competitive relationship has been generated between urban rail transit and bus transit since the operation of the former. Despite different roles in providing services in public transport corridor, they affect each other in actual situations. In terms of urban transportation planning and policy formulation, it is necessary to explore and master the rules of passengers' travel mode choice from different means. In order to study their choices between the rail transit and the bus transit after the operation of the former, taking Xiamen, China as an example, this article analyzed the overall travel features of passenger flow before and after the operation of rail transit by using the public transit IC card data from two consecutive weeks in November 2017 and November 2018. Some features of travel distance, travel time, travel cost, whether to travel in peak hours, the number of collinear stations between bus transit and rail transit or that of rail transit stations are sorted out. With random forest algorithm, a model is set up for the travel mode choice of passengers after urban rail transit is put into use to find out the impact of different travel features. The result shows that travel cost is the most crucial factor that affects passengers' decisions, followed by the number of collinear stations between bus transit and rail transit or that of rail transit stations, travel time and travel distance. Whether to travel in peak hours have less impact on their choices. This study is constructive for cities in the stage of facing competition between newly opened rail transit and bus transit and support transportation decision-making.

Passenger Travel Behavior Analysis under Unplanned Metro Service Disruption: Using Stated Preference Data in Guangzhou, China

AbstractPassengers may change their original travel plans when unplanned metro service disruption occurs, which causes excessive crowding and endangers the safety of passengers. However, it’s not c...

Perspectives on stability and mobility of transit passenger's travel behaviour through smart card data

Existing studies have extensively used spatiotemporal data to discover the mobility patterns of various types of travellers. Smart card data (SCD) collected by the automated fare collection systems can reflect a general view of the mobility pattern of public transit riders. Mobility patterns of transit riders are temporally and spatially dynamic, and therefore difficult to measure. However, few existing studies measure both the mobility and stability of transit riders' travel patterns over a long period of time. To analyse the long-term changes of transit riders' travel behaviour, the authors define a metric for measuring the similarity between SCD, in this study. Also an improved density-based clustering algorithm, simplified smoothed ordering points to identify the clustering structure (SS-OPTICS), to identify transit rider clusters is proposed. Compared to the original OPTICS, SS-OPTICS needs fewer parameters and has better generalisation ability. Further, the generated clusters are categorized according to their features of regularity and occasionality. Based on the generated clusters and categories, fine- and coarse-grained travel pattern transitions of transit riders over four years from 2010 to 2014 are measured. By combining socioeconomic data of Beijing in the year of 2010 and 2014, the interdependence between stability and mobility of transit riders' travel behaviour is also discussed.

Discovering the Hidden Community Structure of Public Transportation Networks

Advances in public transit modeling and smart card technologies can reveal detailed contact patterns of passengers. A natural way to represent such contact patterns is in the form of networks. In this paper we utilize known contact patterns from a public transit assignment model in a major metropolitan city, and propose the development of two novel network structures, each of which elucidate certain aspects of passenger travel behavior. We first propose the development of a transfer network, which can reveal passenger groups that travel together on a given day. Second, we propose the development of a community network, which is derived from the transfer network, and captures the similarity of travel patterns among passengers. We then explore the application of each of these network structures to identify the most frequently used travel paths, i.e., routes and transfers, in the public transit system, and model epidemic spreading risk among passengers of a public transit network, respectively. In the latter our conclusions reinforce previous observations, that routes crossing or connecting to the city center in the morning and afternoon peak hours are the most “dangerous” during an outbreak.

Passenger travel behaviour on Chinese high‐speed railways using machine learning based on revealed‐preference data

AbstractExisting studies on the selection of railway travel modes almost always compare high‐speed trains and regular‐speed trains. In this study, three high‐speed trains (Revival, Harmony, and Electric multiple unites (EMU)) on the Beijing–Shanghai line, and the first‐ and second‐class seats on these three trains are selected as research objects to analyse the travel selection behaviour of Chinese high‐speed train passengers. Three methods are selected to study this behaviour: the Support Vector Machine, Nested Logit model, and Multiple Logit model. The results of these three models are calibrated using LIBSVM software and STATA software and show that age, funding source, income level, and purpose of travel are major factors that affect Chinese railway passengers' choices on high‐speed railways. Finally, it is shown that the Support Vector Machine is the most accurate of these three methods, followed by the Nested Logit model. The results of this study can complement existing research on the travel selection behaviour of Chinese railway passengers and has important implications for enabling China's high‐speed rail operators to adjust their passenger transportation products.

Analysis of spatio-temporal distribution characteristics of passenger travel behaviour based on online ride-sharing trajectory data

With the rapid development of information technology, the travel mode of “ Internet + Transportation ” has brought convenience to residents while causing a series of urban traffic problems. Based on GPS data from Didi Chuxing, this paper relates the residents’ travel behaviour to their daily activities, and studies the regularity of the pick-up and drop-off activities of residents from the dimensions of time and space. The results are as follows: Firstly, the daily variation trend of residents’ travel is unbalanced. At the same time, the travel volume of residents is at a higher level during night. Secondly, the travel of urban residents is closely related to urban areas and urban functional structures. On the administrative scale, the distribution of residents’ travel is concentric, and the farther the distance from the downtown area is, the smaller the number of orders is. On the functional scale, the hot functional areas of residents’ travel are related to residents’ needs. At last, mastering the distribution of residents’ travel hot spots and time will help to understand the residents’ travel mode and the current situation of urban traffic, and provide new ideas for solving traffic problems in big cities.

Optimizing the release of passenger flow guidance information in urban rail transit network via agent-based simulation

• A agent-based simulation optimization model is proposed to optimize the release of passenger guidance information in a rail network. • A hybrid heuristic solution algorithm , integrated with passenger simulator and genetic algorithm , is developed. • A case study of Beijing subway is carried out with the large-scale smart card data. The passenger flow guidance is an effective demand management strategy to alleviate the excessive congestion in the urban rail transit network. In order to determine the scope and the timing, a simulation-based optimization model is proposed to optimize the release of passenger flow guidance information in the rail transit network in this paper. In the optimization model, we mainly focus on three aspects namely; where, when and what type of the guidance information should be released to the passengers. In the simulation model, the passenger choice behavior is captured by the agent-based simulation method, which responses to the congestion and the guidance information. Based on this, the dynamic passenger flow distribution can be derived. Furthermore, the adoption rate of the displayed guidance information on passenger information system as well as its impact on passenger travel behavior are also considered in the model. A hybrid heuristic solution algorithm, integrated with passenger simulator and genetic algorithm, is developed to solve the proposed simulation-based optimization model. Finally, a case study of Beijing subway is carried out with the large-scale smart card data. The numerical study shows that the passenger flow demand affects the guidance effect significantly and the best guidance effect can be met with sufficiently high passenger flow demand. And the guidance rate is also found to affect the guidance results. The results also show that the proposed model can provide a detailed guidance scheme for every station at selected time intervals. The results show that the dynamic releasing scheme can save up to a total of 46,319 min in passenger travel time during a single guidance period.

A Research on Passenger’s Travel Choice Behavior in High-speed Railway Based on Passenger Survey Questionnaire

With the development of high-speed railways, the transportation service level of trains has been continuously improved, and the basic travel demand of passengers has been met to a certain extent. At the same time, the relationship between the transportation services provided by railways and the demand for passenger travel has begun to change, and passengers need to be safer. Comfortable, convenient, fast and economical transportation services, in order to better meet the needs of passengers, it is necessary to conduct statistical analysis of passengers' travel choices, so that railway enterprises can provide better services for passengers. In order to study the travel behavior of high-speed railway passengers, this paper analyzes the influencing factors of passengers' choice of passengers based on the non-aggregate discrete selection model theory, and combines the 2016 high-speed railway passenger travel questionnaire to set the characteristic variables that affect passengers' choice of passengers. Including passenger characteristics, travel characteristics, train service characteristics and corresponding value methods, establish a passenger ride plan selection model. We use SPSS to calibrate the model parameters and draw relevant conclusions.

A framework to help destinations manage carbon risk from aviation emissions

Global tourism is booming, and so is demand for air travel. Recognising that travellers come with a carbon footprint increases the complexity of decision-making for destinations that seek to attract more visitors. The carbon risk inherent in travel to and from a destination could be substantial but current approaches of accounting and lack of transparent data impede a full understanding of exposure and trends. This research therefore takes a demand-focused approach and proposes ten carbon risk indicators that help destinations assess their absolute and relative risk to the economic, financial, social and environmental costs of carbon. The analysis generates global benchmarks for carbon-, passenger- and itinerary-related indicators, and presents a list of most exposed destinations, approximated by departure airports. A comparative assessment of four airports highlights how differences in passenger volumes, geography, route network and travel behaviour by markets influence exposure. Recommendations for destinations are provided.

Passenger Transfer Conflict Delay in Large Passenger Transport Hubs

In the passenger transfer process, passenger congestion conflict is a very important factor in transfer delays in large passenger transport hubs. By analyzing passenger walking speed, passenger arrival rate, the capacity of walking facilities and passengers’ expected travel time, a model for calculating conflict delay in the passenger transfer process is established in this paper. The relationship between the average delay due to passenger conflict, the arrival rate and the absolute value of the difference in passenger arrival rates from different directions is calculated, and a simulation platform of passenger travel behavior, which is based on NetLogo, is established to simulate the passenger transfer conflict process. The outcomes verify that the model is effective, feasible and accurate. Therefore, the research results make great contributions towards the evaluation of the layout plans of passenger transfer facilities in large hubs, and provide a theoretical foundation for perfecting hub transfer facilities.

Characteristics analysis for travel behavior of transportation hub passengers using mobile phone data

The travel behavior of passengers from the transportation hub within the city area is critical for travel demand analysis, security monitoring, and supporting traffic facilities designing. However, the traditional methods used to study the travel behavior of the passengers inside the city are time and labor consuming. The records of the cellular communication provide a potential huge data source for this study to follow the movement of passengers. This study focuses on the passengers’ travel behavior of the Hongqiao transportation hub in Shanghai, China, utilizing the mobile phone data. First, a systematic and novel method is presented to extract the trip information from the mobile phone data. Several key travel characteristics of passengers, including passengers traveling inside the city and between cities, are analyzed and compared. The results show that the proposed method is effective to obtain the travel trajectories of mobile phone users. Besides, the travel behavior of incity passengers and external passengers are quite different. Then, the correlation analysis of the passengers’ travel trajectories is provided to research the availability of the comprehensive area. Moreover, the results of the correlation analysis further indicate that the comprehensive area of the Hongqiao hub plays a relatively important role in passengers’ daily travel.

Individual Travel Behavior Modeling of Public Transport Passenger Based on Graph Construction

This paper presents a novel method for mining the individual travel behavior regularity of different public transport passengers through constructing travel behavior graph based model. The individual travel behavior graph is developed to represent spatial positions, time distributions, and travel routes and further forecasts the public transport passenger’s behavior choice. The proposed travel behavior graph is composed of macronodes, arcs, and transfer probability. Each macronode corresponds to a travel association map and represents a travel behavior. A travel association map also contains its own nodes. The nodes of a travel association map are created when the processed travel chain data shows significant change. Thus, each node of three layers represents a significant change of spatial travel positions, travel time, and routes, respectively. Since a travel association map represents a travel behavior, the graph can be considered a sequence of travel behaviors. Through integrating travel association map and calculating the probabilities of the arcs, it is possible to construct a unique travel behavior graph for each passenger. The data used in this study are multimode data matched by certain rules based on the data of public transport smart card transactions and network features. The case study results show that graph based method to model the individual travel behavior of public transport passengers is effective and feasible. Travel behavior graphs support customized public transport travel characteristics analysis and demand prediction.

Changing travel behaviour of high-speed rail passengers in China

ABSTRACTThis paper studies the impact of high-speed rail on travellers’ behaviour in China through the study of travellers’ characteristics and their satisfaction with the railway. A sample of 328 passengers from the Shenzhen-Xiamen Railway (XSR) is surveyed by questionnaires. The results confirm that the XSR development has partly changed the respondents’ decision to take the railway or choose its tourism-related products. Socio-demographic characteristics including age, occupation, monthly personal income, and annual travel expenditure show a significant influence on the changed travel behaviour though the tendency of such influence is not very apparent. Convenience and comfort lead to travellers’ satisfaction and these two aspects shorten the duration of stay of travellers and provide more short-haul rail-connected destination options. However, railway satisfaction is not highly associated with XSR-related product and service provision, but is related to the condition and the quality of the railway itself.

High speed rail and coastal tourism: Identifying passenger profiles and travel behaviour.

In this paper, we characterise tourists most likely to visit a coastal destination by high-speed rail (HSR). Our data came from a survey conducted among HSR passengers during 2014’s high season (July and August) at Spain’s Camp de Tarragona and Alicante Stations, each of which is near a mass tourism destination on the Mediterranean coast: the Costa Daurada and the Costa Blanca, respectively. We used responses to the survey, which presented binary discrete-choice situations, to construct a database necessary for a logistic regression model that allowed us to examine how passenger profile, trip characteristics, and stay conditions influenced the use of HSR services on visits to each coastal destination. Results highlighted significant differences in the profiles of tourists who arrived at each destination by HSR and, in turn, that no specific tourist profile is associated with HSR, even for two stations that serve sunny beach destinations. Among its implications, to analyse travellers that HSR can attract, it is vital to consider the specific characteristics of each destination and its current market.

Behavioral pattern of commercial public transport passengers in Lagos metropolis

This study examined the travel behavior of commercial public transport passengers in Lagos State, Nigeria. The descriptive research survey was used in order to assess the opinions of the respondents using the questionnaire. A total of 84 samples were used as representative population, while two null hypotheses were formulated and tested using the Pearson Product Moment Correlation Coefficient at 0.05 level of significant. The results that were obtained indicated that a positive correlation exists between frequency of travel and commuters income in Lagos Metropolis and also that a positive correlation exists between frequency of travel and distance covered by commuters in Lagos Metropolis. Secondary data was also sourced to serve as complement to the primary data, thus allowing for a robust research. Descriptive statistical tools such as percentages were also adopted to present the socio-economic characteristics in the area. Findings showed that about 57% of sampled population are male, 62 % are civil servants, 48% of respondents travel for business purposes. Also, that majority of the respondents (50%) said that the commercial public transport is highly prone to accident, while about 64% of the respondents commute on daily basis. It was established that lack of transport infrastructure coupled with poor road maintenance were seen as the leading causes of inaccessibility of the area.

Evaluation of particulate matter concentration in Shanghai’s metro system and strategy for improvement

The air quality in Shanghai’s subway system has become a big concern. At present, the system is the longest in the world, and its daily passenger volume exceeds 9×106 travelers every work day. In this study, we comprehensively assessed the fine particulate matter (PM2.5) concentrations in the 14 lines of Shanghai’s metro system through field measurements in the metro (subway) system and real-time data acquisition at the nearest state-run air sampling sites. We ranked and clustered the 14 lines according to the PM2.5 concentrations and the relative concentrations in the halls and on the platform of the metro station and inside the train for each line. We identified the factors that influence the PM2.5 concentration, and found that the external environment appears to have the strongest influence on air quality. In addition, the age of the line, type of platform (screen door versus half-height security door), air-conditioning filtration system, and other factors influenced the PM2.5 concentration for each line. Based on our evaluation of the contamination and its causes, we propose potential solutions, such as reducing particulate matter invasion from pollution sources, updating the environmental protection hardware (i.e., filtration systems), developing a more scientific cleaning program, and optimizing the travel behavior of passengers and working conditions of merchants to improve the air quality and reduce traveler exposure to pollution in the metro system.

Passenger Travel behavior Model in Railway Network Simulation

Passenger Travel behavior Model in Railway Network Simulation

Longitudinal assessment of high-speed rail service delivery, satisfaction and operations: A study of Taiwan and Korea systems

This article compares Taiwan and Korea high speed rail systems to identify service factors that affect their long-term development. Data were collected by questionnaires and interviews administered over several years to study passenger travel behavior and perceptions of service quality. Analysis results indicate that improving operational effectiveness requires enhanced service quality and that Taiwan High Speed Rail (THSR) passengers are very concerned about facility of infrastructure services. In contrast, the main concern of Korea Train eXpress (KTX) passengers is frontline staff interaction. Notably, the data show that THSR passenger satisfaction increased steadily to levels superior to those expressed by KTX passengers. Another finding is that, in terms of resource allocation, both Taiwan and Korea should improve the handling of passenger complaints, provide improved scheduling information, and strive to improve arrival and departure punctuality. The contribution of this study is the development of a systematic method of assessing the long-term performance of high-speed rail transport services, by which management units can adjust operating strategies to continuously improve services. The analysis results can facilitate the THSR and KTX to formulating planning and operational strategies that can achieve the goal of sustainable operations.