Passenger Behavior Research Articles

Analyzing passenger behavior in airport terminals based on activity preferences

Airport terminals are facilities that provide a variety of activities related to both the preparation of the passengers for their air trip (aeronautical) and their free time inside the terminal (non-aeronautical). In the last years, the number of non-aeronautical activities has substantially increased and significantly diversified both before and after the security checkpoint. The established role of non-aeronautical activities forces planners and managers to better understand passenger behavior. The potential of discrete choice models for the exploration of passenger behavior is analyzed in this paper. For the demonstration of the methodology, Lisbon Humberto Delgado International airport is used as a case study. Data is collected through a revealed and stated preference survey inside the terminal at the area before the security checkpoint. Activity-choice models are developed to identify the factors that affect the choices of the passengers over the area where they conduct non-aeronautical activities. Forecasts show that when increasing the percentage of passengers who conduct the check-in online and have planned their activities before arriving at the airport, the passengers’ preferences to conduct non-aeronautical activities only after the security checkpoint increase. This paper shows the contribution of developing discrete choice models in the better comprehension of passenger decisions over the activities they perform in an airport terminal.

Will modal shift occur from subway to other modes of transportation in the post-corona world in developing countries?

Developing countries are more fragile in the face of the crippling Covid-19 pandemic. Transportation is one of the major industries that has been hardly hit worldwide, and it is more worrying for these countries that already have challenges such as high modal share of private cars, air pollution, and high fatalities due to car accidents. This paper is one of its first kinds that studies the impact of this pandemic on the transportation of Tehran, the capital of Iran, which is one of the forefronts of the battle. In the first step and to get better insights from the travel behavior of passengers due to the pandemic, an online questionnaire is developed and distributed. Priorities for mode choice before and during the pandemic decrease and increase in the share of different modes and the impact of having a high-risk person in the family is studied. Subway had the most decrease and private cars had the highest increase. Hence, two logit models are developed to explain the variables that affect shifting away from the subway and shifting to private cars. Based on the results, a follow-up survey some months later and ridership trends of public transportation during the pandemic, four scenarios are envisaged for the post-corona world, the most probable one is highlighted and policies are recommended to better manage the situation.

Changes in Passengers’ Travel Behavior Due to COVID-19

The COVID-19 outbreak in 2020 has changed the way people travel due to its highly contagious nature. In this study, changes in the travel behavior of passengers due to COVID-19 in the first half of 2020 were examined. To determine whether COVID-19 has affected the use of transportation by passengers, paired t-tests were conducted between the passenger volume of private vehicles in Seoul prior to and after the pandemic. Additionally, the passenger occupancy rate of different modes of transportation during the similar time periods were compared and analyzed to identify the changes in monthly usage rate for each mode. In the case of private vehicles and public bicycles, the usage rates have recovered or increased when compared to those of before the pandemic. Conversely, bus and rail passenger service rates have decreased from the previous year before the pandemic. Furthermore, it is found that existing bus and rail users have switched to the private auto mode due to COVID-19. Based on the results, traffic patterns of travelers after the outbreak and implications responding to the pandemic are discussed.

Managing passenger etiquette in Tokyo: between social control and customer service

ABSTRACT Public transport providers often attempt to prevent passenger behaviour they consider dangerous, deviant, or otherwise undesirable through media technologies such as posters, signage, and overhead announcements. This paper explores the rationale of such mediated regulatory endeavours by taking up the example of ‘manner improvement’ poster campaigns by urban rail providers in Tokyo. Based on expert interviews with individuals involved in the production of poster campaigns and analysis of industry documents, it examines the motives and considerations guiding company interventions into passengers’ everyday mobility practices. While previous scholarship has largely viewed such initiatives as a form of social control, this paper interprets manner improvement efforts as a customer service strategy. The paper examines posters’ content, design, and limitations to argue that manner improvement efforts by urban transport providers are not primarily concerned with disciplining passengers but satisfying customer sensibilities. Enquiring into transit etiquette posters from the perspective of transport and design companies involved in creating them, this paper presents a novel contribution to the study of urban mobilities.

Self-reported likely behaviour of rail passengers during an emergency evacuation-A case study of Kuala Lumpur, Malaysia

Prior knowledge on how passengers behave during a potential emergency evacuation could be advantageous in designing efficient crowd management and emergency procedures. This study examines the likely behaviours of Malaysian passengers during a potential future emergency evacuation situation. Four key behaviours, i.e., reactive, proactive, cooperative and competitive behaviours, were considered. A questionnaire survey was conducted at one major rail transit terminal in Kuala Lumpur, Malaysia to collect the data. All 301 questionnaire responses displayed non-uniformity in their likely behaviour. Results explained that the passengers are more likely to be proactive (e.g., move to exit immediately as soon as the warning siren is sounded) than reactive (e.g., go to assembly point after being instructed) during the pre-evacuation. Further, people are more likely to be cooperative (e.g., help other people who may have difficulties in getting out) than competitive (e.g., push or shove other peoples to get out quickly) during the evacuation. In terms of demographic influences on behaviours, results demonstrated that there could be significant differences in certain behaviours between males and females and between different age groups. The findings of this study provide valuable information for developing models for simulating passengers' evacuation at rail transit terminals. Further, the managers of emergency response could utilize such data and outcomes in devising effective crowd management strategies and developing appropriate training and educational campaigns.

Evacuation Behavior in a Subway Train Emergency: A Video-based Analysis

How do people behave in the seconds after they become aware they have been caught up in a real-life transport emergency? This paper presents the first micro-behavioral, video-based analysis of the behavior of passengers during a small explosion and subsequent fire on a subway train. We analyzed the behavior of 40 passengers present in the same carriage as the explosion. We documented the first action of the passengers following the onset of the emergency and described evidence of pro- and anti-social behavior. Passengers’ first actions varied widely. Moreover, anti-social behavior was rare and displays of pro-sociality were more common. In a quantitative analysis, we examined spatial clustering of running behavior and patterns in passenger exit choices. We found both homogeneity and heterogeneity in the running behavior and exiting choices of passengers. We discuss the implications of these findings for the mass emergency literature and for evacuation modeling.

The Influence of Demographic Matters and Interest on Passenger’s Switching Intention: Evidence of Travelers in the Developing Country Indonesia

Background: After the official operation of the Jakarta-Cikampek elevated highway, a socio-demographic picture of Indonesian travel passengers, as well as their interest in switching transportation preferences was provided. This elevated highway shortened travel time and discouraged users from changing to other transportation modes. This study is likely to become a future research foundation for the switching behavior of passengers. Objective: This study aims to analyze demographic factors and interests on passenger’s switching of transportation preference. Methods: A total of 720 questionnaires were distributed to land transportation passengers at concentrated points with a response rate of 89.17% and 642 valid answers. The obtained data were analyzed using quantitative descriptive techniques with cross tab methods. Results: The results showed that age, education level, and type of work influenced the desire of passengers to switch transportation modes, as opposed to gender. Most of the passengers interested in using land modes, such as the highway, were dominated by intercity between provinces travel buses and refused to change to other means of transportation. Meanwhile, passengers that used rented cars tended to switch to other forms of transportation. Conclusion: This research is useful and acts as a reference for managers of each transportation mode to set high priorities for particular consumers based on detailed socio-demographics to retain or attract new potential customers. JEL Classification Code: D12, L92, R41.

Understanding changes in perceptions and behaviour of train passengers during the Covid 19 pandemic

Transportation is considered as one important area that contribute to energy emission. The transportation sector accounts for 15% of total GHG emissions and 23% of CO 2 emissions, of which 30% of CO 2 comes from burning fossil fuels. Government policies in the context of preventing the spread of Covid 19 and enforcement of restrictions on Community Activities (PPKM), have provided benefits for the environment in terms of reducing air pollution by up to 42% (liputan6.com). The Covid 19 pandemic has an impact on the public transportation business, based on sales data from PT KAI (Persero) that there was a decrease in the number of longdistance train passengers using rail transportation services, namely in April 2019 there were 3,879,792 passengers while in April 2020 it fell to 286 268 passengers. This research aims at understanding the risk perception that affects the planned attitudes and behaviour of potential passengers to travel by train during the pandemic. 368 samples were collected from Operation Area 1 to Operation Area 9. Employing SEM analysis with the SmartPLS, the result shows that the customers intend to continue traveling using railway public services. However, a certain level of anxiety and perceived risk grows due to a lack of information. namely, uncertainty whether the use of public transportation may bear the potential for covid 19 transmissions. Hence the government’s actions in dealing with the pandemic could reassure the customers to continue using the railway services.

Railway timetabling with integrated passenger distribution

Timetabling for railway services often aims at optimizing travel times for passengers. At the same time, restricting assumptions on passenger behavior and passenger modeling are made. While research has shown that discrete choice models are suitable to estimate the distribution of passengers on routes, this has not been considered in timetabling yet. We investigate how to integrate a passenger distribution into an optimization framework for timetabling and present two mixed integer linear programs for this problem. Both approaches design timetables and simultaneously find a corresponding passenger distribution on available routes. One model uses a linear distribution model to estimate passenger route choices. The other model uses an integrated simulation framework to approximate a passenger distribution according to the logit model, a commonly used route choice model. We compare both new approaches with three state-of-the-art timetabling methods and a heuristic approach on a set of artificial instances and a partial network of Netherlands Railways (NS). Our experiments provide insights into the impact of considering multiple routes instead of a single route, and of integrated route choice versus predetermined route assignment with respect to the solution quality.

Multi-stage deep learning approaches to predict boarding behaviour of bus passengers

Smart card data has emerged in recent years and provide a comprehensive, and cheap source of information for planning and managing public transport systems. This paper presents a multi-stage machine learning framework to predict passengers’ boarding stops using smart card data. The framework addresses the challenges arising from the imbalanced nature of the data (e.g. many non-travelling data) and the ‘many-class’ issues (e.g. many possible boarding stops) by decomposing the prediction of hourly ridership into three stages: whether to travel or not in that one-hour time slot, which bus line to use, and at which stop to board. A simple neural network architecture, fully connected networks (FCN), and two deep learning architectures, recurrent neural networks (RNN) and long short-term memory networks (LSTM) are implemented. The proposed approach is applied to a real-life bus network. We show that the data imbalance has a profound impact on the accuracy of prediction at individual level. At aggregated level, FCN is able to accurately predict the rideship at individual stops, it is poor at capturing the temporal distribution of ridership. RNN and LSTM are able to measure the temporal distribution but lack the ability to capture the spatial distribution through bus lines.

Impact of political and market-based measures on aviation emissions and passenger behaviors (a Swiss case study)

The global aviation industry has been increasingly urged to reduce their CO2 emissions. To achieve this, the International Air Transport Association (IATA) and International Civil Aviation Organization (ICAO) have successfully adopted various operational, technological, and air traffic management/infrastructural measures. However, they have also implemented market-based regulatory measures, including the Carbon Offsetting and Reduction Scheme for International Aviation (CORSIA). Additionally, regional measures, such as the Europeanemission trading system (ETS), nationwide political measures, such as flight taxes, and compensation programs by airlines also exist. Therefore, this study surveyed the impact of such measures, primarily on business travelers and their behavior, with a focus on Switzerland. Additionally, not only the impact of the first-last mile (airport access) was discussed, but also intermodal aspects like high-speed rails were debated. Results indicated that flight tax programs were found to have a weak impact on demand. The impact of COVID-19 was addressed and decreased travel frequency from COVID-19 may impact global flight emissions in the long term. Furthermore, passengers supported investments of flight-tax revenues in sustainable aviation technology; they did not support flight contingents. Conclusions are that taxes might generate additional airport traffic. An analysis about booking behaviors revealed fundamental differences in environmental terms. Finally, voluntary compensation was highly favored.

Unplanned Disruption Analysis in Urban Railway Systems Using Smart Card Data

Metro system disruptions are a big concern due to their impacts on safety, service quality, and operating efficiency. A better understanding of system performance and passenger behavior under unplanned disruptions is critical for efficient decision making, effective customer communication, and identifying potential improvements. However, few studies explore disruption impacts on individual passenger behavior, and most studies use manually collected survey data. This study examines the potential of using automated collection data to comprehensively analyze unplanned disruption impacts. We propose a systematic approach to evaluate disruption impacts on system performance and individual responses in urban railway systems using automated fare collection (AFC) data. We develop a set of performance metrics to evaluate performance from the perspectives of train operations, information provision (communication), and bridging strategy (shuttle bus services to connect stations impacted by a disruption). We also propose an inference method to quantify the individual response to disruptions (e.g. travel or not, change stations or modes) depending on their trip characteristics with respect to the location and timing of the disruption. The proposed approach is demonstrated using data from a busy metro system. The results highlight the ability of AFC data in providing new insights for the analysis of unplanned disruptions, which are difficult to extract from traditional data collection methods. The case study shows that the disruption impacts are network-wide, and the impacts on passengers continue for a significant amount of time after the incident ended. The behavior highlights the importance of real-time information and the need for timely dissemination.

An elevator passenger behavior recognition method based on two-stream convolution neural network

In order to reduce elevator accidents caused by passengers’ incorrect use, a method for elevator passenger dangerous behaviour recognition based on two-stream convolutional neural network is proposed after analysing existing elevator passenger behavior recognition methods. A two-stream convolution neural network model framework and its parameters for elevator passenger behavior recognition are also given. Herein, the appearance features of human behavior are extracted from space domain, and the motion features of human behavior are extracted from time domain. Finally, the softmax outputs of the two-stream are merged with the liner weighting to realize human behavior recognition. The model is trained and evaluated on the video dataset of elevator passenger behavior. The results show that this method can be used to identify the unsafe behavior of elevator passengers, and the average recognition rate is 96.87%.

Analysis of the Integration Usage Patterns of Multiple Shared Mobility Modes and Metro System

Although metro systems are established in many Asian cities including Chengdu, they have yet to cover every corner of a city. Understanding the transfer behavior of passengers can provide insight into achieving efficient and sustainable urban transport systems. Combining shared mobility programs with metro to improve the weaknesses of traditional feeder modes is viewed as the most promising line of business in sustainable transportation for the near future. Therefore, this study aims to comprehend the factors affecting the usage regularity of shared mobility by deepening the knowledge on endogenous and exogenous effects, and integrating two modes, namely bike-sharing and ridesourcing. Two systems are cross-compared, first in respect of their travel characteristics. Then, a binary logistic model is employed to capture the influences of trip characteristics and travel environment characteristics on their usage frequency. Researchers found that trip distance is significantly associated with users’ mode options, indicating that bike-sharing and ridesourcing mainly serve short-distance and long-distance transfer users, respectively, although some users may be confused which feeder mode to choose for the journeys of 2 km to 4 km. There were also meteorological and temporal influences, with the competition and complementation of multiple shared mobility feeder modes being likely to change under extreme weather conditions, during peak hours, or on weekends. Besides, metro-shared mobility users value the accessibility of two kinds of transport service, which is affected by the metro station and its surrounding built environment. This study and the proposed policy implications are helpful for embracing a sustainable mobility design from general optimum.

Understanding the relations between crowd density, safety perception and risk-taking behavior on train station platforms: A case study from Switzerland

Railway platforms are becoming increasingly crowded, especially at peak hours. In this observational study, we investigated how the density of people is perceived by passengers and how this perceived density correlates with safety perception and risk-taking behavior. Risk-taking behavior here means stepping into the danger zone, the area of the platform bordering the tracks where individuals are at risk to their physical integrity by a train passing through, arriving at or leaving the station. The investigation of perceived density and actual behavior on the platform poses methodological challenges. Therefore, we used a stereo sensor technology to collect anonymized behavioral data on a train station platform over two months. Data regarding passenger density and oversteps into the danger zone was collected during rush hours and analyzed for this study. Additionally, subjective data, such as estimation and perception of passenger density and safety perception were collected in a survey with 179 participants. Survey links were distributed during rush hours in three different train stations on platforms over two weeks. While distributing the links for the online survey in the field (two-hour sessions during rush hours), an observation was conducted (i.e., oversteps into the danger zone, general passenger behavior). The results indicate that increased measured passenger density is related to more oversteps. Subjective perception of crowd density, regarding how comfortable someone feels in the given situation, correlates with safety perception and also significantly predicts overstepping into the danger zone. Increased estimated density also correlates with reduced safety perception but is not a predictor of oversteps. We suggest optimizing the passenger distribution on the platform by motivating passengers to move to less crowded areas, e.g. with approaches such as “nudging” so that passengers feel more comfortable on the platform. This can both improve both safety and the customer experience on the platform.

Impact of possible random factors on queue behaviors of passengers and taxis at taxi stand of transport hubs

The waiting processes of both passengers and taxis at taxi stand of transport hubs are a complicated random queueing problem because of some typical random characteristics, including the random number of passengers carried by per taxi, random arrival behaviors of passengers, and taxis, random length of boarding time for a batch of passengers, etc. In particular, the arriving process of passengers at transport hubs often has multiple uncertain peak hours within a day, which makes the waiting times of both the passengers and the taxis highly variable and unpredictable at a certain time. Hence, it is necessary to explore and understand the dynamic queues of the passengers and taxis through fully studying the random nature of this system. In this paper, a matching model with two random inputs is developed to simulate the dynamic queueing behaviors of passengers and taxis by considering the random features of the matching number of passengers, matching time, and time-dependent arrival processes, etc. We obtain some important quantities of queues along the time. A series of numerical simulations is conducted to find how the queues of passengers and taxis are affected by the most possible factors. The proposed model in this paper can provide a methodological possibility of clarifying the dynamic queues of passengers and taxis for not only the computer simulation but also the statistical inference, and will further help to improve the service of the passenger–taxi system.

Experimental study on evacuation behaviour of passengers in a high-deck coach: A Chinese case study

High-deck coaches form an essential component of mass transportation systems in China. Safe evacuation from high-deck coaches is facing dire challenges. However, evacuation behaviour from high-deck coaches has not been deeply understood yet. In this study, a novel conceptual framework is firstly proposed to capture the evacuation behaviour of coach passengers, and next based on which 22 full-scale experiments have been conducted to examine the effect of three selected factors: available exits, lighting conditions and age groups on the evacuation behaviour of Chinese passengers in a high-deck coach, in a systematic and quantitative way. Four performance indicators of evacuation behaviour, i.e., evacuation time, pre-evacuation time, flow rate and crowdedness, were collected and analysed. The results indicate that limited available exits and the dim lighting condition (less than 1 lux) significantly reduce the evacuation efficiency and increase the crowdedness within the aisle area regardless of the age groups. Compared to young students, the evacuation of middle-aged people is observed to have a significantly longer pre-evacuation time, lower evacuation efficiency, and higher level of crowdedness. In addition, young students’ pre-evacuation times are found to conform to the Weibull distribution, whereas middle-aged people’ pre-evacuation times could be modelled with the Loglogistic distribution. Empirical results of this study could be helpful for the improvement of the safety design of high-deck coaches, and provide valuable benchmarks for the development of coach evacuation behaviour and simulation models.

Challenges for public transportation: Consequences and possible alternatives for the Covid-19 pandemic through strategic digital city application

Public transport was already one of the biggest issues for all municipalities where people are highly concentrated at the same space at the same time. With COVID-19 pandemic and social distancing consequences, mass transportation is actually the main barrier for students and workers dependents of transport to go back to their daily routines with comfort and safety. Thus, the objective is to determine a demand control able to equalize the number of passengers in each car, respecting the COVID-19 social distancing protocols. The number of passengers in each time-of-day range were combined in four different models that included independent variables related to passenger's behavior indicating that almost 90% of all passengers are following a very strict and straight daily routine that can be coordinated and scheduled creating enough time space one from the other to avoid undesirable concentrations inside buses and bus stops. In conclusion, a very accurate urban management tool can arise from the study and may be able to solve not only the pandemic issues but also to improve local public services efficiency, to attract private investments and to improve citizen's quality of life.

Manner Posters: A Genre Approach

ABSTRACT Illustrated posters appealing to passenger conduct – so-called ‘manner posters’ – are ubiquitous in Japanese public transport spaces. This article draws on archival research and interviews with railway and design professionals to introduce transport company-issued manner posters as a genre of public communication, and link its development to social shifts in post-war Japan. The article first examines passenger behaviours targeted by posters, discussing efficiency, comfort and safety as underlying concerns of company appeals, and analyses the textual structure of posters. This review of the genre’s defining characteristics is followed by an exploration of its history. While manner posters are frequently traced back to a 1974 poster series issued by Eidan (now Tokyo Metro), this article argues that although posters became more complex and widespread in the 1970s, their history reaches back to the Taishō era. The article explores the historical context of the genre’s development by highlighting changes in the railway and advertising sectors. I argue that it was railway companies’ increased efforts to improve the quality of public transport, along with advances of posters as an advertising medium and changes in the style of public communication, that spurred greater adoption of elaborate manner posters in the mid-1970s.

Analysis of the effect of bundled airport privatization on an airline network

Depopulated regions face declines in traffic demand for airline service. The airline network in Hokkaido, Japan, plays an important role in the regional economy, healthcare, and sightseeing. Toward maintaining this airline network, the “bundled privatization” of airports has been discussed. Under such a system, several airports are privatized as a package for management by a single company. This study proposes a model for analyzing the behaviors of airport operators, airline companies, and passengers when bundled privatization is introduced to an airline network. Under bundled privatization, an airport management company can determine the landing fees of all airports such as to maximize the profits of all its airports, while an airline company can determine its service frequencies and aircraft sizes such as to maximize its profit. As a result of these two profit-maximization behaviors, an equilibrium state is obtained. The proposed model is applied to the airline network in Hokkaido, and the results of numerical calculations are discussed.