Passenger Behavior Research Articles

Passenger Distribution Regulation of Rail Transit Platform in Standard Station Environment

Based on the analysis of the construction characteristics and passenger behavior characteristics of the standard station platform of rail transit, combined with the actual data, under the environment of the standard station, the distribution law of the platform passengers is studied. Through the change of different flow, the influence of the change on the overall distribution is studied, which provides a new idea and method for the study of the passenger distribution on the standard platform of rail transit.

USERS’ BEHAVIOURAL INTENTIONS TOWARDS PUBLIC TRANSPORT: CITY OF KUALA LUMPUR AS A CASE

Kuala Lumpur is one of Southeast Asia’s most prominent modern cosmopolitan cities, having evolved from the federal capital. In consequence of this transformation, the population of Kuala Lumpur has expanded outward, leading to its city centre being mostly occupied by businesses and residents relocated to suburbs and more affordable areas. Increased commuting distances also facilitated the use of private automobiles which brings about the environmental issues. This research aims to determine factors influencing the behaviour and preferences of public transport passengers particularly Light Rail Transit (LRT) in Kuala Lumpur. In an effort to verify the theoretical model and the relationship between the variables, a structural equation model (SEM) was developed. Perceived Costs (PC), Attractive Alternatives (AA), Feeling towards Transit (FTT), and Service Quality’ (SQ) were considered as the parameters of the public transport services that could be the drivers for the key variables such as Perceived Benefits (PB), Satisfaction (SA), and Behavioural Intentions (BI). This model incorporates direct and indirect effects with the intention of providing insight into how the behaviour of transit passengers is influenced. The results indicate that Service Quality contributes to Satisfaction and Satisfaction contributes to Behavioural Intentions. According to the results, perceived benefits are positively related to satisfaction. The findings do not support the Service Quality and Attractive Alternatives as the significant indicators for the behavioural intentions. The reason for this outcome may have to do with the adverse impact of the COVID-19 pandemic on public transportation services, which in turn affected riders’ perception of these services.

Modeling non-compensatory strategies on path choices in a complex urban rail transit network considering characteristics of transfer passengers and trips

The path choice behavior of passengers is highly affected by transfer across different lines in a complex urban rail transit network that provides passengers with multiple path alternatives. Hence, the design of a transfer station in terms of its layout and transfer paths is gaining impact on passengers’ path choices in an expanding urban rail transit network. A comprehensive understanding of passenger behavior is essential for transportation planning and management to better design transfer stations and network operations. A questionnaire survey is conducted for passengers’ path choices and perception of transfer stations. Based on the collected data, this study develops a joint path choice model that combines Multinomial Logit Model (MNL) and Constrained Multinomial Logit Model (CMNL) to capture both compensatory and non-compensatory behavior, especially the latter one, which has not been fully explored in the current literature. Further, a membership model is constructed to analyze the heterogeneity of passengers’ non-compensatory behavior on transfer walking time. Four behavioral segments are identified in regard to passenger and trip characteristics. The estimation results show that the proposed model outperforms the general MNL model and allows a more convincing explanation of urban rail transit passenger behavior. Several research findings are derived, including that transfer walking time is more negatively perceived by passengers without non-compensatory behavior. Moreover, in-vehicle travel time is likely to be a minor consideration for passengers with non-compensatory behavior. The proposed model and framework enable urban rail transit operators and planners to retrieve more precise prediction of passenger flows and enhance service quality.

DEVELOPMENT OF MODE CHOICE BEHAVIOR MODEL FOR INTER-REGIONAL PUBLIC TRANSPORT– A CASE STUDY OF INDIA

Mode choice behavior of the inter-regional public transport passengers is important for proficient planning and operation of transport systems in developing countries. It is the responsibility of the authority to satisfy the demand for long regional trips having a significant movement of passengers by providing efficient public transport services. Most of the studies for the mode choice behavior of the passengers have been done for the urban mass transport within the urban conglomerate. The study aims to improve knowledge of the factor affecting passengers' decisions on the mode of travel for inter-regional public transportation in India. The Multinomial Logit (MNL) model and Artificial Neural Network (ANN) models among Gujarat State Road Transport Corporation (GSRTC) buses, Railways, and privately operated buses were developed for the trips between Surat city and Bhavnagar region of Gujarat, India. The final ANN model reflects a difference of attributes has 90.52% mode prediction capability against 70.11% of the MNL model. The developed model gives the proper insight for improving the transportation facilities in the region. It is revealed that the improvement in the service level and egress parameters are more important to attract travelers. The sleeper seat availability, lesser travel cost, lesser egress distance, and availability of night journey have been found important during travelers' thinking process to select the traveling mode. There are 13.1% and 20.1% rises in the probability of choosing GSRTC bus and train mode, respectively, with the improvement in the service level. Moreover, the GSRTC bus should improve its connectivity.

COVID-19 VIRUS PANDEMIC – CRASH OF THE SERBIAN TOURISM OR THE OPPORTUNITY TO PROSPER?

The year 2020 proved to be challenging in many fields. The entire economy suffered huge losses and the tourism is no exception. The research offered in this paper was conducted in this regard, the aim of which was to examine the tourist habits of the Serbian residents before the pandemic, and then to compare them with the behaviour of passengers in this new situation. Surveys designed partly as open questions, and partly as questions with offered answers, and their analysis, revealed that the habits of the citizens of our country change forcibly and that the preferences of a typical Serbian tourist always gravitate to foreign destinations. Only the pandemic led tourists to travel around Serbia, which, as they admit, they do not know enough about. An important role in that context played the government’s tourist vouchers, which to some extent helped overcome the economic difficulties of travel. The conclusion is therefore that although tourism has suffered damage, the imposed global isolation may be just an opportunity to attract domestic tourists. As could easily be concluded, tourism in 2020 faced great challenges globally, because of the Covid-19 virus epidemic. World stock markets and the world economy are in crisis and the extent and consequences of this crisis are still unknown. Tourism as an economic branch does not include only travel but relates to other economic activities that are an integral part of sustainable development. Some tourist destinations in the world once facing too many tourists, now are facing flights cancelled during the pandemic, and the number of visits dropped in a short period of time. On the other hand, we are witnessing events that are a consequence of the declining number of tourists, and which are a kind of nature’s recovery from human presence (dolphins in Venice, animals on city streets, reduced air pollution, etc.). It seems that nature needed a break from man, and viewed from that side, one can see the positive effects of limited movement.

Application of sustainable transport at the university campus level in the face of the COVID-19 pandemic

University campuses are considered small cities where all ages and backgrounds come together to study and work. Hence, applying sustainability policies to improve the travel behavior of university passengers can have significant benefits. In this study, several binomial logits were modeled to evaluate the choice of active modes and the willingness of university passengers to accept different types of policies on sustainable modes. Besides, transportation modes were examined for their share, accessibility, and feasibility. The questionnaire and variable correlations were analyzed by SPSS, and the models’ coefficients were estimated using NLOGIT software. This study is unique in several aspects. It simultaneously investigates both intra- and inter-campus travels under two different conditions (before and during COVID-19) at different levels. Furthermore, it estimates several brand-new variables whose effects have long been unknown to policymakers. Our methods and findings were validated using data from the University of Isfahan, Iran. Results indicated that factors such as type of policies, attendance history, inter and intra modes, accessibility, frequency of visits, occupation, education, and destinations affected the mode choice. The analysis of policies indicates that it can be of value to shift the mode choice of campus commuters towards more sustainable practices.

A data-driven performance analysis and prediction method for electric vehicle cabin thermal management system

Urban conditions and passenger behaviors primarily affect the performance of the electric vehicle (EV) cabin thermal management system (CTMS). The systematic analysis and accurate prediction of performance variations are crucial for the holistic optimization of the system. This study evaluated the effect of different ambient temperatures, solar radiations, driving patterns, and preset temperatures in Beijing, China. Energy analysis, exergy analysis, and the adaptive predictive mean vote method were employed to comprehensively analyze the performance of air conditioning (AC) system and cabin thermal comfort. A support vector machine model based on the multi-improved snake optimizer (MISO-SVM) was proposed to predict the CTMS performance. Chaotic mapping, subtraction-average-based optimizer, and lens imaging reverse learning strategy were applied to enhance the optimization capabilities of the traditional snake optimizer. A CTMS model was developed and validated to facilitate analysis and dataset acquisition. The results indicate that the energy consumption increases dramatically with elevated ambient temperatures (9.27 times increase from 26 °C to 40 °C) and solar radiation (11.42 times increase from 0 to 1200 W/m2). Conversely, high-speed driving patterns and higher preset temperatures (16 °C to 26 °C) are associated with improved AC system performance, leading to a reduction in energy consumption by up to 79.59 %. Moreover, the MISO-SVM model outperforms the traditional prediction models regarding CTMS performance prediction efficiency. The optimized MISO algorithm reduces the average convergence iterations by 20.59 % and achieves an average reduction of 18.92 % in fitness values. Across diverse prediction tasks, the MISO-SVM consistently maintains R2 above 0.99 and the lowest mean squared error. This study may provide new insights into the performance analysis and prediction of CTMS in EVs.

Perceived Competence of Ground Staff in Dealing with Unruly Passenger Behavior at Manila International Airport Authority Airports

Abstract: Unruly passenger behavior poses a significant challenge to the airline industry, affecting safety, security, and customer satisfaction. Ground staff play a crucial role in managing these behaviors, and their perceived competence is pivotal in shaping passengers' overall experiences. Passengers' perception of ground staff competence depends on factors such as staff knowledge of airline policies, de-escalation techniques, and their projection of confidence in handling unruly passengers. Airlines have an opportunity to enhance ground staff competence through comprehensive training, clear policies, and a culture of support and teamwork. The study aims to assess and describe the perceived competence of ground staff in dealing with unruly passenger behavior at Manila International Airport Authority airports. A quantitative research design used a survey as the primary data collection method. Stratified and convenience sampling methods were combined to select 1,475 respondents, representing a diverse sample. Data was analyzed using mean scores, standard deviation, and variance to assess the perceived competence of ground staff. The data suggests that ground staff generally exhibit competence in dealing with unruly passenger behavior. However, competence may vary in specific situations, such as when passengers exploit minor anomalies or use their social status for additional services. Overall, ground staff are perceived as competent, particularly in handling passengers displaying potentially violent tendencies. The findings of this study reveal that ground staff at Manila International Airport Authority airports are generally perceived as competent in handling unruly passenger behavior. While competence may vary in specific situations, such as when passengers exploit minor anomalies, overall, ground staff are well-equipped to ensure passenger safety and a smooth travel experience. The study highlights the importance of ongoing investment in staff training and resources to improve the competence of ground staff in addressing unruly passenger behavior, ultimately contributing to passenger satisfaction and safety.

Study on departure passenger movement during security check process at airport terminal based on an improved cellular automata model

Airport security check is a crucial part of air travel, and the efficiency of this process directly impacts the overall airport experience. This article presents a study on the modelling and simulation of movement of departure passengers during security check at airport terminals, with a particular emphasis on elucidating microscopic properties such as passenger flow patterns, individual movement dynamics and especially queuing phenomena. In order to solve the unreasonable path problem caused by direct application of cellular automata model, we propose an improved cellular automata model in which a new concept of horizontal restrain floor field is innovatively introduced. Considering the factors of spatial distance, number of queuing people and potential queuing people, a checkpoint selection probability model is embedded into the CA model. The results show that the queuing behavior of passengers is influenced by various factors from three aspects: (i) selection strategy of checkpoint, (ii) spatial layout of inspection area, (iii) arrival interval and verification time. The findings of this study can be used by airport operators to have better understanding of departure passengers’ movements at a micro level, improve the efficiency of the security check process and enhance the airport experience for passengers. Some measures can be taken to reduce the queue waiting time, such as personnel guidance, information signage, planning wider entrance and more queue lanes, replacing the manual verification with ticket inspection machines and so on.

Falling Behavior Detection System for Elevator Passengers Based on Deep Learning and Edge Computing

Due to factors such as dense populations and narrow viewing angles, previous deep-learning models for detecting passenger behavior in elevators often lack effectiveness. Traditional cloud-based data transmission methods have issues with high latency, high resource usage, and privacy threats, particularly during periods of high usage. To address these issues, we proposed a falling behavior detection system for elevator passengers based on deep learning and edge computing. A two-stream neural network model improved by 3D ResNet is presented, which utilizes edge computing for elevator passenger fall detection. Our homemade dataset of elevator passenger falling behavior is utilized to train and evaluate the system. The results demonstrate that the system is effective in detecting passengers’ falling behavior in elevators, with an average accuracy of 89.2%. The feasibility of the system in an elevator is also verified, and it has performed well. The application of this system in this field holds significant research value.

Aviation Industry and Covid-19: New Normal in the Pandemic Era, the Experience of Passenger, Post-Pandemic Traveling, and Long-term Impact on the Airlines

The Covid-19 pandemic is currently ravaging the entire world and the aviation industry is one of the hardest hit industries. This paper aims to analyze and understand the change in consumers behavior with regards to the aviation industry due to the Covid-19 pandemic. A questionnaire was administered to 100 respondents to glean insights into passenger preferences and travel decisions. The results reveal a strong willingness among respondents to travel when a cure for COVID-19 becomes available, emphasizing the crucial role of medical solutions in boosting passenger confidence. Additionally, the importance of low COVID-19 case counts at destinations and the tension between safety measures and passenger freedom is evident. Moreover, the research highlights the value passengers place on convenience and adaptability, as well as their evolving expectations regarding personal space and hygiene. These findings provide comprehensive insights into the complex dynamics of passenger behavior and satisfaction within the aviation industry during the pandemic, offering valuable reference points for industry stakeholders and policymakers.

SAFETY RISK MANAGEMENT AS RELATED TO AIRPORT PASSENGER EXPERIENCE AT MANILA INTERNATIONAL AND DOMESTIC AIRPORTS

This paper analyzed how employee attitudes towards safety risk management, perceived competence of ground staff in dealing with unruly passenger behavior, and perceived waiting time for airport security screening service can influence aviation safety performance and passenger experience at the airports operated by the Manila International Airport Authority. Descriptive survey, descriptive correlation, and thematic analysis methods were used to analyze the data. The results of the analysis showed that there is a weak, positive relationship between employees attitudes towards safety risk management and perceived competence of ground staff in dealing with unruly passenger behavior, however this correlation is not statistically significant. Additionally, there is a weak, positive relationship between perceived airport passenger experience and employee’s attitudes towards safety risk management, but this is not statistically significant. Furthermore, there is a moderate, negative correlation between perceived airport passenger experience and perceived waiting time for security screening service, which is statistically significant. The findings of the study suggest that employee attitudes towards safety risk management have no significant impact on the overall experience of airport passengers. In order to improve the airport passenger experience, the Manila International Airport Authority should implement a comprehensive safety training program for all airport staff, develop a safety risk management system and procedure manual, provide regular training and refresher courses for ground staff, and implement comprehensive customer feedback and satisfaction surveys. KEYWORDS: safety risk management, perceived competence of ground staff in dealing with unruly passenger behavior, perceived waiting time, airport passenger experience

Cascading failure in urban rail transit network considering demand variation and time delay

To study the cascading failure in real-world urban rail transit network, the cascading failure model is proposed by considering two essential characteristics of demand variation and time delay, which has rarely been considered in most existing studies. Specifically, the model utilizes a simulation-based approach where passengers in the rail transit network are abstracted as packets moving through the network. The movement of these packets, including their response to station failures, is planned to model the travel behavior of passengers. The Shanghai rail transit network is chosen as the case study for this research. Comparative experiments suggest that the proposed model may produce very different results for stations with high closeness and enough travel demand. The proposed model can depict the dynamic failure process and yield a longer and more dynamic failure process, which is beneficial for managers to identify the period for prevention. Besides, sensitivity analysis shows that higher travel demand is more likely to cause a cascading failure, while time delay slightly impacts the final cascade size but affects propagation duration. In summary, travel demand plays a vital role in cascading failures, and the presence of time delays complicates the cascading failure propagation process. The research results provide references and support for studying cascading failure in real complex environments.

Analyzing Escalator Infrastructures: A Pilot Study in Santiago Metro

The behavior of passengers in urban railway stations (i.e., metro stations) is dependent on environmental, cultural, and temporal factors. This affects how escalator infrastructures are used, with some passengers standing on one side of the steps to allow others to walk and, in other cases, others standing or walking on both sides of the steps. In this research, escalator infrastructures were studied to better understand the relationship between different conditions and passenger behaviors through a method based on video cameras, passenger detection techniques, and a simulation framework. The results indicate that passenger behavior could affect escalator infrastructure as it varies significantly depending on the time of day, type of station, local culture, and other environmental factors. The implications of these findings for the planning and design of the infrastructure of railway stations are discussed, and practical recommendations are proposed to improve the efficiency of escalator usage in urban railway stations.

Pre-peak fare discount policy for managing morning peak demand of interregional bus travel: a case study in Seoul metropolitan area

ABSTRACT Pre-peak fare discount policies have become increasingly common in urban transit services and show potential to spread morning peak-hour demand. This study analyzes the departure time choice behavior of interregional bus passengers during morning hours. A mixed logit model is applied to estimate the probability of shifting departure time as a function of fare discount rate, in-vehicle congestion, and other influential factors, accommodating heterogeneity of passenger preferences. The analysis data are obtained through a stated preference survey, which is conducted to passengers who regularly use the interregional bus in Seoul metropolitan area. Choice models are segmented by departure time periods (peak, pre-peak, and post-peak) and occupation types (fixed time worker, flexible time worker, and student). Marginal utilities of the unobserved preferences are estimated to suggest policy implications. From the experimental analysis, target passengers for shifting departure time during peak hour are suggested as flexible time workers and students.

The factors affecting people’s willingness in participating in and paying for upgrading their seats from the economy class to the premium class

As information technology develops rapidly and network information becomes more and more transparent, it is urgent for airlines to increase their flight carrying rates through marketing to maximize profits before take-off. While the seat bidding strategy has been one of the business models adopted by many airlines in recent years, it has not been implemented yet in Taiwan’s airlines. In order to understand the seat bidding behavior of passengers, this study adopted the double-hurdle model to explore the factors affecting passengers’ participation in seat bidding and calculated the prices they were willing to pay. Moreover, this study investigated the effects of long and short flight distances on passengers’ participation in seat bidding and the prices they were willing to pay for either flight distance. According to the results, the factors affecting the participation of passengers in seat bidding were different for long-distance and short-distance trips, while a common factor between them was that young people and passengers with preliminary knowledge of such activities were more willing to participate. According to the analysis on the prices people were willing to pay, the price difference that passengers of long-haul flights and short-haul flights were willing to pay for upgrades was about 55 and 24% of the reference price, respectively. The information obtained in this study can serve as a reference for future research or for airlines.

Model cascading overload failure and dynamic vulnerability analysis of facility network of metro station

The existing studies mainly focused on the cascading failure of the whole metro but rarely on stations. In this paper, we map a metro station into a directed and hierarchical network, and propose a cascading overload failure model to study the network dynamic vulnerability. The passenger behavior and node load update are considered based on impedance function and mutual flow redistribution. Without changing the network topology, a flow optimization strategy is proposed to improve the network performance against cascading failure. Yuantong station of Nanjing Metro is studied. The results show the network is vulnerable and global failure occurs easily under small node tolerance. When the node tolerance excesses a critical value, the network can transit abruptly from global failure to balance state. We obtain that the critical node tolerances are 0.17 for the largest degree-based attack(LDA) and 0.19 for the largest load-based attack(LLA). The ranges of the optimal flow redistribution coefficient are identified by the flow optimization strategy, which are [0.15 0.2] for LDA and [0.2 0.25] for LLA. The result indicates that a proper guidance of passenger flow can be beneficial to the network performance while a strong intervention to passenger flow can instead increase the cascading failure risk.

Coupling development of high-speed rail network and innovative cooperation based on travel behavior of depressed passengers

Background Depression is a common mental illness. Patients with depression show somatization symptoms such as loss of appetite, insomnia and low mood. The etiology and pathogenesis of depression are still unclear, and the pathogenesis is related to biochemical, neuroendocrine, sleep and brain electrophysiological abnormalities. This study takes depressed passengers as the research object to explore the intervention and influence of innovative high-speed rail services on patients with depressive episodes when they travel, so as to provide ideas for the coupling development of high-speed rail network and innovative cooperation.Subjects and Methods38 cases of high-speed rail passengers with depression were randomly divided into the experimental group and the control group. The experimental group was treated with innovative psychological services of high-speed rail. Hamilton Depression Scale (HAMD) was used to evaluate the degree of depression before and after treatment, and Hamilton Anxiety Scale (HAMA) was used to evaluate the degree of anxiety.ResultsThe scores of HAMD and HAMA in the experimental group after innovative psychological intervention services were lower than those before participation (P<0.05), and the difference was statistically significant.ConclusionsThe innovative high-speed rail depression psychological intervention service has a positive impact on depressed passengers. After the intervention, depressed passengers’ depressed mood and pessimistic thoughts are improved, they no longer resist others, and their discomfort symptoms are significantly reduced. At the same time, this intervention mode opens new ideas for the coupling development of high-speed rail network and innovative cooperationAcknowledgementFoundation: National Natural Science Foundation of China (No. 41301104); Foundation: National Natural Science Foundation of China (No. 41871108).

Popularization of high-speed railway reduces the infection risk via close contact route during journey

The risk of COVID-19 infection has increased due to the prolonged duration of travel and frequent close interactions due to popularization of railway transportations. This study utilized depth detection devices to analyze the close contact behaviors of passengers in high-speed train (HST), traditional trains (TT), waiting area in waiting room (WWR), and ticket check area in waiting room (CWR). A multi-route COVID-19 transmission model was developed to assess the risk of virus exposure in these scenarios under various non-pharmaceutical interventions. A total of 163,740 s of data was collected. The close contact ratios in HST, TT, WWR, and CWR was 5.8%, 64.0%, 7.7%, and 49.0%, respectively. The average interpersonal distance between passengers was 0.85 m, 0.92 m, 1.25 m, and 0.88 m, respectively. The probability of face-to-face contact was 9.5%, 70.0%, 64.2%, and 5.8% across each environment, respectively. When all passengers wore N95 respirators and surgical masks, the personal virus exposure via close contact can be reduced by 94.1% and 51.9%, respectively. The virus exposure in TT is about dozens of times of it in HST. In China, if all current railway traffic was replaced by HST, the total virus exposure of passengers can be reduced by roughly 50%.

A Case Study on the Economic Impact of Covid-19 Pandemic on the Travel and Tourism Industry With Special to Travel Agencies in Kerala

Covid-19 is the most discussed subject in our current society. It affects the lifestyles of every human individual around the world. In this pandemic era, the most affected sector is the travel and tourism sector and travel agencies play a very vital role in the field of tourism.. But in this current scenario, the Covid-19 outbreak has hit travel agencies hard – which could stall development in emerging markets. The change in the behavior of passengers following the COVID-19 crisis, travel restrictions and the ensuing economic crisis have resulted in a dramatic drop in demand for travel agencies. . This project will help to ensure how Covid-19 affects the travel agencies and also check what type of Travelers mostly traveld during this situation through travel agencies . It also ensure how Covid-19 affect the economic stability of the travel agencies. This work aims to get a clear picture of how this pandemic era affects the travel agencies in Kerala.