Passenger Behavior Research Articles

Analyzing Behavioral Patterns of Bus Passengers Using Data Mining Methods (Case Study: Rapid Transportation Systems)

The aim of analyzing passengers' behavioral patterns is providing support for transportation management. In other words, to improve services like scheduling, evacuation policies, and marketing, it is essential to understand spatial and temporal patterns of passengers' trips. Smart Card Automated Fare Collection System (SCAFCS) makes it possible to utilize data mining tools for the purpose of passengers' behavioral pattern analysis. The specific goal of this research is to obtain functional information for passenger's behavioral pattern analysis in city express bus which is called BRT, and classification of passengers to improve performance of bus fast transportation system. Additionally, it is attempted to predict usage and traffic status in a line through predicting passenger's behavior in a bus line. In this paper, smart card data is applied to provide combinational algorithms for clustering and analysis based on data mining. To this end, we have used a combination of data mining methods and particle swarm optimisation algorithm and leveraged multivariate time series prediction to estimate behavioral patterns. Results show that price and compression ratio features are the most influencing features in the separability of transportation smart card data. According to obtained Pareto front, four features include a card identification number, bus identification number, bus line number, and charge times are influencing clustering criteria.

Corporate social responsibility, cost structures, and COVID-19: Impact of passenger behavior on business models

The present work is concerned with the distinction between low-cost and network carriers within the airline industry. These distinct business models follow different approaches in their external strategy and, thus, in their communication, marketing, and internal cost structure. The differences between the two models are discussed in general and in light of the ongoing trend toward more sustainable and transparent business models, thus focusing on the customer perspective. A quantitative empirical study was conducted to assess the perception of the two airline business models. Additionally, an analysis of changing flyer behavior owing to the COVID-19 crisis was conducted, and the importance of corporate social responsibility (CSR) in the airline industry was evaluated. Using a sample of self-reported frequent flyers (n = 99), the present study used a correlative approach to evaluate the relationship between the individual variables. Among the core findings of the study were that CSR plays an important role in the mindset of passengers, especially with respect to philanthropic and legal responsibility, and that during the COVID-19 pandemic, concerns about ecological responsibility gained momentum. Apparently, COVID-19 did not cause a demand shift within business models, and no differences in passengers’ perceptions regarding CSR were detected among models. However, significant differences between low-cost and full-service airlines were found regarding their respective perceived cost structures. Passengers are aware of this difference, which implies that they might see fare differences up to 30% as reasonable.

INFLUENCE OF PASSENGER CHARACTERISTICS AND BEHAVIOUR ON RAILWAY STATION DESIGN: REVIEW PAPER

This research paper summarizes published research work on how the passenger behaviour and characteristics influences the railway station design. Due to the growing population in cities and increased intercity trips, efficient public transportation is becoming an essential need. One of the best options for this problem is use of mass transit which accommodates transportation of people in high numbers per single trip. As a way of making Mass transit attractive to passengers, the utter most important objective of station design is to satisfy the various demands of passengers, translating to a people-oriented design. Public transportation systems such as rail transport pay dividends in key areas which include less traffic congestion, less pollution, safer travels, lower expenditures, less effort and better predictability. Passengers in railway transport exhibit different requirements during travel, which are reflected in terms of requirements regarding wished for offers and in specific behaviour patterns hence proper investigation on passenger characteristics and behavior mapping will aid station operators to offer services which are in sync with user requirements in a cost effective manner.

Review of Transit Data Sources: Potentials, Challenges and Complementarity

Public transport has become one of the major transport options, especially when it comes to reducing motorized individual transport and achieving sustainability while reducing emissions, noise and so on. The use of public transport data has evolved and rapidly improved over the past decades. Indeed, the availability of data from different sources, coupled with advances in analytical and predictive approaches, has contributed to increased attention being paid to the exploitation of available data to improve public transport service. In this paper, we review the current state of the art of public transport data sources. More precisely, we summarize and analyze the potential and challenges of the main data sources. In addition, we show the complementary aspects of these data sources and how to merge them to broaden their contributions and face their challenges. This is complemented by an information management framework to enhance the use of data sources. Specifically, we seek to bridge the gap between traditional data sources and recent ones, present a unified overview of them and show how they can all leverage recent advances in data-driven methods and how they can help achieve a balance between transit service and passenger behavior.

Personal safety of passengers during the first phase Covid-19 pandemic in the opinion of public transport drivers in Krakow

The Covid-19 pandemic impact all public spheres of city inhabitants' lives, also changed the conditions of travel-ing by public transport, especially in the field of personal passenger safety. The introduced limits on people in public transport vehicles and the need to maintain social distancing or cover the mouth and nose in a public transport vehicle - for various reasons - have been met with different understanding by users. The question then arose: are passengers actually complying with the rules of the pandemic when traveling by tram or bus? The paper attempts to assess the behavior of passengers, based on the opinions of people who observe these behav-iors in their daily work, driving public transport vehicles. For this purpose, a survey was carried out among bus and tram drivers of the main public transport operator in Krakow. On the basis of 334 fulfilled questionnaires, it was found that - according to the drivers - passengers often quite freely approach the limits of people in vehicles, and often do not respect the social distancing, especially when alighting and boarding. However, what is opti-mistic, the drivers highly assessed their own safety when performing their official duties - which is extremely important in the context of ensuring the continuity of public transport during a pandemic. Only 10% of drivers considered that the current solutions to protect passengers (and drivers themselves) are sufficient, therefore they called for an increase in the scope of protective measures: an increase in the number of public transport courses (to keep existing passengers), the obligatory use of protective masks (instead of ineffective loosely obligation to cover the face and nose), or the introduction of closed driver cabins in buses (as in trams). The obtained re-search results are of a cognitive nature, they can be a supplement to the travel studies at this specific time. They can also provide support in taking action in the case of a return of the Covid-19 pandemic or similar incidents in the future.

Post Covid-19 Analysis of the Aviation Sector

Abstract: Many countries' economic prosperity and strategic development are dependent on the aviation industry, and it plays an indispensable role in the global transportation system. The emergence of a pandemic, which is likely to shatter the sector and disrupt its operations fundamentally, is one of the most common and unprecedented challenges to this industry. Beginning in 2020, the terrible impact of coronavirus (COVID-19) disease, which has evolved into a worldwide pandemic and resulted in a radical reestablishment of society's daily existence, has been faced by aviation. The pandemic has caused a shift in the formerly usual method of air travel. This thesis investigates the ramifications of COVID-19 for the aviation business, as well as the measures employed by this industry in reaction to the crisis and the intention of passengers to travel by air during pandemics. This thesis' literature analysis covers the issues that the airline sector is facing as a result of the global pandemic, as well as the restructured onboard experience, including the new safety measures that have been implemented across the entire aviation network. The report also looks at the new passenger behaviour trends. In April-May 2021, an online empirical experiment using a video advertising as a stimulus in two separate experimental circumstances was done using a quantitative research approach. The impact of aircraft safety measures on travel intention, customer happiness, value for money, and perceived health risk on the general public was investigated. According to the findings, airline safety measures are likely to have a favourable impact on expected consumer satisfaction. At the same time, direct exposure to aircraft safety measures did not lessen their perceived health risk or raise their considered value for money. According to the findings, improving customer satisfaction and expected value for money might have a favourable impact on individual travel intentions. On the contrary, the greater the perception of a health danger, the less inclined people are to fly. This would be a negative scenario for the air traffic sector, whose future development is now difficult to foresee, and the amount of time required to recover is unknown. Keywords: Covid-19, Air traffic, Analysis, Python, Pre-processing

Identifying and Quantifying Factors Determining Dynamic Vanpooling Use

Nowadays, the growth of traffic congestion and emissions has led to the emergence of an innovative and sustainable transportation service, called dynamic vanpooling. The main aim of this study is to identify factors affecting the travel behavior of passengers due to the introduction of dynamic vanpooling in the transportation system. A web-based mode choice survey was designed and implemented for this scope. The stated-preference experiments offered respondents binary hypothetical scenarios with an ordered choice between dynamic vanpool and the conventional modes of transport, private car and public transportation. In-vehicle travel time, total travel cost and walking and waiting time or searching time for parking varies across the choice scenarios. An ordered probit model, a multinomial logit model and two binary logit models were specified. The model estimation results indicate that respondents who are aged between 26 and 35 years old, commute with PT or are members of bike-sharing services were significantly more likely to choose dynamic vanpool or PT than private car. Moreover, respondents who are worried about climate change and are willing to spend more for environmentally friendly products are significantly more likely to use dynamic vanpool in comparison with private cars. Finally, to indicate the model estimation results for dynamic vanpool, the value of in-vehicle travel time is found to be 12.2€ per hour (13.4€ for Munich subsample).

Roadmap to Early Implementation of Passenger Air Mobility: Findings from a Delphi Study

Urban air mobility (UAM) has recently increased in popularity as an emerging mode of transportation, covering a wide range of applications, for on-demand or scheduled operations of smaller aircraft, in and around metropolitan areas. Due to its novelty and as it has not yet been implemented, UAM research still faces uncertainties. In particular, there is a need to develop a roadmap for the early implementation of passenger air mobility, aiming to identify the most prominent challenges, opportunities, hazards, and risks, but also to highlight the most promising use cases, or on the contrary, the ones associated with the least benefits compared to the risks or complexity they entail. To answer the previous questions, and therefore address this research gap, this study used a two-round Delphi questionnaire, targeting various stakeholder groups (product owners, policymakers, researchers, consultants, investors), leading to a total of 51 experts, out of which 34 also participated in the second round. In the first round, the main challenges, opportunities, and hazards facing the implementation of passenger UAM were identified. Findings on challenges and opportunities that were dependent on use cases only (as opposed to being dependent on technology or external factors) were then fed back into the second round, which helped evaluate the use cases based both on their complexities, as well as the associated benefits. Accordingly, medical/emergency was identified as the best use case and intracity transport as the worst (in terms of complexity vs. benefits). Similarly, a risk analysis evaluated the potential hazards associated with the implementation of UAM and their impacts on the system viability. Community backlash was found to be the most hazardous one, while malicious passenger behavior and improperly designed infrastructure as the least. Findings from this study can help better understand stakeholders’ opinions, highlighting promising use cases, but also risks to be aware of, constituting therefore a roadmap for future implementation.

Application research of urban subway traffic mode based on behavior entropy in the background of big data

With the development of society and the Internet and the advent of the cloud era, people began to pay attention to big data. The background of big data brings opportunities and challenges to the research of urban intelligent transportation networks. Urban transportation system is one of the important foundations for maintaining urban operation. The rapid development of the city has brought tremendous pressure on the traffic, and the congestion of urban traffic has restricted the healthy development of the city. Therefore, how to improve the urban transportation network model and improve transportation and transportation has become an urgent problem to be solved in urban development. Specific patterns hidden in large-scale crowd movements can be studied through transportation networks such as subway networks to explore urban subway transportation modes to support corresponding decisions in urban planning, transportation planning, public health, social networks, and so on. Research on urban subway traffic patterns is crucial. At the same time, a correct understanding of the behavior patterns and laws of residents’ travel is a key factor in solving urban traffic problems. Therefore, this paper takes the metro operation big data as the background, takes the passenger travel behavior in the urban subway transportation system as the research object, uses the behavior entropy to measure the human behavior, and actively explores the urban subway traffic mode based on the metro passenger behavior entropy in the context of big data. At the same time, the congestion degree of the subway station is analyzed, and the redundancy time optimization model of the subway train stop is established to improve the efficiency of the subway operation, so as to provide important and objective data and theoretical support for the traveler, planner and decision maker. Compared to the operation graph without redundant time, the total travel time optimization effect of passengers is 7.74%, and the waiting time optimization effect of passengers is 6.583%.

An integrated SWOT based fuzzy AHP and fuzzy MARCOS methodology for digital transformation strategy analysis in airline industry

Driven by business strategies, digital transformation (DT) facilitates dramatic change in air passenger behavior. This study aims to determine and analyze different DT strategies (DTSs) with the help of an integrated SWOT-based fuzzy AHP-MARCOS methodology that is proposed for the first time in the literature for this purpose. This methodology is validated with a case study concerning the airline industry in Turkey. The weights of the SWOT factors are determined with the fuzzy AHP method. The fuzzy MARCOS approach is used to select the most suitable DTS. The most appropriate strategy is obtained as “focusing on differentiated digital customer experience and service quality by the adaptation of business models to DT to provide benefits”.

Walking model on passenger in merging passage of subway station considering overtaking behavior

As bottlenecks of walking facilities in subway stations, merging passages are prone to congestion when passengers walk and evacuate under daily operation and emergency conditions. The general social force model (SFM) can accurately reproduce the self-organization phenomena of passenger crowds, such as the strip and lane formation. However, the SFM cannot model the overtaking behaviors of individuals who try to avoid collisions with the preceding walkers. To depict the walking behaviors of passengers in merging passages, the SFM is improved by considering the overtaking behaviors of passengers (OSFM) in this paper. Firstly, the trajectories of heterogeneous passengers are abstracted from video footages including the processes of overtaking behaviors. Next, the principle of overtaking, which leads passenger to detour with the shortest route and avoid colliding with the preceding passenger, is put forward to reproduce the overtaking processes of heterogeneous passengers in merging passages. The OSFM is verified to be more accurate than the social force model with only a centripetal force appended (ISFM). Then, the probability of overtaking behavior is estimated based on analysis of the simulation scenarios with various population densities. Finally, some simulation experiments of heterogeneous passengers with different arrival intervals in the merging passages are conducted based on the OSFM. The results show that when population density is below 0.08 ped/m2 or exceeds 1.56 ped/m2, the overtaking behavior scarcely occurs, but when the density is in the range of 0.25–0.4 ped/m2, the overtaking behaviors are mostly likely to occur. The probability of overtaking behavior will decrease with the density increasing when the density exceeds 0.4 ped/m2. What is​ more, the total passing time and walking speeds are more sensitive to the change of layouts of merging passages when the arrival interval of passengers obeys exponential distribution.

Departure Time Choice in Schedule-Based Transit Assignment

This paper investigates existing departure time models for a schedule-based transit assignment and their parametrization. It analyzes the impact of the temporal resolution of travel demand and suggests functions for evaluating the adaptation time as part of the utility of a path. The adaptation time quantifies the time between the preferred and the scheduled departure times. The findings of the analysis suggested that travel demand should be discretized into intervals of 1 min, with interval borders right between the full minute, that is, ±0.5 min. It was shown that longer time intervals led to arbitrary run volumes, even for origin–destination pairs with just one transit line and a fixed headway. Although a linear relationship between adaptation time and adaptation disutility is a common assumption in several publications, it cannot represent certain types of passenger behavior. For some trip purposes, passengers may be insensitive to small adaptation times, but highly sensitive to large adaptations. This requires a nonlinear evaluation function.

An investigation into the attitude and behaviours of ferry passenger towards general waste management

The research study empirically investigates ferry passenger's perception and behaviours towards general waste management and recycling. Structural equation modelling and confirmatory factor analysis were used on survey data gathering from 400 ferry passengers in Hong Kong. The exploratory factor analysis recognized four key dimensions of general waste management and five main dimensions of recycling. Results showed that ferry passengers' attitude on general waste management and recycling are closely associated. Promotion on recycling or display of recycle initiative/guideline can stimulate passengers' attitude on recycling, as well as perceived convenience of recycling can improve passengers' attitude on recycling. However, promotion on recycling or display of recycle initiative/guideline and perceived convenience of recycling only slightly improve ferry passengers' attitude on general waste management suggesting each type of waste need to dedicated information to enhance proper disposal habits by passengers. Implications of research findings on waste management in ferry operations improvements and recommendations for waste management development are also provided.

Impacts of long-term service disruptions on passenger travel behaviour: A smart card analysis from the Greater Copenhagen area

Disruptions in public transport are a major source of frustration for passengers and result in lower public transport usage. Previous studies on the effect of disruptions on passenger travel behaviour have mainly focused on shorter disruptions, while the few studies on impacts of long-term disruptions have had limited focus on individual passenger behaviour. This paper fills the gap in research by proposing a novel methodology based on smart card data for analysing the impacts of long-term planned disruptions on passenger travel behaviour. We use k-means clustering to group passengers based on their travel behaviour before and after the closure. We can thus observe how different passenger groups changed travel behaviour after the disruption. We compare these observations to a group of reference lines without disruption to account for general trends. Using hierarchical clustering of daily travel patterns, we are able to in-depth analyse the reactions of certain passenger groups to the disruption. We apply the method on a 3-month closure of a rail line in the Greater Copenhagen area. The results suggest that, in particular, passengers with an everyday commuting behaviour have decreased after the disruption. The proposed methodology enables explicit analysis of the impact of disruptions on diverse passengers segments, while the specific results are useful for public transport agencies when planning long-term maintenance projects.

Analyzing Influencing Factors of Transfer Passenger Flow of Urban Rail Transit: A New Approach Based on Nested Logit Model Considering Transfer Choices.

With the continuous improvement of the operation line network of urban rail transit, analyzing influencing factors of transfer passenger flow of urban rail transit is critical to improve the transfer demand analysis of urban rail transit. Using data collected from questionnaires, transfer passenger flow surveys and smart cards, this study proposes an approach base on nested logit passenger flow assignment model considering transfer choice behaviours of passengers. The transfer passenger flow at seven transfer stations in Nanjing is obtained. Subsequently, this study investigates the potential influencing factors of transfer passenger flow, including the node degree, geographic location (located in the city center, urban fringe, suburbs or suburban fringe), economic location (distance from the city center) and transportation locations (if it is close to a transportation hub or in combination with the hub) of rail transit transfer stations. The results indicate that a positive correlation between the transfer passenger flow and the node degrees of transfer stations. However, the relationship between transfer passenger flow and the economic, geographic, and transportation locations of transfer stations is not clear. The finding have reference value for the network design of rail transit transfer stations and transfer facilities, and provide reference for the analysis of passenger flow under network operation.

Analyzing the Newest Interchange Connection at Cairo Metro Network Pertaining to Passenger Flow

The French standard governing occupanckes dedicated for gatherings of individuals and institutions receiving the public, including railway stations and tunnels was used in the design of Cairo’s transit network, which consists of three lines connected through three existing interchange connections and one that is currently under construction. There is a lack of studies quantifying passenger evacuation in relation to Cairo transit, especially with regard to the interchange connections. This study assessed the viability of the evacuation methods applied to the design of interchange connections in Egypt. The emergency evacuation procedures for the future interchange connection layout between Lines 1 and 3 were assessed using the French standard and NFPA 130 Standard for Fixed Guideway Transit and Passenger Rail Systems analytical methods. The analysis accounts for the traffic forecast as presented in the Egyptian company for operation and maintenance report for Line 1 and the feasibility study of Line 3. The results demonstrated that the connection was compliant with the French standard, but it did not fully meet NFPA requirements. It was concluded that there is still room for enhancement and for gaps to be considered. Recommendations include undertaking investigations into passenger characteristics and behavior in the context of passenger flow in emergency situations using approaches such as virtual research and field surveys, and simulation techniques.

What Causes Changes in Passenger Behavior in South-East Europe during the COVID-19 Pandemic?

The appearance of the COVID-19 virus in Europe, at the beginning of 2020, brought many challenges and changes to society. These changes affected the behavior, desires, and needs of passengers in vehicles. The change in passenger behavior has contributed to the more difficult organization of passenger transport and traffic management. For these reasons, in the countries of South-East Europe (Serbia, Montenegro, Slovenia, Bosnia and Herzegovina, Greece, the Republic of Northern Macedonia and Croatia), this survey was conducted in order to examine which demographic characteristics of respondents (age, gender, residence, education, and health) influence choice of transport, with the aim to optimize the transport system in times of crisis in this region. 786 respondents participated in the research. The results showed that the acceptability of vehicle occupancy most often differs with respect to age, education, and health conditions of the respondents. The obtained results will greatly help the organizers of public transport and the transport system in the region, since based on these results they can have an insight into the demographic factors that influence the choice of transport mode during a crisis.

Comparison of Door-to-Door Transit Travel Time Estimation Using Schedules, Real-Time Vehicle Arrivals, and Smartcard Inference Methods

Estimating passengers’ door-to-door travel time, for journeys that combine walking and public transit, can be complex for large networks with many available path alternatives. Additional complications arise in tap-on only transit systems, where passengers alightings are not recorded. For one such system, the Chicago Transit Authority, this study compares three methods for estimating door-to-door travel time: assuming optimal path choice given scheduled service, as represented in the General Transit Feed Specification (GTFS); assuming optimal path choice given actually operated bus service, as recorded by automatic vehicle location systems; and using inferred path choices based on automated fare collection smartcard records, as processed with an origin-destination-interchange (ODX) inference algorithm. As expected, ODX-derived travel times are found to be longer than those derived from GTFS, indicating that purely schedule-based travel times underestimate the travel times that are actually available and experienced, which can be attributed primarily to suboptimal passenger route choice. These discrepancies additionally manifest in significant spatial variations, raising concerns about potential biases in travel time estimates that do not account for reliability. The findings bring about a more comprehensive understanding of the interactions between transit reliability and passenger behavior in transportation research. Furthermore, these discrepancies suggest areas of future research into the implications of systematic and behavioral assumptions implied by using conventional schedule-based travel time estimates.

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.