Passenger Behavior Research Articles

Towards simulation optimization of subway station considering refined passenger behaviors

Towards simulation optimization of subway station considering refined passenger behaviors

Design-led solutions for reducing airline food waste: A participatory approach to develop behaviour change interventions

Airline food waste is a critical issue which is affected by passenger behaviour. In parallel with the unexpected advent of COVID-19, passengers have exhibited changes in eating behaviour, generating more food waste during flights. In this paper, the authors explore and reflect on the use of participatory design workshops conducted with designers and airline passengers with the aim to develop behaviour change interventions towards reducing airline food waste. This paper identifies seven types of behaviour change intervention, whilst empirically evidencing the effectiveness of participatory design in facilitating the development of behaviour change interventions, towards achieving airline food waste reduction.

When The Bus Hits The Road: The Lived Experiences of School Bus Stewards in Dealing with Student Passengers

Introduction/Methodology: School bus stewards providing student passengers with a secure means of transportation from home to school is essential to fostering a supportive learning environment. School bus stewards play a vital role in guaranteeing the security and welfare of pupils on their daily commute. This research employed a qualitative approach, specifically a phenomenological research design, to delve into the experiences of the school bus stewards. Result: The three themes that can be found are: Behavioral Support, Management Effort, and Emotional Comfort. With three sub themes under each theme; School bus stewards utilize strategies to address and mitigate disruptive behaviors among student passengers, They also effectively manage student passenger behavior, ensuring a safe and orderly transportation environment while showcasing to the resilience of school bus stewards amidst challenges posed by disruptive behaviors of student passengers. Discussion: School bus stewards highlighted proactive approaches like open communication to reinforce positive student passenger behavior, along with clear rules and consequences. Collaborating with parents fosters a connected bus environment. School bus stewards use problem-solving, distractions, and positive relationships to manage disruptions effectively. They prioritize emotional comfort by resolving conflicts, promoting relaxation, and addressing environmental stressors, creating a positive transportation atmosphere. Conclusion: School bus stewards prioritize supporting, and comforting to manage student passenger behavior, advocating for ongoing program improvement and further research to enhance safety and transportation experiences.

Understanding the route choice behavior of metro passenger using the smartphone applications

Understanding the distribution of passenger flow is one of the crucial prerequisites for improving operational efficiency and implementing travel demand management strategies. This paper proposes an improved Random Regret Minimization (i-RRM) model based on the regret theory, where the passenger’s regret on selecting routes is minimized. Considering the growing trend of passengers relying on smartphone apps for route planning and navigation, three factors are investigated, namely, travel time, the number of transfer stations, and the total number of stations traveled. Automatic Fare Collection (AFC) is used to get the proportion of each route, and mobile phone signaling data is used to obtain the ground truth of passengers’ routes proportions for parameter calibration and model validation. The results show that, compared to the ground truth data, the average errors of the proposed i-RRM model, the classic RRM model, and the direct path matching algorithm are 6.03%, 8.96%, and 10.45%, respectively.

Accounting for passenger loss in bus bunching reduction: a robust real-time speed control method

ABSTRACT Bus bunching is a common phenomenon resulting from the inherent instability influenced by factors such as traffic and passenger behaviour. However, the issue of passenger loss caused by extended waiting time, is often overlooked despite being a significant concern for passengers. This paper addresses this problem by modelling passenger arrival and loss as a Markov birth–death process. We present a transit route model that incorporates passenger loss while considering additional factors, such as stochasticity, overtaking, passenger alighting, and bus capacity constraints. The primary objective of our model is to minimise passenger loss. To achieve this, we conduct an analysis about the characteristics of passenger loss. Under mild assumptions, we prove that passenger loss increases as the headways between buses exhibit greater variance. We also develop a robust optimisation (RO) method and corresponding algorithms to minimise passenger loss. To validate the effectiveness of our control method, we conduct numerical experiments using both computer-generated and real-world data. The results demonstrate that our method effectively reduces bus bunching, passenger loss, and waiting time compared to the existing literature.

Characterising travel behaviour patterns of transport hub station area users using mobile phone data

Understanding the travel behaviour of transport hub users is vital for improving transport services. Although previous research has explored passenger behaviour in and around transport hubs, there is a lack of comprehensive studies on travel patterns within hub station areas. To this end, this study harnessed mobile phone data to analyse the travel behaviour within a hub station area, using Beijing South Railway Station as a representative case. A series of recognition criteria was set up based on spatio-temporal information to categorize users within the station area. We categorised station area users into transport hub users (THU) and non-transport-hub users (NTHU) and examined their distinct travel patterns. The results reveal distinct travel patterns for THU and NTHU. NTHU are predominantly concentrated in and around the station area. THU, however, display longer trip distances and more widely distributed endpoints. In terms of travel time distribution, NTHU show evident morning and evening peak phenomena, while the travel time distribution of THU fluctuates throughout the day. The study also employed association rule analysis to illustrate strong connections between the station area, Beijing's core, central areas, and even urban fringe areas. The results reveal that the station area is most closely connected to the core and central areas of Beijing, from where many users are attracted to the station area. In addition, some urban fringe areas also have strong connections with the station area. These findings inform urban transport planning and personalised travel services.

Disruptive Passenger Behavior Impact on Overall Service Experience: An Appraisal Theory Perspective

The aim of this research is to examine the impact of bystander appraisal components, specifically congruence and relevance, on the intention to share negative experiences resulting from disruptive passengers on a flight. The investigation focuses on the mediating factors of negative emotions, satisfaction with service recovery, and trust in the airline. Employing a scenario-based approach, the detailed perceptions of passengers who may not have directly encountered disruptive behavior were explored. A total of 368 questionnaires were collected, and a structural equation model (SEM) was utilized to assess the relationship between bystanders’ appraisal and their intention to share. The results revealed that relevance positively correlated with negative emotions, and congruence significantly affected negative emotions. Moreover, negative emotions had a negative impact on both satisfaction with service recovery (SSR) and airline trust. Both SSR and airline trust, influenced by negative emotions, were found to negatively affect the intention to share. The results will help inform strategies to manage and mitigate in-flight disturbances, so that a high-quality cabin service can be maintained and the overall airline reputation does not suffer.

Dynamic joint decision of matching parameters and relocation strategies in ride-sourcing systems interacting with traffic congestion

As the ride-sourcing market expands, ride-sourcing fleets have increased urban traffic congestion, and in turn, road congestion is affecting ride-sourcing operations. It is crucial to incorporate this interaction mechanism into the operational decision of ride-sourcing systems. This mechanism can be described in detail by simulation models. Applying simulation-based optimization methods to the real-time operation of ride-sourcing systems is challenging due to the limitations of simulation efficiency. To tackle this issue, this study first proposes a modular simulation model combining time-driven and event-driven mechanisms. The simulation model comprises two layers: the traffic flow module at the bottom, defined by trip-based and multi-region macroscopic fundamental diagrams and cell transmission models, and the ride-sourcing operation module at the top, which considers the interaction between the traffic congestion and ride-sourcing operation and models the random behavior of passengers and drivers. We verify the efficiency and accuracy of the simulation model. The simulation model is utilized as an evaluator of the objective functions and performance measures. Then, we integrate the Bayesian optimization, parallel sampling, and rolling horizon approaches to develop an efficient and effective simulation-based optimization framework for the dynamic joint decision-making of matching radius, matching intervals, and threshold relocation strategies. Six groups of experiments for the traffic scenario in Berlin reveal some interesting findings. (1) The matching rate first increases and then decreases with the matching radius (a concave function), while the closest study does not think so. (2) As the fleet size grows, passenger waiting time reaches its minimum and then rises because of the increased endogenous congestion generated by ride-sourcing fleets. (3) The optimal operation strategy considering endogenous congestion not only leads to an increase in the system revenue and efficiency but also leads to congestion relief, even though the latter was not our initial objective. This benefit is the most pronounced when the fleet size and background traffic are moderate.

Impact of IoT System Imperfections and Passenger Errors on Cruise Ship Evacuation Delay.

Cruise ships and other naval vessels include automated Internet of Things (IoT)-based evacuation systems for the passengers and crew to assist them in case of emergencies and accidents. The technical challenges of assisting passengers and crew to safety during emergencies include various aspects such as sensor failures, imperfections in the sound or display systems that are used to direct evacuees, the timely selection of optimum evacuation routes for the evacuees, as well as computation and communication delays that may occur in the IoT infrastructure due to intense activities during an emergency. In addition, during an emergency, the evacuees may be confused or in a panic, and may make mistakes in following the directions offered by the evacuation system. Therefore, the purpose of this work is to analyze the effect of two important aspects that can have an adverse effect on the passengers' evacuation time, namely (a) the computer processing and communication delays, and (b) the errors that may be made by the evacuees in following instructions. The approach we take uses simulation with a representative existing cruise ship model, which dynamically computes the best exit paths for each passenger, with a deadline-driven Adaptive Navigation Strategy (ANS). Our simulation results reveal that delays in the evacuees' reception of instructions can significantly increase the total time needed for passenger evacuation. In contrast, we observe that passenger behavior errors also affect the evacuation duration, but with less effect on the total time needed to evacuate passengers. These findings demonstrate the importance of the design of passenger evacuation systems in a way that takes into account all realistic features of the ship's indoor evacuation environment, including the importance of having high-performance data processing and communication systems that will not result in congestion and communication delays.

Moving from classical towards machine learning stances for bus passengers’ alighting estimation: A comparison of state-of-the-art approaches in the city of Lisbon

Passenger alighting estimation is a critical task in public transport (PT) management, especially for entry-only Automatic Fare Collection (AFC) transport systems where passenger alighting are not recorded. Effective estimation methods are necessary for trip analysis and route planning, offering valuable insights into passengers’ mobility patterns and, subsequently, improving the quality of service. However, the stochastic nature of passenger behaviour challenges the degree of successful alighting estimates. A classic approach to infer the alighting stops of passengers is the use of trip-chaining principles. Since these principles are dispersed across the literature in the field, their comprehensive review is pivotal to establish the best practice for alighting estimation. Still, trip-chaining approaches are unable to infer the alighting of non-commuting passengers. This paper addresses these two research gaps by: i) providing a critical overview of the existing principles and methods for alighting estimation; ii) proposing an approach to improve alighting estimation that consistently integrates the most effective state-of-the-art principles on trip-chaining; and iii) further introducing a frequent pattern mining and density-based clustering solutions to support alighting estimation for non-commuting passengers. Considering the public bus transport in Lisbon city as the guiding case study, the achieved estimation rate by the proposed assembled model is 92%. Moreover, the density-based clustering solution is found to improve the estimation of 11pp against classic trip-chaining principles. Furthermore, the proposed model and acquired results yield actionable value to enhance PT operations and services, ultimately leading to improved bus routing and quality of service.

Unsupervised Learning-Based Exploration of Urban Rail Transit Passenger Flow and Travel Pattern Mining

This study delves into the realm of urban rail transit systems, leveraging unsupervised learning techniques to analyze passenger flow characteristics and unearth travel patterns. Focused on the dynamic and complex nature of urban rail networks, the research utilizes extensive datasets, primarily derived from Automated Fare Collection (AFC) systems, to provide a comprehensive analysis of passenger behaviors and movement trends. Employing advanced algorithms like DBSCAN, the study categorizes passengers into distinct groups, including tourists, shoppers, thieves, commuters, and station staff. These classifications reveal intricate patterns in travel behaviors, significantly contributing to a deeper understanding of urban transit dynamics. The findings offer valuable insights into peak travel times, popular routes, and station congestion, highlighting potential areas for operational improvements and infrastructure development. The study’s application of unsupervised learning in analyzing vast, unstructured data sets a precedent in urban transportation research, showcasing the potential of artificial intelligence in enhancing the efficiency and sustainability of urban transit systems. The insights garnered are pivotal not only for optimizing current operations but also for shaping future expansion and adaptation strategies, ensuring urban rail systems continue to meet the evolving needs of growing urban populations.

Socialization of Safety Factors for Passengers of Traditional Ship Crossing Transport at Muara Angke Port Jakarta

The orderliness of service and operation of facilities and infrastructure is relatively low, and many factors contribute to it, such as the weak awareness of ship owners and companies in implementing an effective and implementable safety system in the field, the seaworthiness of ships that are more oriented towards certification which in fact is not supported by careful inspection, as well as supervision carried out by the government on the implementation (drilling) of shipping safety requirements is inconsistent. The purpose of socialization to passengers who use traditional wooden ships is "Socialization of Safety Factors for Passengers of Traditional Ship Crossing Transport at Muara Angke Port Jakarta" the purpose of socialization is to prevent opportunities for violations in shipping not using Ring Life Buoys, Life Jackets and unethical behavior of ships passengers.

Modeling Urban Railway Passengers’ Willingness to Wait Based on Latent Class Analysis

Overcrowding in confined spaces, like inside trains, is a social issue that, in the long-term, could lead to accidents. To alleviate congestion in trains, a reduction in train headways is planned, made possible by technological advances such as the introduction of advanced autonomous trains (e.g., 60-s headway). However, simply reducing a train’s headway may be challenging because this does not consider the travel behavior of passengers who engage in the disruptive act of excessively boarding trains, thereby causing delays in their departure. Furthermore, the behavior of passengers may vary depending on an individual’s latent preferences, however, there is insufficient research that reflects these latent preferences. In this study, a survey was conducted with 971 urban rail passengers in the Seoul Metropolitan Area of Korea to identify their latent preferences using latent class analysis, resulting in a four-type classification: a congestion avoidance type, a time-sensitive type, a subway-preference type, and a type that does not prefer public transportation. Based on this, willingness to wait (WTW; i.e., waiting for the next less crowded train to reduce passenger discomfort) according to their latent preferences, was calculated. WTW was found to increase as the headway of the train was reduced. Choice modeling was conducted based on variables, including socioeconomic variables, to suggest effects related to WTW. The results indicated demand dispersion effects according to the headway reduction at peak times for various types of people. This study’s findings could be utilized to offer less crowded and safer train operations.

A Study on Bus Passengers Behaviour with Special Reference to Bangalore City

The transport system provides the basic infrastructure and it affects the economic activities of the country. The transport sector in India is divided into passenger and freight. The passenger services are available in both government private of bus category. At present private sector owns majority of the buses compared to government. In Andhra Pradesh and Karnataka also the private transport players dominating the passenger’s transportation market, especially if the travelling distance is more than 250 KM. Initially consumers (passengers) have been attracted with the facilities and service given by private bus operators. With the increasing competition and other policy issues, the private bus operators are implementing peak load pricing strategy. This pricing strategy is unexpected and unjustifiable to any passenger and even unbearable to them. And the facilities and service are same. Therefore a study has been made by the researcher to bring out the passengers behavior with peak load pricing by private operators with reference to Bangalore city in the state of Karnataka . By using judgment random sampling technique, 110 respondents were selected as sample size and information gathered through interview method. The outcome of the research has shown very interesting results such as customers’ dissatisfaction on Government failure to control the Private Bus operators and the price exploitation, in connection with their no alternative to avoid the services

Forecasting Airline Passenger Growth: Comparative Study LSTM VS Prophet VS Neural Prophet

To conduct an exhaustive examination of airline passenger growth prediction methods, this study compares the performance of three distinct strategies: LSTM, Prophet, and Neural Prophet. To forecast passenger volumes accurately, the aviation industry needs robust prediction models due to rising demand. This research evaluates the performance of LSTM, Prophet, and Neural Prophet models in passenger growth forecasting by utilizing historical airline passenger data. A comprehensive examination of these methodologies is conducted via a rigorous comparative analysis, encompassing prediction accuracy, computational efficiency, and adaptability to ever-changing passenger traffic trends. The research methodology consists of various approaches for preprocessing time series data, engineering features, and training models. The findings elucidate the merits and drawbacks of each method, furnishing knowledge regarding their capacity to capture intricate patterns, fluctuations in passenger behavior across seasons, and abrupt shifts. The results of this study enhance comprehension regarding the relative efficacy of LSTM, Prophet, and Neural Prophet in prognosticating the expansion of airline passenger numbers. As a result, professionals and scholars can gain valuable guidance in determining which methodologies are most suitable for precise predictions of forthcoming passenger demand. This comparative study serves as a significant point of reference for enhancing aviation prediction models to optimize the industry's resource allocation, operational planning, and strategic decision-making.

Voluntary carbon offset programs in aviation: A systematic literature review

A comprehensive literature review on voluntary carbon offsetting (VCO) programs in aviation highlights a predominant focus on offsetters' sociodemographic traits, behavioral triggers, and propensity to pay for offsets, inadvertently overshadowing the critical exploration of policy efficacy. Despite enriching our understanding of air passenger behavior, these studies afford limited insights into the role of VCO programs in enhancing aviation sustainability. The potential of VCO programs to significantly curtail aviation emissions becomes questionable, as increased participation could paradoxically raise offset costs, necessitating public policy intervention. Compounding the challenge is the low level of awareness about VCO programs and the aviation industry's misleading claims, suggesting a propensity toward greenwashing. While air passenger preference leans toward pro-environmental aviation policies and mandatory offsetting, several countries resort to non-earmarked, distance-based air passenger taxes, ostensibly to encourage sustainable travel. This review underscores the urgency for a balanced approach, marrying individual initiatives and structural reforms to effectively manage VCO programs in aviation and yield significant emission reductions. The role of public policy in ensuring transparency in VCO communications is also strongly emphasized.

Real-Time Detection of Abnormal Behavior of Escalator Passengers Based on YOLOv5s

Real-Time Detection of Abnormal Behavior of Escalator Passengers Based on YOLOv5s

Unraveling passengers’ preferences: investigating airport access mode choice in Tehran, Iran

Airports are imperative elements in fueling the economic growth of a country by reducing the distances between origins and destinations for the movement of both passengers and goods. Managing ground access modes of travel has become essential as the majority of trips are made by private cars, leading to increased environmental issues and congestion. To alleviate these problems, it is vital to identify the factors that shape air passengers' choice of ground access mode. In this work, we utilized inclusive Revealed Preference data (RP) collected through face-to-face interviews and applied a Multinomial Logit (MNL) model to study passengers' ground access mode choice behavior at Mehrabad International Airport, the busiest airport in Iran. The results showed that different factors such as socioeconomic status, travel characteristics, and distance to the airport affect the mode choice behavior of passengers in reaching the airport.

ROBIN: Rail mOBIlity simulatioN

The use of simulators is frequent in the study of complex systems. They replicate a real system and allow obtaining data from the simulated process as well as providing a mathematical model that helps answer What If? questions. This opens up the possibility of evaluating laboratory environment techniques and processes that will later be implemented in the real system. Competitive passenger rail services are complex systems. The efficient design of market mechanisms that encourage competition to offer more and better services requires analytical tools on which to test these new policies. This makes it essential to develop a simulator, as real and complete as possible, to carry out this analysis. This paper presents the development of a microscopic simulator, called ROBIN (Rail mOBIlity simulatioN), to simulate rail mobility in a competitive regime. The developed simulator is parameterizable and general enough to simulate passenger flows in competing railway systems. The tests carried out with ROBIN using the Spanish railway market as a case of study are proof of its usefulness, allowing disaggregated modelling of passenger behaviour and their travel choices in a competitive railway market.

СИСТЕМЫ УПРАВЛЕНИЯ ТРАНСПОРТНЫМ ПОВЕДЕНИЕМ НАСЕЛЕНИЯ ГОРОДСКИХ АГЛОМЕРАЦИЙ. ПАТТЕРНЫ ПОВЕДЕНИЯ, МЕТОДЫ ИХ РАСПОЗНАВАНИЯ

This paper examines the issues of studying decision support systems for passenger travel in terms of identifying patterns of such behavior, their classification and clustering. A review of the literature of domestic and foreign authors concerning the analysis of patterns of transport behavior of the population and their recognition is given. The relevance of studying the transport behavior of the population using the theory of patterns is substantiated. An approach to describing patterns of behavior from the point of view of the theory of macrosystems has been formed. Examples of mobile applications for route planning, modern models for studying transport behavior based on BigData technologies, neural networks, and parsing theory are given. The problem of formalizing the description of transport behavior has been solved in order to obtain a tool for analyzing the influence of control actions on transport behavior. The conclusion is made about the need for a generalized representation of transport behavior, and, consequently, its pattern, in the form of logical models. The purpose of the study is to compile a generalized representation of transport behavior and its patterns within the framework of the theory of macrosystems by compiling a digital pattern that can be displayed as a set of graphical schemes and as a subsequent logical description. The results obtained consist in a detailed review of the literature on the topic of decision support systems when making a trip, determining patterns of passenger behavior, and the influence of individual behavior on the behavior of the entire transport system as a whole. A toolkit for determining patterns based on the theory of macrosystems has been developed. The analysis of various methods of data processing and visualization of results used by researchers is carried out, which additionally serves to determine the possibilities of qualitative and quantitative description of transport behavior. At the same time, the set of states of the elements of the transport system, specific measurable and/or calculated values, their relation to the micro- or macro-levels of the description of the transport system were determined. It is determined that the most convenient and comprehensive tool for representing and studying patterns is the theory of transport macrosystems.