Passenger Experience Research Articles

Scheduling and Evaluation of a Power-Concentrated EMU on a Conventional Intercity Railway Based on the Minimum Connection Time

Power-concentrated EMU trains have the advantages of being fast and comfortable, having a flexible formation and a short turn-back time, and so on. They can effectively release the transportation capacity of tense lines and hubs (the replacement of conventional trains with power-concentrated EMUs can reduce the time it takes to enter and exit locomotive yards by 40 min per train), optimize operating structures, improve the quality and efficiency of passenger products for conventional railways, and enhance the travel experience of passengers. Moreover, they have certain cost advantages and practical operational value for improving the market competitiveness of conventional railways. In this study, a two-stage, two-layer cycle method is adopted to solve the application plan of an EMU with the minimum total connection time. Through the decomposition of optimization objectives, the search space and the solution scale in each stage are reduced. In the first stage, the feasible number of routes and the number division plan of internal running lines are listed. In the second stage, an improved ant colony algorithm is designed to arrange and combine the internal running lines in each plan to improve the search quality and convergence speed, which changes the pheromone volatilization coefficient with iteration. The optimal number of routes, the number of internal routes, and the optimal sequence between routes are obtained. The study also puts forward a method of route division according to the passenger load factor, which can help railway bureaus adjust the capacity according to fluctuations in demand. A running diagram of six pairs of power-concentrated EMUs on an intercity railway is used as the background to solve the problem. The optimal connection plan with 14 groups of different route division plans was evaluated by using the entropy weight–TOPSIS method, and the optimal plan was obtained in the form of a route division method with two groups of routes with three pairs of trains in each group. Compared with the actual operation plan, the number of routes and the number of first-level repairs are reduced by 50%, respectively, which can effectively reduce the operation and maintenance costs of EMUs. Compared with the actual plan, the average operation mileage is increased by 100%, the average mileage loss is decreased by 54.6%, and the minimum distance traveled by EMUs is increased by 200%, which indicates that the mileage maintenance cycle of the actual operation plan is not fully used. The average number of tasks of EMUs is increased by 100%, indicating that the efficiency of EMUs in the actual operation plan needs to be improved. The traffic mileage balance is improved by 100%, indicating that the EMUs in different routes are more balanced.

Dataset on passenger experience after riding an autonomous ferry

Dataset on passenger experience after riding an autonomous ferry

Enhanced Security Taxonomy for Fog-Enabled VANETs: A Comprehensive Survey on Attacks, Challenges, Applications and Architectures

Vehicular Ad Hoc Networks (VANETs) are integral to Intelligent Transportation Systems (ITS), enabling real-time communication between vehicles and infrastructure to enhance traffic flow, road safety, and passenger experience. However, the open and dynamic nature of VANETs presents significant privacy and security challenges, including data eavesdropping, message manipulation, and unauthorized access. This study addresses these concerns by leveraging advancements in Fog Computing (FC), which offers lowlatency, distributed data processing near-end devices to enhance the resilience and security of VANET communications. The paper comprehensively analyzes the security frameworks for fog-enabled VANETs, introducing a novel taxonomy that classifies threats, attack vectors, and mitigation strategies across various applications and architectures. Additionally, this study outlines methods for secure data sharing, real-time authentication, and threat mitigation while evaluating emerging security frameworks. By highlighting the advantages of FC—such as localized data privacy management, mobility support, and real-time decision-making—this survey establishes a foundation for future research and innovation in developing secure, scalable, and robust VANET systems. The findings offer practical implications for designing next-generation ITS with enhanced security and operational efficiency.

An Experimental Pilot Approach to Evaluate the Infrastructure Accessibility and Level of Service at Metro Station Platforms

Metro stations are essential for daily commuting, but overcrowding due to increased demand can severely impact infrastructure quality and passenger experience. The Level of Service (LOS), a key indicator of congestion, is influenced by factors such as density, flow, and speed, and poor LOS leads to issues like longer boarding times, overcrowded platforms, and reduced accessibility, especially for vulnerable populations. To address these challenges, the study explores innovative solutions to improve platform design infrastructure for better accessibility and LOS, aligning sustainable development goals to create safer, more inclusive transport systems. This study presents two strategies designed to reduce passenger congestion at Francia station on the Valparaíso metro platform infrastructures. The strategies, tested in experimental scenarios, showed minimal differences in boarding and alighting times, with less than a one-second average variation between the two. However, survey results revealed that passengers preferred the strategy “let passengers alight before boarding the train”, as it provided greater comfort and accessibility, reducing the number of passengers per door and improving the LOS from level C to B. Despite the minimal intervention in the experiments, the results suggest potential operational improvements. Future research will focus on measuring passengers’ emotional responses using psychophysiological data to further evaluate the suitability of the proposed strategies.

A Computer Vision-Based Multidimensional Data Modelling Study of the Psychological Responses of Metro Car Colour Lighting Design on Passengers

With the rapid urbanization and expansion of subway rail transit, the subway has become an essential mode of public transportation. This study explores the impact of subway car color design on passengers’ psychological responses. Utilizing computer vision technology and a pruning algorithm, a target detection model for passenger expression recognition was developed, serving as an intuitive measure of psychological reactions. An optimized expression feature extraction network was constructed for facial expression recognition, while a multidimensional data analysis model, based on data mining, provided comprehensive insights. The study reveals that green, red, and yellow lighting evoke positive psychological responses, whereas blue and purple induce calmer or more somber reactions. These findings offer valuable guidance for urban subway carriage color lighting design, enhancing passenger experience.

The Role of Artificial Intelligence in Aviation Construction Projects in the United Arab Emirates: Insights from Construction Professionals

The applications of Artificial Intelligence (AI) in the airport industry are significantly transforming operational efficiency, safety, and passenger experiences. This study investigates the integration of AI within aviation construction projects, with a focus on the United Arab Emirates (UAE). While AI technologies such as facial recognition, IoT, and biometric systems have advanced airport security and operations, their use in construction project management remains limited. A survey was conducted among 101 engineering professionals and experts with experience or involvement in managing aviation-related construction projects. Participants, many of whom had familiarity with AI tools, provided insights into the applicability of AI in areas such as planning, scheduling, and safety monitoring. The majority agreed that AI has the potential to revolutionize project management processes, improving decision-making, and efficiency. AI tools can predict delays, optimize workflows, and enhance safety through real-time data analytics and machine learning algorithms, reducing risks and human error. Despite the UAE’s leadership in AI-driven security advancements, its use in aviation construction is still underdeveloped. This research highlights the potential for broader AI integration across the entire lifecycle of aviation projects. By adopting AI in these areas, UAE airports could set new benchmarks for cost effectiveness, sustainability, and project delivery, reinforcing the region’s status as a leader in technological innovation within the aviation industry.

Leveraging complex network theory to enhance IoT applications in airport management

The tremendous growth in air traffic and passengers has made airports busier than ever before. This change requires airports to become smarter, more responsive and more efficient to create a seamless airport experience. Airports are increasingly adopting a variety of digital technologies to manage and improve airport performance in terms of resource consumption, carbon emissions and overall passenger journey quality. This graduation project explores the concepts of smart airports and Internet of Things (IoT) technologies. The main objective of the project is to study specific technologies that will have a strong impact on the development of smart airports. The Internet of Things (IoT) technology forms the basis of this project as we believe it is the core technology behind smart airports. By connecting physical assets, equipment, people and applications, IoT plays a vital role in increasing operational efficiency, improving the passenger experience and creating new revenue streams for airports. The project also focuses on the role of specific digital technologies such as blockchain, biometrics, indoor positioning systems, cloud computing and big data in smart airports. The research methodology of the project is based on quantitative literature research with a focus on big data analysis, including white papers, articles, press releases, annual reports and various studies.

Safety After Dark: A Privacy Compliant and Real-Time Edge Computing Intelligent Video Analytics for Safer Public Transportation.

Public transportation systems play a vital role in modern cities, but they face growing security challenges, particularly related to incidents of violence. Detecting and responding to violence in real time is crucial for ensuring passenger safety and the smooth operation of these transport networks. To address this issue, we propose an advanced artificial intelligence (AI) solution for identifying unsafe behaviours in public transport. The proposed approach employs deep learning action recognition models and utilises technologies like NVIDIA DeepStream SDK, Amazon Web Services (AWS) DirectConnect, local edge computing server, ONNXRuntime and MQTT to accelerate the end-to-end pipeline. The solution captures video streams from remote train stations closed circuit television (CCTV) networks, processes the data in the cloud, applies the action recognition model, and transmits the results to a live web application. A temporal pyramid network (TPN) action recognition model was trained on a newly curated video dataset mixing open-source resources and live simulated trials to identify the unsafe behaviours. The base model was able to achieve a validation accuracy of 93% when trained using open-source dataset samples and was improved to 97% when live simulated dataset was included during the training. The developed AI system was deployed at Wollongong Train Station (NSW, Australia) and showcased impressive accuracy in detecting violence incidents during an 8-week test period, achieving a reliable false-positive (FP) rate of 23%. While the AI correctly identified 30 true-positive incidents, there were 6 cases of false negatives (FNs) where violence incidents were missed during the rainy weather suggesting more data in the training dataset related to bad weather. The AI model's continuous retraining capability ensures its adaptability to various real-world scenarios, making it a valuable tool for enhancing safety and the overall passenger experience in public transport settings.

Joint Fault Diagnosis of IGBT and Current Sensor in LLC Resonant Converter Module Based on Reduced Order Interval Sliding Mode Observer.

LLC resonant converters have emerged as essential components in DC charging station modules, thanks to their outstanding performance attributes such as high power density, efficiency, and compact size. The stability of these converters is crucial for vehicle endurance and passenger experience, making reliability a top priority. However, malfunctions in the switching transistor or current sensor can hinder the converter's ability to maintain a resonant state and stable output voltage, leading to a notable reduction in system efficiency and output capability. This article proposes a fault diagnosis strategy for LLC resonant converters utilizing a reduced-order interval sliding mode observer. Initially, an augmented generalized system for the LLC resonant converter is developed to convert current sensor faults into generalized state vectors. Next, the application of matrix transformations plays a critical role in decoupling open-circuit faults from the inverter system's state and current sensor faults. To achieve accurate estimation of phase currents and detection of current sensor faults, a reduced-order interval sliding mode observer has been designed. Building upon the estimation results generated by this observer, a diagnostic algorithm featuring adaptive thresholds has been introduced. This innovative algorithm effectively differentiates between current sensor faults and open switch faults, enhancing fault detection accuracy. Furthermore, it is capable of localizing faulty power switches and estimating various types of current sensor faults, thereby providing valuable insights for maintenance and repair. The robustness and effectiveness of the proposed fault diagnosis algorithm have been validated through experimental results and comparisons with existing methods, confirming its practical applicability in real-world inverter systems.

Unlocking Trust and Acceptance in Tomorrow’s Ride: How In-Vehicle Intelligent Agents Redefine SAE Level 5 Autonomy

As fully automated vehicles (FAVs) advance towards SAE Level 5 automation, the role of in-vehicle intelligent agents (IVIAs) in shaping passenger experience becomes critical. Even at SAE Level 5 automation, effective communication between the vehicle and the passenger will remain crucial to ensure a sense of safety, trust, and engagement. This study explores how different types and combinations of information provided by IVIAs influence user experience, acceptance, and trust. A sample of 25 participants was recruited for the study, which experienced a fully automated ride in a driving simulator, interacting with Iris, an IVIA designed for voice-only communication. The study utilized both qualitative and quantitative methods to assess participants’ perceptions. Findings indicate that critical and vehicle-status-related information had the highest positive impact on trust and acceptance, while personalized information, though valued, raised privacy concerns. Participants showed high engagement with non-driving-related activities, reflecting a high level of trust in the FAV’s performance. Interaction with the anthropomorphic IVIA was generally well received, but concerns about system transparency and information overload were noted. The study concludes that IVIAs play a crucial role in fostering passenger trust in FAVs, with implications for future design enhancements that emphasize emotional intelligence, personalization, and transparency. These findings contribute to the ongoing development of IVIAs and the broader adoption of automated driving technologies.

Assessing User Contentment in Long-Distance Public Transport: An Analytical Approach Utilizing Principal Component and Regression Analysis

This study assesses the performance of long-route public transport services, considering passenger contentment as a key metric through the application of a passenger satisfaction approach. A self-rating questionnaire with a 5-point Likert scale was used to rate 16 different variables. The variables were selected after a literature review and consulting with relevant stakeholders. Data collected from com passengers were analyzed to determine the passengers’ comfort and satisfaction using Principal Components Analysis (PCA) and Regression Analysis. Variables with higher-than-average scores indicate that passengers are satisfied with that variable.16 variables with strong correlation among each other were collapsed into different component using PCA. Furthermore, the components extracted from PCA along with two extra variables that are age and sex were regressed to find the mean passenger satisfaction score. This research fills the gap by specifically targeting long-distance public transport in Nepal, an area that has not been extensively studied. It explores the perceptions of passengers regarding the facilities provided by long-route public transport services and identifies the major factors contributing to passenger satisfaction. Previous studies have often emphasized factors such as service quality, comfort, and safety in urban bus services, but there is a notable lack of research addressing the unique challenges and passenger experiences associated with long-distance bus travel. This study can offer theoretical assistance in devising relevant strategies to enhance the satisfaction level of public commuters.

Research on ride comfort evaluation method of EMU based on CART algorithm

In this paper, the research team presents the findings of long-term tracking tests on power-centralized EMU. The results demonstrate clear discrepancies between the established ride comfort evaluation results and the actual vibration state of the vehicle body and the subjective experiences of the drivers and passengers. A fuzzy evaluation was employed to establish the data set, while the entropy weight method was used to determine the key indexes. The CART decision tree algorithm was then used to establish a model for the evaluation of ride comfort in power-centralized EMU. The resulting evaluation was verified through dynamic simulation and field testing. The findings of the research demonstrate that: The lateral ride index can be employed as the primary index for evaluating the vibration state of the vehicle. By implementing three consecutive lateral ride comfort evaluations of the vehicle, the false positive rate and false negative rate can be maintained below 4%. The accuracy of the evaluation results is greater than 95% when a CART decision tree model is constructed using the minimum, maximum, standard deviation, and 50th percentile of the lateral ride index of the vehicle. The results of the simulation and field tests demonstrate that the ride comfort evaluation results are highly consistent with the actual vibration state of the moving EMU, the annoyance rate, and the human subjective feeling. This paper presents a comprehensive analysis of the research content, taking into account the subjective experiences of drivers and passengers, as well as the practical considerations of railway maintenance personnel. The findings offer a valuable contribution to the decision-making process of railway maintenance professionals.

Efficient User Pairing and Resource Optimization for NOMA-OMA Switching Enabled Dynamic Urban Vehicular Networks

Vehicular communication is revolutionizing transportation by enhancing passenger experience and improving safety through seamless message exchanges with nearby vehicles and roadside units (RSUs). To accommodate the growing number of vehicles in dense urban traffic with limited channel availability, non-orthogonal multiple access (NOMA) is a promising solution due to its ability to improve spectral efficiency by sharing channels among multiple users. However, to completely leverage NOMA on mobile vehicular networks, a chain of operations and resources must be optimized, including vehicle user (VU) and RSU association, channel assignment, and optimal power control. In contrast, traditional orthogonal multiple access (OMA) allocates separate channels to users, simplifying management but falling short in high-density environments. Additionally, enabling NOMA-OMA switching can further enhance the system performance while significantly increasing the complexity of the optimization task. In this study, we propose an optimized framework to jointly utilize the power domain NOMA in a vehicular network, where dynamic NOMA-OMA switching is enabled, by integrating the optimization of vehicle-to-RSU association, channel assignment, NOMA-OMA switching, and transmit power allocation into a single solution. To handle the complexity of these operations, we also propose simplified formulations that make the solution practical for real-time applications. The proposed framework reduces total power consumption by up to 27% compared to Util&LB/opt, improves fairness in user association by 18%, and operates efficiently with minimal computational overhead. These findings highlight the potential of the proposed framework to enhance communication performance in dynamic, densely populated urban environments.

Revisiting Passengers’ Perceptions of Airline Service Quality: A Theory-Driven Machine Learning Approach Using Big Data

ABSTRACT Understanding the discrepancy between passenger expectations and airline offerings is critical for improving service quality and customer satisfaction. This study expands the current knowledge regarding airline service quality dimensions by investigating actual passenger experiences. Analyzing 1,389,689 online reviews from TripAdvisor.com, structural topic modeling identified 29 airline service quality factors. Drawing upon AIRQUAL and SERVQUAL as theoretical frameworks, five unique dimensions emerged: flight attendance service, flight schedule, in-flight entertainment, in-flight meal, and baggage. Moreover, regression analysis highlights the significance of flight attendant services and hidden critical factors, such as attention to child passengers and special requests, in enhancing customer satisfaction. This study provides theoretical implications by uncovering airline service quality factors recognized by passengers and suggesting five potential service quality dimensions suitable for the airline context. Practically, the current study guides airline managers in understanding a passenger viewpoint and avoiding the service gap by combining machine learning, econometric approaches, and existing theories.

The intersection of data and human behaviour in aviation

This paper explores the critical intersection of data analytics and human behaviour within the aviation industry, emphasising the need for a balanced approach to enhance operational efficiency and passenger experience. As airports increasingly rely on data-driven decision making, the paper identifies key areas where data can optimise operations while also highlighting the importance of understanding human stressors in dynamic airport environments. Readers can expect to gain insights into how various stressors influence traveller behaviour and decision making. By employing a human-centred design approach and recognising the complexities of passenger experiences, stakeholders can develop strategies that effectively address both operational goals and individual needs. The paper illustrates the significance of context in interpreting data and the necessity of collaboration among diverse teams to create guest-centric solutions. Ultimately, it underscores that integrating human understanding with data analytics is essential for navigating the challenges of modern air travel, ensuring that advancements truly enhance the travel experience rather than complicate it.

Data governance best practices for the AI-ready airport

Airports generate vast amounts of data across various systems that are crucial for operational efficiency, safety and enhanced passenger experience. As airports increasingly rely on this data in order to adapt to evolving passenger experience demands, business environments and regulatory requirements, data governance becomes essential for managing, safeguarding and leveraging data effectively. A robust data governance framework provides the structure for ensuring data quality, security and compliance while enabling airports to harness data for artificial intelligence (AI) applications such as predictive maintenance and passenger flow management. By starting with a clear scope, objectives and policies, airports can build a data governance framework that addresses both current needs and future challenges. This paper explores the role of data governance in making airports AI-ready, outlining best practices for implementing a governance programme. It highlights the importance of tools such as data lineage tracking and the need for strong data security measures to comply with regulations such as General Data Protection Regulation (GDPR). The paper also emphasises the need for a culture of accountability, outlining key roles such as data stewards and chief data officers (CDOs) to ensure consistent, robust data management. As AI adoption grows, airports must focus on maintaining data integrity, fostering transparency and ensuring regulatory compliance to unlock the full potential of their data assets while safeguarding privacy and building stakeholder trust.

A data-driven approach to elevating airport experiences: Insights from Ontario International Airport’s journey mapping analysis

At Ontario International Airport (ONT), elevating the customer experience goes beyond increasing operational efficiency. By using a comprehensive, data-driven approach to creating a customer journey map, ONT gains a clear and comprehensive understanding of a passenger’s experience throughout the airport. This paper identifies key problems, areas of success and opportunities for improvement, leading to informed decision making and strategic investments that elevate overall customer satisfaction. Readers will learn the purpose and application of a customer journey map, including how to identify critical touchpoints and visualise the user experience from start to finish. The paper also introduces behavioural science tools, such as retina-scanning glasses, which help benchmark current experiences and uncover gaps in service. Additionally, it highlights the role of data-driven insights in influencing future design strategies, considering spatial features, emerging trends, policy changes and technology integration. The paper describes how to analyse and prioritise recommendations across each touchpoint, fostering cross-organisational consensus and guiding strategic investments in enhancing the airport experience. This knowledge is vital for professionals looking to innovate in service and infrastructure improvements, ensuring a world-class experience for airport passengers.

Experiences of Women who Faced Insecurity in Public Transport and its Impact on their Emotional Well-being: A Qualitative Study from Urban Area of Tamil Nadu, India

Introduction: Harassment in public transport faced by women passengers has been pushed to the sidelines failing to acknowledge the impact it has on both physical and mental health. Most incidents do not reach the authorities or the media, who often turn a blind eye to the existing problems. As a result, women develop a sense of insecurity that prevents them from using the public transport system. Women from economically challenged backgrounds often have no choice but to use public transport, as it is the cheapest and sometimes the only available mode of transportation. Aim: To understand the experiences of women passengers commuting by public transport in Chennai city, Tamil Nadu, and the incidents that led to their insecurity about using the public transport system. Materials and Methods: This qualitative study was conducted in the Department of Community Medicine, Sree Balaji Medical College and Hospital, Chennai, Tamil Nadu, India between November 2023 and March 2024 among women over 18 years of age who had been using public transport for daily transit for atleast six months. Qualitative data were collected through indepth interviews based on grounded theory among women who faced insecurity while traveling alone on public transport. Data saturation was reached after interviewing 20 participants. The transcribed data were analysed using Qualcoder software to identify relevant themes and subthemes. Results: In the current study, the majority of the women interviewed were between the ages of 18 and 50 years. Four major themes and 18 subthemes were identified through qualitative analysis. The four major themes were factors related to fear, social life, personal factors, and travel safety. Conclusion: The themes and subthemes highlight the gaps that have been overlooked by the authorities in the public transport system. It is crucial to understand that harassment occurring during transit, if not addressed immediately, will leave a lasting impression on women travelers, leading them to develop an irrational fear of crime that can have detrimental effects on their mental, physical, and social well-being. Further, research can be conducted using these themes, subthemes, and codes to develop a relevant research hypothesis that could be tested using quantitative study designs such as case-control and cohort studies.

Don’t be left at the gate: A practical guide to AI adoption for airports

Modern airports face increasing challenges due to the growing number of passengers and evolving security threats. The successful operation of an airport requires a delicate balance between efficiency, security and passenger satisfaction. Artificial intelligence (AI) offers the potential to revolutionise airport operations by improving passenger experience, enhancing security and optimising efficiency. Implementing AI presents challenges, however, including the need for a strong data foundation and a focused approach. Organisations should avoid trying to do too much at once and focus on specific AI applications. Building a data-driven culture and investing in data teams and chief data officers (CDOs) are essential for successful AI adoption. By addressing these challenges and embracing AI, airports can create a more efficient, secure and passenger-friendly environment.

Sustainable Passenger Services and Child-Friendly Airport Experience: A Case Study of Istanbul Airport

This study explores the concept of child-friendly airports, using Istanbul Airport as a case study to understand how such environments can enhance the travel experience for families with children. Through qualitative research methods, including focus group discussion and in-depth interviews with 12 mothers and 12 field specialists, the research identified key attributes that constitute a child-friendly airport. Building upon the Place Diagram model, the results revealed that a child-friendly airport should prioritize sociability, comfort and image, uses and activities, and access and linkages, aligning with the model’s core themes. The results further identified numerous sub-themes linked to these four themes. Accordingly, airports should offer diverse play areas, family-friendly seating, efficient wayfinding, and high-quality, sustainable materials to create a safe, inclusive, and engaging environment for passengers with children. The study emphasizes the importance of designing airports that cater to the needs of children and their families, contributing to social equity and enhancing the overall passenger experience. These insights can serve as a benchmark for other airports aiming to improve their service offerings for families, supporting sustainable development goals related to reducing inequalities and promoting inclusive, safe, resilient, and sustainable environments. This study represents the first academic attempt focusing specifically on comprehensive services for passengers with children and the broader concept of child-friendly airports.