Passenger Experience Research Articles

Satisfaction of passengers - process comparison between three different cruise ship classes

In cruise ship design, a ship designer often focuses on the ship’s function, while a cruise operator’s focus is on the creation of a comfortable and enjoyable cruise experience for passengers. Today, these two viewpoints are strongly connected, and thus, the way the inside of a ship is designed can impact how satisfied passengers are on their cruise journey. Thus, we need to figure out the best way to design a passenger ship from the perspective of the passengers themselves. In this paper, we analyse the differences in the combination passenger ship environmental elements and overall consumer satisfaction in two different cruise ship classes from the same ship operator, but from different eras. First, we present a theoretical framework and model for the cruise ship environment that consist of ambient, layout/design, social, product/service and onboard enjoyment factors. Then, by using data collected from the public domain, we compare two types of cruise ships using open-source data (N=755). This allows us to identify the factors contributing to the discrepancy in expectations across cruise guests. Based on this limited data, we create several linear regression models which indicates a favourable and statistically significant link between environmental elements and passengers' conduct while on board. Information processed this manner can be utilised to make informed decisions on cruise ship layout and amenities. In addition, the developed innovative KPI proved instrumental in influencing decision-making processes related to cruise ship designs and operations. Therefore, the findings from our research show a positive link between the onboard environment and the overall happiness of passengers.

Machine learning for predicting off-block delays: A case study at Paris — Charles de Gaulle International Airport

Punctuality is a sensitive issue in large airports and hubs for passenger experience and for controlling operational costs. This paper presents a real and challenging problem of predicting and explaining flight off-block delays. We study the case of the international airport Paris Charles de Gaulle (Paris-CDG) starting from the specificities of this problem at Paris-CDG until the proposal of modelings then solutions and the analysis of the results on real data covering an entire year of activity. The proof of concept provided in this paper allows us to believe that the proposed approach could help improve the management of delays and reduce the impact of the resulting consequences.

Working of E-Ticketing for Public Buses

The transition from manual, cash-based ticketing procedures to a cutting-edge digital system represents a pivotal advancement in modernizing India's public bus transportation. This research paper delves into the working model of e-ticketing for public buses, aimed at addressing long-standing challenges of wait times, operational hurdles, and inefficiencies inherent in traditional systems. By leveraging state-of-the-art digital technology, this initiative aims to revolutionize the public transportation experience, enhancing security, convenience, and operational efficiency for both administrators and passengers. Thus, to provide an agile and smooth ticketing experience, we have proposed the smart application. [4] Key objectives of this research include the elimination of manual cash transactions, reduction of wait times, and mitigation of operational inefficiencies. Through the implementation of a digital ticketing system, the paper explores how transaction processes are streamlined, providing passengers with a seamless interface for ticket purchases. Not only does this innovation enhance transaction security, but it also fosters a more agile and responsive public bus transportation system. The research assesses the tangible benefits of the digital ticketing solution, highlighting its role in increasing passenger flexibility and reducing congestion through shorter wait times and improved traffic flow. Real-time data analytics are examined as a critical tool for administrators, enabling enhanced resource allocation and informed decision-making processes. This paper presents a comprehensive analysis of the working model of e-ticketing for public buses, offering insights into its functionality, implementation challenges, and the transformative impact it has on India's public transport sector. By promoting a more sophisticated and user-friendly framework, the research underscores the potential of digital ticketing solutions to redefine the landscape of public transportation, paving the way for a more efficient and accessible urban mobility system in India. Key Word: E-ticketing, Public buses, Digital technology, Operational efficiency, Urban mobility

Bus Pass with Barcode Card Scan Code

Abstract: The "Bus Pass with Barcode Scan Project" aims to enhance the efficiency and security of bus transportation services by implementing a barcode-based pass system. This theoretical project focuses on the conceptual framework and key components required for the successful implementation of such a system. Implementing a secure and convenient bus pass system using barcode technology. Improves the accuracy and speed of passenger boarding by automating the pass verification process. Enhancing data management and analytics for better service planning and passenger experience. Developing a system for generating unique barcodesfor each bus pass holder. Ensures that the generated barcodes are tamper-proof and secure against duplication. Installing barcode scanners on buses to read the passenger's bus pass barcode. Ensures compatibility with various barcode types (e.g., QR codes, traditional barcodes). Creates a centralized database that store passenger information, including name, pass details, and barcode data. Implementing data encryption and security protocols to protect passenger information. Developing a mobile application for passengers to purchase, renew, or display their bus pass on their smartphones. Enabling easy scanning of the mobile app's barcode at the bus entry point.

Assessing Passenger Satisfaction with Smart Airport Technologies: An Empirical Study

Airports serve as crucial components within the air transport value chain, necessitating continual adaptation to meet evolving technological demands and enhance passenger experiences. The evolution towards Smart Airports or Airport 3.0 underscores the imperative for modernization to facilitate safe, efficient, and seamless travel experiences, particularly pertinent amidst the challenges posed by events like the COVID-19 pandemic. This study delves into the concept of Smart Airports, focusing on passenger perceptions of technology and its role in shaping competitiveness within the tourism sector. Through an analysis of passenger perceptions and expectations, this research aims to provide a clearer understanding of the Smart Airport concept, considering gender perspectives, which represent a novel contribution to existing literature. Recognizing the pivotal role of air transport infrastructures in destination competitiveness, this study investigates the impact of technology improvements on passenger satisfaction and overall travel experience. By examining the relationship between airport technologies and passenger perceptions, satisfaction, and enjoyment, this research underscores the importance of integrating new technologies to enhance airport services and meet evolving passenger needs. The study further explores the potential of ICT in reducing travel time and enhancing overall passenger experiences, with a particular emphasis on understanding gender-specific influences on perceptions of airport technologies. Through a comprehensive analysis, this paper seeks to contribute to the ongoing discourse on Smart Airports and their significance in shaping the future of air travel.

Towards balancing efficiency and customer satisfaction in airplane boarding: An agent-based approach

The airplane boarding process, which can have a significant impact on a flight’s turnaround time, is often viewed by researchers and airlines primarily in terms of minimizing total boarding time (TBT). Airplane capacity, number of passengers on board, amount of luggage, and boarding strategy are common factors that affect TBT. However, besides operational efficiency, airlines are also concerned with customer satisfaction, which affects customer loyalty and financial return. One factor that influences passenger experience is the individual boarding time (IBT), here defined by the time passengers stand inside the cabin. Considering these two aspects, an agent-based model is presented that compares the performance of three alternative mainstream boarding strategies in a 132-seat and a 160-seat single-aisle commercial airplane. An important characteristic of the model that differentiates it from previous work is that overhead bins have a physical limitation, which could lead to an increase in aisle interferences on full flights as passengers take longer to find a place for their carry-on luggage. Another important contribution is the analysis of how passenger seat location affects IBT. Our results show that outside-in (OI) produces shorter TBT than random and back-to-front boarding, and also shorter IBT and much shorter maximum IBT than BTF, particularly for passengers seated in the middle of the airplane. This suggests that among the three most popular boarding strategies used by airlines across the world, OI is the best when it comes to balancing airplane boarding efficiency with individual customer satisfaction.

Railing through reality: Trains and mobility in Victorian ghost stories

This paper explores the multifaceted cultural history of ghost trains in Victorian fiction by situating three little-known ghost stories in the publishing and social history of the second half of the nineteenth century. The figure of the ghost train offers a route into the entangled history of publishing and railways by contextualising the anxieties presented in railway ghost stories with the real-world experiences of passengers. Taking ideas of mobility as a focal point, this paper brings together discussions of virtual travel and the supernatural to demonstrate some of the impact railways had on reading and writing about train travel. More so than tales of other haunted transport technologies, it is the ghost train's unnatural capacity for movement that disturbs both passengers and readers. By both enhancing and warping reality, railways are ripe source material for Victorian ghost stories to entertain and demand questions of spatio-temporal experience from their reading passengers.

Airport security: the impact of AI on safety, efficiency, and the passenger experience

Airport security: the impact of AI on safety, efficiency, and the passenger experience

NondBREM: Nondeterministic Offline Reinforcement Learning for Large-Scale Order Dispatching

One of the most important tasks in ride-hailing is order dispatching, i.e., assigning unserved orders to available drivers. Recent order dispatching has achieved a significant improvement due to the advance of reinforcement learning, which has been approved to be able to effectively address sequential decision-making problems like order dispatching. However, most existing reinforcement learning methods require agents to learn the optimal policy by interacting with environments online, which is challenging or impractical for real-world deployment due to high costs or safety concerns. For example, due to the spatiotemporally unbalanced supply and demand, online reinforcement learning-based order dispatching may significantly impact the revenue of the ride-hailing platform and passenger experience during the policy learning period. Hence, in this work, we develop an offline deep reinforcement learning framework called NondBREM for large-scale order dispatching, which learns policy from only the accumulated logged data to avoid costly and unsafe interactions with the environment. In NondBREM, a Nondeterministic Batch-Constrained Q-learning (NondBCQ) module is developed to reduce the algorithm extrapolation error and a Random Ensemble Mixture (REM) module that integrates multiple value networks with multi-head networks is utilized to improve the model generalization and robustness. Extensive experiments on large-scale real-world ride-hailing datasets show the superiority of our design.

Persona-PhysioSync AV: Personalized Interaction through Personality and Physiology Monitoring in Autonomous Vehicles.

The emergence of autonomous vehicles (AVs) marks a transformative leap in transportation technology. Central to the success of AVs is ensuring user safety, but this endeavor is accompanied by the challenge of establishing trust and acceptance of this novel technology. The traditional "one size fits all" approach to AVs may limit their broader societal, economic, and cultural impact. Here, we introduce the Persona-PhysioSync AV (PPS-AV). It adopts a comprehensive approach by combining personality traits with physiological and emotional indicators to personalize the AV experience to enhance trust and comfort. A significant aspect of the PPS-AV framework is its real-time monitoring of passenger engagement and comfort levels within AVs. It considers a passenger's personality traits and their interaction with physiological and emotional responses. The framework can alert passengers when their engagement drops to critical levels or when they exhibit low situational awareness, ensuring they regain attentiveness promptly, especially during Take-Over Request (TOR) events. This approach fosters a heightened sense of Human-Vehicle Interaction (HVI), thereby building trust in AV technology. While the PPS-AV framework currently provides a foundational level of state diagnosis, future developments are expected to include interaction protocols that utilize interfaces like haptic alerts, visual cues, and auditory signals. In summary, the PPS-AV framework is a pivotal tool for the future of autonomous transportation. By prioritizing safety, comfort, and trust, it aims to make AVs not just a mode of transport but a personalized and trusted experience for passengers, accelerating the adoption and societal integration of autonomous vehicles.

Blind Passengers’ Travel Experiences: An Interpretative Phenomenology Analysis of the Challenges and Opportunities of Traveling by Train in Indonesia

This research investigates the experiences of blind passengers using long-distance intercity trains. Although prior research has been done on transportation use by blind consumers, their customer experience in Indonesia has yet to be thoroughly examined. This study employs Smith’s Interpretative Phenomenology Analysis (IPA) method, conducting video conference interviews with six blind passengers who frequently travel on long-distance intercity trains. These in-depth interviews were analyzed using double hermeneutics to develop the Blind Passenger Journey Mapping (BPJM) and Blind Passenger Journey & Experience (BPJE) models. The findings reveal that blind passengers often require assistance at nearly all touchpoints and need more accessible services throughout their journey. Two main themes emerged: Blind Passenger Experience (BPE) and Sighted Mobility Oriented Assistant (SMOA). The paper identifies significant gaps and opportunities in the current service design for blind passengers, suggesting that addressing these through entrepreneurial initiatives cannot only improve inclusivity and accessibility but also revolutionize the travel industry by opening a potentially underserved market, promoting a more equitable and sustainable travel ecosystem.

Railway Cloud: Management and Orchestration Functionality Designed as Microservices

The cloudification and virtualisation of railway functions have the potential to improve railway operation efficiency, reliability, safety, and security, as well as to enhance passenger experience by offering innovative services. This paper considers issues related to the management and orchestration of railway clouds that host cloudified railway functions. A microservices-based approach to the design of railway cloud management and orchestration functionality is proposed. The basic railway cloud concepts were defined, and functionality related to the basic orchestration of the railway cloud and deployments is analysed in order to derive the requirements of platform resources and workload management. This functionality is further designed in the form of microservices, meaning that they could possibly be used in orchestration applications to enable improvements in scalability, fault isolation, and data security. The design of microservices follows the principles of the Representational State of Transfer (REST) application programming interface (API) as a set of interlinked resources. Resources related to railway cloud orchestration are identified with their associated data, relationships to other resources, and applicable methods. The resources’ methods are used in railway applications to implement the required orchestration functionality and to maintain the state of railway cloud orchestration processes. To verify the synthesised microservices, the common orchestration application logic and microservices’ logic were modelled, and it was proved that the orchestration processes, which run concurrently, expose equivalent behaviour. The proposed approach was validated using a simulation, aiming to evaluate injected latency as a key performance indicator for the reliability and safety of railway operations. Additionally, some safety and security issues related to railway cloud management and orchestration are considered.

Balancing commercial return with passenger experience

As the aviation industry recovers from the COVID-19 pandemic, airports need to diversify income streams to generate commercial return, with on-site retail, leisure, food and beverage (F&B) and other non-aeronautical activities becoming vital to the operator’s bottom line. This paper argues that in an age of virtual transaction, operators need to provide an immersive commercial experience within terminals to drive up footfall, dwell time and spend. It also discusses how airports first need to engage their target audience virtually, making the initial connection via online platforms, then deliver on that promise in the physical world to strengthen and deepen the passenger relationship.

Enhancing sustainable passenger experience through duty-free shopping at King Fahd Airport: A multimodal perspective

With the increasing significance of travel retail within the global tourism industry, understanding the effects of duty-free shopping experiences on traveler well-being and brand perceptions has become crucial for enhancing customer satisfaction and engagement. This study examines the relationships between duty-free shopping experiences, traveler well-being, and brand perceptions at King Fahd International Airport. Utilizing a quantitative research approach, data were collected from 412 passengers who had recently engaged with duty-free shops. The study incorporated four key experiential dimensions—pragmatic, sociability, hedonic, and usability experiences—alongside well-being, brand engagement, brand experience, and word-of-mouth (WOM) advocacy. The findings indicated positive associations between well-being and pragmatic, hedonic, and usability experiences. Moreover, well-being significantly influenced brand engagement and brand experience, but not WOM. Brand engagement was found to be a significant predictor of brand experience, while it had no significant effect on WOM. Additionally, WOM did not significantly influence brand experience. These insights contribute to a comprehensive understanding of the intricate interplay between shopping experiences, well-being, and brand-related outcomes, providing valuable guidance for airport retailers and marketers to optimize traveler satisfaction and engagement.

Enhancing Public Transit System Through AI and IoT

The paper investigates the synergy between AI and IoT in revolutionizing public transit systems, emphasizing their role in addressing existing challenges and improving overall efficiency. It explores the application of IoT devices in creating a smart infrastructure that enables real-time data collection, monitoring, and management of transit operations. The research delves into the realm of predictive maintenance, showcasing how AI algorithms, powered by IoT data, can anticipate potential issues in transit vehicles. By analyzing sensor data, transit authorities can proactively address maintenance needs, minimizing downtime, reducing repair costs, and ensuring a more reliable and sustainable public transit service. Route optimization emerges as another crucial aspect of the study, highlighting how AI algorithms leverage historical and real-time data to recommend the most efficient transit routes. Factors such as traffic patterns, weather conditions, and passenger demand are considered to enhance overall system efficiency and reduce travel time for passengers. The paper introduces the concept of dynamic scheduling, illustrating how AI-driven algorithms adapt transit schedules in real-time based on changing passenger needs and external factors. This dynamic approach aims to provide more responsive services, ultimately reducing wait times and improving overall user satisfaction. Passenger information systems are explored as a pivotal component, illustrating how AI and IoT technologies enhance the passenger experience. Real-time communication through mobile apps, digital displays, and other channels ensures that passengers have accurate and timely information about arrival times, delays, and alternative routes, empowering them to make informed decisions. The researchers also delve into fare optimization, examining how AI algorithms analyze data on passenger demographics, travel patterns, and economic factors to create fair and affordable fare structures. This approach aims to encourage ridership, increase revenue, and improve the financial sustainability of public transit systems. The abstract presents a comprehensive overview of how the integration of AI and IoT technologies in public transit systems transforms urban mobility. The findings suggest that leveraging real-time data, predictive analytics, and dynamic solutions can significantly enhance the reliability, accessibility, and sustainability of public transit. As cities continue to explore innovative solutions, the abstract serves as a roadmap for developing smarter, user-friendly, and efficient urban transportation networks, ultimately contributing to improved quality of life for residents.

Enhancing Public Wellbeing Through Autogate at Soekarno-Hatta International Airports

The rapid advancement of information technology (IT), marked by innovations such as artificial intelligence (AI) is reshaping various sectors and challenging traditional frameworks, particularly towards automation. This transformation is particularly evident in public policy, as governments strive to enhance efficiency and citizen satisfaction. The adoption of information and communication technology, such as Autogate systems, emerges as a crucial strategy. In the context of immigration services, Autogate facilitates expedited immigration clearance procedures through biometric technology, enhancing security and efficiency. However, the implementation of such technologies raises concerns about public wellbeing. This research aims to assess the use and impact of Autogate technology on public wellbeing, focusing on its implementationat at Soekarno-Hatta Airport. The research methodology employed in this study involves two key approaches, interviews and observational studies. Findings reveal significant improvements in passenger experience and immigration efficiency, highlighting the potential of Autogate to enhance border management and public wellbeing. This research contributes to understanding the implications of technological advancements in immigration management and informs policymakers and stakeholders about the opportunities and challenges associated with Autogate implementation and public well being.

Exploring community satisfaction in building an innovative android-based mobile survey application for people travelling to Mentawai Island

This research aims to develop and evaluate an android-based mobile survey application to measure passenger satisfaction with transportation service standards to Mentawai Island. The application that has been developed will help in the evaluation process so that the improvement of sea transportation services can be improved. This study used the 4D development model and involved 30 passengers of the Mentawai Fest Ship as research subjects. The research instruments used were a validation questionnaire and a practicality test. A survey of public satisfaction with transportation services to Mentawai Island using an Android-based application will be developed, implemented, and evaluated. The results show that the application effectively meets the community's needs, shows high validity, and provides a convenient feedback mechanism for users. Overall, these findings contribute to efforts aimed at improving the quality of transportation services and the overall passenger experience.

IoT Based Railway Track Faults Detection and Localization using Acoustic Analysis and Signalling

Ensuring the safety and efficiency of rail transportation is paramount, necessitating timely inspection of railway tracks to prevent potential hazards. This research introduces an innovative approach by implementing an Internet of Things (IoT)-based autonomous railway track fault detection system. Various track components, such as rail surface defects, broken sleepers, missing fasteners, and irregular ballast levels, can lead to hazardous situations if not properly monitored. This master's thesis proposes an efficient method for classifying track components using image processing techniques and deep learning algorithms. Specifically, it focuses on detecting missing fasteners by analyzing images captured by a line camera. Experimental results demonstrate the method's effectiveness and robustness in complex environments. Future work aims to develop a comprehensive model capable of classifying all track components simultaneously. Image processing is utilized to extract individual components such as fasteners, rail, ballast, and sleepers, with the model trained to classify fastener states. This research contributes to advancing railway inspection methods, mitigating operational costs, preventing accidents, and enhancing passenger experience

An assessment of factors for the cruise port of call selection: The modified fuzzy Analytic Hierarchy Process.

Improving cruise ports of call is essential for enhancing the overall cruise experience for passengers, promoting tourism, and supporting the economic development of the regions served by these ports. Therefore, this article aims to assess selection factors (SFs) for the cruise port of call from the perspective of cruise operators (COs) and port operators (POs). In doing so, this paper first identifies SFs for the cruise port of call and establishes their hierarchical structure thanks to the extensive literature and expert brainstorming. Afterwards, The Modified Fuzzy Analytic Hierarchy Process (MFAHP) is developed to gauge the discrepancy in SFs between COs and POs. Empirical results from MFAHP pinpoint two significant SFs for POs to improve and attract their customers: customs, immigration control and quarantine (CIQ), and incentive measures. Besides, theoretical and managerial implications, and potential limitations for the next research are discussed.

Behavioural modelling of metro car choice

Overcrowding in metro systems can exert a negative impact on the travel experience of the passengers and on the system’s capacity. This can affect passengers in different ways, worsening their perception of safety, altering their behaviour, decreasing their satisfaction, and leading to higher travel-time sensitivities. One of the causes of overcrowding can be attributed to some metro cars being selected more often than others, and thus, the distribution of passengers inside the train not being homogeneous. This study adopts a disaggregated approach based on the observed behaviour to understand why some specific metro cars are preferred. The characteristics of passengers and metro cars that generate heterogeneity in the load profiles are identified and the motives behind passenger choices are understood by formulating a hybrid latent-class choice model. The behavioural models consider sociodemographic characteristics, level-of-service information, and attitudinal latent variables. Once a metro car choice model was obtained, an operational micro-simulator of the metro was programmed to evaluate how passengers' choices would be affected by altering variables related to the design of the stations, assessing it based on the current situation. Subsequently, interventions to station layouts are made based on the forecast of the behavioural models, to affect passenger choices and produce more homogeneous passenger distributions in the train. The intervention presented in this study reduced 46.49 h of total travel time during the morning peak hours; the impact was greater when considering individual perceptions. Incentives can be developed for optimising the distribution of passengers in metro cars by understanding passenger choice using a behavioural approach, and therefore, they can improve the passenger experience.