Passenger Experience Research Articles

Research On the Design of Public Transit Services for The Elderly in China

As China enters an aging society, the elderly in China are increasingly becoming an important user group of public transportation. This study uses service design thinking to optimize the inclusiveness of Chinese elderly people's travel by bus and subway, and explores the feasibility of applying service design thinking to the field of public transportation. This paper adopts the observation method, with Guangzhou and Beijing as the research areas. The researchers conduct service design research based on the entire process of elderly travel, summarize the travel needs and pain points of elderly passengers, optimize the station guidance system, intelligent interface and service platform, make the public transportation service environment more friendly, and thus make the public transportation service environment more inclusive. Analyze the passenger flow, contact points, and stakeholders of the station, draw character portraits and journey maps, and output service design strategies to improve the travel experience of Chinese elderly passengers. In summary, this study aims to improve the convenience of Chinese elderly passengers taking public transportation and provide theoretical support for the transformation and optimization of China's public transportation services.

How can open data help airports to improve customer satisfaction?

In the evolving landscape of airport management, leveraging public data emerges as a transformative strategy to enhance operational efficiency and customer satisfaction. This paper explores the integration of data analytics in airport operations, illustrating through case studies how airports can utilise open data to optimise performance, improve passenger experience and streamline security processes. We highlight the significant benefits of adopting data-driven approaches, including increased operational transparency, improved decision making and enhanced service quality. Our findings underscore the pivotal role of public data in driving innovation and sustainability in airport management, offering actionable insights for airport authorities and stakeholders. This paper serves as a stimulus on how to benefit from the use of open data.

Simulation Optimization of Station-Level Control of Large-Scale Passenger Flow Based on Queueing Network and Surrogate Model

Urban rail transit encounters supply–demand contradictions during peak hours, seriously affecting passenger experience. Therefore, it is necessary to explore and optimize passenger-flow control strategies for urban rail transit stations during peak hours. However, current research mostly focuses on passenger-flow control at the network level, and there is insufficient exploration of specific operational strategies at the station level. At the same time, the microscopic simulation model for passenger-flow control at the station level faces the challenge of balancing efficiency and accuracy. This paper presents a simulation optimization approach to optimize the station-level passenger-flow controlling measures, based on a queueing network and surrogate model, aiming to improve throughput, minimize congestion, and enhance passenger experience. The first stage of the method modeled the urban railway station using queueing network theory and multi-agent theory, and then built a mesoscale simulation model that was based on an urban railway station. In the second stage, a passenger flow management and control model for ingress flow was established by combining the Kriging model with a queuing network model, and the particle swarm optimization algorithm was used to solve the model. On this basis, a simulation optimization method for station passenger-flow control was established. Finally, we conducted an example analysis of Zhongguancun Station on the Beijing subway. By comparing the simulation results before and after control, as well as comparing the optimal control scheme obtained by this method with the results of other control schemes, the results showed that the simulation optimization method proposed in this paper can propose an optimal passenger-flow control scheme. By using this method, stations can significantly enhance sustainability. For example, the method not only saves human resources but also effectively avoids or reduces congestion, boosting passenger travel efficiency and safety. By minimizing wait times, these methods lower energy consumption and support the sustainable development of public transportation systems, contributing to more sustainable urban environments.

A review of carsickness mitigation: Navigating challenges and exploiting opportunities in the era of intelligent vehicles

Motion sickness (MS) has long been a common complaint in road transportation. However, in the era of driving automation, MS has become an increasingly significant issue. The future intelligent vehicle is envisioned as a mobile space for work or entertainment, but unfortunately passengers’ engagement in non-driving tasks may exacerbate MS. Finding effective MS countermeasures is crucial to ensure a pleasant passenger experience. Nevertheless, due to the complex mechanism of MS, there are numerous challenges in mitigating it, hindering the development of practical countermeasures. To address this, we first review two prevalent theories explaining the mechanism of MS. Subsequently, this paper provides a summary of current subjective and objective approaches for quantifying motion sickness levels. Then, it surveys existing methods for alleviating MS, including passenger adjustment, intelligent vehicle solutions, and motion cues of various modalities. Furthermore, we outline the limitations and remaining challenges of current research and highlight novel opportunities in the context of intelligent vehicles. Finally, we propose an integrated framework for alleviating MS. The findings of this review will enhance our understanding of carsickness and offer valuable insights for future research and practice in MS mitigation within modern vehicles.

Optimizing passengers’ experience: A goal-oriented reinforcement learning speed control approach for urban railway trains

Prolonged vibration can be uncomfortable for passengers utilizing urban rail transit systems. This study proposes an automatic speed control framework for urban railway trains to reduce vertical vibrations experienced by passengers. We suggest the concept of the “segmented comfort speed limit” to represent the vertical passing comfort of oncoming sections. This speed limit is calculated from 1/3 octave band acceleration and smoothed through lag-type speed control mode. The deep deterministic policy gradient algorithm with hindsight experience replay mechanism (HER-DDPG) is designed, to balance safety, comfort, and energy efficiency driving. Verify the speed control framework based on HER-DDPG through the rail data collected from Dalian Metro Line 12. Compared with the DDPG-based model, the vertical comfort is improved by 2.34%, and the longitudinal acceleration and total energy consumption are reduced by 45% and 8.1%. Compared with the real-world train control trajectory, HER-DDPG improves vertical comfort by 9.76% and reduces energy consumption by 12.4%. The results show that the proposed framework can effectively improve the ride experience of passengers.

Towards a Personalized Seat Selection System of High-Speed Rail Based on Feasibility and Desirability

To conduct this research, we identified the current high-speed rail (HSR) market and passenger experience satisfaction with HSR. Then, we developed a personalized seat selection system that could assign passengers to different seats based on their own preferences by collecting their basic information. In the current HSR field, the existing research mainly focuses on improving hardware, such as seat comfort, high-speed rail traveling speed, and high-speed rail traveling smoothness. It does not consider travelers' environmental preferences and individual needs to a great extent. After several weeks of market research through the questionnaire and 4P model study, we concluded that the vast majority of passengers are dissatisfied with the current travel environment full of interruptions and are really willing to use a system that will improve their travel comfort. Therefore, the system will be added to a few major OTA platforms as a plug-in in order to reach a large number of potential customers. After that, we designed many features for our system based on the needs and interests of our target customers, as shown in the survey results, to confirm the desirability and feasibility of our product.

Analysis of passenger perception heterogeneity and differentiated service strategy for air-rail intermodal travel

Air-rail intermodal services (ARISs) represent a highly promising multimodal solution within the transportation sector. Nonetheless, various uncertainties and challenges persist across multiple dimensions of air-rail interline travel, with discrepancies in passenger perceptions being a notable aspect. In an effort to pinpoint the pivotal factors contributing to these disparities among distinct passenger profiles, this study employs the Structural Equation Modeling-Multiple Indicator Multiple Cause-Artificial Neural Network (SEM-MIMIC-ANN) methodology. This approach explores the impact of numerous attributes on passenger perceptions in the context of air-rail intermodal travel, leveraging questionnaire data gathered from Shijiazhuang multimodal passengers. Furthermore, the study utilizes the Classification and Regression Tree (CART) decision tree algorithm to categorize actual passengers into distinct characteristic groups. Subsequently, the perception levels of these diverse passenger groups are quantified through the calculation of comprehensive evaluation function values. In conclusion, taking into account the real-world conditions of air-rail interline travel, this research formulates a tailored service strategy aimed at enhancing the overall passenger experience.

Measuring the service reliability and the passenger experience in frequence-based transport systems

This article explores the application and limitations of operational Key Performance Indicators (KPI) in frequency-based metro systems, specifically focusing on the Copenhagen Metro and Greater Copenhagen Light Rail. Recognizing that currently used KPIs, which primarily measure the frequency of station departures, fails to capture crucial aspects such as travel time reliability and system responsiveness during disruptions, the study proposes a reevaluation of the assessment focus. It delves into alternative measures that consider both the reliability of departures and the quality of the passenger experience. The research methodology evaluates metro performance through a variety of new KPIs, emphasizing time metrics like deviation and stability, thereby providing a more nuanced understanding of service reliability. This preliminary exploration seeks not only to enhance operational assessments but also to aid strategic planning and the execution of improvements within high-frequency public transportation systems. The article underscores the need for more adaptive, comprehensive KPIs that reflect the true operational realities of metro systems, suggesting that such advancements could significantly improve passenger satisfaction and system efficiency.

A proposal for enhanced accountability in train station facilities management: The case of the commuter trains (KCI) in Jakarta, Indonesia

Background: The commuter trains (KCI), the commuter train system in Jakarta and its surrounding areas, faces significant challenges in station operation and maintenance, impacting the passenger experience. The current competitive tendering approach for procuring station facilities lacks a clear framework for accountability, resulting in inconsistent service delivery and passenger dissatisfaction. This paper proposes solutions to enhance accountability in train station facility management, aiming to improve the passenger experience. Method: By utilizing Moore's strategic triangle, the study identifies fundamental reasons for the accountability issues and examines the challenges posed by the restricted flexibility of the existing competitive tendering system. Potential solutions, including enhanced competitive tendering with performance-based negotiation, in-house production, and collaborative arrangements, are explored. Findings: The most feasible solution is recommended to address KCI's operational challenges. Despite KCI's popularity and extensive network, weak accountability, particularly in unresponsive management, disrupts the efficiency of train station facilities. Conclusion: This paper emphasizes the need for KCI to improve its accountability mechanisms to ensure safety, security, trustworthiness, convenience, ease of access, and equity for all passengers, ultimately enhancing the public value delivered by the commuter train system. Novelty/Originality of this study: The novelty of this research is developing a performance-based solution to improve accountability in KCI station facility management through enhanced competitive tendering and collaborative arrangements to enhance passenger experience and operational efficiency.

Characteristics analysis of leakage diseases of Beijing underground subway stations based on the field investigation and data statistics

With a continuous increasing of the underground water level of Beijing, many leakage diseases induced by the structural defect have been exposed in the subway tunnels that have been put into operation. The leakage diseases of underground subway stations may severely cause the safety and durability problems of the structure. Moreover, it could affect the passenger experience and seriously threaten the operation safety. Therefore, this paper collected the leakage states of a total of 258 underground subway stations in 16 lines of Beijing by field investigation, which include the number of average leakage points, leakage types and classification, distribution scenes of leakage, the construction methods and operation ages of subway stations and so on. Besides, in order to further investigate the causes of the leakage diseases, three typical cases of Beijing subway stations with severe leakage diseases, which were constructed by different methods, were selected to carry out the statistical analysis of leakage disease. The typical positions of leakage diseases of Beijing subway stations with different construction methods were presented by three-dimensional modelling, and the leakage characteristics of each station were analyzed. Finally, based on the field investigation and data statistics, a data-based Bayesian network leakage prediction model to evaluate the leakage risk of subway stations was constructed. This research can provide an intuitive reference for early warning, monitoring and prevention of leakage diseases of metro subways in operation.

Are airports like cities? Affordances and people’s micro embodied interactions during the arrival experience

Airports are designed to work efficiently. Specifically, they seek to efficiently process and ‘control’ people’s movements across landside and airside spaces, as well as to entice consumption through the architectural engineering of atmospheres and passenger experiences using architecture (Adey 2008; Fuller and Harley 2004; Hubregtse 2020). However, studies of the design and human sensorial perception of spaces of mobilities have shown that the way people move through and inhabit such spaces transcends the intended functionality of their design (Jensen and Lanng 2017). This includes work that considers airports as not only machines of movement, control and profit, but also city-like spaces (Hernandez-Bueno 2021; Nikolaeva 2013). Based on the conceptualisation of the airport as an ambiguous and city-like space, this paper uses thermal camera video recordings, ethnography, and design mappings to analyse the passenger movements, practices, embodied interactions, and mobile affordances of Copenhagen Airport’s meet and greet area. Focusing on a micro scale, the analysis shows the comparability of people’s practices and ways of inhabiting the airport with practices common to urban public spaces. This facilitates broader reflection on how to speculatively re-design and re-imagine the airport like an urban ‘public’ space.

A passenger service revolution? Transport design and passenger experience on Tokyo’s urban railway network, c. 1945–2010

This article discusses the history of efforts to shape passenger experience aboard urban railways through transport design. Taking Japan National Railways (JNR) trains as a case study, it examines the development of design interventions and organisational polices seeking to increase passenger comfort on Tokyo’s urban railway network in the second half of the 20th century. Industry and popular accounts have often tied the improvement of transport quality and passenger experience aboard JNR trains to its privatisation in 1987. In contrast, this article shows that passenger-oriented transport design has been an integral component in a continuous project to make urban railway travel more pleasant that long preceded this 1987 change in governance structures but has been held back by organisational and structural challenges. In doing so, it highlights that transport design involves the conscious shaping of both the “hardware” (i.e. physical transport infrastructure) and “software” (i.e. passenger-staff interactions) of service, as well as the backstage modification of organisational structures and processes that facilitate this framework. Built on a qualitative analysis of Japanese industry publications, newspapers, and secondary sources, the article thus contributes to literature on passenger experience, mobilities design, and transport history.

Comprehensive examination of regional railway passenger behavior and dwell time components: Insights from video-based observations in Victoria, Australia

In understudied regional railway operations, this study explores passenger boarding and alighting patterns and how station design impacts them, particularly dwell times. Despite extensive metropolitan and suburban train research, regional railways have been overlooked. This study investigates regional rail passenger flow and dwell time to bridge this gap. This article studies dwell times and passenger boarding and alighting at two regional stations in Victoria, Australia, using CCTV data. The objective is to identify insights that might improve regional railway services' efficiency and user experience and advocate for sector-specific solutions. Analysis indicates distinct station boarding and alighting features, highlighting the discovery of the ‘blinded phenomenon’ for train conductors particularly in the afternoon peak (PMP). The results of the study showed that PMP services, which prioritise alighting passengers, had higher dwell times than the morning peak (AMP) services, which emphasise boarding passengers. Obstructed views make it difficult for train conductors to monitor passenger alighting, prolonging dwell times. Better human resource strategies, artificial intelligence for crowd surveillance, and strategic CCTV system deployment to streamline operations and improve passenger experience on regional railways are proposed in the paper, laying the groundwork for future research and operational changes in this vital transportation sector.

Application and Innovation of Information Technology in Rail Transit Courses in Higher Vocational Colleges

The innovation of information technology in the field of rail transit mainly includes big data analysis and intelligent decision making and the application of artificial intelligence technology. Through big data analysis and intelligent decision-making technology, the operational efficiency and passenger experience of rail transit can be improved. Artificial intelligence technology can realize intelligent functions such as train operation prediction, passenger demand analysis, and safety monitoring, so as to improve the safety and reliability of the rail transit system.

Railway Service Dynamics: Factors Shaping Customer Satisfaction in Indian Railways

This paper explores how customer satisfaction and service quality are perceived in the context of Indian Railways, focusing on factors that significantly influence passenger experiences. Drawing from both extensive literature and empirical data collected through a structured questionnaire from 305 respondents in Vijayawada, the study identifies key elements such as punctuality, cleanliness, ticketing procedures, staff behavior, and overall service reliability. Analysis using ANOVA reveals varying impacts of these factors on customer satisfaction across different service dimensions. While punctuality and staff behavior emerged as significant influencers, amenities like seating comfort and digital services showed less impact. Managerial implications highlight the importance of customer-centric strategies, staff training, and infrastructure improvements to enhance service quality and passenger satisfaction. The study concludes with suggestions for future research, emphasizing the need for exploring additional variables and conducting longitudinal studies to track changes in customer perceptions over time. This research not only enriches understanding of service quality in Indian Railways but also offers insights applicable to global transportation systems.

Air and Land Transportation Development: A Mini Systematic Review of Ergonomics Contributions

Indonesia's transportation sector has experienced rapid growth. Research conducted in the field of ergonomics has resulted in a transportation mode development, that provides a comfortable and safe experience for passengers according to their basic needs. The research covers interior design, work environment, and workload in the transportation sector. However, there has yet to be a systematic review of the role and contribution of ergonomics in seeking ergonomic and human-centered transportation, as per the author's research. Therefore, this study examines ergonomics research (cognitive, physical, organizational) applied to Indonesia's innovation in air and land transportation over the past decade (2013-2022). A total of 23 articles were reviewed (6, 6, and 10 for air transportation, land and rail sectors, the land transportation sector other than trains, respectively). The results show that ergonomics impacts the development of ergonomic transportation in Indonesia. The potential for various studies is available in physical, cognitive, and organizational ergonomics.

The Role of Digital Innovation in Modernizing Railway Networks: Case Studies and Lessons Learned

Digital innovation has become a cornerstone in the modernization of railway networks, offering transformative potential to address the complex challenges faced by this critical infrastructure. As global transportation demands evolve, rail systems are increasingly incorporating advanced technologies to enhance efficiency, safety, and passenger experience. This paper explores the pivotal role of digital innovation in modernizing railway networks, presenting detailed case studies and lessons learned from various global implementations. The first section of the paper examines several case studies highlighting successful digital transformation initiatives in railway systems. It includes an analysis of the deployment of Internet of Things (IoT) devices for real-time monitoring and predictive maintenance, advanced data analytics for optimizing scheduling and capacity management, and the integration of smart ticketing systems to improve customer convenience. By analyzing these case studies, the paper identifies key success factors and common challenges encountered during the implementation of digital technologies.

Prediction of Operational Noise Uncertainty in Automotive Micro-Motors Based on Multi-Branch Channel–Spatial Adaptive Weighting Strategy

The acoustic performance of automotive micro-motors directly impacts the comfort and driving experience of both drivers and passengers. However, various motor production and testing uncertainties can lead to noise fluctuations during operation. Thus, predicting the operational noise range of motors on the production line in advance becomes crucial for timely adjustments to production parameters and process optimization. This paper introduces a prediction model based on a Multi-Branch Channel–Spatial Adaptive Weighting Strategy (MCSAWS). The model includes a multi-branch feature extraction (MFE) network and a channel–spatial attention module (CSAM). It uses the vibration and noise data from micro-motors’ idle operations on the production line as input to efficiently predict the operational noise uncertainty interval of automotive micro-motors. The model employs the VAE-GAN approach for data augmentation (DA) and uses Gammatone filters to emphasize the noise at the commutation frequency of the motor. The model was compared with Convolutional Neural Networks (CNNs) and Multilayer Perceptrons (MLPs). Experimental results demonstrate that the MCSAWS method is superior to conventional methods in prediction accuracy and reliability, confirming the feasibility of the proposed approach. This research can help control noise uncertainty in micro-motors’ production and manufacturing processes in advance.

FLYWISE AIRWAYS WEBSITE

Abstract— The FLYWISE Airways website is a user-centric platform designed to streamline the booking process and enhance the overall travel experience for passengers. With its intuitive interface and robust functionality, the website allows users to easily search for flights, compare fares, and make secure online bookings. In addition to booking flights, the website also provides valuable information about FLYWISE Airways’ services, fleet, and destinations. The website’s responsive design ensures a seamless experience across all devices, making it easy for passengers to access the information they need, whenever they need it. With its focus on user convenience and accessibility, the FLYWISE Airways website is poised to become the go-to platform for travelers seeking a hassle-free booking experience. The FLYWISE Airways website is designed with mobile responsiveness in mind, ensuring that passengers can access the website from any device, including smartphones and tablets. This ensures that passengers can easily book flights and access important information while on the go. Keywords – Flight booking, air travel, online reservations, flight search, compare fares, secure booking, flight information, manage bookings, seat selection, extra baggage, in-flight meals, frequent flyer program, mobile responsiveness, user-friendly interface.

Artificial intelligence as a driver of efficiency in air passenger transport: A systematic literature review and future research avenues

Despite the claim that artificial intelligence (AI) has the potential to increase efficiency in and for airlines, current literature is limited concerning models and frameworks to assess AI applications and their implications for airline efficiency. In response, we a) conceptualize and propose an AI-Airline-Efficiency-Model (AAEM) that allows for a more structured management approach for a systematic review and analysis of existing literature, and b) present a framework explicating the identified areas of AI application for airline efficiency based on a the AAEM model. In particular, using the four AI elements Machine Learning, Deep Learning, Reinforcement Learning and Natural Language Processing and their applications within six identified airline departments, we systematically review and analyze key attributes and characteristics of both AI and airline efficiencies to critically assess current research efforts. We found that AI applications are built around four overarching improvement areas predictive analytics, resource optimization, safety & autonomous processes and passenger experience, but lack a cross-department and inter-organizational focus and are often theoretical in nature. This study provides insight into most prevalent AI applications and the less popular applications applied in and for passenger transport, thereby presenting the dominating AI techniques that are covered by existing literature as well as highlighting a wide range of emerging AI techniques with the potential to become more influential for future studies. We discuss theoretical and managerial implications and offer avenues for future research.