Passenger Experience Research Articles

Design of civil aviation tableware based on user experience

In-flight meals are an important touchpoint between passengers and airline services, tableware design is the carrier of air service and brand culture. This paper analyzes the in-flight meal experience of passengers in a special high-altitude environment, It is proposed that there is a "pressure space" in the in-flight meal experience, and takes tableware design as the entry point, from the tableware shape, color, material, and other elements, the plan of the cabin tableware design is proposed. Make aviation tableware artistic, aesthetic and economical, relieve the fatigue of passengers due to high-altitude flights, and improve the service level of airlines.

A Novel Spatiotemporal Data Low-Rank Imputation Approach for Traffic Sensor Network

The Internet of Things (IoT) has enormous potential to transform the transport industry by improving passenger experiences, safety, and efficiency. However, the collected spatiotemporal data by traffic sensor network often suffer from missing values (MVs), which affect the overall performance of the system. As a result, accurate recovery of MVs is essential for the successful application of IoT in transportation. In this article, we propose a novel MVs imputation model by integrating low-rank tensor completion (LRTC) and sparse self-representation into a unified framework. In doing so, the global multidimensional correlation, as well as sample self-similarity, can be well leveraged for imputation. In order to solve the proposed model, an elaborate solution algorithm is developed, following the principle of alternating direction method of multipliers (ADMMs). Importantly, each step in ADMM can be implemented efficiently by analyzing the problem structure. Moreover, in order to select proper parameters for the model, an improved harmony search heuristic algorithm based on dual harmonies generation strategy is developed, thus sufficiently considering the information contained in current harmony memory. The experiments on two real-world traffic data are carried out to evaluate the proposed approach. The results verify that in comparison with the classic matrix/tensor completion and other competing algorithms, our method significantly improves the imputation performance.

Dynamic Order Dispatching With Multiobjective Reward Learning

Traffic supply-demand mismatching has a severe impact on intelligent transportation systems. Fortunately, order dispatching is a promising option to mitigate the traffic supply-demand imbalance. Along this line, this article proposes the Multi-Driver Multi-Order Dispatching (MDMOD) method to make efficient order dispatching policy and enhance the experience of drivers and passengers. In the proposed MDMOD method, the Dynamic Multi-Objective Reward Learning (DMRL) algorithm is proposed to measure the driver-order-pair value, which illustrates the importance of a driver serving a specific order. A centralized matching algorithm is introduced to match all drivers and orders to maximize all driver-order-pair values. The multi-objective reward in the DMRL algorithm considers both immediate gains (i.e., pick-up distance) and future gains (i.e., the future traffic demand of order destination) to effectively improve the experience of drivers and passengers. Furthermore, by introducing the driver service level into the multi-objective reward, the “outstanding driver better reward” mechanism is realized to promote the ecological development of ride-sharing platforms. Notably, the Temporal-Graph Convolutional Network algorithm is proposed to predict the future traffic demand. Some virtual orders, which generated with the predicted future traffic demand, are dispatched to idle drivers to multiplex the traffic supply fully. A simulator is designed to test the performance of the proposed MDMOD method, experimental results demonstrate that the MDMOD method outperforms the state-of-the-art methods in terms of Average Driver Income and Order Response Rate.

Quadratic and higher-order unconstrained binary optimization of railway rescheduling for quantum computing

As consequences of disruptions in railway traffic affect passenger experience and satisfaction, appropriate rerouting and/or rescheduling is necessary. These problems are known to be NP-hard, given the numerous restrictions of traffic nature. With the recent advances in quantum technologies, quantum annealing has become an alternative method to solve such optimization problems. To use quantum annealing, the problem needs to be encoded in quadratic unconstrained binary optimization (QUBO) or higher-order binary optimization (HOBO) formulation that can be recast as a QUBO. This paper introduces QUBO and HOBO representations for rescheduling problems of railway traffic management; the latter is a new approach up to our knowledge. This new approach takes into account not only the single-track lines but also the double- and multi-track lines, as well as stations composed of tracks and switches. We consider the conditions of minimal headway between trains, minimal stay on stations, track occupation, and rolling stock circulation. Furthermore, a hybrid quantum-classical procedure is presented that includes rerouting. We demonstrate the proof of concept implementation on the D-Wave Quantum Processing Unit and D-Wave hybrid solver.

The Impact of Passenger Characteristics on Use of Self-Service Technologies for Check-In Process: A Case Study of Sydney Airport

Due to the severe global competition in the aviation industry, airports give top priority to passenger services. Therefore, a study of characteristics associated with passenger services is one of the most important measurements in the aviation industry. The objective of this study was to analyze the impact of passenger characteristics on the use of self-service technology (SST) for the check-in process at Sydney Kingsford Smith Airport. A survey was conducted online amid the COVID-19 pandemic for data collection. Based on the collected responses, this study analyzed the sociological and demographic characteristics of the passengers and situational factors related to characteristics of SST usage. T-test and Chi-square test were used to gain an understanding of the impact of these characteristics in the context of SST usage. With these analyses, this study was able to show the impact of passengers’ sociological and demographic characteristics such as level of education, flying experience, and age on the use of SST. In addition, this study showed the impact of situational factors related to characteristics such as better signage to locate the service, better corporation by staff members, less crowd or waiting time, and less processing time on the use of SST. Further, the results obtained in this study showed that traveling frequency impacts the passenger processing time at self-service kiosks, and that the use of a self-service kiosk impacts passenger arrival time at the airport. The findings in this study could potentially help airports in improving passenger experience and SST usage.

Spatiotemporal Virtual Graph Convolution Network for Key Origin-Destination Flow Prediction in Metro System

Short-term Origin-Destination (OD) flow prediction plays a major part in the realization of Smart Metro. It can help traffic managers implement dynamic control strategies to improve operation safety. Also, it can assist passengers in making reasonable travel plans to improve the passenger experience. However, there are problems that the dimension of OD short-term traffic prediction is much higher than the base number of metro stations and the OD matrix is sparse. To resolve the above two problems, a threshold-based method is proposed to extract key OD pairs first. OD passenger flow contains the attribute information of the Origin-Destination station and exhibits similar time evolution characteristics, so the spatial and temporal correlation needs to be considered in the prediction. Pearson correlation matrix is used to build a virtual graph and model the virtual connection between OD pairs. A spatiotemporal virtual graph convolutional network (ST-VGCN), which combines the advantages of a graph neural network and gated recurrent neural network, is proposed to identify spatial associations and temporal patterns simultaneously. The proposed method is evaluated on 39 days of real-world data from Shenzhen Metro, which outperforms other benchmarks. The research in this work can contribute to the development of short-term OD flow forecasts and help to provide ideas for the research on real-time operation and management of rail transit. Furthermore, it can help to establish passenger flow prediction and early warning mechanisms to quickly evacuate a large number of passengers in case of emergency.

Valuing Public Transport Customer Amenities: International Transit Agency Practice

Public transport customer amenities cover a range of measures that can enhance the quality of the passenger experience, such as information provision and station quality. While much research has determined the value that users place on amenities, there is little understanding of current practice in the use of customer amenity valuations in project appraisal. A survey of transit agencies in 11 cities (Melbourne, Sydney, Brisbane, Perth, Auckland, London, Paris, Toronto, Vienna, Oslo, and Singapore) was undertaken and showed that Australasian cities generally have widespread inclusion of customer amenities as part of advanced appraisals for all relevant types of public transport projects. Australasian practice tends to include customer amenities more frequently in project appraisal than London, Singapore, and Oslo. Paris, Toronto, and Vienna rarely (if at all) include customer amenities in these appraisals, although they adopt advanced appraisals for some projects. While agencies generally use published sources of customer amenity values specific to their country, Toronto and Singapore tend to use customer amenity values from London, highlighting a lack of local customer amenity values.

Novel group handover mechanism for Cooperative and Coordinated Mobile Femtocells technology in railway environment

Recently, the Mobile Femto (MF) Technology has been debated in many research papers to be a promising solution that will dominate future networks. This small cell technology plays a major role in supporting and maintaining network connectivity, enhancing the communication service as well as user experience for passengers in High-Speed Trains (HSTs) environments. Within the railway environment, there are many MF Technologies placed on HSTs to enhance the train passengers’ internet experience. Those users are more affected by the high penetration loss, path loss, dropped signals, and the unnecessary number of Handovers (HOs). Therefore, it is more appropriate to serve those mobile users by the in-train femtocell technology than being connected to the outside Access Points (APs) or Base Stations (BSs). Hence, having a series of MFs (called Cooperative and Coordinated MFs -CCMF) installed inside the train carriages has been seen to be a promising solution for train environments and future networks. The CCMF Technologies establish Backhaul (BH) links with the serving mother BS (DeNB). However, one of the main drawbacks in such an environment is the frequent and unnecessary number of HO procedures for the MFs and train passengers. Thus, this paper proposes an efficient Group HO mechanism that will improve signal connection and mitigate the impact of a signal outage when train carriages move from one serving cell to another. Unlike most work that uses Fixed Femtocell (FF) architecture, this work uses MF architecture. The achieved results via Matlab simulator show that the proposed HO scheme has achieved less outage probability of 0.055 when the distance between the MF and mobile users is less than 10 m compared to the signal outage probability of the conventional HO scheme. More results have shown that the dropping calls probability has been reduced when mobile users are connected to the MF compared to the direct transmission from the eNB. That is in turn has have improved the call duration of mobile UEs and reduced the dropping calls probability for mobile users who are connected to the MF compared to eNB direct connection UEs.

Improving Air Travel Comfort & Experience: Designing for Infection Prevention and Control in Response to COVID-19

In lieu of the recent impacts of the Coronavirus Disease 2019 (COVID-19) on the health and safety of individuals around the world, domestic and international travel organizations, and more specifically, the aviation industry, rapidly implemented preventative measures to support infection prevention and control (IPAC). Such measures have drastically and understandably changed how passenger interactions and experiences take place through various points in their air travel journey. Research that examines the user experience of passengers in response to changes that emerged from the COVID-19 pandemic is needed. This study focused on developing a better understanding of passenger experience during the COVID-19 pandemic and how the design of the environment may be influencing this experience. Design recommendations to respond quickly to future pandemic conditions or other infectious outbreak scenarios is an expected outcome of this study.

New Technological Solutions to Improve Rail Passenger Services Based on the Example of the In2Stempo and ExtenSive Projects of Shift 2Rail

There is a reason why it is said that stations are the business card of railroads. In the IN2STEMPO and ExtenSive projects, re-search work is looking at solutions to improve the passenger experience at stations. This article presents the scope of research carried out within the projects, including the development of more effective tools for crowd management at high-capacity stations, but also on new methods of designing stations and their elements taking into account the latest architectural trends. An important issue is also a problem still troubling the staff of many railroads, that is, improving accessibility from the plat-form to the train. Rail services, by their universality, should take account of the extensive opportunities offered by information technology. This applies to the complete process of passenger service, from purchase of a ticket to arrival at commercial out-lets and the journey itself. The article also outlines the principles of the ExtenSive project, which is a continuation of previous work aimed at developing both passenger applications and software for transport operators, which at the end of the day will enable the provision of transport services of the highest standard. This paper presents the results of the analysis so far on the eve of the projects’ completion, and identifies possible applications of the results of the work carried out. Keywords: rail passenger services, Shift 2Rail, Horizon 2020

Assessing the Impact of Technological Advancements on the Consumer Experience in Commercial Aviation

Technology has played a major role in changing the passenger experience in commercial aviation. Various laws and regulations in the aviation sector once prevented airlines from setting their own fares, but the eventual deregulation of this sector allowed airlines to create new business models and increase their profitability. This development led to greater competition between airlines and two main models developed: legacy and low-cost. A major trend since deregulation has been the decreased seat pitch in airplanes in order for airlines to maximize their profits. Utilizing classes such as first, business, and economy has allowed companies to negotiate seat pitch with a given price difference per option. The amenities that are available in the air have changed as a result of technological advancements as well. There has been a dramatic impact on inflight entertainment as a result of the better television technology and the development of touch screens. However, travelers believe that the quality of inflight meals has progressively declined. In addition, the impact of artificial intelligence has been massive for airlines, making certain processes more convenient for both the company and passengers. Although most individuals presume that airline companies receive a large profit per passenger, this is not the case since the cost for an average ticket in the commercial aviation industry has increased minimally compared to other industries. This paper analyzes the effects of such factors on the commercial aviation industry and whether the consumer experience has been positively or negatively impacted by them.

Physiological measurements in social acceptance of self driving technologies

The goal of the present study is to examine the cognitive/affective physiological correlates of passenger travel experience in autonomously driven transportation systems. We investigated the social acceptance and cognitive aspects of self-driving technology by measuring physiological responses in real-world experimental settings using eye-tracking and EEG measures simultaneously on 38 volunteers. A typical test run included human-driven (Human) and Autonomous conditions in the same vehicle, in a safe environment. In the spectrum analysis of the eye-tracking data we found significant differences in the complex patterns of eye movements: the structure of movements of different magnitudes were less variable in the Autonomous drive condition. EEG data revealed less positive affectivity in the Autonomous condition compared to the human-driven condition while arousal did not differ between the two conditions. These preliminary findings reinforced our initial hypothesis that passenger experience in human and machine navigated conditions entail different physiological and psychological correlates, and those differences are accessible using state of the art in-world measurements. These useful dimensions of passenger experience may serve as a source of information both for the improvement and design of self-navigating technology and for market-related concerns.

How can airports go from bricks to clicks by accelerating digital retailing strategies?

In addition to minimising fixed costs, optimising operational costs and ensuring an efficient and safe experience for staff and passengers, non aeronautical revenue will continue to play a key role in enhancing financial resilience as airports seek to sustain business operations and promote growth during the ongoing COVID-19 recovery period. Given the decrease of travel demand and the projected total of passenger numbers for 2022 not rising to 2019 levels, non aeronautical revenue will be an integral and critical part of airports' financial health. If the aviation industry is to thrive in a post-pandemic future, all stakeholders must be prepared to adopt new methodologies that are more in line with public expectations and legal requirements for health, sanitisation and safety. This extends to all revenue streams, and puts the onus on stakeholders to transform digitally, and to adopt key data-based technologies that combine biometric and biographic data capture, automation and a digital identity management platform. This paper lays down guidelines for such digital transformation, and strategies for making the transition from physical processes to contactless and digital methodologies. Data monetisation will help airports to ‘go from bricks to clicks’, by leveraging data shared to provide a better and more personalised experience for the customer. The paper also details a roadmap for collaboration between airport stakeholders and considers digital imperatives for travel retail.

How can airports fully embrace multimodal and interoperable mobility ecosystems to deliver sustainability goals and offer a superior passenger experience?

The future of mobility will be fundamentally different from what we know today. While demand for mobility will thrive again after the pandemic, new means of transport and new market players will shape everyday customer mobility of the 2030s and 2040s. To overcome the challenges that the mobility market of 2050 will pose, airports need to fully embrace multimodal and interoperable mobility ecosystems in order to deliver sustainability goals and offer a superior passenger experience. Using the example of Frankfurt Airport, this article addresses the complexity of a mobility landscape in flux. It will be shown how intermodality can both address sustainability demands and provide a customer-centric approach to innovation and digitalisation with regard to mobility. The purpose of this paper is to present one possible approach to the set of challenges it describes: the mobility hub. Current mobility options and customer needs are analysed and, supported by the findings of a customer survey, focus areas allowing airports to evolve towards mobility ecosystems of the future are discovered. Two main goals are identified that support the airport's transformation into a mobility hub, thus suggesting and promoting a possible best practice and providing strategic direction: quality of connectivity and quantity of means of transport. For both, suggestions for fields of action are provided that range from short to long-term strategy.

Continuance intention of autonomous buses: An empirical analysis based on passenger experience

The aim of this study was to understand passengers’ continuance use intention with respect to autonomous buses (ABs) based on actual riding experience. Therefore, an extended technology acceptance model taking characteristics of both autonomous driving and buses into account was proposed, and 576 passengers with ABs riding experience in China responded to the survey. Several findings were revealed. First, characteristics of buses (including perceived in-vehicle safety, service quality and general attitudes toward buses) had positive effects on continuance use intention. Second, perceived road safety was not directly associated with continuance intention but had positive effects on perceived usefulness. Third, two significant moderating variables were revealed, namely, past bus riding habits and driver reliance (i.e., the degree to which passengers considered it important for ABs to have a driver). These observations suggest practical implications for policymakers and automakers.

Urban air mobility infrastructure design: Using virtual reality to capture user experience within the world's first urban airport

Human factors research can play an important role in the successful design of infrastructure to support future mobility. Through engaging users and stakeholders early in the design process we can gain insights before the physical environments are built. This paper presents data from a truly novel application of Virtual Reality (VR), where user experience and wayfinding were evaluated within an emerging future transport infrastructure to support urban air mobility (UAM) – the urban airport (aka vertiports). Urban airports are located in city centres where drones or ‘flying cars’ would land and take off from. Previous quantitative studies have investigated passenger experience in traditional airports using field observation and surveys, but this paper is the first to present qualitative research on user experience in this emerging mobility infrastructure using an immersive VR environment. Twenty participants completed a series of six scenarios aimed at understanding customer ‘exciters’ and ‘pain points’ within an urban airport. Results and recommendations from this empirical research will help inform the design of all future mobility infrastructure solutions, through improving user experience before the infrastructure is physically deployed. Finally, this paper highlights the benefits of engaging users at an early stage of the design process to ensure that future transport infrastructure will be accessible, easy to navigate and a pleasure to use.

Assessing Feasibility of overnight-charging electric bus in a real-world BRT system in the context of a developing country

Air pollution, as a major urban problem in metropolises, has harmful impacts on societies in many aspects. According to the worn-out fleet of diesel buses and fossil fuel dependencies in Tehran, alternative fuels have become more popular in sustainable public transportation. Although battery electric buses (BEBs) provide many benefits, their purchase price and required infrastructure are the main challenges for decision-makers. This paper provides a systematic approach to examine the environmental, traffic and economic efficiency of overnight-charging electric buses (OCEBs) in Tehran, Iran. Environmental analysis shows that Carbon Oxide and Nitrogen Oxide will reduce to zero, and eliminate dependence on fossil fuels. The payback period predicted to be 7 years. Due to the better acceleration of OCEBs, the travel time, delay and stop time reduce about 4%, 10.67%, and 5.15% on average, respectively which leads to a better experience of passengers and an increase in public transportation utility that cause more people attract to OCEBs. The present results indicate the feasibility of OCEBs implementation as a sustainable transportation mode and can be useful in decision-making of policymakers and planning for future public transport system.

Exploring the acceptance of connected and automated vehicles: Focus group discussions with experts and non-experts in transport

The successful deployment of connected and automated vehicles (CAVs) will largely depend on public opinion. Expectations and concerns are essential components driving the speed of market uptake and final adoption of these technologies. We have studied user expectations and concerns, as well as their provenance, by conducting a series of Focus Group (FG) discussions with transport area experts and non-expert participants, in Italy, Germany, and Spain. Together with user acceptance, potential advantages and disadvantages that CAVs may bring at a societal level were explored. Results show that benefits like increased safety and accessibility and improved travel experience for both driver and passengers could support CAVs’ deployment. Nevertheless, a variety of concerns were raised during the discussions, related to privacy, responsibility in case of an accident, and increase in maintenance and repair costs. These aspects could significantly hamper or delay CAVs acceptability and need to be carefully considered by stakeholders. The reported insights from the FG discussions and the fact that some participants noted positive changes of their initial idea of CAVs due to their participation in the discussion, exemplify the importance of citizen engagement activities to address mobility challenges.

ASPIRES: Airport Shuttle Planning and Improved Routing Event-driven Simulation

Most of the existing traffic simulation packages require significant calibration work to be able to reflect reality. To evaluate special operations including emerging technologies, a microscopic simulation that tracks detailed interactions of all the elements of the traffic systems is usually needed. This type of simulation is usually computationally demanding. This work developed an Airport Shuttle Planning and Improved Routing Event-driven Simulation (ASPIRES) package to simulate and evaluate current, potential, and future airport shuttle operations. The simulation was driven by data and thus did not require much calibration effort. The discrete-event simulation nature of ASPIRES makes the simulation computationally efficient. Simulating 1 day of shuttle operations takes less than 2 s. The study site of this work is the Dallas/Fort Worth International Airport in the U.S. The shuttle service that connects the five terminals of the airport and the rental car center was studied. Travel times, dwell times, and passenger arrivals were simulated using empirical distributions derived mainly from real data to capture the stochastic nature of the rental car center shuttle bus operations. Data on bus miles traveled, bus energy consumption, passenger wait times, and passengers left behind at stops were collected to study the trade-off between energy use and passenger experience. Electric bus and on-demand bus operations were also included. The simulation outputs can show passengers statistics at terminals, shuttles statistics, and charging station statistics. ASPIRES cannot be used to model a generic traffic system but is well-suited for fleet systems.

An Evaluation Model for the Comfort of Vehicle Intelligent Cockpits Based on Passenger Experience

With the development of intelligence and network connectivity, the development of the automotive industry is also moving toward intelligent systems. For passengers, the utility of intelligence is to achieve more convenience and comfort. The intelligent cockpit is the place where passengers directly interact with the car, which directly affects the experience of passengers in the car. For the intelligent cockpits that have emerged in recent years, a reasonable and accurate comfort evaluation model is urgently needed. Therefore, in this article, from the passenger’s perspective, a subjective evaluation experiment was set up to collect data on four important indicators affecting the comfort of the intelligent cockpit: sound, light, heat, and human–computer interaction. The subjective evaluation weights were derived from a questionnaire, and the entropy weighting method was used to obtain the objective weights. Finally, the two weights were combined using the idea of game theory combination assignment to get the final accurate weights. Using the idea of penalty type substitution, the four index models were then synthesized to get the final evaluation model. The feasibility of the model was verified when measuring the car cockpit. The feasibility of the method means it can evaluate the comfort level of an intelligent cockpit more reasonably, facilitate the enhancement and improvement of the model, and promote the development of the model to achieve maximum passenger comfort.