Search results

Connecting autonomous drones to ground operations and services is a prerequisite for the adoption of scalable and sustainable drone services in the built environment. Despite the rapid advance in the field of autonomous drones, the development of ground infrastructure has received less attention. Contemporary airport design offers potential solutions for the infrastructure serving autonomous drone services. To that end, this paper aims to construct a framework for connecting air and ground operations for autonomous drone services. Furthermore, the paper defines the minimum facilities needed to support unmanned aerial vehicles for autonomous logistics and the collection of aerial data.

The paper reviews the state-of-the-art in airport design literature as the basis for analysing the guidelines of manned aviation applicable to the development of ground infrastructure for autonomous drone services. Socio-technical system analysis was used for identifying the service needs of drones.

The key findings are functional modularity based on the principles of airport design applies to micro-airports and modular service functions can be connected efficiently with an autonomous ground handling system in a sustainable manner addressing the concerns on maintenance, reliability and lifecycle.

As the study was limited to the airport design literature findings, the evolution of solutions may provide features supporting deviating approaches. The role of autonomy and cloud-based service processes are quintessentially different from the conventional airport design and are likely to impact real-life solutions as the area of future research.

The findings of this study provided a framework for establishing the connection between the airside and the landside for the operations of autonomous aerial services. The lack of such framework and ground infrastructure has hindered the large-scale adoption and easy-to-use solutions for sustainable logistics and aerial data collection for decision-making in the built environment.

The evolution of future autonomous aerial services should be accessible to all users, “democratising” the use of drones. The data collected by drones should comply with the privacy-preserving use of the data. The proposed ground infrastructure can contribute to offloading, storing and handling aerial data to support drone services’ acceptability.

To the best of the authors’ knowledge, the paper describes the first design framework for creating a design concept for a modular and autonomous micro-airport system for unmanned aviation based on the applied functions of full-size conventional airports.

Perception has been identified as the main cause underlying most autonomous vehicle related accidents. As the key technology in perception, deep learning (DL) based computer vision models are generally considered to be black boxes due to poor interpretability. These have exacerbated user distrust and further forestalled their widespread deployment in practical usage. This paper aims to develop explainable DL models for autonomous driving by jointly predicting potential driving actions with corresponding explanations. The explainable DL models can not only boost user trust in autonomy but also serve as a diagnostic approach to identify any model deficiencies or limitations during the system development phase.

This paper proposes an explainable end-to-end autonomous driving system based on “Transformer,” a state-of-the-art self-attention (SA) based model. The model maps visual features from images collected by onboard cameras to guide potential driving actions with corresponding explanations, and aims to achieve soft attention over the image’s global features.

The results demonstrate the efficacy of the proposed model as it exhibits superior performance (in terms of correct prediction of actions and explanations) compared to the benchmark model by a significant margin with much lower computational cost on a public data set (BDD-OIA). From the ablation studies, the proposed SA module also outperforms other attention mechanisms in feature fusion and can generate meaningful representations for downstream prediction.

In the contexts of situational awareness and driver assistance, the proposed model can perform as a driving alarm system for both human-driven vehicles and autonomous vehicles because it is capable of quickly understanding/characterizing the environment and identifying any infeasible driving actions. In addition, the extra explanation head of the proposed model provides an extra channel for sanity checks to guarantee that the model learns the ideal causal relationships. This provision is critical in the development of autonomous systems.

This article proposes an architecture of an intelligent and autonomous recommendation system to be applied to any virtual learning environment, with the objective of efficiently recommending digital resources. The paper presents the architectural details of the intelligent and autonomous dimensions of the recommendation system. The paper describes a hybrid recommendation model that orchestrates and manages the available information and the specific recommendation needs, in order to determine the recommendation algorithms to be used. The hybrid model allows the integration of the approaches based on collaborative filter, content or knowledge. In the architecture, information is extracted from four sources: the context, the students, the course and the digital resources, identifying variables, such as individual learning styles, socioeconomic information, connection characteristics, location, etc. Tests were carried out for the creation of an academic course, in order to analyse the intelligent and autonomous capabilities of the architecture.

Recent studies on commuter parking in an age of fully autonomous vehicles (FAVs) suggest, that the number of parking spaces close to the workplace demanded by commuters will decline because of the capability of FAVs to return home, to seek out (free) parking elsewhere or just cruise. This would be good news because, as of today, parking is one of the largest consumers of urban land and is associated with substantial costs to society. None of the studies, however, is concerned with the special case of employer-provided parking, although workplace parking is a widespread phenomenon and, in many instances, the dominant form of commuter parking. The purpose of this paper is to analyze whether commuter parking will decline with the advent of self-driving cars when parking is provided by the employer.

This study looks at commuter parking from the perspective of both the employer and the employee because in the case of employer-provided parking, the firm’s decision to offer a parking space and the incentive of employees to accept that offer are closely interrelated because of the fringe benefit character of workplace parking. This study develops an economic equilibrium model that explicitly maps the employer–employee relationship, considering the treatment of parking provision and parking policy in the income tax code and accounting for adverse effects from commuting, parking and public transit. This study determines the market level of employer-provided parking in the absence and presence of FAVs and identifies the factors that drive the difference. This study then approximates the magnitude of each factor, relying on recent (first) empirical evidence on the impacts of FAVs.

This paper’s analysis suggests that as long as distortive (tax) policy favors employer-provided parking, FAVs are no guarantee to end up with less commuter parking.

This study’s findings imply that in a world of self-driving cars, policy intervention related to work commuting (e.g. fringe benefit taxation or transport pricing) might be even more warranted than today.

This systematic literature review focuses on the following future advanced information and communication technologies (ICTs) applied in the maritime transport of cargo: Internet of Things (IoT), big data, cloud computing and autonomous ships/vessels (including unmanned ships/vessels). The review question is: “RQ: In what context and by means of what mechanism does the implementation of future advanced ICTs have disruptive impact on maritime transport?”.

The paper complies with the methodological requirements of systematic reviews. The information analysis and synthesis are based on the CIMO logic, referring to the context (C), intervention (I), mechanism (M) and outcome (O) of the implementation of future advanced ICTs in maritime transport.

The review identifies the contextual factors and components of the mechanism that lead to the disruptive impact of different types of future advanced ICT interventions on maritime transport.

The review approaches only the most important future advanced ICTs that will disrupt maritime transport.

The maritime transport organizations should consider: intended outcome as intervention trigger; increased efficiency and responsiveness; benchmarking.

For the first time, the CIMO logic is applied in a systematic review focused on future advanced ICTs in maritime transport. The CIMO-DMT model is elaborated as a basis for further research. Ten directions of study are recommended in a future research agenda.

This study aims to investigate the safety and liability of autonomous vehicles (AVs), and identify the contributing factors quantitatively so as to provide potential insights on safety and liability of AVs.

The actual crash data were obtained from California DMV and Sohu websites involved in collisions of AVs from 2015 to 2021 with 210 observations. The Bayesian random parameter ordered probit model was proposed to reflect the safety and liability of AVs, respectively, as well as accommodating the heterogeneity issue simultaneously.

The findings show that day, location and crash type were significant factors of injury severity while location and crash reason were significant influencing the liability.

The results provide meaningful countermeasures to support the policymakers or practitioners making strategies or regulations about AV safety and liability.

The purpose of the article is to analyze the influence of output control, autonomous motivation and controlled motivation on task performance of public servants at home office owing to the COVID-19 pandemic.

A survey was conducted based on the perception of 236 public servants, and structural equation modeling (SEM) technique was used for data analysis.

Both autonomous motivation and output control are positively and significantly related to task performance.

The present study expands the literature with information about individual performance and management control, which can be related to the control, motivation and task performance of public servants at home office in a pandemic context.

The results could offer a basis for understanding how managers can deal with the challenges while at home office. They can also provide managers with information that they can use to build management strategies to foster the performance of public servants at home office.

Home office can decrease commuting to a central workplace, alleviate traffic problems and reduce car pollution. It also allows for business continuity in the face of storms and pandemics.

Most studies about home office, COVID-19 and task performance have addressed personal, professional and organizational characteristics. However, little is known about the analysis of combinations of the following constructs: output control, autonomous and controlled motivations, and how both affect task performance of public servants.

This study aims to identify the current state of the art and the gaps in the application of computable general equilibrium (CGE) models on studying climate change adaptation.

A systematic review is conducted to select, classify and analyze relevant studies from two databases of Web of Science and Scopus.

Totally, 170 articles based on selected keywords were found from both databases, where 56 articles were duplicates. The authors further excluded 17 articles owing to preliminary exclusion criteria. Hence, 97 papers were selected for full-text review and more detailed assessment. Only a few of the studies explicitly have addressed the role of autonomous adaptation embodied in the CGE models. Over one-third of the studies have focused on planned adaptation without explicitly mentioning autonomous adaptation. Agriculture was the most addressed sector, and country-level models are the most adopted. Only one article has focused on South America.

The review suggests that autonomous adaptation embodied in CGE models was not well addressed in the literature. As the limited studies have shown that autonomous adaptation can dramatically mitigate direct climate change impacts, further studies are needed to examine the importance of the autonomous adaptation for better understanding of climate change impacts. Furthermore, CGE models can provide a joint assessment considering both mitigation and adaptation strategies and management measures as such models have also been widely used to address effects of mitigation measures in the literature.

The studies on climate change adaptation based on CGE models have been systematically reviewed, and state-of-the-art knowledge and research gaps have been identified.

The successful and commercial use of self-driving/driverless/unmanned/automated car will make human life easier. The paper aims to discuss this issue.

This paper reviews the key technology of a self-driving car. In this paper, the four key technologies in self-driving car, namely, car navigation system, path planning, environment perception and car control, are addressed and surveyed. The main research institutions and groups in different countries are summarized. Finally, the debates of self-driving car are discussed and the development trend of self-driving car is predicted.

This paper analyzes the key technology of self-driving car and illuminates the state-of-art of the self-driving car.

The main research contents and key technology have been introduced. The research progress as well as the research institution has been summarized.

This paper aims to analyze the antecedents of two variables concerning the presence of quality certifications in hotel chains: the (ex ante) decision to become a member of the quality system and the (ex post) trend to increase or decrease the number of certified properties. Six hypotheses are posed and tested.

The empirical investigation is carried out on the Spanish Q for Quality in Tourism using a database including 295 hotel chains and 2,727 hotels.

The results evidence the presence of differences in the behavior of hotel chains relative to certification depending on their size, market segment, customer origin and the geographical concentration of their establishments.

This research deepens in how the hotel chain characteristics affect the effectiveness of a quality certification. The consideration of two stages in investment decisions allows the authors to identify differences in the ex ante and ex post decision processes. As a result, one factor (geographical concentration) has been detected as being underrated by managers in the first stage.

Este artículo analiza los antecedentes de dos variables relacionadas con las certificaciones de calidad en cadenas hoteleras: La (ex-ante) decisión de formar parte de un sistema de calidad, y la (ex-post) tendencia a incrementar o reducir el número de establecimientos certificados. Seis hipótesis han sido propuestas y contrastadas.

La investigación empírica ha sido desarrollada en el marco de la marca Q de calidad para el turismo en España usando una base de datos que incluye 295 cadenas hoteleras y 2,727 hoteles.

Los resultados ponen de manifiesto la presencia de diferencias en el comportamiento de las cadenas hoteleras en materia de certificación dependiendo de su tamaño, segmento de mercado atendido, origen de la clientela y del grado de concentración geográfica de sus establecimientos.

El artículo profundiza en cómo las carfacterísticas de la cadena hotelera afectan a la eficacia de la certificación de calidad. Tener en consideración la existencia de dos etapas en las decisones de inversión nos permite identificar diferencias entre los procesos de decisión ex-ante y ex-post. Como resultado, hemos observado que un factor (la concentración geográfica) está siendo infravalorado por parte de os directivos en sus decisiones en la primera etapa.