Personal Mobility Services Research Articles

Predição de cobertura e Tráfego SMP no município de Chapadinha no Maranhão

This study focuses on the region of Chapadinha, located in Maranhão, and explores the importance of predicting the coverage and traffic of Personal Mobile Service (PMS) to meet local communication and connectivity needs. The municipality faces challenges related to the quality of PMS coverage and traffic capacity, negatively impacting the lives of citizens and the performance of businesses. The study employs data analysis methods to evaluate PMS coverage and traffic demand in Chapadinha, with the aim of promoting technological development and digital inclusion in the region.

A Privacy-Preserving and Edge-Collaborating Architecture for Personalized Mobility

Driven by technologies and demands, the modern transportation system has developed from intelligent transportation systems (ITS) to autonomous transportation systems (ATS) to resolve intertwined demands and supplies with few human interventions. In ATS, personal mobility service (PMS) is the service that can sense real-time traffic conditions comprehensively, learn travelers’ preferences accurately, recommend multimodal travel options appropriately, and provide service responses timely to elevate the level of personalization and intelligence in smart mobility services. Since current PMS widely employs centralized approaches (CPMS) to process massive sensitive data from individuals and support diverse edge devices, resulting in high pressure in privacy protection and performance balancing, this paper presents a federated PMS (FPMS) and its design architecture in logical and physical views by adopting federated learning to provide multimodal, dynamic, and personalized travel options with system-saving safety and efficiency guaranteed. Moreover, through an extensive evaluation, the performances of CPMS and FPMS are compared to reveal the merits of FPMS in reducing costs and latency.

A Federated Personal Mobility Service in Autonomous Transportation Systems

A personal mobility service (PMS) is developed to support personalized travel options for users in autonomous transportation systems (ATS), based on a macro-system state and micro-user behavior. However, this functionality necessitates processing and transmitting vast amounts of data, raising concerns about user privacy protection during data processing and transmission within the PMS. Furthermore, the PMS must be maintained and perform well, while preserving privacy. Therefore, we propose a novel federated PMS, denoted as a FPMS. Specifically, the FPMS can serve users’ personal mobility needs by facilitating the collaboration between the physical and information domains. Then, a common framework for FPMS architectures, which captures the features of ATSs, is proposed and discussed from both physical and logical perspectives, which include both the logical architecture and physical architecture; and we present the key algorithms for the FPMS, in conjunction with a artificial neural network (ANN). Additionally, in static estimation scenarios, the FPMS demonstrated a similar accuracy for three different models compared to the traditional PMS, while reducing the computing time by approximately 60% and communication resource consumption by approximately 85%.

A Knowledge-Based AI Framework for Mobility as a Service

Mobility as a Service (MaaS) combines various modes of transportation to present mobility services to travellers based on their transport needs. This paper proposes a knowledge-based framework based on Artificial Intelligence (AI) to integrate various mobility data types and provide travellers with customized services. The proposed framework includes a knowledge acquisition process to extract and structure data from multiple sources of information (such as mobility experts and weather data). It also adds new information to a knowledge base and improves the quality of previously acquired knowledge. We discuss how AI can help discover knowledge from various data sources and recommend sustainable and personalized mobility services with explanations. The proposed knowledge-based AI framework is implemented using a synthetic dataset as a proof of concept. Combining different information sources to generate valuable knowledge is identified as one of the challenges in this study. Finally, explanations of the proposed decisions provide a criterion for evaluating and understanding the proposed knowledge-based AI framework.

Dynamic and Ordinary Capabilities in Industry Transformation: The Case of the Electric Vehicle Industry

In 2018, in the Dialogue, Debate, and Discussion section of MOR 14.3, an interesting series of articles was published in the ‘Forum on Tesla and the Global Automotive Industry’, where researchers discussed the future dynamics of the global automotive sector. In their work, Perkins and Murmann (2018) contended that, based on Tesla's success, a well-funded company could develop a new electric vehicle (EV) from scratch and move it into production within three to five years if it would invest one to two billion USD in design, development, and manufacturing. Expressing a contrasting view, MacDuffie (2018) questioned this possibility, arguing that EV product architecture is unlikely to become substantially more modular and any new entrant would therefore have to develop the ordinary capabilities that current automotive original equipment manufacturers possess, and there is no guarantee that a firm can develop such capabilities. Teece (2018) joined the debate by proposing a capability-based framework within which to analyze four paradigm shifts that have marked progress in the global automotive industry: EVs, autonomous vehicles, connected cars, and personal mobility services. He argues that these paradigm shifts have created opportunities for new entrants while posing challenges to incumbent firms. To navigate through the uncertainty associated with these paradigm shifts, incumbent firms need to enhance and refine their dynamic capabilities and leverage their integration skills. Jiang and Lu (2018) based their contribution to this debate on the development of the Chinese EV market. In MOR 15.1, published in 2019, Teece (2019) further elaborated his framework to facilitate analysis of the prospects for Chinese firms seeking a stronger foothold in the global automobile market. All these articles have been well received by MOR readers and were ranked among the top 20 articles in full-text view times between June 2021 and June 2022.

A Federated Mixed Logit Model for Personal Mobility Service in Autonomous Transportation Systems

Looking ahead to the future-stage autonomous transportation system (ATS), personal mobility service (PMS) aims to provide the recommended travel options based on both microscopic individual travel demand and the macroscopic supply system objectives. Such a goal relies on massive heterogeneous data to interpret and predict user travel intentions, facing the challenges caused by prevalent centralized approaches, such as an unbalanced utilization rate between cloud and edge, and data privacy. To fill the gap, we propose a federated logit model (FMXL), for estimating user preferences, which integrates a discrete choice model—the mixed logit model (MXL), with a novel decentralized learning paradigm—federated learning (FL). FMXL supports PMS by (1) respectively performing local and global estimation at the client and server to optimize the load, (2) collaboratively approximating the posterior of the standard mixed logit model through a continuous interaction mechanism, and (3) flexibly configuring two specific global estimation methods (sampling and aggregation) to accommodate different estimation scenarios. Moreover, the predicted rates of FMXL are about 10% higher compared to a flat logit model in both static and dynamic estimation. Meanwhile, the estimation time has been reduced by about 40% compared to a centralized MXL model. Our model can not only protect user privacy and improve the utilization of edge resources but also significantly improve the accuracy and timeliness of recommendations, thus enhancing the performance of PMS in ATS.

Ensemble-Based Methodology to Identify Optimal Personal Mobility Service Areas Using Public Data

Public transportation networks are well established in main cities, but there are some inconveniences in using public transportation in some cities. Public transportation is less accessible and walking distance of getting to public transportation is too long in some cities. Compared to other cities, Seoul has a higher satisfaction rate with public transportation. There are many cases, however, where short-distance taxis are used because walking to destinations after using public transportation is inconvenient; instead, Personal mobility (PM) devices can be used for these short-distances trip. This study aims to find the optimal PM service area using GIS(Geographic Information System)-based public transportation big data analyses. Variables were generated by collecting socio-economic factors, public transportation data, and geographic data and Extreme gradient boosting and Random forest, which are representative ensemble methods, were used for evaluation. We divided Seoul into a hexagonal grid and developed the optimal PM location service model by creating hexagonal cell data units and analyzing the areas with the models. We found that residential complexes, parks, and near subway stations (all areas with high foot traffic) are best suited for optimal placement. We also determined deployment should be in lower sloped areas. We expect this work to help determine public transportation stop and shared mobility station locations as well as contribute to public transportation demand surveys and accessibility analyses.

Autonomous Transportation Systems and Services Enabled by the Next-Generation Network

The vast development of the next-generation network (NGN) impels its integration with emerging technologies, such as big data, artificial intelligence, and federated learning, to deliver autonomous and intelligent services in various areas. Notably, in modern transportation systems (TSs), the advances of NGN enable a transformation toward an autonomous transportation system (ATS), which can bridge the demand and supply through a self-actuating cycle (sensing, learning, rearranging, and reacting). Since NGN-enabled ATS is still in its infancy, a concrete vision is missing to forge a common research ground. To fill the gap, this article is intended to elucidate NGN-enabled ATS by first discussing its intrinsic difference against the conventional TSs (CTSs) and then depicting its service blueprint in fostering more intelligent and autonomous mobility services. After that, a full-scale ATS service design reference is proposed to ensure the generality, adaptivity, compatibility, interoperability, and scal-ability of services in and across its development stages, representing the levels of autonomy from partial to high to full automation. Furthermore, its superiority is discussed through a preliminary evaluation of personal mobility service based on centralized and federated learning. Finally, open questions and future research directions of this emerging topic are also discussed.

Crash test-based assessment of injury risks for adults and children when colliding with personal mobility devices and service robots

Autonomous mobility devices such as transport, cleaning, and delivery robots, hold a massive economic and social benefit. However, their deployment should not endanger bystanders, particularly vulnerable populations such as children and older adults who are inherently smaller and fragile. This study compared the risks faced by different pedestrian categories and determined risks through crash testing involving a service robot hitting an adult and a child dummy. Results of collisions at 3.1 m/s (11.1 km/h/6.9 mph) showed risks of serious head (14%), neck (20%), and chest (50%) injuries in children, and tibia fracture (33%) in adults. Furthermore, secondary impact analysis resulted in both populations at risk of severe head injuries, namely, from falling to the ground. Our data and simulations show mitigation strategies for reducing impact injury risks below 5% by either lowering the differential speed at impact below 1.5 m/s (5.4 km/h/3.3 mph) or through the usage of absorbent materials. The results presented herein may influence the design of controllers, sensing awareness, and assessment methods for robots and small vehicles standardization, as well as, policymaking and regulations for the speed, design, and usage of these devices in populated areas.

공유경제 플랫폼 기반의 퍼스널 모빌리티 서비스 지속사용의도에 영향을 미치는 요인에 관한 연구

인터넷 플랫폼과 SNS의 사용자확대는 공유경제를 활용한 비즈니스의 성장을 가속화하고, 수요자와 공급자를 긴밀하게 연결시켰다. 이에 따라 등장하게 된 공유경제 플랫폼을 기반으로 한 서비스 중 최근 퍼스널 모빌리티 서비스는 매우 빠른 속도로 성장하고 있다. 본 연구는 기대일치이론을 기반으로 퍼스널 모빌리티 특성, 공유경제 플랫폼 특성, 지각된 위험을 활용하여 연구모형을 구성하였다. 연구모형의 가설검증을 위해 20대 이상의 사용자 인원을 대상으로 설문을 수집하였다. 수집된 설문지를 활용하여 실증분석을 실시하고, 독립 요인들이 매개요인을 거쳐 최종적으로 지속사용의도에 어떠한 영향을 미치는지 검증하였다.

ON THE CHALLENGE OF SERVICE RECOMMENDATION TO MOBILE USERS IN SMART CITIES: CONTEXT AND ARCHITECTURE

Abstract. The industrial and academic interest of the research on mobile service recommendation systems based on a wide range of potential applications has significantly increased, owing to the rapid progress of mobile technologies. These systems aim to recommend the right product, service or information to the right mobile users at anytime and anywhere. In smart cities, recommending such services becomes more interesting but also more challenging due to the wide range of information that can be obtained on the user and his surrounding. This quantity and variety of information create problems in terms of processing as well as the problem of choosing the right information to use to offer services. We consider that to provide personalized mobile services in a smart city and know which information is relevant for the recommendation process, identifying and understanding the context of the mobile user is the key.This paper aims to address the issue of recommending personalized mobile services in smart cities by considering two steps: defining the context of the mobile user and designing an architecture of a system that can collect and process context data. Firstly, we propose an UML-based context model to show the contextual parameters to consider in recommending mobile services in a smart city. The model is based on three main classes from which others are divided: the user, his device and the environment. Secondly, we describe a general architecture based on the proposed context model for the collection and processing of context data.

Advances in Personalized Mobile Services

Advances in Personalized Mobile Services

Advancements, prospects, and impacts of automated driving systems

Abstract Over the last decade, significant progress has been made in automated driving systems (ADS). Given the current momentum and progress, ADS can be expected to continue to advance and a variety of ADS products will become commercially available within a decade. It is envisioned that automated driving technology will lead to a paradigm shift in transportation systems in terms of user experience, mode choices, and business models. In this paper, we start with a review of the state-of-the-art in the field of ADS and their deployment paths. It is followed by a discussion of the future prospects of ADS and their effects on various aspects of the transportation field. We then identify two specific use cases of ADS where the impacts can be significant – personal mobility services and vehicle automation for aging society. A survey of impact assessment studies and the associated methodologies for evaluating ADS is given, which is followed by concluding remarks at the end of the paper.

Multi-armed bandits with application to 5G small cells

Due to the pervasive demand for mobile services, next generation wireless networks are expected to be able to deliver high date rates while wireless resources become more and more scarce. This requires the next generation wireless networks to move towards new networking paradigms that are able to efficiently support resource-demanding applications such as personalized mobile services. Examples of such paradigms foreseen for the emerging fifth generation (5G) cellular networks include very densely deployed small cells and device-to-device communications. For 5G networks, it will be imperative to search for spectrum and energy-efficient solutions to the resource allocation problems that i) are amenable to distributed implementation, ii) are capable of dealing with uncertainty and lack of information, and iii) can cope with users' selfishness. The core objective of this article is to investigate and to establish the potential of multi-armed bandit (MAB) framework to address this challenge. In particular, we provide a brief tutorial on bandit problems, including different variations and solution approaches. Furthermore, we discuss recent applications as well as future research directions. In addition, we provide a detailed example of using an MAB model for energy-efficient small cell planning in 5G networks.

Scrutable Mobile Client-side Personalization

Personalization has become an essential feature of mobile services of different domains. On the other hand, users have conflicting needs of personalized experience and privacy. This leads to the question of how to maximize the user’s experience of personalized mobile services while keeping privacy. One possible solution is to provide user’s control of their personal data by keeping their user model on their personal mobile devices. In this way, a user can scrutinize the data while sharing with service providers depending on her/his requirements. The client-side personalization approach can shift the control of privacy to the users and can involve them in personalization process. Transparency and user control can increase the user’s trust. In this paper, we have proposed a solution with the objective of scrutable client-side personalization while keeping the user in control of both privacy and personalization process.

Framework for an Autonomic Transport System in Smart Cities

Abstract Today the concept of smart cities is discussed in scientific society and politics. A core function of smart cities is transportation. This paper gives a short overview on the concepts for Intelligent Transport Systems (ITS) for smart cities and proposes a framework for the design of an autonomic transportation system that provides personalized mobility services to its users in a smart city setting. The transportation poses extreme environment for ICT systems due to fast moving vehicles and users, requiring real-time acquisition and high performance processing of large scale data, and rapidly changing communication networks topologies and node densities. The aim of this paper is to propose a framework that will act as a reference for the design of future transportation systems that are able to cope with the ever rising system complexities and users’ demands. Therefore, a backbone system providing information at different levels was designed following the principles of a corporative ICT that were proposed in [1]. The framework fulfills the main requirement providing suitable information about the local decision engines in vehicles and infrastructure interacting in smart cities traffic systems.

Impact of future trends on personal mobility services

This paper looks into long term future trends of personal mobility services by providing a literature review and analysis of future studies. The future trends include long spanning megatrends in addition to shorter term forecasts of the forthcoming phenomena in mobility, transportation, traffic and logistics. Based on the analysis, some key trends are recognised based on their potential impact on personal mobility services. These key trends are analysed and discussed in more detail. Finally, we draw some conclusions of the main development directions in personal mobility.

Personal mobile services

Ubiquitous information access through mobile devices has become a typical practice in everyday life. The mobile service paradigm shifts the role of mobile devices from consumers to providers, opening up new opportunities for a multitude of collaborative services and applications ranging from sharing personal information to collaborative participatory sensing. Although many basic principles of the standard Web service approach continue to apply, the inherent constraints of mobile devices and broadband wireless access render the deployment of the standard architecture in mobile environments inefficient. This paper introduced personal services, a user-centric paradigm that enables service-oriented interactions among mobile devices that are controlled via user-specified authorization policies. Personal services exploit the user's contact list (ranging from phonebook to social lists) in order to publish and discover Web services while placing users in full control of their own personal data and privacy. Experimental validation demonstrates the ability of personal services to foster a new generation of collaborative mobile services. Performance evaluation results show that the publication and discovery through contact lists are efficient and that service announcements and discovery requests can reach a huge number of users in a few seconds. Results also support a conclusion that resources-constrained devices can collaborate to carry out functionalities beyond the ability of their resources limitations.

Personal Decision Support Mobile Service for Extreme Situations

This article discusses aspects of implementation of a massive personal decision support mobile service for evacuation process in extreme situations, based on cloud computation platform CLAVIRE and a virtual society model. The virtual society model was constructed using an agent-based approach. To increase credibility the individual motivation methods (personal decision support and user training) were used.

A lightweight convergent personal mobile service delivery approach based on phone book

SUMMARYMobile phone users are demanding an efficient convergent communication approach for efficient communication and information sharing with their real life social circles. Unfortunately, the existing telecom services and the popular Internet services are not organically integrated to provide convenient convergent services. To overcome this issue, one of the promising convergent communication services considered by telecom carriers is RCS (Rich Communication Suite). However, existing issues, such as insufficient interoperability among different operators, shortage of terminals and heavy dependence on large‐scale IP multimedia subsystem (IMS) network deployment, mean that it will take time to implement RCS across worldwide networks and for it to become a service equally universal as voice and short message. Instead of a heavyweight RCS solution, in this paper, we propose a lightweight, quick deployment mobile phone book application system based on cloud computing without a fully deployed IP multimedia subsystem infrastructure, which seamlessly integrates traditional telecommunication services, instant messaging services, social networking services and automatic contact information management. Based on this, ‘Telco‐OTT’ service provision approach, telecom carriers can allow their subscribers to enjoy conveniently the rich convergent personal service experience quickly without a large initial investment. Thus, it is a low‐risk opportunity for telecom carriers to meet subscriber demand for advanced user‐centric convergent service features. Copyright © 2013 John Wiley & Sons, Ltd.