A Survey of Artificial Intelligence-Related Cybersecurity Risks and Countermeasures in Mobility-as-a-Service DOI
Kai-Fung Chu, Haiyue Yuan, Yuan Jinsheng

и другие.

IEEE Intelligent Transportation Systems Magazine, Год журнала: 2024, Номер 16(6), С. 37 - 55

Опубликована: Авг. 5, 2024

Mobility-as-a-Service (MaaS) integrates different transport modalities and can support more personalisation of travellers' journey planning based on their individual preferences, behaviours wishes. To fully achieve the potential MaaS, a range AI (including machine learning data mining) algorithms are needed to learn personal requirements needs, optimise each traveller all travellers as whole, help service operators relevant governmental bodies operate plan services, detect prevent cyber attacks from various threat actors including dishonest malicious operators. The increasing use processing in both centralised distributed settings opens MaaS ecosystem up diverse privacy at algorithm level connectivity surfaces. In this paper, we present first comprehensive review coupling between AI-driven design security challenges related countermeasures. particular, focus how current emerging AI-facilitated risks (profiling, inference, third-party threats) adversarial (evasion, extraction, gamification) may impact ecosystem. These often combine novel (e.g., inverse learning) with traditional attack vectors man-in-the-middle attacks), exacerbating for wider participation emergence new business models.

Язык: Английский

Exploring the potential adoption of Mobility-as-a-Service in Beijing: A spatial agent-based model DOI
Justin Hayse Chiwing G. Tang, Junbei Liu, Anthony Chen

и другие.

Transportation Research Part A Policy and Practice, Год журнала: 2025, Номер 194, С. 104430 - 104430

Опубликована: Март 1, 2025

Язык: Английский

Процитировано

0
Assessment of the barriers in establishing passenger mobility-as-a-service (MaaS) systems: An analogy with multimodal freight transport DOI
Chenyang Wu, Scott Le Vine, Aruna Sivakumar

и другие.

Case Studies on Transport Policy, Год журнала: 2025, Номер unknown, С. 101433 - 101433

Опубликована: Март 1, 2025

Язык: Английский

Процитировано

0
Emerging Research Issues and Directions on MaaS, Sustainability and Shared Mobility in Smart Cities with Multi-Modal Transport Systems DOI Creative Commons
Fu-Shiung Hsieh

Applied Sciences, Год журнала: 2025, Номер 15(10), С. 5709 - 5709

Опубликована: Май 20, 2025

In recent years, several emerging transport modes have appeared in cities all over the world and been widely adopted by commuters travelers. This leads to strong growth popularity of multi-modal Mobility as a Service (MaaS) cities. These not only received much attention from service providers practitioners but also attracted researchers related communities. are reflected growing number published papers research issues mobility The factors that driving deficiencies effective solution methods accommodate needs users with modes. Although existing literature is still deficient offering seamless end-to-end services, it provides valuable sources clues for finding potential future subjects/issues/directions. this study, we attempt identify directions based on review transport. By searching WOS database, analyze profile trends mobility. results study pave way assessment subjects/issues/directions under umbrella term paper significantly reduces time required readers prospective subjects, issues, or without delving into literature.

Язык: Английский

Процитировано

0
A Survey of Artificial Intelligence-Related Cybersecurity Risks and Countermeasures in Mobility-as-a-Service DOI
Kai-Fung Chu, Haiyue Yuan, Yuan Jinsheng

и другие.

IEEE Intelligent Transportation Systems Magazine, Год журнала: 2024, Номер 16(6), С. 37 - 55

Опубликована: Авг. 5, 2024

Mobility-as-a-Service (MaaS) integrates different transport modalities and can support more personalisation of travellers' journey planning based on their individual preferences, behaviours wishes. To fully achieve the potential MaaS, a range AI (including machine learning data mining) algorithms are needed to learn personal requirements needs, optimise each traveller all travellers as whole, help service operators relevant governmental bodies operate plan services, detect prevent cyber attacks from various threat actors including dishonest malicious operators. The increasing use processing in both centralised distributed settings opens MaaS ecosystem up diverse privacy at algorithm level connectivity surfaces. In this paper, we present first comprehensive review coupling between AI-driven design security challenges related countermeasures. particular, focus how current emerging AI-facilitated risks (profiling, inference, third-party threats) adversarial (evasion, extraction, gamification) may impact ecosystem. These often combine novel (e.g., inverse learning) with traditional attack vectors man-in-the-middle attacks), exacerbating for wider participation emergence new business models.

Язык: Английский

Процитировано

2