A Hybrid Knowledge-based Recommender for Mobility-as-a-Service

Arnaoutaki, Konstantina; Magoutas, Babis; Bothos, Efthimios; Mentzas, Gregoris

doi:10.5220/0007921400950103

Cited by 7 publications

(7 citation statements)

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“…Although several studies applied various AIbased models in MaaS, relatively few of them considered leveraging knowledge-based systems in the models to provide personalized and explainable mobility services. Arnaoutaki et al [1], as an example, proposed a hybrid knowledge-based system that uses constraint programming mechanisms to provide mobility plans to travellers based on their preferences and exclude the routes that do not match those preferences. Close to this study and in conjunction with the other AIbased models, we propose a knowledge-based AI mobility framework that utilizes context information and knowledge of mobility (acquired from travellers and vehicles) to provide personalized mobility services while being interpretable and explainable for both travellers and domain experts.…”

Section: Related Workmentioning

confidence: 99%

An Explainable Knowledge-based AI Framework for Mobility as a Service

Rajabi¹,

Nowaczyk²,

Pashami³

et al. 2022

Proceedings of the 34th International Conference on Software Engineering and Knowledge Engineering

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Mobility as a Service (MaaS) is a relatively new domain where new types of knowledge systems have recently emerged. It combines various modes of transportation and different kinds of data to present personalized services to travellers based on transport needs. A knowledge-based framework based on Artificial Intelligence (AI) is proposed in this paper to integrate, analyze, and process different types of mobility data. The framework includes a knowledge acquisition process to extract and structure data from various sources, including mobility experts and add new information to a knowledge base. The role of AI in this framework is to aid in automatically discovering knowledge from various data sets and recommend efficient and personalized mobility services with explanations. A scenario is also presented to demonstrate the interaction of the proposed framework's modules.

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Section: Related Workmentioning

confidence: 99%

An Explainable Knowledge-based AI Framework for Mobility as a Service

Rajabi¹,

Nowaczyk²,

Pashami³

et al. 2022

Proceedings of the 34th International Conference on Software Engineering and Knowledge Engineering

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“…This work builds on top of our approach previously described in [35], where we introduced the use of constraint models for MaaS Plans recommendations and set the ground for tackling the MaaS Plans selection problem. In this work, we have introduced improvements in the constraint models and the employed similarity metric mechanism, including a data-driven mechanism aiming to exploit past users' data; we have implemented and integrated the approach in a real MaaS application; we have evaluated the approach in both experimental and real settings; and we have analyzed the results of the evaluation, which provide useful insights for both research and practice.…”

Section: Related Workmentioning

confidence: 99%

A Recommender System for Mobility-as-a-Service Plans Selection

et al. 2021

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Transportation and mobility in smart cities are undergoing a grave transformation as new ways of mobility are introduced to facilitate seamless traveling, addressing travelers’ needs in a personalized manner. A novel concept that has been recently introduced is Mobility-as-a-Service (MaaS), where mobility services are bundled in MaaS Plans and offered to end-users through a single digital platform. The present paper introduces a recommender system for MaaS Plans selection that supports travelers to select bundles of mobility services that fit their everyday transportation needs. The recommender filters out unsuitable plans and then ranks the remaining ones on the basis of their similarity to the users’ characteristics, habits and preferences. The recommendation approach is based on Constraint Satisfaction Problem (CSP) formalisms combined with cosine similarity techniques. The proposed method was evaluated in experimental settings and was further embedded in real-life pilot MaaS applications. The experimental results showed that the proposed approach provides lists of MaaS PlanMaaS Plans that users would choose in a real-life MaaS setting, in most of the cases. Moreover, the results of the real-life pilots showed that the majority of the participants chose an actual MaaS Plan from the top three places of the recommendation lists.

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“…1 The identification process of selecting papers supply side concentrated on service package creation [36] and pricing schemes [37]. The platform side focused on matching both users and vehicles, route planning [38][39][40][41][42][43][44][45][46][47][48][49], and the impact of MPO on users [50][51][52]. The modeling part focused on modeling the demand-supply interactions [48,[53][54][55][56][57][58][59][60][61][62][63][64][65][66][67].…”

Section: Inclusionmentioning

confidence: 99%

“…Users make decisions on either accepting or rejecting journeys offered by providers according to their preferences for maximum price, maximum waiting time, and departure time intervals, while service providers aim to jointly optimize the scheduling, routing, and pricing to maximize profits; however, this study is unrelated to multimodal integration and the MaaS context. Furthermore, the MPO captured user needs and preferences for travel modes and service features to develop MaaS plans [50], although there were few models considering the assumptions of user preferences for different available mobility services to estimate user demand in a multimodal context [53,62]. There is a limitation in the integration of psychological indicators into modeling the interactions in existing studies, especially considering that users' willingness to share is a major limitation in modeling ridesharing and/or on-demand services.…”

Section: Modeling Interactions Between Demand-supplymentioning

confidence: 99%

A Literature Review on Interactions Between Stakeholders Through Accessibility Indicators Under Mobility as a Service Context

2021

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This study aims to explore accessibility indicators influencing the interactions between users, transport service providers (TSPs), and a platform operator, generating a conceptual framework for modeling these interactions under Mobility as a Service context. A systematic literature review was conducted to identify all studies focusing on indicators and modeling the interactions. There are limitations in integrating psychological indicators and dynamic pricing into the existing models. Moreover, there are gaps in considering monthly service packages, the efficiency of transport systems, and the perspectives of the TSPs for modeling the demand–supply interactions. The study ends with conclusions, discussions, and directions for further studies.

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A Hybrid Knowledge-based Recommender for Mobility-as-a-Service

Cited by 7 publications

References 0 publications

An Explainable Knowledge-based AI Framework for Mobility as a Service

An Explainable Knowledge-based AI Framework for Mobility as a Service

A Recommender System for Mobility-as-a-Service Plans Selection

A Literature Review on Interactions Between Stakeholders Through Accessibility Indicators Under Mobility as a Service Context

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