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A Knowledge-Based AI Framework for Mobility as a Service

Authors :
Rajabi, Enayat
Nowaczyk, Sławomir
Pashami, Sepideh
Bergquist, Magnus
Ebby, Geethu Susan
Wajid, Summrina
Rajabi, Enayat
Nowaczyk, Sławomir
Pashami, Sepideh
Bergquist, Magnus
Ebby, Geethu Susan
Wajid, Summrina
Publication Year :
2023

Abstract

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. © 2023 by the authors.

Details

Database :
OAIster
Notes :
application/pdf, English
Publication Type :
Electronic Resource
Accession number :
edsoai.on1372247221
Document Type :
Electronic Resource
Full Text :
https://doi.org/10.3390.su15032717