hh.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
An Explainable Knowledge-based AI Framework for Mobility as a Service
Halmstad University, School of Information Technology, Center for Applied Intelligent Systems Research (CAISR). Shannon School of Business, Cape Breton University, Canada.ORCID iD: 0000-0002-9557-0043
Halmstad University, School of Information Technology.ORCID iD: 0000-0002-7796-5201
Halmstad University, School of Information Technology.ORCID iD: 0000-0003-3272-4145
Halmstad University, School of Information Technology.ORCID iD: 0000-0002-6453-3653
2022 (English)In: Proceedings of the International Conference on Software Engineering and Knowledge Engineering, Skokie, IL: Knowledge Systems Institute, 2022, p. 312-316Conference paper, Published paper (Refereed)
Abstract [en]

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.

Place, publisher, year, edition, pages
Skokie, IL: Knowledge Systems Institute, 2022. p. 312-316
Series
Proceedings of the International Conference on Software Engineering and Knowledge Engineering, E-ISSN 2325-9000 ; 2022
Keywords [en]
Acquisition process, Data set, Knowledge based, Knowledge based framework, Knowledge system, Mobility datum, Mobility service, Personalized service, Structure data
National Category
Computer Systems
Research subject
Smart Cities and Communities
Identifiers
URN: urn:nbn:se:hh:diva-48498DOI: 10.18293/SEKE2022-0020Scopus ID: 2-s2.0-85137156006ISBN: 9781891706547 (print)ISBN: 1891706543 (print)OAI: oai:DiVA.org:hh-48498DiVA, id: diva2:1704870
Conference
34th International Conference on Software Engineering and Knowledge Engineering, SEKE 2022; KSIR Virtual Conference CenterPittsburgh; United States; 1 July 2022 through 10 July 2022
Available from: 2022-10-19 Created: 2022-10-19 Last updated: 2022-12-05Bibliographically approved

Open Access in DiVA

fulltext(1125 kB)112 downloads
File information
File name FULLTEXT01.pdfFile size 1125 kBChecksum SHA-512
1cf3027d6077ff6134efd498601ccd74d7f39b638f248a45c20499e0963f9ac76267b7dffd014c7feb5e4a9b97323b3c3fedb0ca79c00da70ce9b9bd743f5d2c
Type fulltextMimetype application/pdf

Other links

Publisher's full textScopus

Authority records

Rajabi, EnayatNowaczyk, SławomirPashami, SepidehBergquist, Magnus

Search in DiVA

By author/editor
Rajabi, EnayatNowaczyk, SławomirPashami, SepidehBergquist, Magnus
By organisation
Center for Applied Intelligent Systems Research (CAISR)School of Information Technology
Computer Systems

Search outside of DiVA

GoogleGoogle Scholar
Total: 112 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

doi
isbn
urn-nbn

Altmetric score

doi
isbn
urn-nbn
Total: 250 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf