A Knowledge-Based AI Framework for Mobility as a Service

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.

Outgrowing the Private Car—Learnings from a Mobility-as-a-Service Intervention in Greater Copenhagen

“…What it fails to address is Jitter's third conceptualization; MaaS, a phenomenon (new forms of mobility habits and behaviors). Hersher et al provided a definition that is very workable for particular developers of concrete MaaS solutions, who tend to focus primarily on the technical side of MaaS in terms of, e.g., developing new app solutions for smartphones that integrate different transport solutions [1,7]. From such a perspective, the challenge of creating the sustainable and seamlessly integrated MaaS system of the future is first and foremost for developing coherent digital infrastructure and an attractive user interface.…”