Artificial Intelligence Supporting Sustainable and Individual Mobility: Development of an Algorithm for Mobility Planning and Choice of Means of Transport

Rebecca Heckmann (), Sören Kock and Lutz Gaspers
Additional contact information
Rebecca Heckmann: University of Applied Sciences Stuttgart
Sören Kock: Hochschule für Technik Stuttgart
Lutz Gaspers: Hochschule für Technik Stuttgart

Chapter 3 in iCity. Transformative Research for the Livable, Intelligent, and Sustainable City, 2022, pp 27-41 from Springer

Abstract: Abstract Mobility planning is rarely individually tailored. Instead people have to make an active effort to adapt standard solutions to their requirements. Routing apps like Google Maps allow for personalization only by saving important places like home and a workplace but do not allow the user to influence the routing suggestions or choice of mode of transport based on preferences, limitations, or situation. It becomes even more difficult when different means of transport are to interact since most routing applications offer very little multimodal optimization aside from the last mile. Thus, the objective of this article is to present a concept for the utilization of artificial intelligence and regression models in order to enable individual and sustainable mobility planning. To achieve this objective, initially existing routing and mobility planning applications are examined and are conceptually expanded in order to outlay the benefits of personalized route planning. The concrete objective alongside with a method for the development of a new solution is summarized. An algorithm fulfilling these objectives based on multiple linear regression is conceptualized. Relevant factors with coefficient are identified, as well as necessary data sources and interfaces. This algorithm is then implemented in a limited prototype as a proof of concept. Finally, this prototype is tested based on a set of mobility scenarios in order to validate the achievement of the defined objective.

Keywords: Sustainable mobility; Personalized routing; Artificial intelligence; Multiple linear regression (search for similar items in EconPapers)
Date: 2022
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-030-92096-8_3

Ordering information: This item can be ordered from
http://www.springer.com/9783030920968

DOI: 10.1007/978-3-030-92096-8_3

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().