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Routing for on-street parking search using probabilistic data

  • A significant percentage of urban traffic is caused by the search for parking spots. One possible approach to improve this situation is to guide drivers along routes which are likely to have free parking spots. The task of finding such a route can be modeled as a probabilistic graph problem which is NP-complete. Thus, we propose heuristic approaches for solving this problem and evaluate them experimentally. For this, we use probabilities of finding a parking spot, which are based on publicly available empirical data from TomTom International B.V. Additionally, we propose a heuristic that relies exclusively on conventional road attributes. Our experiments show that this algorithm comes close to the baseline by a factor of 1.3 in our cost measure. Last, we complement our experiments with results from a field study, comparing the success rates of our algorithms against real human drivers.

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Author details:Tobias FriedrichORCiDGND, Martin Stefan KrejcaORCiDGND, Ralf RothenbergerORCiD, Tobias Arndt, Danijar Hafner, Thomas Kellermeier, Simon Krogmann, Armin Razmjou
DOI:https://doi.org/10.3233/AIC-180574
ISSN:0921-7126
ISSN:1875-8452
Title of parent work (English):AI communications : AICOM ; the European journal on artificial intelligence
Publisher:IOS Press
Place of publishing:Amsterdam
Publication type:Article
Language:English
Date of first publication:2019/05/16
Publication year:2019
Release date:2021/05/14
Tag:Parking search; constrained optimization; field study; probabilistic routing
Volume:32
Issue:2
Number of pages:12
First page:113
Last Page:124
Organizational units:Digital Engineering Fakultät / Hasso-Plattner-Institut für Digital Engineering GmbH
DDC classification:0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 000 Informatik, Informationswissenschaft, allgemeine Werke
Peer review:Referiert
Publishing method:Open Access / Green Open-Access
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