@article{FriedrichKrejcaRothenbergeretal.2019,
  author    = {Friedrich, Tobias and Krejca, Martin Stefan and Rothenberger, Ralf and Arndt, Tobias and Hafner, Danijar and Kellermeier, Thomas and Krogmann, Simon and Razmjou, Armin},
  title     = {Routing for on-street parking search using probabilistic data},
  series = {AI communications : AICOM ; the European journal on artificial intelligence},
  volume    = {32},
  journal   = {AI communications : AICOM ; the European journal on artificial intelligence},
  number    = {2},
  publisher = {IOS Press},
  address   = {Amsterdam},
  issn      = {0921-7126},
  doi       = {10.3233/AIC-180574},
  pages     = {113 -- 124},
  year      = {2019},
  abstract  = {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.},
  language  = {en}
}