TY  - JOUR
A1  - Friedrich, Tobias
A1  - Krejca, Martin Stefan
A1  - Rothenberger, Ralf
A1  - Arndt, Tobias
A1  - Hafner, Danijar
A1  - Kellermeier, Thomas
A1  - Krogmann, Simon
A1  - Razmjou, Armin
T1  - Routing for on-street parking search using probabilistic data
JF  - AI communications : AICOM ; the European journal on artificial intelligence
N2  - 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.
KW  - Parking search
KW  - probabilistic routing
KW  - constrained optimization
KW  - field study
Y1  - 2019
U6  - https://doi.org/10.3233/AIC-180574
SN  - 0921-7126
SN  - 1875-8452
VL  - 32
IS  - 2
SP  - 113
EP  - 124
PB  - IOS Press
CY  - Amsterdam
ER  -