Parking Search Optimization in Urban Area

This study is a first step towards solving the parking search time optimization problem in urban area. By using adaptive multi-criteria optimisation model with system feedback for simulation of parking choice behaviour and drivers' preferences, presented by adequate utility function, we shown on real case that parking search time can by reduced by 70 %. We use publicly available demographic study as input data and Rockwell Automation Arena 14 software for processing and modelling. Various categories of data were evaluated based on results from 2,057 interviews with parking users. Our comparison of two models, everyday driver behaviour model and adaptive experimental optimisation model, shows a great potential in reducing parking search time. The analysed results show that search time decreases with information availability about three main criteria: acceptable walking distance, price and driving time.