Reservation-based multi-objective smart parking approach for smart cities

It has been revealed that cruising for parking is one of the main sources of road congestion and pollution, as well as daily discomfort and stress experienced by drivers while on road to attend their everyday businesses. Therefore, there is a substantial need to design efficient car parking mechanisms that can be easily deployed into future intelligent transportation systems. In this paper, we tackle this important issue and we propose a new Reservation-based multi-Objective SmArt Parking approach denoted as ROSAP. Our approach uses a simulated annealing based meta-heuristic to optimize the parking slot assignment problem formulated as a multi-objective Integer Linear Program (ILP). ROSAP helps drivers to find the most suitable parking slot within their areas of interest and with respect to their specified constraints. In order to gauge the effectiveness of our proposal, we conducted extensive simulation experiments considering a real-like environment. Results show significant gains in both request satisfaction ratio and parking occupancy while keeping a minimal walking distance between the parking and the user's destination, in comparison with greedy approaches where each vehicle is assigned a free parking slot, which is closer to the destination.