Temporal Modeling of On-Street Parking Data for Detection of Parking Violation in Smart Cities

With the increase in number of vehicles, the requirement of intelligent parking management is indispensable in smart cities. One of the major requirements in smart parking system is handling parking violations efficiently. The parking violation generally includes parking beyond allowed time. To detect parking violations and to manage it efficiently, the parking data collected through field sensor devices need to be analyzed intensively and thoroughly. To this end, this paper has presented temporal analysis of on-street parking data of Melbourne city and proposed a novel mathematical model and curve-fitting algorithm using quasi-Newton method to detect parking violation. The proposed model is validated with real dataset through simulation with a sum of squared error of \(4.888 \times 10^{-7}\).