Enhancing the smart parking assignment system through constraints optimization

Abstract

Traffic in big cities has become a black spot for drivers. One of the major concerns is the parking problem that hindering urban mobility particularly in the big city and other congested areas; Drivers lose a significant amount of time looking for looking for a parking spot. This leads to an increase in accidents, a big consumption of fuel and a spectacular augmentation of pollution. We present a parking assignment system based on constraint programming in this paper, to meet the need for effective parking management in smart cities, for a group of drivers booking in the same time and area. In this work, we suggest two formulations of the Parking Assignment Problem, The first was established by using Constraint Satisfaction Problems (CSP) and the second is based on Mixed Integer Linear Programing (MILP). An implementation of the model taking advantage of Choco solver dedicate to the constraint programming and the evaluation of its scalability compared to the Mixed Integer Linear Programing solvers. The experiments conducted with Choco and MILP solvers on a real case study in the city of Casablanca showed that the two methods generates promising solutions in terms of scalability and response time.