Abstract
The parking problem in big cities has become one of the key causes of the city traffic congestion, driver frustration and air pollution.So to avoid these problems, parking monitoring is an important solution. Recently many new technologies have been developed that allows vehicle drivers to effectively find the free parking places in the city but these systems still limited because they don 't take into consideration road networks constraints. In this paper, We design a distributed system that will help drivers to find the optimal route between their positions and an indoor parking in the city taking into consideration a set of constraints such as ( distance, traffic, amount of fuel in the car, available places in te parking, and parking cost). We propose a distributed technique based on multi objective Ant Colony Optimisation (ACO). The proposed method aim to manage multi objective parking problem in real time using the behavior of real ants and multi agent systems to decrease the traffic flow and to find the optimal route for drivers.
Highlights
Cities noticed that their drivers had real problems to find a parking space especially during peak hours, the difficulty roots from not knowing where the parking spaces are available at the given time
We notice that the guidance systems proposed by researches dont provide an optimal multi objective solution to find an indoor parking with available places.We design a distributed system that will help drivers to find the optimal route between their positions and an indoor parking in the city taking into consideration a set of constraints such as
This paper presents a distributed solution based on multi objective Ant Colony Optimisation (ACO) and multi agent systems to solve multi constraints parking problem .The proposed solution uses the behavior of real ants based on artificial agents
Summary
Cities noticed that their drivers had real problems to find a parking space especially during peak hours, the difficulty roots from not knowing where the parking spaces are available at the given time. We propose a mathematical model to decrease the time searching for an indoor parking taking into consideration a set of constraints ( distance, traffic, amount of fuel in the car, available places and parking cost). With this approach, we propose a new method to optimize parking timing based on a distributed multi-objective ACO algorithm and multi agent systems. The Mathematical model for solving the multi objective parking problem is proposed in Section 4 that aims to find the optimal solution for drivers.
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