Embedded system for indoor guidance parking with Dijkstra’s algorithm and ant colony optimization

The population increases rapidly and many parking bays are needed, especially during weekends when shopping malls face heavy traffic congestion. Consequently, during peak hours, finding a vacant parking bay is more of a difficult task. This study proposes a car parking management system which applies Dijkstra’s algorithm, Ant Colony Optimization (ACO) and Binary Search Tree (BST) in structuring a guidance system for indoor parking. In this system, the layout was designed under two categories which are the standard bay size and the small bay size to increase the parking bays. Based on the proposed layout of the parking system, the number of parking bays have increased by 21.5% compared with the standard parking design. The proposed embedded system for guidance parking is a system that assigns the nearest available vacant bay to the entrance with the shortest driving path. The system will automatically check for the nearest vacant bay and reserve it for the current user allowing a different bay reservation for the next user. The circuits have been designed by proteus, the microcontrollers have been programmed by micro C, and the Graphical User Interface (GUI) has been implemented in Java. Few by electronic components such as RFID, multiplexer, XBee, and servo motors have been used to realize the system. Dijkstra and ACO with BST are integrated to produce the embedded system for parking guidance for the indoor parking system. BST inserts the nodes in the way that the Dijkstra’s can find the empty parking in fastest way. Dijkstra’s algorithm initials the paths to finding the shortest path while ACO optimizes the paths. This study is aimed at helping to calculate the shortest path as well as to guide the driver towards the nearest vacant available bay near the entrance by considering both the walking distance and the driving distance. It also presents the realtime simulation of the parking system and validates any information regarding the parking status by dual switches, multiplexers and microcontroller. The proposed embedded system has achieved positive outcomes in comparison to the current system and the traditional algorithm with regards to the shortest path. The results show a range of 8.3% to 26.8% improvement in the proposed path compared to the traditional Dijkstra’s algorithm. The findings also indicate that the proposed embedded system for indoor guidance parking using Dijkstra-ACO algorithm with the proposed layout of parking bay for indoor parking system, will help in reducing the time wasted in searching for a parking bay and will increase the efficiency of the parking system in shopping malls.

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