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Paper Details

Modeling and Optimizing a Vehicle Navigation System by G-Network

 Mohammad Kouchaki Pahnekolaei, Morteza Romoozi,  Mahshid Ghorbani, Hamideh Babaei

Journal Title:INTERNATIONAL JOURNAL OF HUMANITIES AND ART RESEARCHES
Abstract


Increasing the production of vehicles and necessity to use private and public cars have led to heavy traffic that has negative effects in that respect. The aim of intelligent transportation systems (ITS) is improving the quality of transportation, reducing travelling time and reducing fuel consumption via advanced technologies. Clearly, analyzing the routing problems of vehicles and finding optimized routes are among the considerable challenges in intelligent transportation systems. Vehicle navigation systems are the systems used for leading and routing. Using GPRS communication, these systems provide on-line services for collecting instant traffic information, such as vehicles coordination, speed and type, for enhancing them for efficient routing of vehicles. Furthermore, they can prepare diverse traffic reports regarding time, period, max. and min. speeds, the total driven distance in a desired specific date or time limit. Many navigation systems have used offline city maps and pre-set maps together with the history of navigation data obtained from GPS. These systems are not suitable due to rapid changes in the traffic conditions.     Since, online systems are preferred. Focusing on online navigation systems and dynamic VRP, we presented a navigation system for the vehicles to receive updated traffic information on reaching each junction, and select the best route with lower traffic to their destination, in case they are permitted to move in it.  In this paper, we used G-Network for modeling the proposed vehicles navigation system. G-Networks are queuing networks with the idea of considering negative customers against positive ones. Negative customers or signals can be considered actual or virtual, operating in different manners in the network. They can destruct positive customers in a queue [1], cause momentary passing of the customers to another queue [2], or remove a group of customers from the network [3]. Vehicles in our proposed model are positive customers and routing decisions are negative customers, here with considered virtual. The queue network is the map of an assumed city. Vehicles may be of different types, such as cars, heavy vehicles and rescue vehicles. Therefore, positive customers in the modeling include different classes. In this graph, each junction and also segments distributed uniformly in each pathway establish the queues of the queuing network. Accordingly, the relevant performance metrics of the network are presented.  The given model provide the possibility for us to use gradient descent method for optimization of the routing. Gradient descent is a first order optimization algorithm, used for finding the minimum rate of functions. In optimizing the behavior of the network, it was attempted to minimize the cost function, which includes parameters such as the probability of passing a type of vehicle from a junction and also probability of a routing decision in the junction. The obtained results from optimization show that the routing problems are improved by considering different criteria including average delay for the vehicles, average delay for routing decisions, average delay for the whole network and average usefulness.     Increasing the production of vehicles and necessity to use private and public cars have led to heavy traffic that has negative effects in that respect. The aim of intelligent transportation systems (ITS) is improving the quality of transportation, reducing travelling time and reducing fuel consumption via advanced technologies. Clearly, analyzing the routing problems of vehicles and finding optimized routes are among the considerable challenges in intelligent transportation systems. Vehicle navigation systems are the systems used for leading and routing. Using GPRS communication, these systems provide on-line services for collecting instant traffic information, such as vehicles coordination, speed and type, for enhancing them for efficient routing of vehicles. Furthermore, they can prepare diverse traffic reports regarding time, period, max. and min. speeds, the total driven distance in a desired specific date or time limit. Many navigation systems have used offline city maps and pre-set maps together with the history of navigation data obtained from GPS. These systems are not suitable due to rapid changes in the traffic conditions.     Since, online systems are preferred. Focusing on online navigation systems and dynamic VRP, we presented a navigation system for the vehicles to receive updated traffic information on reaching each junction, and select the best route with lower traffic to their destination, in case they are permitted to move in it.  In this paper, we used G-Network for modeling the proposed vehicles navigation system. G-Networks are queuing networks with the idea of considering negative customers against positive ones. Negative customers or signals can be considered actual or virtual, operating in different manners in the network. They can destruct positive customers in a queue [1], cause momentary passing of the customers to another queue [2], or remove a group of customers from the network [3]. Vehicles in our proposed model are positive customers and routing decisions are negative customers, here with considered virtual. The queue network is the map of an assumed city. Vehicles may be of different types, such as cars, heavy vehicles and rescue vehicles. Therefore, positive customers in the modeling include different classes. In this graph, each junction and also segments distributed uniformly in each pathway establish the queues of the queuing network. Accordingly, the relevant performance metrics of the network are presented.  The given model provide the possibility for us to use gradient descent method for optimization of the routing. Gradient descent is a first order optimization algorithm, used for finding the minimum rate of functions. In optimizing the behavior of the network, it was attempted to minimize the cost function, which includes parameters such as the probability of passing a type of vehicle from a junction and also probability of a routing decision in the junction. The obtained results from optimization show that the routing problems are improved by considering different criteria including average delay for the vehicles, average delay for routing decisions, average delay for the whole network and average usefulness.  

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