Context-Aware Services Using MANETs for Long-Distance Vehicular Systems: A Cognitive Agent-Based Model

Abstract

Long-distance transportation systems play an important role in economic growth. Yet, these systems are incurred with multifaceted delays and cost problems. The major incites affecting transportation systems are congestion, breakdowns, emergencies, and inclement weather. Scarcity of information about the environment also exacerbates travel problems. It is essential to employ monitoring and guidance that aid in making timely decisions through premediated information. This work aims to provide a flexible model for the long-distance transport system. The model is based on problems faced in long-distance transportation. Moreover, we examine the possible use of emerging Information and Communication Technologies (ICTs) for better transportation. The system dynamics study the problem at hand through cognitive agent-based modelling (ABM) concepts. The integrated model lays the rules to abate traffic delays. In this model, the distance travelled by vehicles is divided into sections using checkpoints. Every section is composed of different agents such as medical units, police stations, workshops, and petrol pumps. The vehicle shifts connection over the mobile ad hoc network (MANET) when enters or leaves a section. We used NetLogo for simulation of the model. A monitoring and guidance system is tested, and obtained results are analyzed by addressing problems causing delays. The guidance system helps vehicles to take optimal decisions for the time, congestion, and rests. The model can be used to improve decision-making for vehicles through premediated decisions. The proposed model can help to improve the efficiency of the transportation systems by reducing travel time.