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.

中文翻译：

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使用MANET进行远程车辆系统的上下文感知服务：基于认知代理的模型

长途运输系统在经济增长中起着重要作用。然而，这些系统存在多方面的延迟和成本问题。影响交通系统的主要因素是交通拥堵，故障，紧急情况和恶劣天气。有关环境的信息稀缺也加剧了旅行问题。必须采用监视和指导，以通过中介信息及时做出决定，这一点至关重要。这项工作旨在为长途运输系统提供一个灵活的模型。该模型基于长途运输中面临的问题。此外，我们研究了可能使用新兴的信息和通信技术（ICT）改善交通运输的情况。系统动力学通过基于认知主体的建模（ABM）概念研究当前的问题。集成模型为减少交通延误奠定了规则。在此模型中，使用检查点将车辆行驶的距离分为多个部分。每个部分都由不同的人员组成，例如医疗队，派出所，车间和加油站。进入或离开路段时，车辆会通过移动自组织网络（MANET）切换连接。我们使用NetLogo进行了模型仿真。测试了监视和引导系统，并通过解决引起延迟的问题来分析获得的结果。引导系统可帮助车辆针对时间，交通拥堵和休息做出最佳决策。该模型可用于通过预先确定的决策来改善车辆的决策。所提出的模型可以通过减少出行时间来帮助提高运输系统的效率。