Investigations of Agents, Transportation and Data Communication Networks

代理、运输和数据通信网络的调查

• 批准号: RGPIN-2014-04528
• 负责人: Lawniczak, Anna
• 金额: $ 1.89万
• 依托单位: University of Guelph
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2014
• 资助国家: 加拿大
• 起止时间: 2014-01-01 至 2015-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=566578
• 关键词: Investigations; Agents; Transportation; Data; Communication

Vehicular traffic congestion affects many aspects of everyday life. It causes waste of time, economic losses, increased fuel consumption & pollution. Due to financial, spatial and environmental constraints extending the road network is not feasible solution to solve congestion, reliability and safety problems. The hope is in designing Intelligent Transportation Systems (ITSs) build on progress in the area of information & communication technology such as Vehicular Ad Hoc Networks (VANETs) which in principle, could allow vehicles, traffic lights, or road side units to exchange information and to coordinate their behavior. Thus, vehicle-to-vehicle communication and applications of advances in information technologies could help to optimize traffic flow and make transportation more efficient. However, before many of these advances can be implemented in real life in the most optimal way their scenarios must be studied and analyzed via simulation models. This research develops multi-agent based simulation models to be used for evaluating design options prior to making changes in road/street networks and to study the effects of these changes on traffic flow and congestion, and travel times. This research investigates how application of information and communication technologies can be used for vehicular traffic flow monitoring, detection, forecasting and control. The problems of existence of anomalous traffic and congestion are not unique to vehicular transportation networks. Also, they are encountered in data communication networks everyday. This research develops simulation models of data communication networks, i.e. of Packet Switching Networks (PSNs) to study spatio-temporal packet traffic dynamics under normal and anomalous traffic conditions (e.g., under distributed denial of service attacks). It investigates: (1) how function, structure, and scalability affect collective dynamics and performance of PSNs; (2) how well aggregate network performance indicators capture changes in PSNs flow and congestion dynamics caused by anomalous conditions. Additionally, this research develops novel: (1) detection methods of anomalous traffic based on multiscale analysis of spatio-temporal large scale flow patterns in vehicular transportation and data communication networks; (2) flow and congestion control strategies based on the understanding of spatio-temporal dynamics of large scale flow patterns and on correlation and cooperation in vehicular transportation and data communication networks; (3) real-time implementable flow and congestion control strategies, congestion detection and forecasting methods for traffic flow management in vehicular transportation networks. The discussed research covers methodology and application domains. In the methodology domain it studies also cognitive agents with emphasis on minimal entities, both in terms of storage and in terms of logical primitives and unable to express concepts analytically and unable to use crisp values. The described research will produce novel results of importance to the advancement of methodology, theoretical understanding of flow and congestion phenomena and useful for practical applications to solve some real life problems of economical value in vehicular transportation and data communication networks.

交通拥堵影响着人们日常生活的方方面面。它会造成时间浪费、经济损失、增加燃料消耗和污染。由于资金、空间和环境的限制，扩建道路网并不是解决交通拥挤、可靠性和安全性问题的可行方案。希望是在设计智能交通系统（ITS）的基础上，在信息和通信技术领域的进展，如车辆自组织网络（VANTIONAL），原则上，可以允许车辆，交通灯，或路边单元交换信息，并协调他们的行为。因此，车辆之间的通信和信息技术进步的应用有助于优化交通流量，提高运输效率。然而，在这些进步中的许多能够以最佳方式在真实的生活中实施之前，必须通过模拟模型来研究和分析它们的场景。本研究开发了基于多智能体的仿真模型，用于评估设计方案之前，在道路/街道网络的变化，并研究这些变化对交通流量和拥堵，出行时间的影响。本研究探讨如何应用资讯及通讯科技进行车辆交通流量监测、侦测、预测及控制。异常交通和拥挤的存在问题并不是车辆运输网络所独有的。而且，它们每天都在数据通信网络中遇到。本研究开发了数据通信网络的仿真模型，即分组交换网络（PSN）的仿真模型，以研究正常和异常业务条件下的时空分组业务动态（例如，在分布式拒绝服务攻击下）。它调查：（1）功能、结构和可扩展性如何影响PSN的集体动态和性能;（2）聚合网络性能指标如何捕获由异常条件引起的PSN流量和拥塞动态的变化。此外，本研究还提出了以下新的研究方法：（1）基于大规模时空流模式多尺度分析的异常交通检测方法;（2）基于大规模时空流模式的动态特性和交通网络中的相关性和协作性的交通流量和拥塞控制策略;（3）可实时实现的交通流和拥塞控制策略、拥塞检测和预测方法。所讨论的研究涵盖方法和应用领域。在方法论领域，它还研究认知代理，强调最小实体，无论是在存储方面还是在逻辑原语方面，都不能分析地表达概念，也不能使用清晰的值。所描述的研究将产生新的结果的重要性，先进的方法，理论上的理解流和拥塞现象和有用的实际应用，以解决一些真实的生活中的问题，在车辆运输和数据通信网络的经济价值。