Cyber-physical Distributed Simulation of Intelligent Transportation Systems

智能交通系统的信息物理分布式仿真

• 批准号: RGPIN-2018-06882
• 负责人: DeGrande, Robson
• 金额: $ 2.04万
• 依托单位: Brock University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=749800
• 关键词: Cyber; physical; Distributed; Simulation; Intelligent

Intelligent Transportation Systems (ITS) has been steadily growing in recent years due to the latest advancements in Information and Communications Technology (ICT). Vehicles have become smarter and embedded with sensors and processing units. Also, new communication paradigms have enabled connected vehicles, allowing the exchange of information, which significantly supports new trends in autonomous driving and transportation electrification. All these elements come along with incentives for developing Smart Cities, in which there is an inherent interaction between vehicles and mobile devices and the infrastructure, allowing to happen built-in context-awareness.Highly complex systems are built upon ITS, demanding more precise control and efficient management of resources, vehicles, systems, and the environment. Modelling and simulation move closely alongside such demands, becoming an essential tool for ITS training, emergency preparedness and response, estimation and prediction, and scenario analysis. However, the features and requirements of intelligent transportation have conditioned simulations to support high consistency and fidelity on reproducing actual environments. As a consequence, models for and of ITS become highly complex and detailed; the complexity grows as IoT-oriented sensed data sources are integrated into such simulation systems.Existing models and simulators have been explored to provide estimations and analyze traffic scenarios to support transportation systems on the traffic control. Macroscopic models have allowed predicting traffic load through abstraction of details and strong assumptions. Such assumptions might be unrealistic but effective for simplifying and speeding up processing towards timely optimizations. On the other hand, microscopic simulations enable exploratory analysis from traffic scenarios, which are generated based on a broad set of parameters. These simulations usually involve just traffic, movement of vehicles, and allow to observe complex virtual, synthetic environments extensively. This proposal aims at defining a robust research program towards the development of a distributed simulation platform for and of ITS. The platform contemplates the modelling of large-scale urban centres, where distributed agent-based simulations represent the individual movement and interaction of vehicles, pedestrians, and infrastructure, and traffic-flow models estimate the load and trends. The platform is intended to provide precise estimations from real or synthetically generated data and serve as a validation and evaluation tool for novel ITS solutions. The input of real-time data in the simulations requires a support system for constant communication/interaction with the real environments, providing high performance through Cloud-based resource management, load balancing, and data dissemination.

近年来，由于信息和通信技术（ICT）的最新进展，智能交通系统（ITS）得到了稳步发展。汽车变得更加智能，并嵌入了传感器和处理单元。此外，新的通信模式使互联汽车成为可能，允许信息交换，这极大地支持了自动驾驶和交通电气化的新趋势。所有这些因素都是发展智能城市的激励因素，在智能城市中，车辆、移动设备和基础设施之间存在固有的互动，从而实现内置的环境感知。高度复杂的系统建立在ITS的基础上，要求对资源、车辆、系统和环境进行更精确的控制和有效的管理。建模和模拟与这些需求密切相关，成为智能交通系统培训、应急准备和反应、估计和预测以及情景分析的基本工具。然而，智能交通的特点和需求使得仿真在再现实际环境时必须支持高一致性和保真度。因此，智能交通系统的模型变得非常复杂和详细；随着面向物联网的传感数据源被集成到这样的仿真系统中，复杂性也在增加。利用现有的模型和仿真器对交通情景进行估计和分析，为交通系统的交通控制提供支持。宏观模型可以通过抽象细节和强假设来预测交通负荷。这样的假设可能是不现实的，但对于简化和加速处理以实现及时优化是有效的。另一方面，微观模拟可以对基于广泛参数集生成的交通场景进行探索性分析。这些模拟通常只涉及交通、车辆的移动，并允许广泛地观察复杂的虚拟合成环境。本提案旨在为ITS的分布式仿真平台的开发定义一个强大的研究计划。该平台考虑对大型城市中心进行建模，其中基于分布式代理的模拟代表了车辆、行人和基础设施的个人运动和互动，交通流量模型估计了负载和趋势。该平台旨在从真实或综合生成的数据中提供精确的估计，并作为新型ITS解决方案的验证和评估工具。仿真中实时数据的输入需要一个与真实环境持续通信/交互的支持系统，通过基于云的资源管理、负载平衡和数据分发提供高性能。