Advancing traffic management using bluetooth/wifi and connected vehicle data

该研究由滑铁卢大学和领先的智能交通系统解决方案提供商SMATS合作开展，旨在开发使用蓝牙/Wifi和联网车辆（CV）数据的新型交通性能测量和信号控制系统。具体而言，建议的研究是为了实现在不同时空尺度上评估道路交通性能的新指标；开发新的基于人工智能的交通信号控制方法，利用新的交通大数据源；并探索创新的基于仿真的技术来评估所提出的绩效指标和控制模型的有效性。拟议的研究将解决两种截然不同的数据源所带来的挑战：高频但低质量的蓝牙/Wifi数据与低频但高质量的CV数据。它将产生新的知识、创新的交通分析和信号控制方法，以及新的解决方案和产品，这些解决方案和产品可以立即在现场进行测试和实施，以提高道路安全性、可持续性和机动性，使安大略省和加拿大所有城市受益。

The proposed research is a collaborative effort between the University of Waterloo and SMATS, a leading intelligent transportation systems solution provider, aiming to develop new traffic performance measurement and signal control systems using Bluetooth/Wifi and connected vehicle (CV) data. Specifically, the proposed research is to achieve the objectives of developing new metrics for assessing the road traffic performance at various spatiotemporal scales; developing new AI-based traffic signal control methods that can take advantage of the new Big Traffic Data sources; and exploring innovative simulation-based techniques for assessing the effectiveness of the proposed performance metrics and control models. The proposed research will address the challenge posed by two strikingly different data sources: high-frequent but low-quality Bluetooth/Wifi data vs. low-frequent but high-quality CV data. It will generate new knowledge, innovative traffic analysis and signal control methodologies, and new solutions and products that can be immediately tested and implemented in the field for improved road safety, sustainability, and mobility, benefiting Ontario and all municipalities across Canada.