Advancing Transportation Safety and Sustainability Using Big Data and Deep Learning

利用大数据和深度学习促进运输安全和可持续发展

• 批准号: RGPIN-2018-03970
• 负责人: Fu, Liping
• 金额: $ 6.27万
• 依托单位: University of Waterloo
• 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=748814
• 关键词: Advancing; Transportation; Safety; Sustainability; Using

Transportation systems around the world continue to face significant challenges such as persistent safety problems, rapidly rising traffic congestion, and deteriorating air quality. These challenges could be potentially addressed with the latest developments in information and sensor technologies, which are making available massive volumes of data from dedicated sensors embedded in the road network, connected vehicles with vehicle-to-vehicle and vehicle-to-infrastructure communication, and various elements of the transportation systems, as well as the traveling public via their personal communication and wearable devices. Availability of these data along with significant advances in artificial intelligence (AI) affords transportation authorities new opportunities to estimate and predict travel demand and traffic conditions more accurately, speed up transportation services, optimize network supply and improve the overall experience and health of the travelling public. However, before these potentials can be realized fully, many technical challenges must be addressed. Specific challenges for transportation applications include incompleteness and heterogeneity of available data, responsiveness and timeliness of decision-making and information provision, and needs to consideration traveler behavior and responses. The proposed research program focuses on development and advancement of big data analytics and machine learning techniques for monitoring, managing and controlling the road transportation system for maximum safety, efficiency, and sustainability. Specifically, it includes three research themes as follows: (i) Development and Evaluation of Innovative Sensor Technologies: design novel dedicated as well as crowdsourcing techniques for collecting real-time road and traffic condition data, develop new integrated driver, road condition, and vehicle monitoring system, (ii) Estimation and Prediction with Big Data: develop models and algorithms for estimating and predicting travel related decisions and road, traffic, air and driver conditions using various big data sources such as video traffic surveillance systems, loop detectors, Bluetooth/Wifi detectors, mobile phones, and social medias, (iii) Enabling Intelligent Decision Support: develop systematic optimization models and algorithms for intelligently managing travel demand, traffic, and travel experience, such as, adaptive traffic signal control, advanced travel information provision, autonomous driver, and ramp-metering and changeable message signs. The research will provide a foundation for developing a wide array of innovative, application-driven technologies for ubiquitous road weather and traffic condition monitoring, integrated driver information and traffic network management, and advanced traffic control schemes.

世界各地的交通运输系统继续面临重大挑战，如持续的安全问题，迅速增加的交通拥堵和不断恶化的空气质量。这些挑战可以通过信息和传感器技术的最新发展来解决，这些技术可以从嵌入道路网络的专用传感器、具有车辆到车辆和车辆到基础设施通信的联网车辆、交通系统的各种元素以及通过个人通信和可穿戴设备的旅行公众提供大量数据。 这些数据的可用性沿着人工智能（AI）的显著进步，为交通管理部门提供了新的机会，可以更准确地估计和预测出行需求和交通状况，加快交通服务，优化网络供应，改善出行公众的整体体验和健康。然而，在充分实现这些潜力之前，必须解决许多技术挑战。 交通应用程序的具体挑战包括可用数据的不完整性和异质性，决策和信息提供的响应性和及时性，以及考虑旅行者行为和响应的需求。 拟议的研究计划侧重于开发和推进大数据分析和机器学习技术，用于监测，管理和控制道路运输系统，以实现最大的安全性，效率和可持续性。 具体而言，它包括以下三个研究主题：（i）创新传感器技术的开发和评估：设计新颖的专用和众包技术，用于收集实时道路和交通状况数据，开发新的集成驾驶员，道路状况和车辆监控系统，（ii）大数据估计和预测：开发模型和算法，用于使用各种大数据源（如视频交通监控系统、环路检测器、蓝牙/Wifi检测器、移动的电话）估计和预测与出行相关的决策以及道路、交通、空气和驾驶员状况，（iii）实现智能决策支持：开发系统优化模型和算法，用于智能管理出行需求、交通和出行体验，例如自适应交通信号控制、高级出行信息提供、自动驾驶、匝道计量和可变信息标志。 该研究将为开发广泛的创新，应用驱动的技术提供基础，这些技术用于无处不在的道路天气和交通状况监测，集成驾驶员信息和交通网络管理以及先进的交通控制方案。