Faculdade de Engenharia, Universidade do Porto, Rua Dr. Roberto Frias, s/n, 4200-465 Porto, Portugal.
Departamento de Engenharia Mecânica, Faculdade de Engenharia, Universidade do Porto, Rua Dr. Roberto Frias, s/n, 4200-465 Porto, Portugal.
Sensors (Basel). 2021 Nov 19;21(22):7705. doi: 10.3390/s21227705.
With the rapid growth and development of cities, Intelligent Traffic Management and Control (ITMC) is becoming a fundamental component to address the challenges of modern urban traffic management, where a wide range of daily problems need to be addressed in a prompt and expedited manner. Issues such as unpredictable traffic dynamics, resource constraints, and abnormal events pose difficulties to city managers. ITMC aims to increase the efficiency of traffic management by minimizing the odds of traffic problems, by providing real-time traffic state forecasts to better schedule the intersection signal controls. Reliable implementations of ITMC improve the safety of inhabitants and the quality of life, leading to economic growth. In recent years, researchers have proposed different solutions to address specific problems concerning traffic management, ranging from image-processing and deep-learning techniques to forecasting the traffic state and deriving policies to control intersection signals. This review article studies the primary public datasets helpful in developing models to address the identified problems, complemented with a deep analysis of the works related to traffic state forecast and intersection-signal-control models. Our analysis found that deep-learning-based approaches for short-term traffic state forecast and multi-intersection signal control showed reasonable results, but lacked robustness for unusual scenarios, particularly during oversaturated situations, which can be resolved by explicitly addressing these cases, potentially leading to significant improvements of the systems overall. However, there is arguably a long path until these models can be used safely and effectively in real-world scenarios.
随着城市的快速增长和发展，智能交通管理和控制（ITMC）正成为应对现代城市交通管理挑战的基本组成部分，其中需要迅速解决各种日常问题。不可预测的交通动态、资源限制和异常事件等问题给城市管理者带来了困难。ITMC 旨在通过最小化交通问题的可能性来提高交通管理效率，通过提供实时交通状态预测来更好地安排交叉口信号控制。可靠的 ITMC 实施提高了居民的安全性和生活质量，从而促进了经济增长。近年来，研究人员提出了不同的解决方案来解决交通管理方面的具体问题，包括图像处理和深度学习技术，以及预测交通状态和制定控制交叉口信号的策略。本文综述了有助于开发模型以解决已确定问题的主要公共数据集，并对与交通状态预测和交叉口信号控制模型相关的工作进行了深入分析。我们的分析发现，基于深度学习的短期交通状态预测和多交叉口信号控制方法取得了合理的结果，但在异常情况下（特别是在过饱和情况下）缺乏稳健性，通过明确解决这些情况，可能会显著提高系统的整体性能。然而，这些模型要在实际场景中安全有效地使用，还有很长的路要走。
