Qatar expects more than a million visitors during the 2022 World Cup, which will pose significant challenges. The high number of people will likely cause a rise in road traffic congestion, vehicle crashes, injuries and deaths. To tackle this problem, Naturalistic Driver Behavior can be utilised which will collect and analyze data to estimate the current Qatar traffic system, including traffic data infrastructure, safety planning, and engineering practices and standards. In this paper, an IoT based solution to facilitate such a study in Qatar is proposed. Different data points from a driver are collected and recorded in an unobtrusive manner, such as trip data, GPS coordinates, compass heading, minimum, average, and maximum speed and his driving behavior, including driver's drowsiness level. Analysis of these data points will help in prediction of crashes and road infrastructure improvements to reduce such events. It will also be used for drivers risk assessment and to detect extreme road user behaviors. A framework that will help to visualize and manage this data is also proposed, along with a Deep Learning-based application that detects drowsy driving behavior that netted an 82 percent accuracy.

中文翻译：

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城市交通监控和建模系统：增强道路安全的物联网解决方案

卡塔尔预计 2022 年世界杯期间将有超过 100 万游客，这将带来重大挑战。大量人口可能会导致道路交通拥堵、车辆碰撞、受伤和死亡的增加。为了解决这个问题，可以利用自然驾驶行为来收集和分析数据来估计当前的卡塔尔交通系统，包括交通数据基础设施、安全规划以及工程实践和标准。在本文中，提出了一种基于物联网的解决方案，以促进卡塔尔的此类研究。以不显眼的方式收集和记录来自驾驶员的不同数据点，例如行程数据、GPS 坐标、罗盘航向、最低、平均和最高速度以及他的驾驶行为，包括驾驶员的困倦程度。对这些数据点的分析将有助于预测碰撞和道路基础设施的改善，以减少此类事件。它还将用于驾驶员风险评估和检测极端道路使用者行为。还提出了一个有助于可视化和管理这些数据的框架，以及一个基于深度学习的应用程序，该应用程序检测昏昏欲睡的驾驶行为，准确率为 82%。