Efficiently predicting traffic congestion benefits various traffic stakeholders, from regular commuters and logistic operators to urban planners and responsible authorities. This study aims to give a high-quality estimation of traffic conditions from a large historical Floating Car Data (FCD) with two main goals: (i) estimation of congestion zones on a large road network, and (ii) estimation of travel times within congestion zones in the form of the time-varying Travel Time Indexes (TTIs). On the micro level, the traffic conditions, in the form of speed profiles were mapped to links in the road network. On the macro level, the observed area was divided into a fine-grained grid and represented as an image where each pixel indicated congestion intensity. Spatio-temporal characteristics of congestion zones were determined by morphological closing operation and Monte Carlo simulation coupled with temporal clustering. As a case study, the road network in Croatia was selected with spatio-temporal analysis differentiating between the summer season and the rest of the year season. To validate the proposed approach, three comparisons were conducted: (i) comparison to real routes' travel times driven in a controlled manner, (ii) comparison to historical trajectory dataset, and (iii) comparison to the state-of-the-art method. Compared to the real measured travel times, using zone's time-varying TTIs for travel time estimation resulted in the mean relative percentage error of 4.13%, with a minor difference to travel times estimated on the micro level, and a significant improvement compared to the current Croatian industrial navigation. The results support the feasibility of estimating congestion zones and time-varying TTIs on a large road network from FCD, with the application in urban planning and time-dependent routing operations due to: significant reduction in the data volume without notable quality loss, and meaningful reduction in the pre-processing computation time.

有效地预测交通拥堵，可以使各种交通利益相关者受益，从常规的通勤者和物流运营商到城市规划者和主管部门。这项研究的目的是根据大型历史浮动车数据（FCD）进行交通状况的高质量估算，其主要目标有两个：（i）估算大型道路网络上的拥堵区，以及（ii）估算路段内的行驶时间时变旅行时间指数（TTI）形式的交通拥堵区。在微观层面上，交通状况以速度曲线的形式被映射到公路网中的路段。在宏观水平上，将观察到的区域划分为细粒度的网格，并表示为图像，其中每个像素都表示拥塞强度。通过形态封闭操作和蒙特卡洛模拟结合时间聚类来确定拥挤区的时空特征。作为案例研究，选择克罗地亚的道路网络时空分析以区分夏季和全年的剩余时间。为了验证所提出的方法，进行了三项比较：（i）与以受控方式驱动的实际路线的行驶时间进行比较；（ii）与历史轨迹数据集进行比较；以及（iii）与最新技术进行比较方法。与实际测得的旅行时间相比，使用区域的时变TTI进行旅行时间估算可得出平均相对百分比误差为4.13％，与微观水平上估算的旅行时间之间存在细微的差异，与目前的克罗地亚工业导航系统相比，有了明显的改进。结果支持通过FCD估算大型道路网络上的拥堵区和时变TTI的可行性，并应用于城市规划和与时间有关的路由操作，原因是：数据量显着减少而没有明显的质量损失，并且有意义减少了预处理计算时间。