Traffic congestion at intersections is a significant issue in urban areas,
leading to increased commute times, safety hazards, and operational
inefficiencies. This study aims to develop a predictive model for congestion at
intersections in major U.S. cities, utilizing a dataset of trip-logging metrics
from commercial vehicles across 4,800 intersections. The dataset encompasses 27
features, including intersection coordinates, street names, time of day, and
traffic metrics (Kashyap et al., 2019). Additional features, such as
rainfall/snowfall percentage, distance from downtown and outskirts, and road
types, were incorporated to enhance the model's predictive power. The
methodology involves data exploration, feature transformation, and handling
missing values through low-rank models and label encoding. The proposed model
has the potential to assist city planners and governments in anticipating
traffic hot spots, optimizing operations, and identifying infrastructure
challenges.

通过商用车辆行程记录数据集，该研究旨在开发预测模型，用于美国主要城市交叉口的交通拥堵，包括正交坐标、街道名称、时间和交通指标等特征，并结合其他特征如降雨 / 降雪百分比、距离市中心和市郊的距离以及道路类型，通过数据探索、特征转换和处理缺失值的方法，提出的模型有助于帮助城市规划者和政府预测交通热点，优化运营，识别基础设施问题。