Road traffic congestion prediction is a crucial component of intelligent transportation systems, since it enables proactive traffic management, enhances suburban experience, reduces environmental impact, and improves overall safety and efficiency. Although there are several public datasets, especially for metropolitan areas, these datasets may not be applicable to practical scenarios due to insufficiency in the scale of data (i.e. number of sensors and road links) and several external factors like different characteristics of the target area such as urban, highways and the data collection location. To address this, this paper introduces a novel IBB Traffic graph dataset as an alternative benchmark dataset to mitigate these limitations and enrich the literature with new geographical characteristics. IBB Traffic graph dataset covers the sensor data collected at 2451 distinct locations. Moreover, we propose a novel Road Traffic Prediction Model that strengthens temporal links through feature engineering, node embedding with GLEE to represent inter-related relationships within the traffic network, and traffic prediction with ExtraTrees. The results indicate that the proposed model consistently outperforms the baseline models, demonstrating an average accuracy improvement of 4%.

翻译：道路交通拥堵预测是智能交通系统的关键组成部分，因为它能够实现主动交通管理、提升郊区出行体验、减少环境影响，并提高整体安全与效率。尽管存在多个公开数据集，特别是针对大都市区域的数据集，但由于数据规模（即传感器数量和道路链路数）的不足，以及目标区域特性（如城市、高速公路）和数据采集位置等多种外部因素的影响，这些数据集可能不适用于实际场景。为解决这一问题，本文引入了一种新颖的IBB交通图数据集作为替代基准数据集，以缓解这些局限性，并通过新的地理特征丰富相关研究文献。IBB交通图数据集覆盖了2451个不同位置采集的传感器数据。此外，我们提出了一种新颖的道路交通预测模型，该模型通过特征工程强化时间关联、使用GLEE进行节点嵌入以表征交通网络内的相互关联关系，并采用ExtraTrees进行交通预测。结果表明，所提出的模型在各项基准测试中均表现优异，平均准确率提升了4%。