  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%.
