Urban Arterial Traffic Volume and Travel Time Estimation with Use of Data Driven Models

Abstract

In recent years, the development of technology and the availability of large amounts of data have transformed the way transport systems are managed, particularly through the forecasting of traffic conditions. This work aims to develop traffic prediction models, with a specific focus on traffic volume and travel time of an urban arterial. These models are based on Machine Learning algorithms, which find frequent application in the literature for various forecasting tasks. The traffic data that were utilized in the development process were sourced from a road section of Alexandras Av. in Athens. According to the results, the models are capable of accurate predictions with an acceptable fit to the data. Finally, a comparison is drawn between Machine Learning models, the BPR Volume-Delay function, and a BPR-ML hybrid approach. Ultimately, the findings reveal that Machine Learning methods exhibit superior forecasting capabilities.