Path-based traffic flow prediction


Efstratios Karkanis, Nikos Pelekis, Eva Chondrodima and Yannis Theodoridis


Predicting traffic flow on a road network is crucial in various aspects of the transportation domain, encompassing safety and logistics. This paper introduces an innovative approach to forecast traffic flow, with a specific emphasis on predicting future traffic along paths within a road network. The proposed framework is tailored to accept GPS trajectory data as input, generating time series data illustrating traffic flow along designated pathways. It achieves this by utilizing only a subset of trajectories that strictly follow the paths without detours. This intuitive approach results in training data that better captures the essence of the forecasting problem. In the final step of our methodology, we employ state-of-the-art time series forecasting methods, including ensemble trees and recurrent neural networks. To validate the effectiveness of our approach, we evaluate it using a real-world dataset, demonstrating its capability in predicting traffic flow.

Other Publications
BMDA Workshop EDBT Conference 2024