Clustering Dynamics for Improved Speed Prediction Deriving from Topographical GPS Registrations
This work addresses missing data challenges in Intelligent Transportation Systems, offering a domain-specific solution for traffic analysis.
The paper tackled the problem of predicting traffic speeds in regions with scarce GPS data by using topographical and road design features, achieving qualitative and quantitative improvements over existing regression methods.
A persistent challenge in the field of Intelligent Transportation Systems is to extract accurate traffic insights from geographic regions with scarce or no data coverage. To this end, we propose solutions for speed prediction using sparse GPS data points and their associated topographical and road design features. Our goal is to investigate whether we can use similarities in the terrain and infrastructure to train a machine learning model that can predict speed in regions where we lack transportation data. For this we create a Temporally Orientated Speed Dictionary Centered on Topographically Clustered Roads, which helps us to provide speed correlations to selected feature configurations. Our results show qualitative and quantitative improvement over new and standard regression methods. The presented framework provides a fresh perspective on devising strategies for missing data traffic analysis.