Abstract:
Traffic speed prediction is one of the challenging task and has many applications. Existing solutions either use crowd-sourced data or sophisticated technologies to perform the task, and hence are costly and unreliable. In this thesis, we propose a machine learning technique that uses the public transport movement data to predict the speed/congestion on a given road segment. Specifically, we use DIMTS buses movement data that comes in the form of GPS trajectories. The technique, we call as STH-Model (Spatio-Temporal-Historical Model), is based on CNN and LSTM models and captures the local spatial dynamics and temporal speed trends for its prediction task. We demonstrate the efficacy of our approach on real-time DIMTS data
