Urban traffic flow prediction, a spatial-temporal approach

Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial TechnologiesCurrent advances in computational technologies such as machine learning combined with traffic data availability are inspiring the development and growth of intelligent transport Systems (ITS). As urban authorities strive for efficient traffic systems, traffic forecasting is a vital element for effective control and management of traffic networks. Traffic forecasting methods have progressed from traditional statistical techniques to optimized data driven methods eulogised with artificial intelligence. Today, most techniques in traffic forecasting are mainly timeseries methods that ignore the spatial impact of traffic networks in traffic flow modelling. The consideration of both spatial and temporal dimensions in traffic forecasting efforts is key to achieving inclusive traffic forecasts. This research paper presents approaches to analyse spatial temporal patterns existing in networks and goes on to use a machine learning model that integrates both spatial and temporal dependency in traffic flow prediction. The application of the model to a traffic dataset for the city of Singapore shows that we can accurately predict traffic flow up to 15 minutes in advance and also accuracy results obtained outperform other classical traffic prediction methods