Resumen
To be able to predict reliably traffic conditions over the short term (15 minutes into the future) may reduce congestion on a transport system. With the emergence of large datasets comes the opportunity to test the effectiveness of pattern recognition techniques to solve complex, non-linear problems such as the one in question. This paper presents the results of applying artificial intelligence, specifically artificial neural networks (ANNs), to estimate traffic conditions a 15 minutes into the future given current / historic traffic information. For this study, data from Highways England's Motorway Incident Detection and Automatic Signalling (MIDAS) system for approximately 20 km of the M60, M62 and M602 motorway near Manchester, UK was used to build a short term prediction model. To reduce the complexity of the problem, the number of input dimensions to the model was successfully reduced using an autoencoder. The final model exhibits very good predictive power with 90% of all predictions within 2.6 veh/km/lane of observed values. The approach adopted in this research is one that can be transferred to other parts of the UK motorway network where MIDAS is installed, and once trained, the application of an ANN is straightforward. An algorithm such as the one derived has multiple applications including: refining predictions within intelligent transport systems (ITS) and / or enabling traffic controllers to take proactive decisions to mitigate the impacts of expected congestion. It could also be the engine behind a ?traffic-cast? system which could provide the public with a forecast of expected traffic conditions. This could result in reduced congestion on the transport system as accessibility to more accurate information could encourage beneficial behavioural changes in users.