PRELONG puts forward artificial intelligence (AI), in the form of machine learning, as an accessible and capable method to predict long-term outcomes and performances of large-scale transport systems. PRELONG will showcase the capability of AI to reliably predict effects of interventions to the transport systems (as road capacity increases and changes to the road toll structure) and that much faster (in a few seconds) than current transport models. Fast computation times and easy access are potential game changers in how we utilize transport models for effective transport planning. The overall data flow includes 1) Establishing synthetic travel populations 2) Run agent-based traffic simulations and iteratively calibrate underlying parameters in the simulator c) Train a binarized DNN on the simulated traffic data d) Implement the trained DNN within an open-access and user-friendly GIS-based sketch planning tool. The scenarios of the multiple runs with the traffic simulator (MATSim) will be careful predefined by an experimental design varying a) road toll structure b) speed limit c) road capacity on single road network links d) population in different zones in the Oslo metropolitan area. To ground DNN predictions to the empirical real world, we calibrate the detailed traffic simulator against real data. This enables the simulator to credibly predict a wide range of future scenarios at a high level of resolution. These simulator predictions are then used to train a neural network that can quickly approximate complex future scenarios in a sketch planning setup. We plan to use binarized neural networks analysed and interpreted using an exact encoding into propositional logic. PRELONG is a collaboration between the Institute of Transport Economics, the Department of Informatics at the University of Bergen, the Swedish National Road and Transport Institute, the AI-company Epigram AS, as well as the Norwegian Public Road Administration.
Project leader: Stefan Flügel
Institution: TRANSPORTØKONOMISK INSTITUTT Stiftelsen Norsk senter for samferdselsforskning