Short-term traffic predictions on large urban traffic networks: applications of network-based machine learning models and dynamic traffic assignment models

The paper discusses the issues to face in applications of short-term traffic predictions on urban road networks and the opportunities provided by explicit and implicit models. Different specifications of Bayesian Networks and Artificial Neural Networks are applied for prediction of road link speed and are tested on a large floating car data set. Moreover, two traffic assignment models of different complexity are applied on a sub-area of the road network of Rome and validated on the same floating car data set

Comparing Emission and Traffic Flow models of different categories

Emission modelling is one of the key applications of traffic simulation because it allows for the detailed evaluation of ITS and other traffic measures before implementation. In order to assess the outcomes correctly it becomes necessary to compare the different emission and traffic models for their applicability to different scenarios. This paper compares two different traffic models and three different emission models of diverse origins in an urban and a highway scenario