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.
Short-term traffic predictions on large urban traffic networks: Applications of network-based machine learning models and dynamic traffic assignment models
01.06.2015
1640597 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
Dynamic Traffic Assignment Models
Springer Verlag | 1992
|Traffic Models for Dynamic Assignment
British Library Conference Proceedings | 1992
|Traffic models for dynamic assignment
British Library Conference Proceedings | 1992
|Analysis of Traffic Models for Dynamic Equilibrium Traffic Assignment
British Library Conference Proceedings | 1998
|Calibration of mesoscopic traffic simulation models for dynamic traffic assignment
DSpace@MIT | 2002
|