Abstract
It is widely acknowledged that traffic information has the potential of increasing the reliability in road networks and in alleviating congestion and its negative environmental and societal side effects. However, for these beneficial collective effects to occur, reliable and accurate traffic information is a prerequisite. Building on previous research, this article presents a reliable framework for online travel time prediction for freeways, which could, for example, be used to generate traffic information messages on so-called dynamic route information panels on freeways. Central in this framework is a so-called state-space neural network (SSNN) model, which learns to predict travel times directly from data obtained from real time traffic data collection systems. In this article we show that by using an ensemble of SSNN models also a measure for the reliability of each prediction can be produced. This enables traffic managers to monitor in real time the reliability of this system without actually measuring travel times.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
