Predicting traffic congestion using driver behavior

Predicting Traffic Congestion with Driving Behavior

Predicting Severity of Road Traffic Congestion Using Semantic Web Technologies

Predictive reasoning, or the problem of estimating future observations given some historical information, is an important inference task for obtaining insight on cities and supporting efficient urban planning. This paper, focusing on transportation, presents how severity of road traffic congestion can be predicted using semantic Web technologies. In particular we present a system which integrat...

Modeling Naturalistic Driver Behavior in Traffic Using Machine Learning

This research is focused on driver behavior in traffic, especially during car-following situations and safety critical events. Driving behavior is considered as a human decision process in this research which provides opportunities for an artificial driver agent simulator to learn according to naturalistic driving data. This thesis presents two mechine learning methodologies that can be applied...

Predicting Driver Behavior during the Yellow Interval Using Video Surveillance

At a signalized intersection, drivers must make a stop/go decision at the onset of the yellow signal. Incorrect decisions would lead to red light running (RLR) violations or crashes. This study aims to predict drivers' stop/go decisions and RLR violations during yellow intervals. Traffic data such as vehicle approaching speed, acceleration, distance to the intersection, and occurrence of RLR vi...

Traffic congestion, type A behavior, and stress.