Stephen G. Ritchie

TL;DR: Results indicate the feasibility of employing the proposed approach to estimate time-varying mandatory lane-changing fractions as well as queue lengths during incidents, and can be used not only in better understanding the phenomena of incident-related inter-lane and intra-lane traffic characteristics, but also in developing real-time incident management technologies.

TL;DR: The proposed methodology based on loop detector data enables to identify collision potentials in real time and would be a valuable tool for operating agencies in developing various strategies and policies toward enhancements of traffic safety.

TL;DR: The methodology developed is able to classify pavement surface cracking by the type, severity, and extent of cracks detected in video images using an integration of artificial neural network models with conventional image-processing techniques.

TL;DR: This paper formulates and solves the vehicle reidentification problem as a lexicographic optimization problem with the potential to yield reliable section measures such as travel times and densities, and enables the measurement of partial dynamic origin/destination demands.

TL;DR: Experimental results indicate that the new PNN-based algorithm is competitive with the Multi Layer Feed Forward (MLF) architecture, which was found in previous studies to yield superior incident detection performance, while being significantly faster to train.