A significant body of research on advanced techniques for automated freeway incident detection has been conducted at the University of California, Irvine (UCI). Such advanced pattern recognition techniques as artificial neural networks (ANNs) have been thoroughly investigated and their potential...

In this project (MOU 336),an initial phase of a field implementation was accomplished of the results of a previous research project (MOU 224),in which a vehicle reidentification algorithm based on loop signature analysis was developed using freeway traffic data.This algorithm was extended to...

This research addressed the first year of a proposed multi-year research effort that would investigate, assess, and develop neural network models from the field of artificial intelligence for automated detection of non- recurring congestion in integrated freeway and signalized surface street...

This report presents the results of Phase I of a multi-year research effort on "Field Investigation of Advanced Vehicle Reidentification Techniques and Detector Technologies," and extends previous PATH research by the authors on MOU 336 "Section-Related Measures of Traffic System Performance:...

This research involved an important extension of existing field-implemented and tested PATH research by the authors on individual vehicle reidentification, to develop methods for assessing freeway and arterial (and transit) system performance for the Caltrans PeMS (Performance Measurement...

In this research, the authors introduce and define a universal incident detection framework that is capable of fulfilling all components of a set of recognized needs. An algorithm is presented that has the potential to fulfill the defined universality requirements. It is a modified form of a...

Travel time is the reciprocal of speed and is a useful measure of road congestion and traffic system performance. Travel time is also a basic traffic variable that is used in many Intelligent Transportation System (ITS) strategies such as route guidance, incident detection, and traveler...

Knowledge of vehicle classes is especially useful for monitoring commercial vehicles (CVs). Accurate CV class information will enhance truck traffic surveillance and fleet management, such as in port areas by providing information for environmental impact investigations. From an implementation...

The San Pedro Bay Ports (SPBP) of Los Angeles and Long Beach in Southern California is one of the largest port container complexes in the world, and the largest one is the United States. To decrease the air pollution associated with port operations, a number of measures have been adopted,...

The goal of this paper is to estimate the health impacts resulting from exposure to PM and NOx emitted by train operations in the Alameda corridor, a crucial rail link that serves the Ports of Los Angeles and Long Beach, also known as the San Pedro Bay Ports (SPBP). We link a pollutant...