The arrival (or departure) information of each vehicle on a detector can be thought of as a time series waveform . Given the high…
Our deep learning approach (called InterTwin as a short form for Intersection Twin) uses a large amount of data from different intersection…
Coordination involves synchronizing multiple intersections to enhance the operation of directional movements in a system. Typically, the…
The focus of our work is on using data just from loop detectors placed at intersections, for estimating queue lengths. Such loop detector…
Turn movement counts (TMCs) are used for a wide variety of applications related to intersection analyses, intersection design, and transport…
Our goal in this paper is to use large scale loop detector data for detecting traffic interruptions. With the advent of new systems, loop…
Our goal in this paper is to find the best mapping of detectors to phases and to classify detectors as stop bar detectors or advance…
Pervasive computing is changing the monitoring landscape for patients to communicate their healthcare information in real-time to clinicians…
We describe a system that leverages machine learning methodologies for data collected from a large number of intersections to derive key…
This work presents a novel framework that combines processing of high resolution controller log data pertaining to certain performance…
Our goal in this paper is to use loop detector data for detecting traffic interruptions. Loop detector data are now widely available to…
