Multi‐modal arterial performance measurement using multi‐source ITS data
PI: Yao-Jan Wu, University of Arizona
$120,000 ($60,000 UCCONNECT, $60,000 matching funds)
Abstract: Due to recent advances in Intelligent Transportation Systems (ITS) technologies, an increasing number of public and private transportation agencies have begun to invest in traffic data collection systems. The ITS traffic data collected provides these agencies with a range of different possibilities for measuring roadway performance and improving traffic flow. However, transportation data are ubiquitous and collected from multiple sources, so full utilization of the multi‐source data remains challenging because of its complexity. This project seeks to develop a best practice protocol for collecting multiple sources of ITS data and demonstrate their optimal utilization to measure multi‐modal arterial performance. The primary ITS data source will be the travel time data collected by in‐house Bluetooth detectors installed on major arterial corridors in Tucson, Ariz. The classification of Bluetooth‐based travel time data for different modes of transportation (bicycles, pedestrians, transit and auto vehicles) is not straightforward, so a data‐driven approach is proposed to fully utilize this multi‐source ITS data. The results and products of this project are expected to enhance Bluetooth‐based travel time estimation for multi‐modal arterial performance measurement.