Anthony Patire

Job title: 
Research & Development Engineer
Partners for Advanced Transportation Technology
Lead Researcher
Having earned Master's and Bachelor's degrees in Electrical Engineering and Computer Science from the Massachusetts Institute of Technology, Anthony brings two years of experience in satellite telecommunication systems engineering, and three years of experience in embedded systems hardware design. While building an on-chip bus architecture to handle the logistical problem of moving bits between multiple consumers and producers of data, he became interested in pursuing analogous problems moving people between origins and destinations in the physical world. 
Mr. Patire completed his Ph.D. in Civil and Environmental Engineering at UC Berkeley, focusing on traffic flow theory and bottlenecks.  For the past several years he has helped to bring a number of PATH projects to completion, including Mobile Century, Mobile Millennium, and the Hybrid Traffic Data Collection Roadmap. Along the way, he has been a team leader on large scale problems that involve GPS-based probe data, data assimilation, and flow models in the context of integrated corridor management. In his present position, Mr. Patire is responsible for analysis, modeling, and simulation of the I-210 corridor as a part of the Connected Corridors Program.
Research interests: 
  • Erroneous HOV Degradation. Applied a range of machine learning methods, including both supervised classification methods, and unsupervised anomaly detection methods to detect HOV misconfiguration errors in the Caltrans data pipeline. Demonstrated that the reported degradation of HOV-lane facilities is greater than the actual degradation, and that potential exists to use machine learning to improve the performance measures that inform operating policies for HOV-lane facilities.

  • Multiple ICM Corridor Management. Formulated strategies in which multiple, adjacent Integrated Corridor Management (ICM) projects may work together. Identified situations in which traffic management decisions on one corridor may affect a nearby corridor.

  • Hybrid Data Implementation. Created a strategic roadmap for Caltrans to integrate third-party, travel-time data with Performance Measurement System (PeMS) data to reduce costs and increase coverage of traffic monitoring, improve existing deployment of point-based sensors, and provide a methodology to calculate delay from a flexible mix of data types. Investigated methods using traditional traffic theory, adaptive smoothing, and machine learning. Demonstrated the performance of the algorithms for a range of operating conditions and infrastructure categories.