November 18, 2011

Embedded Sensing of Transportation Networks

Speaker(s)

Dr. Ram Rajagopal, Assistant Professor, Department of Civil and Environmental Engineering, Stanford University

Abstract

Optimizing planning and operations of existing transportation networks requires an accurate measurement of its state. Some common types of measurement systems are loop detectors and video cameras. These approaches do not provide a complete characterization of the state of the network. Moreover, they are expensive to install and operate. In this talk we describe an alternative measurement platform: road embedded wireless sensor nodes. Combined with new algorithms we have designed, such nodes are capable of providing travel times, queue lengths, vehicle classes and vehicle loads. I will present various algorithms for estimating these quantities and if time permits, a provably optimal decentralized control algorithm for coordinating traffic signals based on the novel measurements. Some of the key advantages of the technology we present are its high reliability, novel measurement capabilities and low installation and operating cost.

Biographical Sketch

Ram Rajagopal is Assistant Professor of Civil and Environmental Engineering at Stanford University. He also coordinates the Stanford Sustainable Systems laboratory. Dr. Rajagopal’s research focuses on developing sensing platforms, statistical data mining and signal processing algorithms and optimization and control systems for monitoring and controlling large infrastructure systems, in particular, to improve renewable energy integration and control, increase building energy efficiency, and optimize transportation energy use. His work has received various awards, and led to several publications, more than 40 patents, 10 commercial products and 3 startup companies. Prior to joining Stanford University, Ram was a DSP research engineer at National Instruments, where he created industrial machine vision, controls and embedded systems products, and a visiting researcher at IBM Research, where he worked on analytics for early warning systems. He holds a Ph.D. in Electrical Engineering and Computer Science and an M.A. in Statistics, both from the University of California Berkeley, and a Bachelor’s in Electrical Engineering from Federal University of Rio de Janeiro.