METRANS UTC

Fine grained "automatic vehicle classification" system development for accurately measuring passenger-freight interactions

Project Number

16-13

Project Summary

This research proposal targets the design of vehicular road sensing networks used in the framework for Intelligent Transportation Systems (ITS). The PI will develop machine-learning models, optimized powersaving algorithms, communications protocols, and a low-power sensing platform to yield a novel and modular multi-node system for the purpose of “automatic vehicular detection and classification” (motorcycles, passenger cars, buses, trucks, etc.). The PI proposes to create smart highways by implanting wireless Micro-Electro-Mechanical System (MEMS) sensors, which will act like neurons to collect traffic data for vehicular movement. The proposed smart sensing and data interpretation system for smart roadways will be scalable, significantly cost-effective, maintain a small foot-print, and will be capable of detecting and classifying a vehicle in real-time. This research will focus on all levels of system design from architecture to computation to communication design.

Project Status

Complete

Year

2016

Topic Area

Integrated Freight and Passenger Systems

P.I. Name & Address

Assistant Professor, Department of Electrical Engineering; College of Engineering
California State University, Long Beach
1250 Bellflower Blvd.
ECS-521
Long Beach, CA 90840-8306
United States
mohammad.mozumdar@csulb.edu

Funding source: California Department of Transportation (Caltrans)

Funding amount: $87,703

Start date: 3/2/2017

End date: 6/30/2018