Smart Sensing System for Real-time Automatic Traffic Analysis of Highway Rest Areas

State transportation agency spends millions of dollars yearly to maintain and improve the service provided to the travelers in the highway rest areas. Getting real time high grained traffic data (from passenger-freight vehicles) could reveal uses of the rest area. Therefore, it could be help immensely to devise better policy to maintain and extend services provided in each of the rest areas. Transportation agencies don’t have Intelligent Transportation System (ITS) that can perform “automatic” and “real time” vehicle identification and classification for highway rest areas. Motivated by a dire need to enhance and modernize the transportation system, the Principal Investigator (PI) proposes an advanced modular system that will bring smart sensing technology to understand rest area traffic pattern efficiently. Currently, California Department of Transportation (Caltrans) collects traffic data from Automated Vehicle Classification (AVC) stations and manual census counts at specific locations to monitor and manage traffic. This technology is expensive, time-consuming, and disruptive to install as well as costly to maintain, therefore it has not been deployed widely.   In the last decade, significant improvements have been achieved in Microelectromechanical systems (MEMS) sensors domain with respect to size, cost and accuracy. Moreover, extreme miniaturization of radio frequency (RF) transceivers and low power microcontrollers motivated the development of small and low power sensors and radio equipped modules, which are now replacing traditional wired sensor systems. These modules which are often called “sensor mote” (size of a quarter) can communicate with other sensor nodes and build an intelligent sensing network. Because of the miniaturization and low power consumption, these sensor motes can remain functional years after years with low power budget. Motivated by these novel advances, the project proposes a wireless MEMS sensor based automatic vehicle classification and identification system for highway rest area.   The proposed Automatic Vehicle Classification and Identification (AVCI) system is mainly composed of two sub parts. Firstly, AVCI sensor nodes which contain magneto-resistive and accelerometer sensors for calculating speed and axles respectively. Secondly, Access Point (AP) which collects filtered sensor data from sensor motes to calculate speed, axles count also classify them based on Federal Highway Administration (FHWA) 13-categories Scheme-F[5]. The AP contains RF transceiver to communicate with sensor motes and a GPRS (General Packet Radio Service) shield to send aggregated traffic data to the county or regional traffic data collection center. The proposed research plan outlined is based on recent collaborative research experiences with METRANS and transportation agency Caltrans (California Department of Transportation).  


    • English


    • Status: Active
    • Contract Numbers:


    • Sponsor Organizations:

      METRANS Transportation Center

      University of Southern California
      Los Angeles, CA  United States  90089-0626

      National Center for Metropolitan Transportation Research

      University of Southern California
      650 Childs Way, RGL 107
      Los Angeles, CA  United States  90089-0626

      California Department of Transportation

      1227 O Street
      Sacramento, CA  United States  95843

      Office of the Assistant Secretary for Research and Technology

      University Transportation Centers Program
      Department of Transportation
      Washington, DC  United States  20590
    • Project Managers:

      Feldman, Doug

    • Principal Investigators:

      Mozumdar, Mohammad

    • Start Date: 20170930
    • Expected Completion Date: 20180930
    • Actual Completion Date: 0

    Subject/Index Terms

    Filing Info

    • Accession Number: 01642996
    • Record Type: Research project
    • Source Agency: National Center for Metropolitan Transportation Research
    • Contract Numbers: 17-11
    • Files: UTC, RiP
    • Created Date: Aug 1 2017 7:14PM