Reducing Truck Emissions and Improving Truck Fuel Economy via Intelligent Transportation System Technologies

The aim of this project is to use intelligent transportation system (ITS) technologies that take into account the presence of trucks in the traffic flow, in order to improve impact on the environment by reducing fuel consumption and pollution levels in areas where the truck volume is relatively high. The work is divided into two parts. In the first part, the authors propose an integrated variable speed limit (VSL), ramp metering (RM) and lane change (LC) controller using feedback linearization. The proposed integrated controller keeps the bottleneck flow at the maximum level and homogenizes the density and speed of the traffic flow along the highway sections. This improvement of the traffic flow characteristics lead to improved fuel economy and reduction in tailpipe emissions of both trucks and passenger vehicles. In order to evaluate the performance of the integrated traffic controller, a microscopic traffic simulation network of the I-710 highway, which is connected to the Ports of Long Beach/Los Angeles and has high truck volume, is developed. The authors use Monte-Carlo traffic flow simulations to demonstrate that the integrated traffic controller can generate consistent improvements with respect to travel time, safety, fuel economy and emissions under different traffic conditions. In the second part, they compared the proposed feedback linearization controller with the widely-used model predictive traffic controller in terms of performance and robustness with respect to perturbations on traffic demand, model parameters and measurement noise. Results show that both controllers are able to improve the total time spent, which leads to improvements in fuel economy and emissions, under different levels of perturbation and noise. The feedback linearization controller however, guarantees good performance and robustness properties than the model predictive controller with much less computational effort.

• Summary URL:
  https://doi.org/10.7922/G2GB229Q
• Record URL:
  https://escholarship.org/uc/item/2rr3b8mz
• 
• Summary URL:
  https://rosap.ntl.bts.gov/view/dot/66959
• Record URL:
  https://rosap.ntl.bts.gov/view/dot/64627
• Supplemental Notes:
  • This document was sponsored by the U.S. Department of Transportation, University Transportation Centers Program.
• Corporate Authors:

  University of Southern California, Los Angeles

  Department of Electrical Engineering
  3740 McClintock Avenue
  Los Angeles, CA  United States  90089-2562

  National Center for Sustainable Transportation

  One Shields Avenue
  Davis, CA  United States  95616

  METRANS Transportation Center

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

  Office of the Assistant Secretary for Research and Technology

  University Transportation Centers Program
  Department of Transportation
  Washington, DC  United States  20590
• Authors:
  • Ioannou, Petros
  • Zhang, Yihang
• Publication Date: 2018-1

Language

• English

Media Info

• Media Type: Digital/other
• Features: Figures; References; Tables;
• Pagination: 37p

Subject/Index Terms

• TRT Terms: Feedback control; Fuel consumption; Lane changing; Monte Carlo method; Pollutants; Ramp metering; Traffic flow; Trucks; Variable speed limits
• Identifier Terms: Interstate 710; Port of Long Beach
• Uncontrolled Terms: Predictive control
• Geographic Terms: Los Angeles Metropolitan Area
• Subject Areas: Energy; Environment; Freight Transportation; Highways; Planning and Forecasting; Vehicles and Equipment;

Filing Info

• Accession Number: 01690077
• Record Type: Publication
• Files: BTRIS, UTC, NTL, TRIS, RITA, ATRI, USDOT
• Created Date: Dec 27 2018 3:43PM