Finding the link driving schedules (LDS) for integrated traffic-emissions (EPA-MOVES) simulator by clustering with dynamic time warping measures

This study develops a novel technique to determine the link driving schedules for Motor Vehicle Emission Simulator (MOVES)2010b. The authors propose a hierarchical clustering technique with dynamic time warping similarity measures (HC-DTW) to find the link driving schedules for MOVES2010b. Test results using the data from a five-intersection corridor show that HC-DTW technique can significantly reduce emissions estimation time without compromising the accuracy. The benefits are found to be most significant when the level of congestion variation is high. This technique is also useful when real world trajectory data are available. With the availability of trajectory data irrespective of the source, HC-DTW can provide link driving schedules and the estimation using MOVES can be done with significantly shorter computational time compared with the exact approach where trajectory of each vehicle is used as input. The error difference lies between 2 to 8% for most pollutants except PM10 as found from the tests.

  • Supplemental Notes:
    • Alternate title: Finding Link Driving Schedules for Integrated Traffic Emission Simulator by Clustering with Dynamic Time-Warping Measures. This paper was sponsored by TRB committee ADC20 Transportation and Air Quality.
  • Corporate Authors:

    Transportation Research Board

    500 Fifth Street, NW
    Washington, DC  United States  20001
  • Authors:
    • Aziz, H M Abdul
    • Ukkusuri, Satish V
  • Conference:
  • Date: 2015


  • English

Media Info

  • Media Type: Digital/other
  • Features: Figures; References; Tables;
  • Pagination: 21p
  • Monograph Title: TRB 94th Annual Meeting Compendium of Papers

Subject/Index Terms

Filing Info

  • Accession Number: 01554282
  • Record Type: Publication
  • Report/Paper Numbers: 15-5060
  • Files: TRIS, TRB, ATRI
  • Created Date: Feb 26 2015 9:49AM