Congestion Reduction via Personalized Incentives [supporting dataset]

The purpose of this research is to develop real-time algorithms to reduce traffic congestion and improve routing efficiency via offering personalized incentives to drivers. The incentives and alternative routes should be chosen smartly in order to maximize the probability of acceptance by drivers and to avoid the creation of new congestion in other areas of the network. To this end, the authors propose to exploit the wide-accessibility of smart communication devices and develop a real-time look-ahead incentive offering mechanism using individuals’ routing and aggregate traffic information. The proposed approach relies on historical data and state-of-the-art traffic prediction methodologies to continually predict congestion and traffic flow of the network. Using this prediction and based on individual preferences, the central controller offers personalized incentives to drivers with the goal of reducing the probability of congestion. The decisions about incentives are made via solving a series of carefully designed large-scale stochastic optimization problems. The performance of the proposed algorithms are evaluated using data from the Los Angeles area. Finally, the authors evaluate the performance of their method using data from the Los Angeles area. The Los Angeles region is ideally suited for being the validation area since there are a number of dedicated carpool lanes in the region and furthermore, there are portions of the freeway network where congestion pricing is employed with the added feature that ridesharing vehicles can travel on these lanes free of charge (e.g., I-110). Additionally, researchers at the University of Southern California (USC) have developed the Archived Data Management System (ADMS) that collects, archives, and integrates a variety of transportation datasets from Los Angeles, Orange, San Bernardino, Riverside, and Ventura Counties.

• Record URL:
  https://doi.org/10.7922/G2KD1W6J
• Dataset URL:
  https://doi.org/10.5061/dryad.ncjsxkst8
• Dataset URL:
  https://rosap.ntl.bts.gov/view/dot/61748
• Record URL:
  https://rosap.ntl.bts.gov/view/dot/59105
• Supplemental Notes:
  • The dataset supports report: Congestion Reduction via Personalized Incentives, available at the URL above. This document was sponsored by the U.S. Department of Transportation, University Transportation Centers Program.
• Corporate Authors:

  METRANS Transportation Center

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

  National Center for Sustainable Transportation

  University of California, Davis
  Davis, CA  United States 

  Office of the Assistant Secretary for Research and Technology

  University Transportation Centers Program
  Department of Transportation
  Washington, DC  United States  20590

  California Department of Transportation

  Division of Research, Innovation and System Information
  1727 30th Street, MS 83
  Sacramento, CA  United States  95816
• Authors:
  • Ghafelebashi, Ali
  • 0000-0001-8339-7960
  • Razaviyayn, Meisam
  • 0000-0003-4342-6661
  • Dessouky, Maged
  • 0000-0002-9630-6201
• Publication Date: 2021-4-21

Language

• English

Media Info

• Media Type: Dataset
• Dataset publisher:

  Dryad

  ,    

Subject/Index Terms

• TRT Terms: Algorithms; Congestion management systems; Incentives; Optimization; Routes and routing; Traffic congestion; Traffic data; Validation
• Geographic Terms: Los Angeles (California)
• Subject Areas: Data and Information Technology; Highways; Operations and Traffic Management;

Filing Info

• Accession Number: 01776286
• Record Type: Publication
• Contract Numbers: Caltrans 65A0686 Task Order 043; USDOT Grant 69A35
• Files: UTC, NTL, TRIS, ATRI, USDOT, STATEDOT
• Created Date: Jul 8 2021 4:31PM