Integrated Optimization of Transportation Demand Management and Traffic Operations Using Bootstrapped Support Vector Regression

Integrating optimization of transportation demand management and traffic operations strategies has been challenging since analytical models that are capable of predicting the joint impact of these strategies can hardly be formulated. On the other hand, simulation tools can capture the performance of transportation systems in response to combinations of various policies at different levels, while they are more often used for evaluation instead of optimization due to their high computational cost. This paper applies the surrogate-based optimization method, which enjoys the advantages of both simulation evaluations and mathematical optimization methods, to jointly optimize a transportation demand management policy (i.e. high-occupancy/toll, HOT) and a traffic operational strategy (i.e. freeway diversion control) for a freeway-arterial corridor (I-270 corridor) under a work zone scenario in the State of Maryland. A distribution based support vector regression model (D-SVR) is developed to take into account the observed asymmetric distribution of simulation noise. In addition, bootstrapping and expected improvement based infill strategies are incorporated into the D-SVR for the global optimization. Results of the numerical test show that the D-SVR can approximate the true response surface more accurately and the bootstrapped D-SVR converges to the global optimal solution much faster than other models. By applying the optimal solution predicted by the D-SVR in the I-270 corridor, the average trip travel time for work zone impacted vehicles is reduced by 7.66% compared to baseline during the PM peak period. With negligible cost in infrastructure construction, the total saved travel time by the D-SVR predicted optima corresponds to a yearly saving of around $11 million for the 5-hour extended PM peak period.

  • Supplemental Notes:
    • This paper was sponsored by TRB committee ADB30 Transportation Network Modeling.
  • Corporate Authors:

    Transportation Research Board

    500 Fifth Street, NW
    Washington, DC  United States  20001
  • Authors:
    • He, Xiang
    • Chen, Xiqun (Michael)
    • Xiong, Chenfeng
    • Zhu, Zheng
    • Zhang, Lei
  • Conference:
  • Date: 2015

Language

  • English

Media Info

  • Media Type: Digital/other
  • Features: Figures; References; Tables;
  • Pagination: 22p
  • Monograph Title: TRB 94th Annual Meeting Compendium of Papers

Subject/Index Terms

Filing Info

  • Accession Number: 01558275
  • Record Type: Publication
  • Report/Paper Numbers: 15-5500
  • Files: TRIS, TRB, ATRI
  • Created Date: Mar 30 2015 9:35AM