Real Time Optimization of Network Control Strategies in DynaMIT2.0

Traffic congestion is a pervasive problem in most urban transportation networks worldwide owing to significant supply and demand side uncertainty. Unfortunately, network capacity augmentation through infrastructure expansion is seldom a feasible solution due to financial, environmental and space restrictions. Consequently, emphasis is shifting towards alleviating network congestion through a more efficient utilization of existing infrastructure. In this context, this paper proposes a framework that integrates the real time optimization of network control strategies (tolls, ramp metering rates, etc.) with the generation of predictive travel time information within DynaMIT2.0, a state of the art Dynamic Traffic Assignment (DTA) model. The proposed approach is novel in that it takes into account the network state estimated using heterogeneous real time data from a variety of sources and short term predictions that incorporate the behavioral response of travelers to the provided travel time guidance and control strategies. This ensures that the optimal control strategies are based on state predictions and that the actual outcomes (traffic conditions) are consistent with the expectation of users when responding to the predictive information/network control. The real time optimization capability of DynaMIT2.0 is demonstrated through an application to the specific problem of optimizing tolls on selected network links. The fixed demand dynamic toll optimization problem is formulated as a non-linear programming problem to minimize predicted network travel times. A scalable efficient genetic algorithm is applied to solve this problem that exploits parallel computing. Numerical experiments conducted on the network of expressways and major arterials in Singapore investigate the performance of the proposed algorithm. In addition, a case study illustrates the benefits of real time optimization of Singapore Electronic Road Pricing (ERP) toll rates and provision of predictive guidance through DynaMIT2.0 in mitigating network congestion under a non-recurrent incident scenario.

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

    Transportation Research Board

    500 Fifth Street, NW
    Washington, DC  United States  20001
  • Authors:
    • Gupta, Samarth
    • Seshadri, Ravi
    • Atasoy, Bilge
    • Pereira, Francisco Câmara
    • Wang, Shi
    • Vu, Vinh-An
    • Tan, Gary
    • Dong, Wang
    • Lu, Yang
    • Antoniou, Constantinos
    • Ben-Akiva, Moshe
  • Conference:
  • Date: 2016


  • English

Media Info

  • Media Type: Digital/other
  • Features: Figures; Maps; References; Tables;
  • Pagination: 17p
  • Monograph Title: TRB 95th Annual Meeting Compendium of Papers

Subject/Index Terms

Filing Info

  • Accession Number: 01594094
  • Record Type: Publication
  • Report/Paper Numbers: 16-5560
  • Files: TRIS, TRB, ATRI
  • Created Date: Jan 12 2016 6:25PM