Stochastic Dynamic Sensor Location Problem with Efficient Solutions

This paper integrates automated traffic signal operation and sensor location problem in a connected vehicle environment with advanced data analytics. As a variant of mobile facility location problem, this study optimally allocates road-side sensors connected to traffic signal controllers to extend green light to prevent queue spillback, considering the future predicted delay of each intersection over the course of the day. Although previously developed author’s two-stage stochastic programming model provides scenario-based solutions with better performance than deterministic model, the high relocation cost has made researchers overlook the benefit of dynamic sensor relocation. With scheduling of autonomous robots, the synchronously commanding robots, drones, autonomous vehicles will present a reliable performance. This research develops multi-period stochastic problem, considering the future sensor locations given budget constraints on the sensor costs and relocation costs, and the effect of control is tested on various demand profiles and penetration rates of an urban transportation network. The curse of dimensionality problem with growing network size is handled with tradeoff between solution quality and computational efficiency. A subproblem decomposed by Lagrangian relaxation enhanced with valid cuts has a better bound, and a variable neighborhood search algorithm quickly finds solutions. Dynamic model that constrain a restricted relocation present higher savings compared to the stationary model without sensor relocation. The flexible relocation model guarantees higher savings than restricted model by achieving the same maximum savings with fewer number of sensors. The gap between two dynamic models decreases when more sensors are available.

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

    Transportation Research Board

  • Authors:
    • Park, Hyoshin
    • Haghani, Ali
  • Conference:
  • Date: 2019


  • English

Media Info

  • Media Type: Digital/other
  • Features: Figures; References; Tables;
  • Pagination: 20p

Subject/Index Terms

Filing Info

  • Accession Number: 01698293
  • Record Type: Publication
  • Report/Paper Numbers: 19-04783
  • Files: TRIS, TRB, ATRI
  • Created Date: Dec 7 2018 9:51AM