01698293

Component

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.

This paper was sponsored by TRB committee ADB30 Standing Committee on Transportation Network Modeling.

19-04783

English

20p

2019

Transportation Research Board 98th Annual Meeting

Digital/other

Figures; References; Tables

Lagrangian functions; Markov processes; Mobile communication systems; Sensors; Stochastic programming; Traffic signal control systems; Traffic signal controllers

Highways; Operations and Traffic Management; Planning and Forecasting

Transportation Research Board Annual Meeting 2019 Paper #19-04783

TRIS, TRB, ATRI

Dec 7 2018 9:51AM