United States Department of Transportation
i
Quantifying Uncertainty and Distributed Control for Unanticipated Traffic Patterns as a Result of Natural and Man-Made Disruptions
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2018-09-01
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[PDF-6.80 MB]
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Abstract:As new sources of traffic and related data are becoming more widely available and at a granularity that was inconceivable only a decade ago, the ability to measure traffic conditions and detect incidents has dramatically improved. Responding to such conditions in real-time via control strategies that are tailored to the nature of the incident is a natural next step in the process. But performing optimal control calculations in real-time and in a way that captures (i) uncertainties in the evolution of traffic conditions and (ii) queue build-up and dissipation dynamics in a network setting cannot be achieved with present state-of-the-art algorithms. This report presents real-time distributed network control techniques capable of utilizing various types of real-time traffic data, from both fixed and mobile sources. The work is divided into two major parts: traffic state estimation when data is limited and adaptive control. Two methodologies for traffic state estimation are presented in Section 3: (i) A conditional random fields (CRF) approach that combines mesoscopic traffic modeling with the statistical power of probabilistic graphical models to learn the traffic patterns from historical data, including both look-ahead dynamics along with vehicle interaction dynamics, and (ii) a stochastic Lagrangian model utilizing the Newell-Franklin equilibrium relation along with a second-order Gaussian approximation are developed. A new Backpressure (BP) algorithm tailored to traffic dynamics (namely, capturing queue buildup and dissipation) is developed in Section 4. The backpressure control technique developed in this report is based on macroscopic traffic flow and is referred to as position-weighted backpressure (PWBP). In Section 5.1, the authors use real world data to test the effects of traffic state estimation and network control. The NGSIM trajectory data along I-80 in the San Francisco Bay area in Emeryville, CA is used. Additionally, a microscopic traffic simulation model of an eleven-intersection network in Abu Dhabi is used to test the proposed PWBP control policy in Section 5.2. Results indicate that PWBP can accommodate higher demand levels than the other three control policies and outperforms them in terms of total network delay, congestion propagation speed, recoverability from heavy congestion, and response to an incident.
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