Bayesian Stochastic Kriging Metamodel for Active Traffic Management of Corridors

Abstract

The transportation system, particularly commuting corridors of freeways and major arterials in metropolitan areas are congested. The purpose of implementing active traffic management (ATM) strategies, e.g. high-occupancy/toll lane management and congestion warning ahead of freeway diversion points, is primarily to improve the corridor-wide performance. Utilizing a simulation-based dynamic traffic assignment model, this paper proposes a Bayesian stochastic Kriging metamodel to optimize integrated planning and operational ATM strategies for corridors. Since transportation simulations are influenced by a variety of uncertainties, e.g. random seed, signal timing, route choice behaviors and etc., we observe stochastic outputs given the same input setting. The developed approach accounts for model uncertainty raised by stochastic travel behaviors and the induced heteroscedasticity in random simulation errors. The simulation based optimization approach is tested both in a synthetic network and a real-world corridor of I-270 (freeway) and MD-355 (arterial) in the State of Maryland. Field measurements by fixed traffic flow detections are used to calibrate the travel demand and supply model. Results show that the joint optimization of travel demand management and operational strategies is promising to reduce the corridor-wide average travel time and enhance the vehicle throughput.

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