Modeling network-wide impacts of traffic bottleneck mitigation strategies under stochastic capacity conditions

Abstract

Traffic congestion occurs because the available capacity cannot serve the desired demand on a portion of the roadway at a particular time. Major sources of congestion include recurring bottlenecks, incidents, work zones, inclement weather, poor signal timing, and day-to-day fluctuations in normal traffic demand. This dissertation addresses a series of critical and challenging issues in evaluating the benefits of Advanced Traveler Information Strategies under different uncertainty sources. In particular, three major modeling approaches are integrated in this dissertation, namely: mathematical programming, dynamic simulation and analytical approximation. The proposed models aim to 1) represent static-state network user equilibrium conditions, knowledge quality and accessibility of traveler information systems under both stochastic capacity and stochastic demand distributions; 2) characterize day-to-day learning behavior with different information groups under stochastic capacity and 3) quantify travel time variability from stochastic capacity distribution functions on critical bottlenecks. First, a nonlinear optimization-based conceptual framework is proposed for incorporating stochastic capacity, stochastic demand, travel time performance functions and varying degrees of traveler knowledge in an advanced traveler information provision environment. This method categorizes commuters into two classes: (1) those with access

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