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

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