MARC 主機 00000nam  2200409   4500 
001    AAI3433948 
005    20120313143401.5 
008    120313s2011    ||||||||||||||||| ||eng d 
020    9781124401706 
035    (UMI)AAI3433948 
040    UMI|cUMI 
100 1  Li, Mingxin 
245 10 Modeling network-wide impacts of traffic bottleneck 
       mitigation strategies under stochastic capacity conditions
300    169 p 
500    Source: Dissertation Abstracts International, Volume: 72-
       02, Section: B, page: 1042 
500    Adviser: Xuesong Zhou 
502    Thesis (Ph.D.)--The University of Utah, 2011 
520    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 
520    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 
520    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 to perfect traffic information every day, and (2) 
       those with knowledge of the expected traffic conditions 
       across different days. Using a gap function framework, two
       mathematical programming models are further formulated to 
       describe the route choice behavior of the perfect 
       information and expected travel time user classes under 
       stochastic day-dependent travel time 
520    This dissertation also presents adaptive day-to-day 
       traveler learning and route choice behavioral models under
       the travel time variability. To account for different 
       levels of information availability and cognitive 
       limitations of individual travelers, a set of "bounded 
       rationality" rules are adapted to describe route choice 
       rules for a traffic system with inherent process noise and
       different information provision strategies. In addition, 
       this dissertation investigates a fundamental problem of 
       quantifying travel time variability from its root sources:
       stochastic capacity and demand variations that follow 
       commonly used log-normal distributions. The proposed 
       models provide theoretically rigorous and practically 
       usefully tools to understand the causes of travel time 
       unreliability and evaluate the system-wide benefit of 
       reducing demand and capacity variability 
590    School code: 0240 
650  4 Engineering, Civil 
650  4 Transportation 
690    0543 
690    0709 
710 2  The University of Utah.|bCivil and Environmental 
773 0  |tDissertation Abstracts International|g72-02B 
856 40 |u