Introduction Modeling travel behavior is a key aspect of demand analysis, where aggregate demand is the accumulation of individuals' decisions. In this chapter, we focus on "short-term" travel decisions. The most important short-term travel decisions include choice of destination for a non-work trip, choice of travel mode, choice of departure time and choice of route. It is important to note that short-term decisions are conditional on long-term travel and mobility decisions such as car ownership and residential and work locations. The analysis of travel behavior is typically disaggregate, meaning that the models represent the choice behavior of individual travelers. Discrete choice analysis is the methodology used to analyze and predict travel decisions. Therefore, we begin this chapter with a review of the theoretical and practical aspects of discrete choice models. After a brief discussion of general assumptions, we introduce the random utility model, which is the most c

Evaluation of Intelligent Transportation Systems (ITS) at the planning level, as well as various short-term planning projects, require the use of appropriate tools that can capture the dynamic and stochastic interactions between demand and supply. The objective of this thesis is to develop a methodological framework for such applications and implement it in the context of an existing dynamic traffic assignment system, DynaMIT. The methodological framework captures the day-to-day evolution of traffic. Furthermore, it models traveler behavior and network performance, in response to special events and situations such as incidents, weather emergencies, sport events etc. The new planning tool DynaMIT-P, consists of a supply (network performance) simulator, a demand simulator and algorithms that capture their interactions. The

Morning commuters adjust their departure times in response to day-to-day changes in congestion. Advanced Traveler Information Systems (ATIS) may enable motorists to employ fundamentally new strategies when adapting their departure times to fluctuations in congestion. At the same time, new driver strategies will likely give rise to different road network behaviors. This paper explores the mutual feedback between driver strategy and traffic system performance through a simulation model of rush hour commuting. Motorists in this model choose departure times according to three adaptive strategies. When commuters apply adaptive strategies that require ATIS in the present model, outcomes for both individual motorists and the system as a whole are by several measures worse than when drivers use a simple strategy that does not require ATIS. These results largely agree with an earlier study of a nearly identical model of rush-hour commuting. This document is available in HTML on the ...

This report describes the methodologies and procedures developed through a contract to the University of Texas at Austin, in collaboration with the University of Maryland, to address these essential needs. Specifically, a simulation-assignment methodology has been developed to describe user's path choices in the network in response to real-time information, and the resulting flow patterns that propagate through the network, yielding information about overall quality of service and effectiveness, as well as localized information pointing to problem spots and opportunities for improvement. This methodology is intended for use off-line for evaluation purposes, or on-line for prediction purpose in support of advanced traffic management functions. In additional, algorithmic procedures have been developed to determine the best paths to which users should be directed so as to optimize overall system performance. Powerful extension

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, IS. Supplementary Notes