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Innovations in Travel Demand Modeling, Volume 2: Papers (2008)

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Suggested Citation:"T57054 txt_072.pdf." National Academies of Sciences, Engineering, and Medicine. 2008. Innovations in Travel Demand Modeling, Volume 2: Papers. Washington, DC: The National Academies Press. doi: 10.17226/13678.
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activity-travel scheduling decisions, within-day reschedul- ing, and learning processes in high resolutions of space and time. It summarizes some concepts and discusses a series of projects and activities that will be conducted to further the operational effectiveness of the models for Flanders. AURORA Key Characteristics Aurora is an agent-based microsimulation system in which each individual of the population is represented as an agent. It is also an activity-based model in the sense that the model simulates the full pattern of activity and travel episodes of each agent and each day of the simu- lated period. At the start of the day, the agent generates a schedule from scratch, and, during the day, the agent exe- cutes the schedule in space and time. It is also dynamic in that (a) perceived utilities of scheduling options depend on the state of the agent, and implementing a schedule changes this state; (b) each time after having implemented a schedule, an agent updates his or her knowledge about the transportation and land use system and develops habits for implementing activities, and (c) each time an agent arrives at a node of the network or has completed an activity during execution of a schedule, the agent may reconsider scheduling decisions for the remaining time of the day. This may happen because an agent’s expectations may differ from reality. This may result from imperfect knowledge, but it may also be due to the nonstationarity of the environment. As a result of the decisions of all other agents, congestion may cause an increase in travel times on links or transaction times at activity locations. Fur- thermore, random events may cause a discrepancy between schedule and reality. BASIC CONCEPTS Utility Function The model is based on a set of utility functions, in which the utility of a schedule is defined as the sum of utilities across the sequence of travel and activity episodes it con- tains. Formally, (1) where Ui = utility of episode i, A = number of activity episodes, and J = number of travel episodes in the schedule. The functional form of utilities differs between activity and travel episodes. For activity episodes, utility is defined as a continuous, S-shaped function of the dura- tion of the activity. This form reflects the notion that with increasing values duration is at first a limiting fac- tor in “producing” utility and after some point other fac- tors become limiting. In particular: (2) where va = duration of episode a, Ua max = asymptotic maximum of the utility the individual can derive from the activity, and αa, βa, and γa = activity-specific parameters. The α, β, and γ parameters determine the duration, slope, and degree of symmetry at the inflection point, respec- tively. In turn, the asymptotic maximum is defined as a function of schedule context, attributes, and history of the activity, as (3) where ta, la, and qa = start time, location, and position in the sequence of activity a, respectively, 0 ≤ f(x) ≤ 1 = factors representing the impact of activity attributes on the maximum utility, Uxa = base level of the maximum utility, and Ta = time elapsed since the last implementation of activity a. The position variable, qa, takes into account possible carryover effects between activities leading to prefer- ences about combinations or sequences of activities (e.g., shopping after a social activity). For this function, the same functional form (an S-shape) is assumed as for the duration function (Equation 2). Thus, it can be assumed that the urgency of an activity increases with an increas- ing rate in the low range and a decreasing rate in the high range of elapsed time (T). The start-time factor of the maximum utility is a func- tion of attributes of the activity: (4) U U Ua j J J a A = + == ∑∑ 11 72 INNOVATIONS IN TRAVEL DEMAND MODELING, VOLUME 2 U U v a a a a a a a = + − max ( exp[ ( )])1 1 γ β α γ U f t f l f q U Ta a a a x x x a a a a max ( )* ( )* ( )* exp[ ( = + −1 β α )] ⎡ ⎣ ⎢⎢ ⎤ ⎦ ⎥⎥ f t t t t t t t t t t a a a a a a a a a a( ) = − − ≥ ∧ < 1 2 1 1 2 0 if if ≥ ∧ < − − ≥ ∧ < t t t t t t t t t t t a a a a a a a a a a a 2 3 3 3 4 3 4 1 if otherwise ⎧ ⎨ ⎪⎪⎪⎪ ⎩ ⎪⎪⎪⎪

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TRB Conference Proceedings 42, Innovations in Travel Demand Modeling, Volume 2: Papers includes the papers that were presented at a May 21-23, 2006, conference that examined advances in travel demand modeling, explored the opportunities and the challenges associated with the implementation of advanced travel models, and reviewed the skills and training necessary to apply new modeling techniques. TRB Conference Proceedings 42, Innovations in Travel Demand Modeling, Volume 1: Session Summaries is available online.

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