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Modeling of Peak Hour Spreading with a Disaggregate Tour-Based Model Rebekah S. Anderson, Ohio Department of Transportation Robert M. Donnelly, PB Consult O ver the last decade in all metropolitan areas, growing The set consists of nine models that are linked and peak period congestion has been accompanied by applied sequentially. They are: population synthesis, increased demand from the peak hour into the shoul- auto ownership, daily activity pattern (mandatory tour der hours of the peak period. Conventional forecasting models generation), joint tour generation, individual non- generally adopt static diurnal factors and do not model time-of- mandatory tour generation, tour destination choice, day (TOD) choice, and are generally not well formulated to TOD choice, tour mode choice, and stops and trip mode extend their capabilities to model travel by hour of day as a choice. function of level of service and other factors, including simula- The tour destination, TOD, and tour mode choices' tion of peak hour spreading. As a disaggregate tour-based models are logit based and applied together. The "Log- model, the Mid-Ohio Regional Planning Commission Sum" composite impedance measure from the mode (MORPC) travel forecasting system fully incorporates a TOD choice model is available to the other choice models, choice model for a 19-h average weekday. Because the TOD making them sensitive to changes in travel times due to model is sensitive to travel times, peak hour spreading as the congestion. The TOD model is based on the "time win- consequence of increased levels of peak period congestion dows" concept, accounting for use of a person's time should be evident in the model's application. This paper budget over the day (16 h available per person). These explores this aspect of the MORPC tour-based model in appli- models are applied at the tour level, yielding the primary cation, comparing observed traffic data with the simulated destination, TOD, and mode choice for the entire tour, hourly demand results from a series of tests of the model. (Ref- and consider both the outbound and inbound portions. erences 111 refer to TOD modeling and the Columbus The TOD model is a hybrid discrete choice departure tour-based model.) time and duration model, with a temporal resolution of 1 h for the modeled period between 5:00 a.m. and 11:00 p.m. All tour departures before 5:00 a.m. were shifted to MORPC TIME-OF-DAY MODEL the 5:00 a.m. hour, and all tour arrivals after 11:00 p.m. were shifted to 11:00 p.m. The TOD model is applied In October 2001, MORPC contracted PB Consult to sequentially among tours, with mandatory (work, uni- develop a set of regional travel forecasting models. It versity, and school) tours scheduled first. The model developed a disaggregate tour-based model applied with determines the departure time of each tour and the dura- the microsimulation of each individual household, per- tion of the activity associated with the tour. Therefore, son, or tour, mostly using Monte Carlo realization of the 190 departure and arrival time combinations can be each possibility estimated by the models, with a random applied with relatively few variables. As a result of this number series to determine which possibility is chosen time-windows constrained formulation, the timing of for that record. the departure and arrival times on both legs of the tour 161