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144 I N N O VAT I O N S I N T R AV E L D E M A N D M O D E L I N G , V O L U M E 2 CS recently completed an FHWA research project on min time periods. When estimating the time-of-day mod- time-of-day models that resulted in a methodology for els, the chosen time period for each trip will be based on time-of-day choice models, trip-based models, and activ- the midpoint between the reported trip departure and ity-based models. These were validated in case studies in arrival times. Trip tables are developed for each time Denver, Colorado, and San Francisco, California. The period, purpose, mode, and direction. These are applied to trip-based time-of-day modeling method was applied to a networks by mode and time period in the trip assignment pricing scenario in the Denver region. Tolls were assumed model. on a (currently toll-free) 20-mi section of a circumferential Multinomial logit (MNL) choice models were esti- freeway. Tolls were highest in the two peak periods (0.2 to mated for six home-based trip purpose and direction com- 3.5 h long), with lower tolls in shoulder periods (1 to 3.5 binations--home to work (HW), work to home (WH), h) and lowest tolls in off-peak periods. The time-of-day home to shop (HS), shop to home (SH), home to other choice method estimated trips by time of day for half-hour (HO), and other to home (OH). periods. The application of the model for this scenario Two features were added to the time-of-day models to showed a modest amount of peak spreading resulting make them more sensitive to congestion pricing: from implementation of the period-based tolls. The tour-based time-of-day modeling method was · The three periods where congestion occurs (a.m. applied to a pricing scenario for downtown San Francisco peak, midday, p.m. peak) were further divided into 30- and estimated trips by time of day for half-hour periods. A min subperiods, in order to model peak-spreading hypothetical $4.00 toll was applied for all auto trips enter- behavior. Because it would be impractical to perform a ing downtown San Francisco during the a.m. peak period separate traffic assignment for each 30-min period, the (6:00 to 9:00 a.m.). The largest effect appears to be on distribution of trips across the subperiods was based on mode choice. About 20% of the reduction in downtown travel times for the same five periods that are included in trips is due to people choosing not to travel downtown at the current model. As the congested travel time in the all, about 70% is due to changes is mode, and about 10% "peak of the peak" increases relative to the free-flow appears to be due to time-of-day shifts. These results seem travel time, the peak tends to flatten, and a higher per- reasonable, as many downtown travelers may not have centage of peak travelers will travel in the shoulders of the flexibility to change their travel times. the peak. Generally, this type of effect is not symmetric For the Washington State Department of Transporta- because there are different constraints for traveling ear- tion, CS updated the time-of-day choice models by divid- lier as opposed to traveling later. In the a.m. peak, for ing the five main periods (a.m. peak, midday, p.m. peak, example, we expect more workers to shift toward the evening, and night) into 30-min subperiods in order to earlier shoulder of the peak rather than the later shoul- model peak-spreading behavior (5). In addition to auto der because many workers have to arrive at work before travel time variations between periods, the model was a specific time. structured to be sensitive to auto travel cost differences · Second, in addition to auto travel time variations between periods (i.e., to emulate time-of-day-specific con- between periods, the model is sensitive to auto travel gestion pricing). The new time-of-day choice models were cost differences between periods, for instance from time- estimated for eight trip purpose and direction combina- of-day-specific congestion pricing. Because there are no tions, using 32 alternatives. data on such cost differences in the household survey, it These five time periods are used for transit, truck, and is necessary to infer the sensitivity to travel cost by using external trips. Auto trips are further subdivided into 32 the sensitivity to travel time multiplied by the appropri- time periods, as shown in Table 1. ate value of time for each income group and travel pur- The auto time-of-day model uses highway travel times pose. The authors use the same values of time as in the from each of the five time periods to predict travel for 30- mode choice models. TABLE 1 Time-of-Day Choice Models A.M. Peak Midday P.M. Peak Evening Night 5:00 a.m.5:29 a.m. 10:00 a.m.10:29 a.m. 3:00 p.m.3:29 p.m. 8:00 p.m.10:59 p.m. 11:00 p.m.4:59 a.m. 5:30 a.m.5:59 a.m. 10:30 a.m.10:59 a.m. 3:30 p.m.3:59 p.m. 6:00 a.m.6:29 a.m. 11:00 a.m.11:29 a.m. 4:00 p.m.4:29 p.m. 6:30 a.m.6:59 a.m. 11:30 a.m.11:59 a.m. 4:30 p.m.4:59 p.m. 7:00 a.m.7:29 a.m. 12:00 a.m.12:29 p.m. 5:00 p.m.5:29 p.m. 7:30 a.m.7:59 a.m. 12:30 p.m.12:59 p.m. 5:30 p.m.5:59 p.m. 8:00 a.m.8:29 a.m. 1:00 p.m.1:29 p.m. 6:00 p.m.6:29 p.m. 8:30 a.m.8:59 a.m. 1:30 p.m.1:59 p.m. 6:30 p.m.6:59 p.m. 9:00 a.m.9:29 a.m. 2:00 p.m.2:29 p.m. 7:00 p.m.7:29 p.m. 9:30 a.m.9:59 a.m. 2:30 p.m.2:59 p.m. 7:30 p.m.7:59 p.m.