Below are the first 10 and last 10 pages of uncorrected machine-read text (when available) of this chapter, followed by the top 30 algorithmically extracted key phrases from the chapter as a whole.
Intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text on the opening pages of each chapter.
Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.
Do not use for reproduction, copying, pasting, or reading; exclusively for search engines.
OCR for page 155
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.
