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 21
21
A range of improvements, including demand management, actual travel rate during the off-peak is higher than the target
advanced traveler information systems, and HOV lanes, value. The BI and delay measures also could be useful in the
have an effect on other hours in the peak period. off-peak period in locations that may be experiencing some
· Daily volume variation is the variability in person or vehi- congestion in the off-peak.
cle volume from day to day. These data are particularly im-
portant in analyses that examine mobility and reliability
2.9.2 Daily Analysis
levels on particularly heavy volume days (e.g., Fridays or
days before holidays) or days/time periods with different Analysis using daily averages is often less useful with the
travel patterns (e.g., special events or weekends). TTI and BI. Using 24-hour speeds for computing the TTI
· Incident information includes the number and duration of is not meaningful because the measure is meant to com-
crashes and vehicle breakdowns that occur on roadway pare peak and off-peak travel conditions. Likewise, the BI
segments and transit routes. This information is used in is intended to be a measure of reliability during a peak
analyses of the variation in mobility and reliability. The re- period. Daily values "wash out" the effect of congestion in
liability of transportation systems is a particular concern in peak periods with the longer off-peak periods. Total delay
analyses of incident management programs, value pricing is more meaningful as a daily congestion measure. Though
projects, and freight movement studies. the total delay in person- or vehicle-hours is less meaning-
· Weather information can explain a significant amount of ful to an individual driver, it is a good measure for analyz-
the variation in travel conditions. Snow, ice, fog, and rain ing trends from year to year. Daily delay is used in this
can be noted in a database used for mobility and reliabil- manner in the FHWA-sponsored Mobility Monitoring
ity analyses. Program (MMP).
· Road work information includes construction and main-
tenance activities and their location. This includes the lo-
2.9.3 Seasonal Analysis
cation, number of lanes affected, and time period.
· Peak direction hourly travel demand and volume are two Investigating variations in mobility and reliability over the
measures of person or vehicle travel used in system analy- seasons of the year also may be of interest. Many areas have
ses. The two may be the same for uncongested corridors. unique peaking characteristics due to seasonal events (e.g.,
Demand is higher than volume in congested corridors, academic calendars, sporting events, and tourism). These ac-
however, and the "excess" volume travels on the main tivities can alter the length and extent of the peak period. All
route in hours adjacent to the peak hour and on alternate of the measures discussed in this chapter can be used in a
routes. Improvements to primary routes or travel modes mobility or reliability analysis that compares peak or off-peak
may result in higher traffic volumes in the peak hour that period measure changes by month of year.
can be predicted if demand is estimated.
2.9.4 Urban or Rural Analysis
2.9 Time Periods for Analysis
The preceding discussion has assumed an urban mobility
Selecting the appropriate time period is an important or reliability analysis. Rural locations also can be the subject
part of building the data collection plan and analysis frame- of mobility and reliability analyses. For example, there might
work. Considerations include the nature of the problem(s) be an interest in freight movements in rural areas. Special
to be addressed through the analysis, the geography of the events and tourism activities also are situations that may
study area, and the presence of any special seasonal events generate interest in a rural analysis.
or conditions that could dramatically alter data or interpre- As mentioned previously, continuous data sources provide
tation of results. speed (travel time), volume, and classification information in
some urban areas. Point-to-point travel-time information
also is of interest for rural freight operations. As with travel
2.9.1 Peak and Off-Peak Period Analysis
conditions on an urban congestion map, such point-to-point
Peak period is the time period most often used for urban travel-time information would allow insight into rural freight
mobility and reliability analyses. Off-peak periods may be of operations. Transponders could be used to provide the
interest to study the extent of peak spreading at one area com- continuous information. The University of Washington is in-
pared to another area. The TTI is computed relative to the vestigating such applications in rural areas in the state of Wash-
FFS or PSL. If the analyst is investigating the TTI of an off- ington. Of the primary measures discussed in this chapter, TTI
peak period that is beginning to experience congestion, the and delay measures could be used for this rural application.
TTI could be used to illustrate the increased congestion if the The TTI could be used to compare current travel rates to a
