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Madison Avenue Dual Bus Lanes
The dual bus lanes were implemented on Madison Avenue in midtown Manhattan in 1981 as part
of a Service and Methods Demonstration (SMD) project. The two right-side lanes on the five-lane
street were reserved between 2:00 PM and 7:00 PM for buses only. Parking was prohibited along the
17-block (0.85-mile) segment during this time period, making three lanes available for GP mixed traf-
fic. Taxis were allowed to make right turns at two intersections and to use a four block section of the
lanes. These changes were made without adverse effects on Madison Avenue mixed traffic. Project
survey results showed that removing the friction between buses and other vehicles improved mixed
traffic speeds by 10 percent during the rush hour period. This improvement occurred despite a
10 percent increase in through volumes (Schwartz et al., 1982; Kuzmyak, 1984).
Over 700 buses operated on Madison Avenue during the 2:00 PM to 7:00 PM time period, with up to
200 during the 5:00 PM to 6:00 PM peak hour. Average express bus travel times along the 17-block
segment were reduced during the peak hour by 42 percent with the implementation of the reserved
lanes, from 15 to 9 minutes in round numbers, a 6 minute savings. Travel times for local buses declined
by 35 percent, from 16 to 11 minutes, a 5 minute savings. Afternoon peak-period bus reliability, using
a variability measure expressed as the standard deviation divided by the mean travel time, improved
from 40 percent to 27 percent for express buses and from 40 percent to 16 percent for local buses.
Ridership on both local and express routes increased during the 17 months after the bus lanes were
implemented. Ridership gains were higher on local service. Average weekday local service riders
increased from 9,450 to 12,385, or 31 percent. Approximately 17 percent indicated they started to use
service on Madison Avenue because of the lane. About half of these were riders changing from other
transit services. Some 62 percent of local service riders reported that their trips were consistently
faster because of the bus lanes.
Ridership increases on express buses were more modest. Daily ridership increased from 14,614 to
15,524, or 6 percent, during the first 17 months of operation. Although express buses saved 6 min-
utes as a result of the bus lanes, this figure represented a small amount of the total travel time for
many express passengers. Nevertheless, some 75 percent of the express passengers felt their trip was
consistently faster due to the bus lanes (Kuzmyak, 1984). Not only the small percentage travel time
savings, but also the low viability of walking and taxis as alternative modes, may have dampened
the relative effect on express bus ridership.
UNDERLYING TRAVELER RESPONSE FACTORS
Reduced travel times and more reliable trip times are key elements provided by many HOV facili-
ties for encouraging choice of a high occupancy commuting mode over driving alone. Other factors
influencing traveler response to HOV facilities include ambient travel patterns, underlying urban
area characteristics, certain features of HOV facilities and their operation, and external incentives to
HOV use such as the degree of transit service provided, park and ride lots, and Travel Demand
Management (TDM). These and other factors are explored here; however, the primary park and ride
coverage is in Chapter 3, "Park-and-Ride/Pool," while TDM is addressed in Chapter 19, "Employer
and Institutional TDM Strategies."
Choice of HOV Facilities
Travel demand model and user survey research provide an overview perspective on the relative
importance of, and interactions among, the various influences affecting the decision to use an HOV
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facility. The results of a major modeling effort and several user surveys are drawn upon here before
examining individual factors.
Insights from Travel Demand Modeling
A late 1980s HOV research project developed a travel mode and carpool occupancy choice model
based on detailed travel data. The data set was rich in surveyed travel choices made in the presence
of a major HOV facility--Northern Virginia's Shirley Highway (I-395) into Washington, DC. The
analysis indicated that trip makers perceive automobile and bus travel as very different choices,
with carpooling and vanpooling viewed more as a subset of auto travel. The study also found that
the least difference in perception and resistance to change was among various shared-ride occu-
pancy levels, such as three-person versus four-person carpools. The decision to share a ride rather
than driving alone was in between the extremes. It thus appeared that the greatest resistance to
mode change was between transit and ridesharing, suggesting that these two primary modes do
not closely compete for the same travelers, at least not when both are offered HOV travel time
advantages.
The modeling results also indicated that the in-vehicle travel time savings offered by an HOV facil-
ity are more important to a potential carpooler or vanpooler in the mode choice decision than ordi-
nary in-vehicle travel time savings. In the Shirley Highway corridor, carpoolers value the travel
time savings from the HOV lane 2-1/2 times more than normal driving or riding time savings. This
effect is believed to reflect perceived travel time savings on the HOV facility and perhaps also the
reliability of the HOV travel time--not otherwise accounted for in the modeling effort.
Characteristics of the workplace were also found to be strong determinants in the decision to
rideshare. Working for the federal government or other large employer was estimated to be equiv-
alent to 8 to 12 minutes of ordinary time savings, parking incentives for ridesharing were worth
8 minutes, and flextime was equivalent to 3 minutes (Comsis, 1989).
Insights from User Surveys
The finding that transit and ridesharing do not closely compete with each other matches results
from HOV lane user surveys. Surveys from the 1970s showed that while buses on HOV facilities
attracted some carpool passengers, a higher proportion of auto drivers changed to riding the bus.
Similarly, some transit riders on HOV lanes were attracted from carpools, but proportionately
more lower occupancy auto commuters were attracted (Pratt and Copple, 1981). This continues to
be the case with more recent HOV lanes observations. (See "Related Information and Impacts"--
"Sources of HOV Users," Tables 2-26 and 2-27.)
Surveys of HOV lane users also provide further information on the importance of the facility and the
other factors that help influence changes in travel behavior. For example, the periodic surveys con-
ducted in Houston indicate that between 54 and 76 percent of passengers riding buses on the
Houston HOV lanes viewed the opening of the HOV facilities as very important in their decision to
ride the bus. Further, between 22 and 39 percent of the survey respondents indicated that they would
not be riding the bus without the presence of the HOV lane (Bullard, 1991; Turnbull, Turner and
Lindquist, 1995).
Surveys of bus riders on the Shirley Highway HOV lanes completed in 1971 and 1974 identified
shorter bus travel times and reduced levels of congestion in the HOV lanes as important factors in
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their decision to use transit (McQueen et al., 1975). Bus riders on the San Bernardino Transitway
in 1977 identified the ability to avoid congestion and travel time savings provided by the facility
as the main reasons for riding the bus. Carpoolers identified similar factors influencing their use
of HOV facilities (Crain & Associates, 1978). Bus and especially carpool and vanpool users of HOV
lanes in Houston likewise put congestion and travel time savings at or close to the top of the list,
but with time to relax, trip time reliability, and cost savings close behind, as shown in Table 2-20
(Christiansen and Morris, 1990).
Travel Time Savings
The economic and travel behavior impacts of HOV facilities depend largely on the amount of time
saved. As time savings increase there are operating cost savings for transit operators and impacts
on mode choice favoring transit and ridesharing.
HOV Facilities
Individual examples of travel time savings that HOV lanes provide to buses, vanpools, and car-
pools relative to travel on the general-purpose (GP) lanes or adjacent facilities were included in
the preceding "Traveler Response by Type of HOV Application" sections. Time savings realized
by travelers in the HOV lanes depend on a number of factors. These include length of the facility,
access treatments, traffic volumes in the HOV lane, and congestion levels in the GP lanes. Without
the presence of mixed traffic congestion, no HOV facility can offer a significant time advantage
for high occupancy vehicles except for exclusive ramps or separate roadways that provide more
direct routes.
Table 2-21 brings together examples of peak-hour travel time savings reported on various HOV
facilities. Except where noted, the information is based on comparisons of the travel times between
the HOV facility and the GP lanes for commuters traveling the full length. The reported time sav-
ings presumably pertain to the peak hour, and may be averages, or normal upper limits. Note that
circa 2000 AM and PM peak-period travel time savings data for 12 additional Los Angeles facilities
opened in the 1990s were included within Table 2-9 of the "Traveler Response by Type of HOV
Application" section.
Table 2-20 Reasons Reported by Houston HOV Lane Users for "Transitway" Use
Katy HOV Lane North HOV Lane
Why Use Transitway Bus Car/Vanpoolers Bus Passengers Vanpoolers
Passengers
Freeway Too Congested 20% 19% 23% 20%
Saves Time 16 20 20 20
Time to Relax 18 14 15 13
Reliable Trip Time 14 12 15 13
Cost Less 14 14 12 15
Dislike Driving 11 -- 10 --
Source: 1986 Texas Transportation Institute surveys as reported in Christiansen and Morris (1990).
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Time savings will vary from day to day, and may be much less in the shoulders of the peak than
in the time span of peak congestion on the GP lanes. The travel time savings assembled in Tables
2-9 and 2-21 range from practically nothing to almost 40 minutes. It may be observed that:
· HOV lanes that function as queue bypasses at toll stations and other bottlenecks such as water
crossings provide substantial savings--from about 6 up to 20 minutes per mile--on HOV facil-
ities that are typically short.
· Longer HOV facilities along freeways save up to about 1.6 minutes per mile, averaging 0.7 min-
utes per mile for the relatively new HOV facility segments in Los Angeles (Table 2-9) and 1.0
for other long freeway facilities nationwide (Table 2-21).
· HOV lanes on arterial streets typically save about 0.5 minutes per mile.
These savings pertain to the full length of the HOV facility (or study segment in the case of Los
Angeles) and relate only to the portion of the trip on the HOV facility. The impact of the HOV facil-
ity on the total trip time of travelers may be different. Changes in travel behavior will be influenced
by the total travel time, not just the HOV section.
Travel time savings, as outlined in the preceding section, have been reported by HOV facility users
as an important factor in their decision to change from driving alone. For example, time savings
provided by Houston's Katy and Northwest HOV lanes were rated an important factor by 72 per-
cent of the carpoolers using both facilities in a 1995 survey (Turnbull, Turner and Lindquist, 1995).
Houston studies suggest a guideline of 7 to 8 minutes travel time savings on the overall facility as
an indicator of success, or alternatively, 5 to 10 minutes (Christiansen and Morris, 1990 and 1991).
Offering meaningful travel time savings is, quite possibly, the most important single function of
HOV lanes in inducing HOV use. However, primary reliance must be placed on results of surveys
and travel demand modeling at the individual trip level for assessing degree of importance (see
"Choice of HOV Facilities"--"Insights from Travel Demand Modeling," above). Examined at the
facility level, corridor characteristics cloud the results.
For example, regression analysis of historical data from Texas HOV evaluations established a pos-
itive relationship between HOV lane person movement (in the Texas context) and HOV lane peak-
hour travel time savings. However, the scatter pattern of the data points suggests that time savings
are overshadowed by other factors associated with individual facilities (Stockton et al., 1997). It
seems likely that factors such as quantity of individual corridor population and employment and
other corridor characteristics cause this result.
HOV lane users in many areas appear to substantially overestimate the travel time savings they
realize, and have been doing so fairly consistently from the outset of HOV operations (Pratt and
Copple, 1981). In a 1995 survey, bus riders on the Katy HOV lane in Houston reported travel time
savings of 23 minutes in their morning commute and carpoolers reported 25 minutes, while travel
time surveys using the floating car technique indicated actual travel time savings of some 17 min-
utes compared to the GP lanes. On the other hand, bus riders and carpoolers on the Northwest
HOV lane reported AM peak-hour travel time savings of 17 minutes and 20 minutes respectively,
compared to actual savings of approximately 22 minutes. Bus riders on the East R. L. Thornton
HOV lanes in Dallas reported travel time savings of 13 minutes in the morning and carpoolers indi-
cated 15 minutes, compared to 5 minutes in measured time savings (Turnbull, Turner and
Lindquist, 1995; Stockton et al., 1997). Carpoolers using the interim I-394 HOV lane in Minneapolis
reported travel time savings of 10 minutes in the morning when the actual travel time savings
recorded in field surveys was 5.2 minutes (SRF, Inc., 1987).
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Table 2-21 Examples of Reported AM Peak-Hour Travel Time Savings Associated with
HOV Facilities and Bus Lanes
Travel Time Savings a
Length Minutes
Facility (miles) Yearb Total (minutes) per Mile
Exclusive Freeway HOV Lanes
Houston, Texas
I-45N (North) 13.5 1996 14 1.0
I-45S (Gulf) 12.1 1996 4 0.3
I-10W (Katy) 13 1996 17 1.3
US 290 (Northwest) 13.5 1996 22 1.6
US 59 (Southwest) 12.2 1996 2 0.2
Los Angeles, California
San Bernardino Transitway 12 1992 17 1.4
Minneapolis, Minnesota
I-394 (exclusive & concurrent flow) 11 1992 5 0.5
Washington, DC
I-95/I-395 (I-95 and Shirley Hwy.) 27 1997 39 1.4
I-66 (exclusive & concurrent flow) 27 1997 28 1.0
Concurrent Flow Freeway HOV Lanes
California
SR 55, Orange County 11 1986 18 1.6
SR 91, Los Angeles 8 1992 10 1.2
SR 101, San Francisco Bay Area 11 1989 5 0.5
SR 237, San Francisco Bay Area 4 1989 4 1.0
Bay Bridge, San Francisco Bay Area c 2 1998 20 10.0
Massachusetts
I-93(N) Boston d 2.5 1999 10 (max) 4.0 (m ax)
Maryland
I-270 8 1997 5-6 (AM peak) 0.6-0.8
9-12 (PM peak) 1.1-1.5
Miami Ft. Lauderdale Palm Beach
I-95 45 1998 6 (AM/northbound) 0.1
7 (PM/northbound) 0.2
16 (AM/southbound) 0.4
Because the time savings reported address only the trip segment on the HOV facility, not the con-
nections to and from the lanes, the impact of an HOV project on the total travel time may be more
or less. For example, picking up carpoolers may add time to a trip compared to driving alone.
Conversely, HOV lane users may save additional time by missing congestion at upstream or
downstream locations, by availing themselves of improved bus service frequencies on the HOV
lanes, or by using preferential carpool parking at their destination.
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Table 2-21 Examples of Reported AM Peak-Hour Travel Time Savings Associated with
HOV Facilities and Bus Lanes, continued
Travel T i m e Savings a
Length Minutes
Facility (m iles) Yearb Total (minutes) per Mile
Contraflow Freeway HOV Lanes
East R. L. Thornton, Dallas 5.2 1996 6 1.2
Route 495, New York/New Jersey c 2.8 1991 18 6.4
Gowanus, New York c 0.9 1982 20 22.2
(max) (max)
Arterial Street HOV Lanes
San Thomas Expressway, San Jose 11 1989 5 0.5
Montague Expressway, San Jose 5 1989 3 0.6
Airport Road, 128th Street, Seattle 3.4 1993 1 0.3
Eglington Avenue, Toronto 7 1996 3 (AM) 2.5 (PM) 0.4 e
Hastings Street, Vancouver 4.4 1996 3 (AM/westbound) 0.7
5 (PM/eastbound) 1.1
Arterial Street Bus Lanes
Second Avenue Contraflow, New York f 0.09 n/a 10 111.1
49t h-50 th Bus/Taxi Street, New York 0.88 n/a 7 8.0
Madison Avenue Bus Lane, New York g 0.85 1981 6-8 (express buses) 7.0-9.4
5-7 (local buses) 5.9-8.2
a
Notes: Comparison of travel time in the HOV lanes over the general-purpose lanes (in known cases, unless otherwise
noted) for commuters traveling the full length of the HOV facility.
b
Year travel time savings documented.
c
Queue bypass on approach to toll plaza.
d
Queue bypass on approach to merge and lane drop.
e
Applies only to buses, negligible time savings for 3+ carpools.
f
Queue bypass on approach to congested bridge entrance (no longer exists).
g
Represents savings from before/after lanes implemented.
Sources: Turnbull (1992b); Stockton et al. (1997); SRF, Inc. (1995); Henderson, Vandervalk and Cromartie (1998);
Kuzmyak (1984); Ho (1996); New York City DOT (1983); Lisco (1999); Schwartz et al. (1982); Municipality
of Metropolitan Toronto (1997).
The overestimation of travel time savings by some users may be partially the result of reductions
in total trip travel times, not just the portion associated with the HOV lane. It may also be the result
of comparing the HOV travel time with the worst case travel time in the GP lanes, or of extrapo-
lating from perceptions of a fast trip. The more successful HOV systems will tend to be those which
combine on-facility time savings with increases in reliability and actions to make HOV door-to-
door trip times competitive with low occupancy auto travel.
Arterial Bus Lanes
Several studies have documented the effectiveness of arterial bus lanes in reducing travel times,
although no analyses have been encountered directly linking the resultant time savings to traveler
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response. Early capacity research cited increases in peak-hour bus speeds of about 1.5 to 2.0 miles
per hour when bus lanes were installed (Rainville et al., 1961). Bus rapid transit studies
have demonstrated how time savings vary inversely with the preexisting bus speed. CBD and
arterial street bus lane applications have been shown to provide time savings ranging from
about 8 minutes per mile of time savings at prior condition operating speeds of 3 to 5 miles per
hour, to 1 to 3 minutes per mile of time savings at prior operating speeds of 6 to 12 miles per
hour (Wilbur Smith and Associates, 1975).3 Reported time savings of bus lanes and bus streets
in New York City are appended to Table 2-21. The benefits shown are greater than those experi-
enced with conventional concurrent flow bus lanes where violations and right-turn conflicts are
common.
Trip Time Reliability
It is not only the higher operating speeds and shorter travel times of HOV lanes that are important
to users. Ongoing reliability of time savings, reflected in travel time consistency and bus on-time
performance, is also important. Measuring travel time reliability requires historical speed and
travel time data on both the HOV facility and the GP lanes. Bus on-time performance data also pro-
vides an indication. Travel time reliability has been found in a number of cases to be significantly
improved by HOV facilities.
Most examinations of HOV facility travel time reliability have utilized periodic surveys using the
floating car data collection technique or monitoring of bus on-time performance. A more detailed
analysis has been conducted using data from the AVI traffic monitoring system in Houston. Eight
months of these data were used to examine peak-period travel time reliability on the Katy HOV
lane and the GP lanes. Trip reliability was assessed by comparing standard deviations of travel
times for weekdays within each month. Figure 2-1 provides an example of the travel times for
the Katy HOV lane (lower set of travel times in the graph) and the GP lanes (higher set of times in
the graph) over the 8 month period. Both the travel time savings offered by the HOV lanes and the
greater variability in travel times in the GP lanes are evident. Figure 2-2 illustrates the travel time
reliability for the HOV lane and GP lanes in terms of the range of times within one standard devi-
ation (Turner, Carlin and Henk, 1995; Turner, 1997).
Among other evaluations of HOV facility travel time reliability is an assessment done of traffic
incidents on the Gowanus Expressway in Brooklyn, when its HOV lane was operating in the
configuration that pertained in 1998, until August. Reported traffic incidents were one per month
on the HOV lane (which was moving 11,000 persons in the AM peak hour) and 18 per month,
total, on the three GP lanes (moving 5,040 persons total, AM peak hour). The HOV lane had
at least one incident requiring more than 15 minutes clearance time on 6 percent of all work
days; the corresponding measure for the GP lane was 54 percent of all workdays (Sverdrup/
Urbitran, 1998). Another study, done in connection with the occupancy requirement change
on the I-5 North HOV lanes in Seattle, found that reliability declined somewhat when the vehi-
cle occupancy requirement was lowered from 3 to 2 (Ulberg et al., 1992). Partly as a result of
this change, Washington State DOT developed guidelines based on minimum average speed and
3
Computation of transit operating speeds such as these, sometimes referred to as effective velocity or (pri-
marily in Europe) commercial speed, includes time spent stopped to load and unload passengers as well as
time incurred in traffic stops and delays along with acceleration and deceleration effects.
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Figure 2-1 Daily and monthly average peak-hour
travel times on Houston's Katy
Freeway
Source: Turner (1997).
Figure 2-2 Morning peak-hour travel time
reliability for Houston's Katy Freeway
Source: Turner (1997).
speed reliability for use in determining when increases in vehicle occupancy levels should be
considered.
Documented improvements in bus on-time performance include the results of opening the Shirley
Highway HOV lanes in 1969. In that case, the percentage of affected bus trips arriving early or on
time in downtown Washington, DC, improved from 33 percent to 92 percent (McQueen et al., 1975).
Improvements in bus reliability from 16 percent "on time" to 55 percent "on time" were observed
with opening of the Oakland Bay Bridge approach HOV lanes. Lesser but positive bus reliability
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improvements have been recorded for other HOV lane openings on freeways, and a wide range of
reductions in bus trip time variance has been reported for arterial street bus lanes. The average
reported improvement is a halving of "late" bus arrivals for all types of facilities (Pratt and Copple,
1981). For the Madison Avenue dual bus lanes example of reliability improvement, refer back to
"Response to Arterial Street Bus-Only Facilities" under "Traveler Response by Type of HOV
Application."
Bus Service, Urban Area, and Facility Characteristics
To assist in examination of other factors potentially important in determining HOV facility usage,
peak-hour HOV facility utilization information from Tables 2-2, 2-8, and 2-10 has been assembled in
a consolidated and augmented tabulation. Available utilization information supports inclusion of
35 observations from HOV facilities along freeways in North America, roughly 40 percent of the total.
(Toll roads, river crossings, and expressways are, for short, subsumed within the term "freeways.")
The data augmentation consists of having added an HOV-persons total along with several descrip-
tors of the operating environment and characteristics. The result is presented as Table 2-22; sorted
in order of decreasing HOV person volume, it provides the sum of HOV facility bus passengers
and van/carpool occupants. Scatter plots were prepared relating several of the HOV facility
descriptors to the person volumes. Figure 2-3, discussed below, is an example.
The information in Table 2-22 supports the finding presented earlier that travel time savings are a
crucially important determinant of HOV facility usage. Of the 17 facilities for which travel time
savings information is listed, five have an estimated saving of 20 minutes or more. Three of these
facilities correspond to the top four in total HOV-person volume, and four correspond to the top
six. There is insufficient data for a comparable assessment of trip time reliability.
The findings from examining several different data-sorts of the information presented in Table 2-22,
and the scatter plots prepared from it, have been combined with conclusions from other sources to
assemble the discussion of bus service, urban area and facility characteristic factors presented next.
Bus Service Levels
Many HOV facilities, but almost entirely those oriented toward downtown CBDs, have relatively
high bus volumes. These applications--facilities with substantial levels of bus service--have dra-
matically higher total HOV person volumes than facilities with little or no bus service. Other facil-
ities, especially those focusing on suburb to suburb travel patterns, fall in the little or no bus service
category. This, in turn, tends to be an indicator of lower HOV facility person volumes. The HOV
facility on New Jersey's I-287, suspended during 1998 in its eleventh month of operation, was in
the latter category (see "Related Information and Impacts"--"Terminations of HOV Projects").
Figure 2-3 illustrates a scatter plot relating AM peak-hour total HOV person volumes to the bus
vehicle volume on each facility during the same time period. The relationship, with bus vehicle
volumes serving as a measure of transit service levels, is extremely strong. The total peak-hour per-
son volume may be approximated on most facilities using the linear regression relationship:
Total peak-hour HOV person volume = 1,864 46 (peak-hour bus vehicle volume)
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Table 2-22 Consolidated Freeway HOV Lane Utilization Data with Urban Area and Facility Descriptors
Bus Bus Van/Car- Total Travel 1996 Combined Conges-
Vehi- Pass- pool Oc- HOV Time Area Pop. Facility Facility tion
Location and HOV Facility cles engers cupants Persons Savings Facility Type (000) Length Orientation Measure
NJ Rte. 495 (to Lincoln Tunnel) 725 34,685 0 34,685 18 min. Contraflow 17,150 3 miles Radial 1.18
Alameda Co., CA I-80 Bay Br. 101 3,535 8,273 11,808 20 Concurrent Flow 3,890 2 Radial, Bridge 1.33
No. VA/DC I-95/I-395 Shirley 118 3,085 8,212 11,297 39 Exclusive Rev. 3,460 27 Radial 1.43
New York City, Gowanus Expy. 202 8,686 899 9,585 20 Contraflow 17,150 2 Radial 1.18
New York City, I-495 L.I. Expy. 165 7,838 394 8,232 n/a Contraflow 17,150 4 Radial 1.18
Los Angeles, I-10 San Bernardino 71 2,750 4,352 7,102 17 Exclusive 2-way 12,220 12 Radial 1.57
No. VA/DC I-66 16 484 6,486 6,970 28 Excl. Rev. & Conc. 3,460 17 Radial 1.43
Seattle, I-5 North 64 2,605 3,039 5,644 n/a Concurrent Flow 1,950 14 Radial 1.27
Montreal, Champlain Bridge 91 5,300 0 5,300 n/a Contraflow 1,016 4 Out.-Radial, Br. n/ a
Minneapolis, I-394 (inner) 56 1,834 3,341 5,175 5 Excl. Rev. & Conc. 2,250 10 Radial 1.12
Houston, I-45 North Fwy. 53 2,100 2,725 4,825 14 Exclusive Rev. 3,060 14 Radial 1.11
Houston, US 59 Southwest 38 1,420 3,147 4,567 2 Exclusive Rev. 3,060 11 Radial/Circ. 1.11
Houston, US 290 Northwest 22 1,035 3,030 4,065 22 Exclusive Rev. 3,060 14 Radial 1.11
Seattle, SR 520 56 3,140 498 3,638 n/a Concurrent Flow 1,950 2 Out.-Radial, Br. 1.27
Dallas, I-30 R.L. Thornton 64 1,041 2,494 3,535 6 Contraflow 2,290 5 Radial 1.11
Marin Co., CA US 101 57 1,995 1,490 3,485 5 Concurrent Flow 3,890 13 Radial, Bridge 1.33
Houston, I-10 Katy Fwy. 40 1,355 2,091 3,446 17 Exclusive Rev. 3,060 13 Radial/Circ. 1.11
Houston, I-45 Gulf Fwy. 31 740 2,682 3,422 4 Exclusive Rev. 3,060 12 Radial 1.11
Boston, I-93 North 35 1,050 2,320 3,370 10 Concurrent Flow 3,010 2 Radial 1.09
Minneapolis, I-394 (outer) 29 1,031 1,797 2,828 5 Conc. & Excl. Rev. 2,250 10 Outer Radial 1.12
Pittsburgh, I-279/579 23 1,050 1,527 2,577 n/a Exclusive Rev. 1,930 4 Radial 0.85
Seattle, I-5 South 28 1,176 1,320 2,496 n/a Concurrent Flow 1,950 16 Radial 1.27
Santa Clara Co., CA SR 237 18 630 1,720 2,350 4 Concurrent Flow 1,595 6 Circumferential 1.11
Norfolk, I-64 0 0 2,130 2,130 n/a Exclusive Rev. 1,010 8 Circumferential 0.96
Dallas, I-35E Stemmons Fwy. 9 310 1,667 1,977 n/a Concurrent Flow 2,290 7 Radial 1.11
Seattle, I-90 34 1,250 660 1,910 n/a Conc. & Excl. Rev. 1,950 13 Out.-Radial, Br. 1.27
Dallas, I-635 LBJ Fwy. 1 10 1,812 1,822 n/a Concurrent Flow 2,290 7 Circumferential 1.11
Minneapolis, I-34W 15 469 1,318 1,787 n/a Concurrent Flow 2,250 5 Radial 1.12
Hartford, I-91 11 280 1,416 1,696 n/a Exclusive 2-way 635 9 Radial 0.93
Norfolk/Va. Beach, SR 44 0 0 1,520 1,520 n/a Concurrent Flow 1,010 4 Out.-Radial, Br. 0.96
Hartford I-84 12 288 1,193 1,481 n/a Exclusive 2-way 635 10 Radial 0.93
Vancouver, BC H-99 27 1,080 0 1,080 n/a Concurrent Flow 514 4 Outer Radi al n/ a
Denver, US 36 Boulder Tpk. 28 1,000 0 1,000 n/a Concurrent Flow 1,770 4 Radial 1.12
Santa Clara Co., CA US 101 3 105 803 908 n/a Concurrent Flow 1,595 25 Radial/Circ. 1.11
New Jersey I-287 2 45 711 756 n/a Concurrent Flow 4,522 20 Circumferential 1.18
Sources: Developed with HOV characteristics and utilization data from Tables 2-1, 2-2, 2-7, 2-8 (see footnote "a"), 2-10, 2-11 and 2-21; 1996 population and congestion measure data from
Texas Transportation Institute (1998c); New Jersey I-287 and Canadian population data from U.S. Census and Canadian Embassy sources, respectively; facility orientation
determinations by Handbook authors.
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Figure 2-3 Total peak-hour peak-direction person volumes on 35 HOV facilities related to bus
vehicle volumes
14,000
CA I-80 Bay Bridge
12,000
No. VA I-395
A.M. Peak Hour Peak Direction Total HOV Persons
10,000
NY Gowanus Expy.
Person Volume = 1863.7 + 45.99(Bus Volume)
NY I-495 L.I. Expy.
R2= 0.92
8,000
No. VA I-66
6,000 40,000
35,000 NJ 495
30,000
25,000
4,000 20,000
15,000
10,000
2,000 5,000
0
0 200 400 600 800
0
0 50 100 150 200 250
A.M. Peak-Hour Peak-Direction Bus Vehicles
Note: Person volumes include bus passengers plus carpool and vanpool occupants. See text for discussion of labeled data points.
Source: Developed from AM peak-hour peak-direction bus vehicle volumes and total HOV facility person volumes data for 35
HOV facilities as consolidated in Table 2-22 from Tables 2-2, 2-8 (see footnote "a"), and 2-11.
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The facilities least well represented by this formula, those whose plots are furthest from the linear regres-
sion line in Figure 2-3, are "outliers" for good reasons. The Northern Virginia I-66 and I-395 facilities,
and CA I-80 San Francisco Bay Bridge, are paralleled by rail rapid transit lines, tending to deflate bus
relative to carpool volumes. The Long Island and Gowanus Expressway facilities in New York City, and
three others below the linear regression line, do not (or did not when the data was collected) permit car-
pools, limiting person volumes. NJ Route 495 (inset) is both paralleled by rail lines and bus-only, but lies
in an exceedingly high volume corridor focused on Midtown Manhattan and its over 500,000 jobs.
The relationship presented above not only reflects the relatively obvious cause and effect of bus
vehicle volume on bus passenger volume, but also an approximate yet robust correlation between
the ability to support substantial bus service and the ability to attract large numbers of carpools.
The travel patterns and parameters that support one also support the other.
When there are only carpools and vanpools in an HOV lane, lane productivity is often limited. In
such cases, an HOV lane might carry more people than a GP lane only in very large urban areas.
When there are less than 15 buses in the hour, total AM peak-hour peak-direction HOV person vol-
umes generally do not exceed 2,200 on any existing facility.
Los Angeles County--a very large multi-nucleated area of dense urban sprawl--does indeed prove to
be an exception, achieving higher HOV volumes with insubstantial bus volumes. Morning peak-hour
volumes in the 2,200 to 3,500 range are encountered there on 7 of 11 facilities that may be presumed to
have fewer than 15 buses in the peak hour. The same 7 facilities also carry more persons in the peak hour
peak direction than the adjacent GP lanes. (See "Traveler Response by Type of HOV Application"--
"Response to Concurrent Flow Freeway HOV Lanes"--"Los Angeles County Examples," noting that
the bus counts in Table 2-9 are not peak hour peak direction, but daily in both directions).
Urban Area Characteristics
The importance to HOV facility usage of underlying travel patterns and parameters, such as pro-
portion of travel headed to the CBD, downtown parking costs, degree of concentration or disper-
sion of traffic, and indeed absolute quantity of travel activity, is fairly obvious. These underlying
travel characteristics are in turn shaped by the size and nature of the urban area in question:
Population. The number of passengers using HOV facilities tends to increase as urbanized popu-
lation increases. Most HOV facilities along freeways are found in urban areas with more than one
million people. Generally, in the larger urban areas, the city centers--and other activity centers--
are stronger, and there is more bus service. The patterns are not fully consistent, however, as indi-
vidual facilities within single urban areas display wide variability. This variability results in part
from differences in the development of individual corridors within regions.
Employment. HOV facilities are heavily work trip oriented, thus the amount of employment and
its distribution should be as important as population. Lack of nationwide consistency in the tabu-
lation of employment by sectors such as CBDs hampers analysis, however. The best that can be
said is that presence of a major employment center with 100,000 or more jobs within the immedi-
ate destination service area of an HOV facility appears to be critical.
Urban Form. Physical barriers such as water bodies or steep topography constrict development,
travel, and traffic flow, creating the travel concentrations and traffic congestion that enhance HOV
facility attractiveness and use. Most freeway-based HOV facilities are clustered along the East
Coast, the West Coast, and in Texas, with only a few facilities in other Midwestern cities. Most of
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the East and West Coast installations are in cities with wide river barriers, if not greater physical
constraints, but the Texas and other Midwestern systems are significant exceptions.
Facility Characteristics
Facility characteristics that affect HOV usage include both physical and operational characteristics
of the HOV facility and the freeway or other roadway along which the HOV facility is installed.
Facility Type. Among freeway HOV facilities, there is little relationship between type of HOV
facility and usage. The three top passenger volume HOV facilities in Table 2-22 are a contraflow
lane, a set of concurrent flow lanes, and a reversible exclusive facility. There is a tendency for con-
traflow lanes to be heavy carriers of person volumes, but this is probably because of selection of
the contraflow design in response to the constrained space and substantial bus service typical of
highly developed areas and river barrier crossings.
Facility Length. Where HOV facility time savings over travel in the GP lanes is uniform throughout
the length of a facility, length is obviously important. Examination of the observations in Table 2-22
shows no pattern, however, relating distance to facility usage. This result reflects the mix of facilities
across North America that gain their time advantage, if any, from operating alongside GP lanes of
varying degrees of congestion, along with short queue bypass lanes that take HOVs around severe
congestion at the approaches to toll barriers, lane drops and other sources of major delay.
Facility Orientation. Most HOV facilities focus on the city center or, in some cases, other very major
employment concentrations. In Table 2-22, it can be seen that the top 60 percent of facilities in the
data set have an orientation that is, at least in part, radial to the center city CBD. These are the facil-
ities in the peak-hour volume range of 2,500 to 35,000 persons. Peak volumes on all purely circum-
ferential HOV facilities are generally in the 750 to 2,400 persons range.4
Eligibility Requirements. Either allowing carpools to use a bus-only lane or reducing HOV carpool
occupancy requirements will result in an increase in HOV lane usage, measured either in terms of vehi-
cle or person volumes, so long as the vehicular capacity of the priority lane is not exceeded (Christiansen
and Morris, 1990 and 1991). For examples and analyses, refer back to "Traveler Response by Type of
HOV Application"--"Response to Changes in Occupancy Requirements and Operating Hours."
Years in Service. Available data covering individual HOV facilities exhibit patterns of strong growth
over 3- to 20-year periods (Christiansen and Morris, 1990 and 1991). Clearly a number of facilities
serve lesser volumes in whole or in part because of fewer years in service. Further information on
this phenomenon is provided under "Related Information and Impacts"--"Time to Establish
Ridership and Use."
Supporting Facilities. HOV facility usage in general, and HOV facility bus ridership in particular,
can be enhanced through provision of supporting facilities. Potential supporting features range
from park-and-ride and park-and-pool lots to downtown bus lanes and even connecting busways,
as in Seattle, which also has a connecting bus tunnel. For further coverage, refer back to "Traveler
Response by Type of HOV Application"--"Response to Arterial Street Bus-Only Facilities" in this
chapter, and to Chapter 3, "Park-and-Ride/Pool," and Chapter 4, "Busways, BRT and Express Bus."
4 Los Angeles County, covered separately in Table 2-9, is ever the exception. Circumferential facility peak-hour
peak-direction person volumes there range from 1,500 (CA 118 Ronald Reagan Freeway, AM and PM) to 3,400
(I-405 San Diego Freeway, southwest segment, PM).
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Congestion. Unless severe congestion exists in the GP lanes on a recurring basis, usage of HOV
facilities will not be high. As previously discussed, provision of meaningful travel time savings is
perhaps the most important single factor influencing HOV facility use (Christiansen and Morris,
1991). Without congestion, there is no way for HOV facilities to generate time savings, except in
the rare case of exclusive ramps (and potentially other installations) that save distance. From a sta-
tistical perspective, HOV person volumes increase with city size and attendant traffic congestion,
although the patterns exhibited by available data show wide ranges. In large part, the variability
is introduced by use of regional rather than facility-specific published indicators of congestion.
Without congestion, there is little reason for HOV lanes.
Carpool Composition and Longevity
Conventional Carpooling
Most carpools draw upon family and co-workers for participants, and carpool users of HOV lanes
are no exception. Surveys of carpoolers on the Houston HOV lanes over the years indicate that
between 56 and 65 percent are formed with family members, 25 to 32 percent are composed of co-
workers, and 8 to 13 percent are with neighbors or other individuals (Bullard, 1991; Turnbull,
Turner and Lindquist, 1995). Further, responses to a 1995 survey on the Katy and Northwest HOV
lanes indicate that most carpools are formed by the members themselves, with little outside assis-
tance. Only 1 to 5 percent of the respondents reported using an employer rideshare program to
help find someone to carpool with, and 1 percent indicated using the METRO Rideshare Program
(Turnbull, Turner and Lindquist, 1995).
In connection with Houston's Katy Freeway HOV lane QuickRide value pricing demonstration, reg-
istrants were surveyed in 1998 to identify the composition of their carpools. The results, repre-
senting carpools prepared to pay $2.00 for entry onto the HOV lanes during periods of 3
occupancy requirement, are outside the range previously identified for Houston HOV lane users.
Family members composed 49 percent of reported members, less than for regular HOV lane car-
poolers. Of these, 37 percent were adults and 12 percent were children. Co-workers accounted for
41 percent, followed by neighbors at 6 percent, and other members at 4 percent (LKC Consulting
Services and Texas Transportation Institute, 1998).
The 1977 survey of 3 carpoolers on the San Bernardino Transitway (El Monte Busway) in Los
Angeles indicated 14 percent were formed with family members, 63 percent with co-workers,
8 percent with neighbors, 4 percent with help from Commuter Computer, and 12 percent in com-
binations of these (Crain & Associates, 1978). A 2001 survey of Los Angeles County HOV lane users
including the El Monte Busway and 15 HOV 2 lanes, in contrast, found carpool partners to be
62 percent family, 42 percent co-workers, 6 percent neighbors, and 5 percent "self" or "other" with
multiple survey responses allowed. Corresponding vanpool responses were 9, 94, 5, and 3 percent,
respectively (Parsons Brinckerhoff et al., 2002a). Results of a 1995 survey of carpoolers on the East
R. L. Thornton HOV lane in Dallas indicated that 65 percent were formed with family members,
31 percent were composed of co-workers or friends, and 4 percent were with other individuals.
The DART rideshare program had been used by 2 percent of the respondents and 1 percent used
an employer sponsored program (Turnbull, Turner and Lindquist, 1995).
Overall declines in carpooling during the 1980s and 1990s have, at least on a regional or national
(rather than facility) basis, been linked with decreasing percentages of carpool members from
beyond the immediate family. Recent surveys reporting particularly high percentages of family
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among carpool members, in addition to the Houston and Dallas surveys noted above, include two
by the Southern California Association of Governments. They found household members com-
posed 49 percent of carpoolers in 1996 and 55 percent in 1999. Analysis of National Household
Travel Survey data found that carpools made up entirely of members from the same family repre-
sented 76 percent of all journey-to-work carpools nationwide in 1990 and 83 percent in 2001. It has
been further inferred by some that HOV 2 carpools composed of family members riding together,
dubbed "fampools," don't take cars off the road and would exist without inducements such as
HOV lanes that are open to use by 2 carpools (Poole and Balaker, 2005).
These various findings over time are indeed suggestive of a shift away from co-worker carpooling
in the transition from the gasoline scarcities of the 1970s to the epoch of plentiful and cheap gaso-
line in the 1990s. Gasoline pricing and availability are, however, probably not the only factor. For
example, the increase of women in the workforce may have increased opportunities for family
members to carpool to work. Many HOV lanes by their very nature emphasize service to persons
going to and from work. Clearly this work trip orientation applies to any facility whose operation
is restricted to peak hours or the peak direction of workday travel flow. The Houston carpool com-
position data provided above pertains to such facilities. It is also likely that all of the various sur-
veys drawn from above were to some degree peak traffic flow oriented. Judging from recent Los
Angeles information and Houston data breakouts, traveling to work remains the dominant reason
for being on an HOV lane in peak periods (see "Related Information and Impacts"--"HOV Facility
User Groups"--"User Trip Purposes and Other Characteristics").
Whatever the broad effects of carpool composition, limited analysis of HOV facility users--focus-
ing on Houston experience--indicates that HOV lanes have a positive influence on the duration
or life of carpools. Comparison of survey results for carpoolers using HOV lanes with those on free-
ways without HOV lanes indicates that the median age of carpools is two to three times higher in
the case of freeway HOV lanes. Median length-of-time in operation for three separate years was
13, 12 and 9 months for HOV lane carpools as compared to 3, 6 and 4 months, respectively, for non-
HOV freeway carpools (Christiansen and Morris, 1990).
Casual Carpooling
In a few locations with supportive conditions, "casual carpooling" has spontaneously developed.
Casual carpooling utilizes "impromptu carpools formed among strangers" (Burris and Winn,
2006). The impromptu carpool formation separates casual carpooling from "dynamic ridesharing,"
which as currently defined, involves matching--by an independent organization--of passengers
with drivers for individual trips (Victoria Transport Policy Institute, 2005).
Casual carpools have no fixed composition or overarching organization. Instead, without evident
pre-arrangement, motorists ("bodysnatchers") pick up willing riders ("slugs") at established loca-
tions in advance of HOV lane entry points. They do this in order to meet the HOV occupancy
requirement and achieve the time saving and reliability offered by the HOV facility. Despite the
personal safety issues raised in theory by this modern urban variant of hitchhiking, casual car-
poolers form a significant niche market for individual HOV lanes. Exact figures are elusive, but
casual carpoolers appear to represent 5,200 of the AM peak-period carpool occupants on Northern
Virginia's I-95/I-395 Shirley Highway HOV lanes, with another 3,500 to 4,000 in the PM; 8,000 of
AM peak-period carpool occupants on the Bay Bridge HOV lanes in the San Francisco Bay Area;
and (after extrapolation to include drivers) around 750 of AM peak-period carpoolers on the I-10W
Katy Freeway and US 290 Northwest Freeway HOV lanes in Houston (Spielberg and Shapiro, 2000;
Beroldo, 1990; Rides for Bay Area Commuters, 1999; Burris and Winn, 2006).
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Necessary conditions for significant casual carpooling include (Beroldo, 1990; Spielberg and
Shapiro, 2000):
· Significant travel time reduction and reliability gain for the driver through use of the HOV
facility--enough to be worthwhile even subtracting out passenger pick-up and drop-off times.
· Need for additional riders to meet HOV access requirements (enhanced by a 3 or greater
occupancy requirement).
· Well-known pickup locations having easy driver and rider access and offering good transit ser-
vice available as backup for prospective riders.
· Very substantial employment concentration(s) as the focus for the morning commute, allow-
ing quick and efficient passenger drop-off and dispersal to ultimate destinations.
Nature of Casual Carpooling. The characteristics and extent of casual carpooling are further illus-
trated here by drawing on observations from the three major casual carpooling locales: the
Northern Virginia suburbs of Washington, the San Francisco East Bay area, and Houston. A key
similarity among these areas is the existence of 3 carpool occupancy requirements, giving spe-
cial impetus to the search for additional occupants (Burris and Winn, 2006).
Observations at the Pentagon and elsewhere in Northern Virginia's I-95/I-395 corridor suggest
that casual carpooling is "a highly egalitarian activity." Passengers and riders appear to make no
differentiation on the basis of gender, race, military versus civilian, or military rank. More males
than females were observed overall at six pickup locations, with females constituting 32 percent
of persons in arriving vehicles and 40 percent of persons taking rides. Percentages varied among
locations, for no readily discernable cause, with 60 percent females accepting rides at two of the
more outlying locations. It has been theorized that rider comfort level is increased by the ability to
pair up in accepting rides, an approach enhanced by 3 or greater HOV occupancy requirements.
Of persons accepting rides, 88 percent did so in groups of two or three. Group gender composition
(number of observed groups or persons) was 40 to 41 percent male-only, 21 percent female only,
and 38 percent mixed. Slugs were observed to line up in destination-specific queues (Spielberg and
Shapiro, 2000).
Casual carpooling in the I-95/I-395 corridor, to and from the Pentagon and the District of
Columbia core, started in the early 1970s. Drivers going the full distance in the 2004 AM peak
period saved 37 minutes of I-95/I-395 travel time in exchange for forming casual carpools, better
than halving their line haul travel time (Spielberg and Shapiro, 2000; Metropolitan Washington
COG, 2005).
A 1998 assessment of I-95/I-395 "slugging activity" concluded that all persons who slugged in the
afternoon probably also did so in the morning. It also presumed that where morning bus ridership
was less than afternoon ridership, the differential represented persons slugging in the morning.
Based on these assumptions, the assessment produced an estimate of 900 persons slugging in the
morning and returning by public transit, and 2,200 persons slugging in both directions, for a total
of 3,100 persons accepting rides in the AM peak period. This estimate, thought to be understated
given that smaller pickup locations were not surveyed, indicates that almost 11 percent of 28,000
carpoolers and bus riders on the HOV facility between 6:00 AM and 9:00 AM were slugs. Casual car-
pool occupancy was observed to be 1.25 before slug pickup and 3.07 after slug pickup, with a filled-
carpool makeup of 32.6 percent drivers, 8.2 percent non-slug passengers, and 59.2 percent slugs
(Spielberg and Shapiro, 2000). The corresponding number of casual carpoolers would be 1.69 per
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slug, implying that some 5,200 persons in the 1998 AM peak period on the I-95/I-395 HOV facil-
ity were casual carpooling. This represents nearly 19 percent of total carpool and bus AM peak-
period facility usage.
Another casual carpooling activity dating from the 1970s is focused on morning passage west-
bound across the San Francisco-Oakland Bay Bridge. Morning pickup points are at AC Transit bus
stops and BART stations. When extensively surveyed in the late 1980s, drivers filling their cars to
3 occupancy could achieve a 10 to 20 minute time savings and save a $1.00 toll. Following a
period of sharp growth in casual carpooling activity it was concluded, on the basis of three sepa-
rate methodological approaches, that about 8,000 people were involved in casual carpooling in
1989. This represented over 45 percent of carpoolers using the bridge. Casual carpooling was
shortly thereafter disrupted by a 1-month earthquake-related Bay Bridge closure, but subsequently
slowly recovered (Beroldo, 1990).
As of 1998 Bay Bridge casual carpooling was again at a level where 8,000 persons was once more
thought to be a valid approximation of weekday inbound activity. In the 2 years prior to the 1998
survey, the mode had been enhanced by a doubling of Bay Bridge tolls to $2.00, BART fare
increases, and opening of I-80 HOV lanes through the northerly East Bay communities. Spurred
by the I-80 HOV lanes and Environmental Defense Fund publicity, almost 10 percent of inbound
casual carpoolers were returning home as casual carpoolers by 1998. Rail or bus transit was the
mode used to get home by 84 percent. One-third had casual carpooled for less than a year, while
15 percent had been doing so for a decade or more.
Many access modes are used by passengers to reach East Bay pickup points. The walk access per-
centage has declined from 42 to 32 percent between 1987 and 1998, while the drive-alone-and-park
percentage has increased from 29 to 41 percent over the same period. Passenger mode of access
percentages in 1998 across eight pickup locations ranged from 3 to 68 percent walk, 17 to 61 per-
cent drive/park, 6 to 25 percent dropped off, and 9 to 17 percent other including transit. For casual
carpool drivers, average distance from home to pickup point has decreased, with access drives
over 5 miles dropping from 28 percent in 1987 to 15 percent in 1998. In 1998, 67 percent of survey
respondents indicated they were normally passengers, 22 percent said they were normally drivers,
and 11 percent reported being sometimes one and sometimes the other. Some 84 percent casual
carpool 4 to 5 days a week, 10 percent do it 2 to 3 days a week, and 6 percent carpool less frequently
(Rides for Bay Area Commuters, 1999).
The San Francisco Bay Area's Commute Profile survey has identified casual carpools areawide as
making up from 4 or 5 percent (2004 and 2002 results, respectively) to 8 percent (2003) of all car-
pools throughout the 9-county region. The reported variation is likely an outcome, at least in part,
of small sample size (Rides for Bay Area Commuters, 2002 and 2004). In any case, the casual car-
pooling percentage implied for the Bay Bridge HOV facility alone would be much higher, as it is
the primary attraction for casual carpooling but serves only the inner portions of three out of a
much larger total of major corridors throughout the Bay Area.
Casual carpooling proceeds in accord with an established passenger pickup and ridesharing eti-
quette, carefully observed and documented in both Northern Virginia and Houston. Rarely is
money exchanged and it is understood that riders may turn down a ride they don't feel comfort-
able with. During a 4-hour morning observation by a newspaper reporter of the pickup process at
Houston's Addicks park-and-ride lot, it normally took only a minute or two for drivers and riders
to match up. The maximum wait was 7 minutes and the maximum queue was 10 persons. If the
line of riders grew too long, people opted for the bus. When drivers were waiting, people heading
for the bus would accept a ride (Wall, 2002). Formal measurements at the same location in 2003
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