Transportation ! 7 :159- - 177, 1990 © 1990 Klu wer A cademic Publishers. Printed in the Netherlands.

Impact of high occupancy vehicle lanes on carpooling behavior

GENEVIEVE GIULIANO, l DOUGLAS W. LEVINE, 2 & ROGER F. TEAL 2

School of Urban and Regional Planning, University of Southern California, Los Angeles, (',4 9O089, USA 2 Institute of Transportation Studies, University of California, Irvine, Ita,ine, CA 92717, USA

Accepted 30 June t 990

Key words: carpooling, HOV Lanes, Orange County, ridesharing

Abstract: High occupancy vehicle lanes have become an integral part of regional transporta- tion planning. Their purpose is to increase ridesharing by offering a travel time advantage to multiple occupant vehicles. This paper examines the extent to which an HOV facility increases ridesharing. Using data from the Route 55 HOV facility in Orange Country, California, changes in the carpooling rate on Route 55 are compared to that of a control group of freeway commuters. The analysis shows that the carpooling rate among peak period commuters, and particularly those who use the entire length of the facility, has increased. However, there has been no significant increase in ridesharing among the entire population of Route 55 commuters. Results suggest that barriers to increased ridesharing are formidable, that travel time savings must be large in order to attract new carpoolers, and that further increases in capooling will likely require development of extensive HOV lane systems.

1. Introduction

Provision of high occupancy vehicle (HOV) facilities is an increasingly common strategy for dealing with peak period congestion in U.S. metro- politan areas. HOV facilities provide an exclusive lane for buses, vanpools and carpools, allowing them to bypass congestion and save travel time. Travel time savings provides an inducement to increased ridesharing and thus more efficient utilization of the transportation system.

HOV failities are currently operating in at least 17 U.S. metropolitan areas. They range from multimillion dollar transitway projects that inte- grate park-and-fide facilities, express bus service and ridesharing assist- ance programs to simple freeway restriping projects. Transportation plan- ners are generally strong advocates of HOV facilities (Lancaster & Lomax 1987), and the HOV concept has been adopted as the core of transporta- tion plans in many U.S. metropolitan areas, for example Seattle (WA), Houston (TX), Pittsburgh (PA), and Orange County (CA).

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Despite the increasing investment in HOV facilities, however, there has been only limited analysis of their effectiveness as a transportation management strategy. Most of the analysis conducted thus far has focused on HOV "through put" such as peak period person volume and associated indirect effects such as estimated auto trip reductions, travel time savings and fuel savings (e.g. Institute of Transportation Engineers 1986; South- worth & Westbrook 1985). Evaluations of the HOV facilities constructed as Federal demonstration projects generally also considered violation rates and safety issues (Curry 1976; Fisher & Simkowitz 1978; Spielberg, Zellner, Andrle & Tcheng 1980: Wattleworth, Courage & Wallace 1978). Only one facility, the San Bernardino Busway (Los Angeles, CA), has been the subject of a cost-benefit analysis (Gordon & Muretta 1983).

None of these studies have addressed the more fundamental question of the source of increased ridesharing. Do these HOV's attract new carpoolers and transit riders, or do they simply divert existing carpoolers and transit users from other routes or time periods? The objective of providing HOV facilities is to attract new riders -- to reduce the propor- tion of single occupant vehicles and thereby improve the efficiency of the transportation system. Whether HOV facilities fulfill this objective is therefore an important public policy question. This paper focuses on the ridesharing effects of HOV lanes. Results are based on a study of the Route 55 HOV project in Orange County, CA. The purpose of the study was to determine the impact of the project on ridesharing behavior: to estimate the net change in carpooling resulting from provision of the HOV project and to identify factors associated with increased carpooling. The analysis focuses on just two modes, drive alone and carpool, since they account for more than 97°/,, of all trips on Route 55. The remainder of this paper is organized as follows. Our research approach is discussed in Section 2, and the data are described in Section 3. We present results in Section 4 and discuss conclusions and policy implications in Section 5.

2. Research approach

Previous research using national travel data indicate that carpooling is most prevalent among a relatively small portion of commuters: those with longer trips, lower incomes, and more restricted auto access than the general commuting population (Teal 1987). Carpooling reduces the monetary cost of commuting compared to driving alone, but requires additional travel time to pick up and drop off passengers. Long distance commuters (those with trip lengths of 15 miles or more) make up the largest carpool market. These commuters have the greatest monetary

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incentive to carpool, and the added travel time makes up a relatively small proportion of the total commute trip. HOV facilities are intended to increase carpooling by offering carpoolers shorter line-haul travel times on the freeway that can offset the carpooling time penalty. HOV facilities are thus attractive options in heavily congested corridors where peak period travel speeds are particularly low.

If an HOV lane stimulates additional carpooling, this effect wilt be manifested as a change in the carpool formation rate and/or the duration of carpoots. Although several other studies of the impact of HOV facilities have reported increases in carpooling, none of them used a methodology that could clearly attribute the increase to the HOV facility (e.g. Curry 1976; Southworth & Westbrook 1985). Rather, these studies compared the peak period carpooling rate on the freeway before and after the HOV lane was implemented. Any increases in observed carpooling were attri- buted to the HOV lane. More recently, estimates of HOV lane impacts have been made from survey data. In these cases, carpoolers who reported their prior mode as drive alone were considered to be new carpoolers attributable to the lane (Christiansen 1990; Lancaster & Lomax 1987). Such comparisons overestimate the number of new carpools attributable to the lane. Additional peak period carpoolers could represent existing carpoolers who previously used other routes or travelled at other (non- peak) times. Those who previously drove alone may also have carpooled at some previous time. Proper assessment of HOV impacts requires deter- mining whether the observed additional carpools are the result of the formation of new carpools, an increase in the duration of existing carpools (which would result in a higher rate of carpooling over time), or simply of temporal or spatial shifts on the part of commuters who were already carpooting. Only increases in new carpoof formation or carpool duration represent true increases in the carpooling rate.

Carpool diversions obviously represent some benefit. Carpoolers di- verted from other routes or time periods redistribute demand, and conse- quently increase the overall capacity of the highway system. Diversions from arterials are particularly beneficial when significant spillover from the freeway has affected arterial flow within the corridor, Temporal shifts occur in response to travel preferences, and therefore generate benefits to the individual. However, the primary purpose of HOV facilities is to expand the market for carpooling and increase the overall carpooling rate.

In order to determine the impact of an HOV facility, we estimate the net change in carpooling relative to before the facility was available, con- trolling for the source of the change, as well as for any other factors that may have affected carpooling over the time period. A panel survey of commuters before and after implementation of the project would provide

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a direct measure of project impacts. However, limitations in the timing of the HOV implementation precluded a panel study. We therefore used a cross-sectional survey sample and collected both current and retrospective data.

Our sample includes carpoolers and solo drivers from the HOV project location and from a control sample of freeway commuters. Current and past carpooling behavior can be compared between the two groups. Our analytical approach consists of comparing the changes in carpooting between the two groups before and after the HOV project was imple- mented.

3. Data

We conducted our analysis on the Route 55 HOV project in Orange County, California, a suburban county located in the greater Los Angeles metropolitan region.

3,1 "lTw Route 55 project

Route 55 is a major commuting and recreational route for Orange County travelers (Fig. 1). It is one of the most congested freeways in the County. Prior to adding the HOV lane, congested conditions prevailed 6 to 8 hours daily; with average peak hour speeds of under 30 mph. Parallel arterials in the corridor were also filled to capacity as a result of spillover from the freeway.

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A reconstruction project provided the opportunity to add a fourth lane in each direction within the freeway median. Local and state officials decided to use the lanes as an HOV demonstration project. The lanes were opened in November, 1985, and became permanent HOV facilities in 1987. The Route 55 HOV facility is spartan. Space for the lanes was created by eliminating the existing median shoulder and narrowing the general purpose lanes. The HOV lanes are separated only by a double- double yellow striping (about 1 foot wide) over most of their 13.5 mile length. There are no park and ride lots along the route, and no express bus service is available. The immediate effect of the HOV project was to improve peak period traffic conditions on the entire freeway. All freeway travelers saved time due to the reduction in congestion. Time savings in the general purpose lanes was short-lived however; by late 1987 traffic congestion had returned to pre-project levels.

The Route 55 HOV project has been implemented in one of the less promising environments for ridesharing. Orange county workers are afflu- ent, have a high rate of auto access, do not pay for parking, and do not have particularly long commute trips. Hence, the carpooling rate among Orange County commuters is less than the national average as well as that of neighboring Los Angeles County. Average I987 peak period freeway vehicle occupancy in Orange County was 1.15. Moreover, although Route 55 serves the single largest employment center in the county (the South Coast Metro area), this center does not have the density or spatial con- centration of a conventional downtown.

On the other hand, Route 55 is a major commuting route from River- side County to Orange County. This commuting market has experienced substantial growth over the past 10 years despite the distance involved (a trip from Riverside to central Orange County is at least 30 miles in length), due to the availability of more affordable housing in Riverside County. Commuters from Riverside to Orange County thus tend to have lower incomes than workers who live in Orange County. Given modest incomes and long trips, commuters from Riverside County may represent a promising market for increased carpooling as a result of the HOV lane.

3.2 Sula'ev data

Data were collected during November and December of 1987. The Route 55 sample was generated from an earlier one day license plate video taping survey. The license plate data were used to identify vehicle owners who were subsequently contacted by phone, or by mail if the phone number was unlisted. The Route 55 sample was drawn on a 50% split between HOV users and mainline users, and on a 66% to 33% split

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between the AM peak (6--10 AM) and the PM peak (3--7 PM). These splits were chosen in order to obtain a large sample of workers and carpoolers. The control sample was generated from two sources: a license plate videotape survey conducted on the 1-405 freeway in Orange County, and a random digit dial telephone survey of selected mail zip code areas in Orange County and adjacent areas in Los Angeles, Riverside and San Bernardino counties. The 1-405 sample was drawn on the same AM/PM peak split as that of the Routs 55 sample.

Survey sample information is given in Table 1. Valid responses are defined as those from persons who use an Orange County freeway at least twice a week during either the AM or PM peak. The final sample consisted of 1,238 observations, of which 1,041 were work trips. It is important to note that the categories listed in Table 1 are the sources from which the sample was drawn, and not necessarily the respondents" usual mode or freeway used as reported in the questionnaire.

Fabh" 1. Survey sample sources and responses.

Sample Mail Telephone

Responses Response rale Responses Response rate

Route 55

HOV Users 183 34% 203 56% Mainline Users 196 36% 226 62°/,,

Subtotal 379 36% 454* 61%

Control Group

1-405 Users 152 38% 154 68% Random digit dial N/A 99 37%

Subtotal 152 38% 253 51%

* There were 25 telephone surveys for which lane information was missing.

The sample used in this analysis includes work trips only, because they constitute the dominant type of peak period trips (80% in this case), and ridesharing policies are focussed on the work trip. Modes considered arc limited to carpooling and drive alone, because together they account for more than 97% of all work trips in the sample. The split by mode and route is given in Table 2. The large number of carpoolers in the Route 55 sample was generated by oversampling the HOV lane. The small number

Table 2. Work trip sample.

Route 55 Control

Drive alone 449 345 Carpool 2(19 38 Column total 658 383

Sample total 11}41

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of carpoolers in the control sample is the result of random sampling and reflects the actual work trip carpooling rate in the control sample.

Some sample characteristics by work trip mode are given in Table 3. Mean trip lengths are long because the sample includes only freeway commuters, the sample is thus not representative of all workers. Differ- ences between drive-alone and carpool commuters are as expected. Carpool trips are significantly longer both in distance and time, with a mean carpool trip time of nearly one hour. Carpoolers have a significantly lower level of auto access than those who drive alone (however still more than 1 car per worker), but do not have significantly lower household incomes. The affluence of the sample is quite remarkable; Orange County 1987 median annual household income was about $42,000. Finally, only a very small proportion of workers pay for parking.

Table 3. Sample characteristics by mode.

Drive akme Carpool

Mean distance to work* 22 miles 28 milcs Median distance to work 18 miles 25 miles Mean travel time to work* 46.6 min. 56.0 rain. Median travel time to work 4{I.0 min. 50.0 min. Percent paying for parking 4% 3% Mean vehicles per worker* 1.41 1.30

Household Income (1987S)

Less than $25,000 9°/,, 9% $25,000 to $45,000 28% 30% $45,000 to $65,000 29% 32% More than $65,000 34% 29%

* Difference of means between groups significant at (p ~< 0.05).

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4. Analysis

After the HOV lane was implemented on Route 55, peak period vehicle occupancy counts increased. Table 4 gives vehicle occupancy counts for two locations on the freeway. Note that the data are limited to a two-hour (6:30--8:30 A.M.) peak period, however, and may or may not be captur- ing actual peak-period trends. Also, the occupancy data give no informa- tion on how much of the observed increase may be due to spatial or temporal shifts. Comparing the changes in occupancy from April 1984 to April 1988 shows an increase of 6.9% at Location 1 and 9.5% at Location 2. This increase compares favorably with the 1-10 San Bernardino Proiect in Los Angeles (12.5% increase from 1976 to 1985), a physically separated facility leading to downtown Los Angeles, and with other more similar concurrent flow facilities, such as 1-95 in Miami (4.1% increase from 1976 to 1984). Freeway average vehicle occupancy is low compared to the I-10 (1.59), but quite comparable to 1-95 (1.28) (Southworth & Westbrook 1986).

7~d}le 4. Route 55 vehicle occupancy counts,

Date Occupancy

Location 1

Before HOV

After HOV

Location 2

Before HOV

After HOV

4/26 /84 1.164 1 0 / 3 0 8 5 1,174 1 2 ' 0 5 8 5 1,195 4/{)3 86 1.213 5/08 86 1.221 4 /02/87 1.238 6 ,04 ,87 1.265 4 /27/88 1,244 Change from 4 /26 /84 = 6.9%

4J24/84 1,164 11"19/85 1,168 5/(16/86 1.233 5 /12/87 1.239 4-'27/88 1,269 Change from 4 / 2 4 / 8 4 = 9.5%

Source: California Department of Transportation.

4.1 Measuring the change in caq~ooling

Given the observed increase in vehicle occupancy on Route 55, we now

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turn to the question of whether this increase represents new carpoolers or diversion from other facilities. The population of current carpoolers consists of those who also carpooled in 1985 ("CP ~ CP"), and those who were driving alone in 1985 ("DA ~ CP'). The population of previous

carpoolers consists of those who are carpooling now CCP --" CP") and those who are driving alone now ("CP --" DA"). Thus the net change in carpooting is equal to:

[(CP --* CP) + (DA --' cP)] - [(CP - CP) + (cP ---- DA)I

o r

(DA -* C P ) - (CP -* DA). (1)

Because our Route 55 sample overrepresents carpoolers, it will also overstate the (DA ~ CP) group. Thus in order to calculate the net change in carpooling, the Route 55 sample must be adjusted to reflect the actual carpooling rate. Vehicle occupancy data from Table 4 were used to weight the sample. Although the analysis is limited to the work trip, the weighting scheme is based on all trips in conformance with the occupancy data.

In order to control for shifts of carpoolers from other facilities, we restrict our definition of new carpooler to someone who previously made the same trip driving alone (either on Route 55 or on another route). Someone who carpooled on another freeway but shifted to Route 55 to take advantage of the HOV lane is previous carpooler (CP --' CP), even though he/she may be new to the Route 55 freeway. Anyone who did not make the same trip in the pre-lane period is not included in the analysis. That is, those who were not working two years prior are excluded. Our survey data also of course excludes those who were commuting on Route 55 in 1985 but not in 1987, as they are not included in the sampling universe. As mentioned earlier, temporal shifts in carpooling may have also taken place. Because of the lack of occupancy data for times outside the two-hour a.m. peak, however, it is not possible to quantify these shifts, In addition, data were not available on the time of day of the pre-lane work trip.

4.2 Results

The change in carpooling rate is measured as the net percent change. This percent change can also be viewed as the mean travel mode change. A positive score indicates there are more people changing from driving alone to carpooling. A negative score indicates more people changing from carpooling to driving alone. Specifically, the net percent change is corn-

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puted as,

Total Carpools Now - Total Carpools Then

Total Commuters

I(CP - CP) + (DA --' CP) - [(CP --" CP) + (CP - DA)]

(CP - CP) + (DA - CP) + (CP ~ DA) + (DA --' DA). (2)

The net percent change for the control group is 1.44%. The net percent change in carpooling for the weighted Route 55 sample is 2.99%. Com- paring the mean mode change for the Route 55 sample against the control mean revealed no significant difference at the 95% confidence level. ~ That is, the number of people changing to carpooling from driving alone was not greater for the Route 55 freeway sample as a whole compared to the naturally occurring rate of mode change in the control freeway sample.

In order to more fully understand the dynamics of carpooling on Route 55, we partition the sample in various ways and examine differences between the sample subgroups. Four different partitions are presented here:

-- by time of day, -- by trip length on the freeway, - - by total trip length, and -- by whether or not a job or residential relocation occurred within the

two-year time period.

4.2.1 Peak period travelers Work trips reported in the sample may have occurred at any time of day, yet the motivation to carpool should be strongest for workers traveling during the peak when potential travel time savings are greatest. For workers commuting outside or on the edge of the peak period, the HOV lane affords a few minutes time savings at best (there are significant commuting flows on Route 55 both after 9:00 a.m. and before 6:00 a.m.). In contrast, time savings of 15 to 20 minutes are possible for peak period users of the HOV lane. We approximate the peak period sample by selecting all trips that begin or end between 6:00 a.m. and 9:00 a.m. Results for tests of carpooling differences are given in Table 5, and show that the increase in carpooling on Route 55 among peak period travelers is 3.54 %, significantly greater than that of the control group.

Table 5. Difference test: workers traveling between 6 and 9 am.

Sample Present change in mode

Route 55 3.54% Control --0.40% t(646) 3.32*

* Significant at p < 0.05.

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4.2.2 Taking advantage of the hme If the HOV lane influences mode choice, it should have the greatest influence on trips that can utilize it for the greatest length. We test this idea by splitting the sample into two groups: group 1, those who use half or more of the total length of the HOV lane and group 2, those who use less than half. Results are presented in Table 6. The group 1 sample results are positive, with significantly greater increases in carpooling compared to the control sample, and the largest increase of all sample segments. The group 2 sample results are just the opposite. Thus, the largest increase in carpooling is coming from commuters who are able to take full advantage of the lane's travel time savings.

f ab l e ~5. Difference test: use of the HOV lane.

Percent change in mode

Group 1 Group 2 Use half Use less than or more of half of HOV HOV lane lane

Route 55 1229% -2 .94% Control 1.44% 1.44% t-value 6.04* -3.68* d.f. (637) (6t6)

* Significant at p < 0.05.

4.2.3 Trip distance and use of the HO V hme Use of the HOV lane is also related to trip distance. Given that long distance commuters are a primary market for carpooling, we explore whether the pattern of trip distances among Route 55 users is different from that of the control group. Aside from expecting carpoolers to report

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longer trips, we have no prior expectations regarding new vs. old (carpool- ing two years ago) carpoolers or new vs, old drive-alones. However, if the HOV lane influences mode choice, new Route 55 carpoolers should travel a longer distance on the lane (a surrogate for travel time savings), whether or not their total trip distance is longer than that of old carpoolers. Similarly, new drive-alones should travel the shortest distance on Route 55, whether or not their total trip distance is shorter than that of old drive-alones. People with the shortest distance on Route 55 have the least incentive to carpool; people with the longest distance have the greatest incentive, And these incentives are most critical for those who change (pre-existing carpoolers obviously had sufficient incentive without the lane).

Table 7 presents mean trip distance of the four mode groups for Route 55 and control group commuters. One-way analysis of variance was used to test whether differences in means between the four mode groups are significant. M e ~ s for both total trip distance and distance on Route 55 are significantly different for the Route 55 sample. The Route 55 results are interesting: carpoolers have longer total trip distances and distances on Route 55 than do solo drivers, and new carpoolers have the longest trip distance on Route 55. New drive-alones have both the shortest total trip and shortest distance on Route 55. None of these differences were significant for the control sample.

7ahle 7. Trip distance by mode group, total samples of Route 55 and control.

Route 55 Control

Average trip Average Route 55 Average trip distancc distancc distance

I)A -" I)A 21.3 miles 6.8 miles 24,1 miles CP - DA 18.6 6.4 t9.2 I)A ~ CP 28.9 9.1 26.7 CP - CP 31.5 8.1 23,4 Total population 23.9 7.4 24.0 n 549 506 337 df 3,545 3,502 3,333 F-statistic 11.39* 13.52* 0.96

* Significant a l p < 0.05.

The conclusion that time savings provide motivation for new carpoolers is further supported by the analysis of a survey item which asked whether time savings were an important factor in determining whether the re-

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spondent carpooled. New Route 55 carpoolers rated time savings as significantly more important than old Route 55 carpoolers.

4.2.4 The role of location changes The survey sample population is highly mobile: slightly more than half of the commuters reported having changed either work or home location within the past two years. Mode choice changes may be made jointly with location change decisions. Workers may move closer to work or take jobs closer to home and shift from carpooling to driving alone. Others may move further away and shift from driving alone to carpooling. Thus it may be argued that our analysis should be limited to those who have made the same trip, that is, those who have not changed job or work location. Results are given in Table 8. Both the Route 55 and control groups consist of "non-movers" only. Results are quite different from the total sample. The increase in carpooling is significantly greater for the Route 55 sample.

Further tests e showed that the Route 55 "movers" tended to change in the opposite direction-toward more drive-alone commuting. However, "'movers" do not necessarily have shorter trips. It therefore cannot be concluded that the reduction in carpooling observed among movers is due to shorter trips. The difference between "movers" and "non-movers" mode changes also exists within the other subsamples. For example, among the commuters traveling less than half the length of Route 55, nearly all of the reduction in carpooling is accounted for by the movers. These results suggest that the HOV lane provides an incentive to shift to carpooling for commuters who are tied to job and home locations.

T.hle S. Difference test: c o m m u t e r s who have nol changed h o m e

or job location within two vcars.

Sample Percent change in mode

Route 55 (~.73% Control - 0 , 6 3 % t(453) 3,71"

* Signif icanlat p < 0.05.

4.3 Calpool duration

In addition to providing an incentive for more carpooling, the HOV lane may also provide an incentive for longer carpoot duration. People cur- rently in carpools are motivated to stay in order to retain the time savings

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benefit of the lane. When carpools are disbanded, there may be motivation to more rapidly join another carpool.

Our survey data provides only one comparison of carpool duration: current Route 55 carpoolers v.s. current control group carpoolers. Table 9 shows that among current carpoolers, the Route 55 group has significantly longer duration than that of the control group. It is possible that this result may be due to other factors, such as lower average incomes, longer trips, ctc. Difference tests of trip characteristics and socio-demographic factors revealed no significant differences between the two groups, However, the small sample size of the control group carpoolers precludes a detailed analysis of this issue.

Tabh" 9. Carpool dura t ion by sample group, current carpoolers*.

Dura t ion interval Route 55 Control

Less than 6 mos. 9% 41%

6 mos. to I year 22 18 1 year to 2 )'ears 33 ] 2 More than 2 years 37 29

n of observa t ion 177 34

* Differences significanl at l) < 0.001,

It would also be useful to compare these results with those of other carpooling studies. Unfortunately, an extensive literature search revealed only one previous study: a survey of carpoolers and vanpoolers who had been placed by a local ridesharing service agency (Berolds 1987). This study found that approximately half of the carpools lasted two years or more, a much greater proportion than in our sample. However, Berolds" sample is biased toward longer durations. First, ridesharing clients are obviously especially motivated to carpool, and therefore and not repre- sentative of the general carpooling population. Second, the results were reported for only 19% of the completed surveys (10% of the total re- spondents). It is possible that the non-respondents were individuals who had stopped carpooling.

4.4 Conwari.s'on q/Route 55 with other HO I"t)rojecls

The proliferation of HOV facilities in long range transportation plans has been based on the reported success of these facilities in increasing the number of peak period person-trips on the freeway. It is useful to compare

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Route 55 results with those of other projects to determine whether the net increase in carpooling observed here may be considered representative of other projects.

Few other surveys of HOV users have been reported. The Texas State Department of Highways and Public Transportation has an ongoing evaluation program for the Houston area transitway systems that includes user surveys (Christiansen 1987, 1990). Only one other recent user survey has been reported: on USI 2 in Minneapolis, MI: an arterial highway HOV facility (Crawford 1987). Table 10 gives data on prior mode for three projects: Route 55, the 1-10 (Katy Freeway) in Houston, and US12. The data were collected approximately two years after project implementation in all cases. Based on the limited data available, the Route 55 project compares well. Route 55 has the highest proportion of previous drive- alone commuters, and US12 has the highest proportion of carpoolers diverted from a different route. Conclusions about project effectiveness (e.g. promoting shifts to carpooling) cannot be drawn because of the lack of data on carpool turnover rates. If the turnover rates are comparable, however, the Route 55 project is at least as effective as the others.

Table 10. Prior mode of HOV carpoolers.

Previous mode Route 55 US 12 (Minn) 1-10 (Houston)

Drove Alone 57% 38% 50%

Same route 35% 26% N,'A Different route 22% 12°/,, N/A

Carpooled/Transit 43% 62";i, 50%

Same route 29% 3 Y'/,, N 'A Different route 14% 29% N A

Source: Calculated from Crawford (1987).

5. Conclusions

This research has shown that the Route 55 HOV project has had a significant impact on carpooling behavior among peak period commuters, and particularly on those able to take full advantage of the lane's travel time savings. The HOV lane is intended to provide an incentive for peak period commuters to carpool, and within this group, the HOV lane had

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the intended effect. If, on the other hand, all work trips (i.e. both peak and non-peak) are considered, carpooling on Route 55 did not increase compared to the control sample. Indeed, in the off-peak, more turnover to carpooling was evident in the control sample+ Our analysis also indicates that the travel time savings of the lane is the motivating factor for new carpoolers, and that these savings are particularly attractive to those who have retained the same job and home location for two years or more.

Finally, the limited data available suggest that the Route 55 results are comparable with those of other HOV projects, both in terms of the overall increase in vehicle occupancy and the prior mode of HOV lane users. This result is particularly significant in view of the special circumstances of the Route 55 project. First, the HOV lanes were added to a heavily congested facility, and the resulting additional capacity provided travel time savings to all commuters. That is, the relati+,e travel time advantage to carpoolers was somewhat offset by the improved conditions in the general purpose lanes. These improved conditions attracted as many drive-alone as carpool trips from other routes. The extra capacity is now used up, and traffic conditions are approaching those of 1985. Further increases in carpooling should result as the travel time difference between the HOV lane and the general purpose lanes increases.

Second, the Route 55 project has a comparative disadvantage due to its geographic location and lack of supporting services. As discussed above, Route 55 does not serve a conventional high density employment con- ccntrafion. The facility has no express bus service, no park and ride lots, and no other characteristics that may be conducive to a higher rate of carpooling. In a sense, then, the Route 55 project provides the most stringent test of the HOV concept.

5.1 TDe promises of riO V strategies

The results presented here indicate that HOV projects can increase ridesharing, but only when the potential gains are relatively large. As noted earlier, use of the entire length of the lane generates travel time

"1 o savings of 15 to 20 minutes, or roughly .5 ~, for the average commuter trip. Commuters who are able to take full advantage of the lane have shifted to carpooling in rather significant numbers (12.7°/.); however the overall shift among peak period commuters has been relatively small (3.5°/.).

These results also suggest that barriers to increased carpooling are significant, even within the most promising market segment: that of the long distance commuter. The shift away from carpooling among off-peak commuters may indicate that if schedules can be arranged to avoid peak

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travel, driving alone remains the preferred alternative. Futhermore, the fact that workers who changed job or home location within the past two years were more likely to shift to driving alone suggest that carpooling may be a last resort option, chosen when other alternatives are not available.

5.2 l~lannin~ implications

Our analysis also has implications for transportations planning. Given the dispersed pattern of work trip travel in decentralized urban environments, development of a comprehensive HOV network would be required in order to provide the travel time savings necessary to attract more solo commuters to ridesharing. An extensive network is necessary to serve the largest number of possible combinations of origins and destinations.

System level planning implies a substantial level of financial investment, and careful analysis would be required to determine whether such in- vestment is justified. It is possible that capital intensive HOV alternatives, such as fully separated transitways with exclusive access/egress systems cannot be justified on the basis of potential ridership, except under the most favorable conditions, e.g. extremely high density travel corridors. On the other hand, freeway median HOV lanes that share access/egress with general purpose traffic may be more appropriate in suburban en- vironments where travel patterns are more dispersed. More research is necessary to estimate the relationship between travel time savings and propensity to rideshare so that the potential benefits of alternative facilities can be evaluated.

The results of this research also have implications for HOV facility design. Since the primary market for HOV users is the long distance commuter, access/egress points could be minimized, thus reducing the number of conflicts with general traffic on shared facilities. Location of access/egress points should be based on traffic patterns so as to provide for the greatest possible number of trips within these constraints. In addition, HOV facilities may benefit from other supportive services and facilities. For example, park and ride facilities should be provided so that alternative carpool arrangements can be accommodated. Recent research on casual earpooling also suggests that park and ride lots provide an alternative means for forming new carpools? Finally, our research leads to the conclusion that preferences for drive-alone commuting are strong, even when traffic is heavy and trips are long. Thus while HOV facilities play a role in solving transportation problems in U.S. urban areas, they cannot be expected to generate major changes in travel behavior.

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Acknowledgements

This research was supported by the Orange County Transportation Com- mission under contract OCTC-11378, and by the Institute of Transporta- tion Studies, University of California, lrvine. Opinions and points of view expressed, as well as possible omissions, are those of the authors.

Notes

1. T-test result: 1(912) = 1.50, p = 0A 3, with degrees of freedom based on the weighted data.

2. T-test result t(264) = 2.09, p < 0.05. 3. Information provided to authors by the Southern California Association of Govern-

merits.

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