HOV or General Purpose © The Author(s) 2009 Lanes? or GPL as...Burris and Lipnicky 131 from single...

Public Works Management & Policy14(2) 130 –147

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DOI: 10.1177/1087724X09350227http://pwmp.sagepub.com

HOV or General Purpose Lanes?

Mark Burris1 and Kevin Lipnicky2

Abstract

High occupancy vehicle (HOV) lanes are used as a congestion management tool in many areas to encourage carpooling and transit by offering faster, more reliable trips than general purpose lanes (GPLs). However, they draw criticism from some groups as some motorists believe that they are underused. This article uses extensive data sets to compare user costs, specifically time and fuel costs, for two scenarios—(1) a single reversible HOV lane and (2) a GPL in each direction on the Katy Freeway in Houston, Texas—over a variety of mode and time shifts. These two scenarios were chosen because they would require approximately the same amount of room in the median of the Katy Freeway. With both high occupancy/toll operations and slugging on the Katy Freeway, the authors set out to see if this highly used HOV lane was the best use of this valuable real estate. Or would a pair of GPLs have provided lower total travel costs? In almost all examined scenarios, the two GPLs case had lower user costs than the HOV lane case.

Keywords

HOV lane, general purpose lane, comparison, planning

Introduction

High occupancy vehicle (HOV) lanes are a congestion mitigation technique commonly employed in urban areas around the United States. These lanes use vehicle occupancy restrictions to ensure free-flow commute conditions. In turn, the travel time savings offered by HOV lanes over congested general purpose lanes (GPLs) encourages drivers to change

1Texas A&M University, College Station, TX, USA2Halff Associates, Inc., San Antonio, TX, USA

Corresponding Author:Mark Burris, Zachry Department of Civil Engineering, Texas A&M University, 3136 TAMU, College Station, TX 77843, USAEmail: [email protected]

Burris and Lipnicky 131

from single occupant vehicle (SOV) commuting to higher occupancy modes such as car-pools and bus transit. This enhances the roadway corridor’s person-carrying capacity dramatically, offering the ability to move as many or more people in the HOV lane as in all adjacent lanes (Turnbull et al., 2006).

HOV Lane OppositionDespite HOV lanes’ potential operational benefits, public resistance to their imple-mentation or continued operation exists. For example, after mainline sections of concurrent-flow HOV lanes had been implemented on I-80 and I-287 in New Jersey, but before interchange direct connector ramps could be completed to connect the HOV lanes into a system, public outcry resulted in the redesignation of the HOV lanes to GPLs. The public was dissatisfied with the low levels of use of these lanes shortly after implementation (Turnbull & DeJohn, 2000). Interestingly, only the HOV lanes on I-287 were critically underused, with peak flows of only 200 to 380 vehicles per hour (vph). In contrast, the I-80 HOV lanes carried between 1,000 and 1,300 vph in the peak hour. Despite the level of use of the I-80 HOV lanes, public sentiment was so negative that these lanes were redesignated as GPLs at the same time as the I-287 HOV lanes were. Whereas the New Jersey Department of Transportation concluded that the HOV lanes were not meeting their objectives, it is unclear whether these lanes would have resulted in greater benefits as HOV lanes or GPLs (Schofer & Czepiel, 2000). Regardless of whether this lane network would have, in time, been better used, it illustrates the power that public sentiment has in the selection of transportation policy. Additionally, this example indicates how underused HOV lanes can affect the viability of entire HOV networks.

Optimizing HOV Lane UseEven HOV lanes with high usage may be underused. Operationally, HOV lanes can be considered successful if they carry more people than adjacent GPLs (Schofer & Czepiel, 2000). However, this does not mean that the HOV lane operates at capacity or that the public will consider it the best option available. Some strategies used to improve HOV lane use, and public approval of HOV lanes, include offering express transit service along the HOV lane, to provide park-and-ride lots adjacent to the lane, and to offer carpool matching services. Another strategy used to increase vehicle and person movement on HOV lanes is to convert them to high occupancy/toll lanes, as on the Katy Freeway. These allow excess capacity to be filled by low occupancy vehicles without exceeding capacity though the use of variable pricing. These measures can increase the number of vehicles using the HOV lane, improving its effectiveness.

Yet another practice that increases HOV lane use is casual carpooling, also known as “slugging.” This informal practice, not sanctioned or facilitated by transit agencies, takes place when motorists who would otherwise use the GPLs arrive at transit stops to pick up passengers. The driver benefits by then being eligible for the HOV lane and

132 Public Works Management & Policy 14(2)

the travel time savings it affords. The passengers, who are mostly transit riders (Burris & Winn, 2006), benefit by reducing their wait time and eliminating their fare. This practice has begun in Houston on the Katy and Northwest freeways during the periods of 3+ occupancy restrictions on the HOV lanes. Approximately 350 casual carpoolers were transported on the Katy Freeway on June 19, 2003 (Winn, 2005). The over-whelming majority of casual carpoolers commute to downtown areas. It is expected that they would switch to transit travel absent an HOV lane.

HOV Lanes in HoustonMost of the HOV lanes in the Houston, Texas, area are of the barrier separated, revers-ible type (Turnbull, 2003). The Katy Freeway reversible lane was constructed where the inside shoulders of the GPLs were originally located. Measuring 19.5 feet, the lane is wide enough for a bus to pass another, disabled, bus (Metropolitan Transit Authority of Harris County, Texas, 2007). In addition to the width of the lane itself, there were barriers on either side of the HOV lane, plus inside shoulder room on the GPLs. The cumulative width of these features was probably sufficient for two GPLs in most of the corridor (but not all), with sacrifices on the outside shoulders.

Previous StudiesPast research has compared the benefits and costs of the installation of an HOV lane or lanes versus the installation of a GPL in each direction on the Katy Freeway in Houston, Texas. One such analysis found that the HOV lane provided additional ben-efits over GPLs (Daniels & Stockton, 2000). However, it was not clear from their article how vehicles that traveled in the existing HOV lane were assigned to the GPLs when GPLs replaced the HOV lane. This is a critical issue when examining the two alternatives. One of the most important factors in encouraging commuters to use a high occupancy vehicle lane is the travel time savings it offers (Ungemah, Goodin, Dusza, & Burris, 2007). Thus, in the case where a GPL is added in each direction instead of the one HOV lane case, it would be expected that carpooling and transit use would decline. If all transit and carpool travelers were assumed to travel in SOVs instead of higher occupancy modes, the benefits of the HOV lane would be inflated. Many travelers currently using high occupancy modes would continue to do so even without the availability of an HOV lane. Assigning all former high occupancy mode travelers to SOV travel would result in more vehicles on the roadway, resulting in greater congestion than a more realistic HOV reassignment. On the other hand, if all current carpool and transit users were assumed to continue traveling in high occu-pancy modes without an HOV lane, the impact of the HOV lane on encouraging carpool and transit use would be ignored. Recent surveys of travelers in this corridor now offer insight into the range of potential shifts from carpools and transit in the absence of the HOV lane. These surveys revealed that a large proportion of transit and carpool travelers choose their mode based on more reasons than travel time. Furthermore, many of these travelers would still carpool or use transit even without


the HOV lane. Using these data, not available to Daniels and Stockton at the time of their analysis, our analysis is better able to account for traveler mode choice, resulting in a better estimate of the benefits of the HOV lane.

Turner, Carlin, and Henk (1995) compared several alternatives, including the existing HOV lanes, for the Katy, Northwest, and North freeways in Houston. Data from the recently installed automatic vehicle identification (AVI) system were used to determine the operating speeds of the GPLs and HOV lanes, as well as the travel time savings of the HOV lane, during the peak period. Then, the AVI data were used to calibrate a macro-simulation model that was then used to compare

• a no-HOV lane alternative (the no-build case), • the existing case—with an HOV lane, • a case where one GPL was added in each direction instead of an HOV lane, • a case where two GPLs were added in each direction in lieu of an HOV lane, and • a traffic system management alternative with no HOV lane, resulting in a

10% increase in capacity over the no-build case.

Turner, Carlin, and Henk’s (1995) cases were not analyzed for the entire day but only from 6 a.m. to noon. In the cases without an HOV lane, existing HOV volumes were reassigned to the GPLs based on the percentage of each vehicle occupancy type present on the freeway prior to the creation of the HOV lane. Furthermore, 25% of peak-hour HOV travelers were reassigned to the hour before and the hour after the peak period to account for the possibility of temporal shift in travel behavior due to the difference in available travel modes.

Their analysis of the simulated scenarios indicated that adding one HOV lane reduced delay less than adding one GPL in each direction on the Northwest and North freeways. Conversely, adding one HOV lane reduced delay more than adding one GPL in each direction on the Katy Freeway.

However, as our research here suggests, when the roadway becomes extremely con-gested (as it has in the 10+ years after the Turner, Carlin, & Henk, 1995, study), it is necessary to also consider off-peak travel times. The limitation of analyzing only the peak period is important because shoulder and off-peak period travel increases substantially once peak periods become highly congested. This trend is seen currently on the Katy Freeway, where volumes remain high in both directions throughout the daylight hours. Congestion now occurs in the off-peak direction such that there may be significant off-peak direction benefits obtained when adding a GPL in each direction. The analysis performed here will include off-peak direction and a full day of travel.

Dahlgren (1996) focused on estimating the general effects of adding an HOV lane to a corridor versus adding a GPL versus doing nothing. Dahlgren used a queuing model to determine which levels of GPL delay and HOV mode share would be neces-sary to make adding an HOV lane a more attractive alternative than adding a GPL. She found that adding HOV lanes was only more beneficial than adding GPLs in a narrow range of cases, when both GPL delay is 30 minutes or greater and the number of HOVs


on the roadway would be close to, but not exceeding, the capacity of the HOV lane (Dahlgren, 1996). This article also pointed out the fact that HOV lanes’ success at attracting new carpool and transit users depends on continued congestion on the GPLs, which is a less than ideal condition. This was one of several studies of HOV versus GP lanes summarized by Mallinckrodt (2003) that generally found the greatest benefits were obtained from GPLs. Mallinckrodt also noted that these studies were unusual as most studies found HOV lanes to provide the greatest benefits but, in his opinion, used flawed methodologies.

Data SourcesData from multiple sources were used in this analysis to estimate traffic flows, speeds, and the resulting costs due to travel time and fuel consumption. These data and their sources are included in Table 1.

Every effort was made to incorporate the most accurate data available at 5-minute intervals, which meant including data from slightly different years. Although not ideal, it should have very little impact on results as the Katy Freeway experienced no significant changes during the period of data collection.

AnalysisIn every scenario, the base case represents the conditions observed on the Katy Freeway with its HOV lane in 2001, and the alternative case included a GPL being added in each direction to the Katy Freeway in place of the existing HOV lane. The alternative case may not have been physically possible in this case, although it would be close—but it is worth examining as many freeway medians could accommodate these alternatives. Thus, the focus is on the best use of the land available—not just one lane type versus another. Each scenario included mode or mode and time shifts applied to the alternative case to determine the effect of the shifts on the overall travel costs of the alternative case. In this framework, the travel time and fuel costs of the base case were compared with those costs in the alternative cases, and the percentage difference between the two was reported for all scenarios. Route shifts were not examined, as few good alternative routes to the Katy Freeway existed. Latent demand from trips not currently undertaken was not explored, as this sort of analysis requires knowledge of travel demand for the area that was not available.

Mode ShiftForty-nine combinations of mode retention were examined. This was accomplished by varying the portions of current travelers using transit and traveling in HOVs who would continue to use those modes without the travel time advantage of the HOV lane. These combinations ranged from 50% to 100% of HOV or transit mode retention in seven equally spaced increments for each group, the HOV users and the transit users.


Table 1. Summary of Data Sets Used

Raw Data

Data Source Use

HOV volume March 2006 Houston HOV opera-tions report (Texas Transportation Institute, 2006)

Determine the number of vehicles on the HOV lane

HOV speed 2002 AVI observations. The TxDOT has an extensive travel speed data collection system based on tracking toll transponders

Determine travel time on the HOV lane. Vehicle speed data were aggregated into 5-minute average directional speeds for all workdays in 2002 for this study

HOV occupancy 2003 AVO counts Determine person movements by vehicle occupancy on the HOV lane

Casual carpooling volume

Report on casual carpooling in Houston (Winn, 2005)

Determine the number of casual carpoolers

GPL volume 2001 loop detector data. Volume data from these detectors for all workdays in 2001

Determine the number of vehicles on the GPLs

EB GPL speed 2001 loop detector data Determine GPL travel timeWB GPL speed 2002 AVI observations Determine GPL travel timeGPL occupancy 2003 AVO counts Determine GPL person

movements by vehicle occupancy on the GPL lane

Fuel consumption West et al. (1999) Establish speed to fuel consump-tion relationship

Fuel cost American Automobile Association (2007)

Determine cost of fuel used. Used the average price minus the tax for Houston, Texas

Value of travel time TxDOT, September 2007 Determine user cost of delayCarpool participant

and transit rider proportions

A 2003 Survey of Katy and North-ewest Freeway Travelers (see Burris & Figueroa, 2006, for survey details) and a 2005 Survey of Houston and Dallas Area Resi-dent’s Attitudes Toward Managed Lanes (see Burris et al., 2007, for survey details)

Determine likelihood that carpool and transit travelers would change mode without HOV lane

Author Developed DataData UseTime-shifted volumes Examine the effect of time-of-day of travel shift, resulting in more

intense peak periods and long shoulder periods, on overall user costs

Speed–flow relationship

Determine how travel speed, and thus fuel consumption and value- of-time costs, vary with traffic volume

Note: HOV, high occupancy vehicle; TxDOT, Texas Department of Transportation; AVI, automatic vehicle identification; AVO, average vehicle occupancy; GPL, general purpose lane.


Travelers who currently use transit who were not retained in the alternative scenarios were assigned to SOV and HOV travel in proportion to the numbers of SOV and HOV travelers currently using the Katy Freeway during the given time. This equates to about 90% SOVs and 10% HOVs. This is similar to other freeways and appears to be a reasonable approximation.

Travelers in HOVs who were not retained were assigned to SOV travel. These mode shifts resulted in an increase in total corridor volume over the base case. The full range of mode shifts was compared to determine the effect of mode choice on conges-tion and travel costs in the alternative scenario.

The mode shift ranges chosen stem from both characteristics of Katy Freeway travelers as well as national trends. A survey was conducted in 2003 of travelers using the Katy and Northwest Freeways in Houston. Questions included whether travelers typically car-pooled, and if so, what was the relationship between the participants in the carpool. A total of 380 respondents to this survey indicated that they traveled on the Katy Freeway in a carpool. In 2005, another survey of travelers in the Houston and Dallas areas was con-ducted. Although this survey focused on travelers’ attitudes toward managed lanes, questions about travelers’ carpooling behavior were also included. A total of 140 respon-dents to this survey indicated that they traveled on the Katy Freeway by carpool. From the responses to these surveys, the proportion of carpool participants by relationship to the survey respondent was determined. Figure 1 displays these proportions.

The proportion of carpools consisting of family members (fampools) on the Katy Freeway, 65.5% in 2003 and 58.2% in 2005, is lower than the national average. The national average proportion of carpools consisting of family members is 74% (Ungemah et al., 2007). It is believed that fampools are usually formed for reasons other than gaining access to HOV lanes, such as the ability to drop a child off at school

Figure 1. Percentage of carpools by participant type

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or daycare on the way to work or for spouses to save fuel and maintenance costs when their workplaces are near each other. Thus, it is reasonable to expect that most fam-pools would exist whether or not an HOV lane was available. Likewise, it is expected that some carpools comprising coworkers or neighbors would be retained absent an HOV lane. Therefore, although the HOV mode retention share was varied from 50% to 100%, it is expected that real-world HOV mode retention would be high, probably greater than 75%.

In November 2003, a survey of peak period transit users in Houston, Texas, was conducted. Of the 687 surveys administered on the Northwest and Katy Freeway express bus routes, 584 surveys were returned. All respondents indicated that there was at least one vehicle in their household, with almost 85% having two or more vehicles in their household (Chum, 2007). This indicates that many transit users in the Katy Freeway corridor were not captive riders and could switch to nontransit modes if they so desired.

In the same survey, stated preference questions were asked regarding those transit users’ mode choice given the ability to travel in the HOV lanes as a SOV for a fee. The fees listed in the survey were often in the same range as the transit fare. In spite of the ability to travel alone while maintaining their travel time savings in the HOV lane, 77% indicated that they would continue traveling by transit.

Based on these results, although many transit riders do have an auto alternative, it is reasonable to expect that many will remain loyal to the transit mode. Therefore, as for HOV mode retention, the transit mode retention rates modeled ranged from 50% to 100%, though it is expected that transit mode retention would be greater than 75%.

Time ShiftAnother factor examined was the potential for travelers to shift their time of travel. The additional capacity and more balanced lane assignment of vehicles offered by the additional GPL over the HOV lane would cause the periods just before and just after the peak to become significantly less congested. For example, just prior to congestion causing the breakdown of the lanes in the morning peak (around 6 to 6:15 a.m.), approximately 2,238 vehicles per hour per lane (vphpl) use the three GPLs and 600 vphpl use the HOV lane. Without mode and time shifts, spreading these vehicles over four lanes results in 1,815 vphpl, which results in nearly free flow conditions. Even with mode shifts, there would likely be good travel conditions. Therefore, some peak period travelers and some early morning travelers would likely alter their time of travel to take advantage of these driving conditions. In the interest of comparison, during the 6 to 6:15 a.m. period, approximately 7,200 people per hour are moved in the eastbound GPLs in 6,650 vehicles per hour, whereas approximately 1,800 people per hour are moved in the HOV lane in 600 vehicles per hour. At the peak of morning HOV lane use, during the 7:30 to 7:45 a.m. period, approximately 5,575 people per hour are moved in the eastbound GPLs in 5,200 vehicles per hour, whereas approxi-mately 4,325 people per hour travel by the HOV lane in 1,025 vehicles per hour.


Vehicles shifting their time of travel can result in congestion occurring on roads shortly after capacity improvements, although the congested periods are usually shorter and less intense than existed prior to the capacity improvement. The effect of three time shift scenarios (small, medium, and large) were compared against the no time shift case (existing traffic by time of day) for the 75% HOV and 75% transit mode share retention scenarios. In each case, the overall person movement for the day was held constant, but the time in which vehicles, and as a result people, traveled was altered to shift people into what appeared to be more convenient and likely travel periods. The time shifts were generated arbitrarily with the goal of determining the effect that increasingly intense peak periods and prolonged shoulder periods would have on delay and fuel consumption. The volumes by time of day for the base and time shifted scenarios are shown in Figure 2 for the eastbound direction.

Volume and Person MovementsTo determine the base case GPL volumes, loop detector observations made on week-days in 20011 were aggregated into 5-minute increments and averaged. These volumes were multiplied by the mode shares observed in 2003 and the average occupancies of those modes to determine the person movement per hour for the GPLs. This also included the number of vehicles of each type (SOV, HOV 2, HOV 3+, bus, vanpools, and motorcycles), which was used later to determine the vehicle volumes of the alter-native cases. The volumes and person movements on the HOV lane were obtained

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Figure 2. Eastbound base and time shifted volumes


through a vehicle classification count conducted for the Houston HOV Operations Quarterly Reports (Texas Transportation Institute [TTI], 2003). The mode share for all modes in the HOV lane, including violating SOVs, but not including casual carpool-ing, was determined from the quarterly report data. The data collection point for vehicle occupancy was well downstream of the locations where casual carpools formed. Therefore, it was impossible to distinguish between a casual carpool and a traditional carpool from these observations.

Casual carpool characteristics, including vehicle occupancy and use counts, were observed and reported in Justin Winn’s (2005) thesis. Casual carpooling was only observed where a substantial travel time savings exists by forming high occupancy, generally HOV 3+, carpools. In other words, casual carpooling would not exist with-out an HOV lane to support it (Winn, 2005). Thus, it was assumed that in the alternative cases, which did not include an HOV lane, casual carpooling flows would drop to zero. It was assumed that all casual carpools were HOV 3, because most casual car-pooling occurs during the HOV 3+ occupancy periods and that two casual carpooler passengers (slugs) entered each participating vehicle. To account for the casual car-pools in the alternative cases, the driver was added to the number of SOVs while the passengers were added to the SOVs and HOVs in the proportion of the person move-ments each group carries. The number of casual carpools was also subtracted from the HOV 3+ lane count so that the calculated HOV lane flow would match with the observed flow.

SpeedsBreakdown. Speeds and flows were recorded by double-loop detectors on both the

eastbound and westbound I-10 Katy GPLs at Kirkwood. These values were plotted together for several weekdays in February 2001. A sample chart is provided in Figure 3, showing the speed and flow in the eastbound direction.

The speed charts were examined to determine what general circumstances lead to flow breakdown each day and what volume levels generally lead to recovery. Three flow regimes were apparent in both the east and westbound data sets. These were uncongested, shoulder, and congested. In the eastbound direction, it appears that flows above 1,950 vphpl often result in the transition from uncongested to shoulder condi-tions and that recovery from shoulder to uncongested tends to occur when flows drop below 1,450 vphpl. The breakdown from shoulder to congested conditions occurs when the maximum volume for the morning or evening period is attained, assuming volumes exceed the threshold for shoulder conditions. Recovery from congested to shoulder occurs when volumes drop below 1,750 vphpl. However, there are major exceptions to these guidelines, particularly due to inclement weather or an incident.

Westbound traffic tended to experience the transition from uncongested to shoulder conditions at about 1,800 vphpl. Recovery from shoulder to uncongested conditions occurred when volumes dropped to 1,700 vphpl. Breakdown from shoulder to con-gested conditions occurred when volumes exceeded 1,900 vphpl, and transitioned


back from congested to shoulder conditions occurred when volumes decreased to 1,800 vphpl. These are all general guidelines used to develop reasonable estimates of the speed–flow relationship in the alternative (no HOV lane) cases.

Speed–flow. The same loop detector data used to estimate the conditions in which breakdown and recovery occur were also used to create speed–flow models. These models have distinct uncongested, shoulder, and congested regions. Figure 4 shows the observed eastbound mainlane speeds with the speeds determined by the model.

The breakdown conditions and speed–flow model were combined to estimate speeds in the alternative (no HOV lane) cases explored. Estimated volumes per lane were analyzed for points of likely breakdown and recovery, and the appropriate speed–flow model was applied to those volumes to estimate the speeds for each 5-minute increment. Observed speeds were used for the base case.

Comparison

Overall travel costs for the corridor were calculated for the base case and the alterna-tive cases for all scenarios examined, and the percentage difference between the two were determined. The overall travel cost comprised the value-of-time cost and the cost of fuel accrued by all travelers on the roadway. Costs such as vehicle capital costs, maintenance costs, and the societal cost of emissions and noise were not calculated. These costs are very difficult to estimate and value, plus they are generally much smaller in value than travel time savings (Burris & Sullivan, 2006; Sullivan & Burris,

Figure 3. Sample eastbound speed and flow chart

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2006; American Association of State Highway and Transportation Officials, 2003). Additionally, construction costs were not calculated, as they were not the focus of this study. It is anticipated, however, that the cost of adding the HOV lane would have been higher than adding a GPL in each direction because of the cost of the elevated ramps from the HOV lane to the transit facilities.

Travel time costs. The first step to determining the cost of travel time was to deter-mine the number of people traveling in each of the 5-minute increments on each portion of the roadway, eastbound, westbound, and (in the base case) the HOV lane. Then, the total travel time cost incurred by travelers over the length of the lane was determined using Equation (1):

(1)

where TTC is the travel time cost ($), 1 to 250 indicates all work days in the year, 1 to 288 indicates all 5-minute increments in a day, L is the corridor length (13.1 mi), PMi is the person movement in the increment (in persons), VoT is the value of time ($15.42/person/hr; David Schrank, personal communication, November 12, 2007), and Si is the speed in the 5-minute increment (mph).

Fuel costs. West, McGill, and Sluder (1999) determined the average fuel consump-tion per hour for several types and sizes of vehicles under many driving conditions at a range of speeds from 0 to 75 miles per hour. This relationship was used to calculate

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Figure 4. Comparison of observed and model speeds on eastbound I-10 Katy Freeway

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the average fuel consumption per mile for a typical passenger vehicle given the range of speeds from 0 to 75 miles per hour. The cost of fuel consumed was determined using Equation (2):

(2)

where FC is the fuel cost ($), 1 to 250 indicates all work days in the year, 1 to 288 indicates all 5-minute increments in a day, FpMi is the fuel consumption per mile in the 5-minutes increment (gal/mi/veh), Vi is the volume in the increment (vehicles), L is the corridor length (13.1 mi), and FP is the fuel price ($2.215/gal). The fuel price reported in Houston on September 20, 2007, minus fuel taxes, was used to determine the total societal cost of fuel consumed. No adjustment was made for buses, as they comprise a fraction of 1% of total vehicles.

Total cost and percentage difference. The overall corridor costs were found by adding the travel time costs and the fuel consumption costs for each 5-minute period. These costs were found for the base case, which represents the existing roadway configura-tion, and a wide array of alternative scenarios where one GPL was built in each direction instead of the HOV lane and mode retention and time shifts were varied. These costs were then compared with one another for each scenario, and the resulting percentage difference in costs is reported. A positive difference indicates that the alter-native case had a lower travel time and fuel cost.

ResultsDifference in User Costs by Average Vehicle Occupancy

For each mode retention scenario, the average vehicle occupancy (AVO) and percent-age difference between user costs were calculated. It is important to note that even though a wide range of HOV and transit mode retention rates were explored, the AVOs only ranged from 1.11 to 1.17. The range of AVOs is small because of the fact that in all periods on the Katy Freeway, travel is dominated by the SOV mode. Even during the peak periods, when transit and carpool travel is the highest, more than half of the person movements in the peak direction are made by SOVs. These values are within the range of a typical freeway without an HOV lane. Additionally, these calculations assumed no time shift but did recalculate travel speeds based on the new per-lane volume estimates. Time shifts are added in the next section.

In all analyzed cases, the case where a GPL was added in each direction was superior to the case where a single reversible HOV lane was added. Generally, when AVO increases (indicating a greater percentage of HOV and transit retention), the relative benefit of the two GPLs added cases increased. This is logical, as higher AVO represents a smaller shift to SOV and a lower volume of vehicles. The decreased volume leads to reduced conges-tion, increased travel speeds, and thus reduced value-of-time costs.

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Difference in User Costs by Percent HOV and Transit Modes Retained

Table 2 illustrates the effects of changing rates of HOV and transit mode retention on the total user costs for the full day in both directions. HOV and transit mode retention have similar effects on the difference in user costs between the alternatives. In cases where either HOV or transit mode retention is low, the difference between the alterna-tives is smaller, whereas high mode retention results in the one GPL added in each direction alternative having much lower total user costs.

Similarly, Table 3 shows the effects of changing rates of HOV and transit mode retention on peak direction user costs. In the high mode retention scenarios, there was very little difference between the peak direction and the full-day percentage differ-ences in user costs. This indicates that both directions benefit equally in the GPL added in each direction alternative. However, in low mode retention scenarios, peak direction user costs changed very little from the base case. Therefore, in low mode retention scenarios, travelers in the off-peak direction received most of the benefits.

Large differences in user costs were due to changes in the start and end times of shoul-der and congested periods. As volume levels per lane decreased below the threshold volumes for a given congestion level, the duration of the shoulder and congested periods decreased, leading to increased travel speeds, reduced fuel consumption, and lower user costs. In many scenarios, especially the scenarios with high levels of HOV and transit mode retention, very little congestion occurred. For example, in the scenario with 100% HOV and transit mode retention, no congested periods were encountered, and only a short shoulder period from 4:30 to 5:15 p.m. in the westbound direction was evident. This was not surprising given the fact that the GPLs were already handling the maximum number of vehicles during the shoulder periods. Furthermore, the GPLs handled less than the maxi-mum possible number of vehicles during the peak period due to reduced capacity after breakdown. Spreading these vehicles, along with a lower number of HOV lane vehicles, over more lanes decreased the throughput to a level where congestion did not occur.

Table 2. Percentage Difference in User Costs by Percentage of High Occupancy Vehicle (HOV) and Transit Users Retained

Percentage of HOV Travelers Retained

Percentage of Transit Users Retained

50 59 67 75 83 91 100

50 8.1 8.2 9.3 10.5 11.8 12.5 15.359 8.7 9.0 10.0 12.2 12.7 12.6 17.067 9.1 9.4 10.9 12.7 12.4 17.3 18.575 9.2 10.7 12.5 12.6 17.3 18.1 19.783 9.1 12.6 14.7 15.1 18.2 18.8 21.991 10.4 12.6 15.1 17.8 18.7 21.9 22.0100 12.9 15.4 15.7 18.6 22.0 22.3 22.5

Note: The percentages indicate the increased cost of the base case (HOV lane) over the alternated case (two general purpose lanes).


Undoubtedly, once travelers observe this congestion free peak period travel, they would alter their time of travel and once again cause a congested peak period. Although this would result in similar levels of peak period congestion as the base case, the shoulder and off-peak periods would experience a reduction in congestion and hence user costs. This would offset the peak period congestion costs.

Difference in User Costs by Intensity of Time ShiftThe final parameter examined was the effect of shifting travel times. It was expected that travelers will respond to reduced peak period congestion by shifting their travel to more convenient times, namely, the morning and evening peak periods. This would have the effect of further reducing delay and user costs during off-peak periods but increasing con-gestion, delay, and user costs during the peak periods. Three levels of time shift, small, moderate, and large, were examined and compared against the scenario of no time shift for the 75% HOV and 75% transit mode retention scenarios. This mode retention scenario was chosen arbitrarily, but it is expected that the majority of travelers currently using the HOV and transit modes would continue to do so even without the HOV lane. The differences between user costs for the cases for each level of time shift for the entire day in both directions ranged from 12.9% benefit of GPLs in the no time shift scenario (as shown in Table 2) to a minimum of 5.9% for a large time shift. As expected, as more travelers shift their travel times to the peak periods, the benefit of the GPL added in each direction case is diminished. In fact, peak period travelers suffer greater delay in the GPLs added scenario with a large time shift.

The user cost difference between the base case (HOV lane) and the alternative scenario (one GPL added in each direction) was similar for the small and medium time shifts. This was despite the fact that the medium time shift scenario had more intense peaks and longer congested and shoulder periods than the small time shift case. Peak period user costs for the alternative scenario exceeded those of the base case due to the long congested periods. This added cost was smaller than the benefit to off-peak direction travelers in the alterna-tive scenario, resulting in lower overall user costs than the base case when all travelers are accounted for. This may be rather unique to only a few hypercongested freeways in the

Table 3. Percentage Difference in Peak Direction User Costs by Percentage of Heavy Occupancy Vehicle (HOV) and Transit Users Retained

Percentage of HOV Travelers Retained

Percentage of Transit Users Retained

50 59 67 75 83 91 100

50 0.6 0.7 2.5 4.4 6.5 7.7 12.259 1.5 2.1 3.6 7.1 8.0 7.9 14.967 2.2 2.6 5.1 8.1 7.5 15.4 17.375 2.3 4.8 7.6 7.9 15.4 16.7 19.383 2.2 7.8 11.2 11.8 16.9 17.8 22.891 4.2 7.9 11.8 16.2 17.6 22.9 23.0100 8.2 12.3 12.9 17.5 23.0 23.4 23.8


country that experience congestion in both directions during peak and shoulder periods. Furthermore, the GPL added in each direction case has the additional, difficult to quantify, benefit of users traveling at their preferred time of travel.

ConclusionsHOV lanes have been shown to reduce congestion, pollution, and fuel consumption in congested corridors when they represent added capacity. HOV lanes are considered successful when they carry at least as many person movements as an adjacent GPL. These statements do not, however, indicate whether that was the optimal use of right-of-way. In corridors where single, reversible HOV lanes were implemented, it may be possible to add two GPLs instead.

Based on 2001 traffic data, a GPL in each direction on the Katy Freeway would have reduced traveler time and fuel costs over a single, reversible HOV lane in all cases examined. Note that this is based on travel conditions that are extremely differ-ent than those experienced when deciding to install the HOV lane 25 years ago. Even where many users who currently travel in carpools or by transit shift to SOV travel, adding a GPL in each direction still reduced total travel time and fuel used over adding a single HOV lane. This also holds true when various time shifts in response to the added capacity along with a reasonable mode shift are modeled.

These benefits occurred for several reasons. One factor is that congestion can be avoided altogether in the off-peak direction due to the fact that the off-peak direction gains an entire lane. This may not be an issue in all but a few extremely congested corridors. Another reason is that demand in each direction is divided equally among all lanes, leading to a more optimal distribution of traffic. No lane is underused compared with any other, meaning that, except in the absolute most congested periods, volume per lane is lower even in the peak direction. Lower volumes per lane lead to shorter congested periods, higher travel speeds, and lower user costs. Additionally, the capacity of the single, barrier sepa-rated, HOV lane is approximately 1,600 vphpl, whereas the capacity of each GPL is closer to 2,200 vphpl in the eastbound direction and 2,000 vphpl in the westbound direction. These factors work together to reduce user costs compared with the single HOV lane case.

These statements do not mean that HOV lanes are not beneficial, that they should not be implemented, or that the Katy HOV lane was less beneficial than two GPLs for most of its operational life. As shown in previous research, during less congested years on the Katy Freeway the benefits of the HOV lane may have outweighed the two GPL case (Daniels & Stockton, 2000; Turner et al., 1995). But, based on our analysis of current operations, GPLs would now be more beneficial. Based on this, TxDOT’s decision to replace the Katy HOV lane with two toll lanes in each direction makes sense. These lanes will be open to all traffic, like GPLs, but only those with higher values of time will use them. This research also highlights the fact that, over time, the best use of highway infrastructure can change and periodic analysis is warranted.

Acknowledgments

This research effort required a great deal of data, much of which were collected by TTI researchers and staff with funding from TxDOT and the Federal Highway Administration. The authors are


grateful for the assistance from these agencies. Additionally, researchers with TTI provided very helpful reviews of draft versions of this article, and three anonymous JPWMP reviewers also pro-vided valuable comments. The authors would also like to thank Houston METRO, who operates the transit system and HOV lanes for their assistance with this research. However, all contents of this article reflect the views of the authors, who are responsible for the facts and the accuracy of the data presented herein. The contents do not necessarily reflect the official views or polices of the Federal Highway Administration, Houston METRO, or the Texas Department of Transportation.

Declaration of Conflicting Interests

The author declared no potential conflicts of interests with respect to the authorship and/or publication of this article.

Funding

TxDOT and FHWA supported research projects that collected the data used in this analysis.

Note

1. Data from loop detectors in Houston more recent than 2001 was unreliable as Houston has discontinued their use.

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Bios

Mark Burris is the E.B. Snead Associate Professor in the Zachry Department of Civil Engineering at Texas A & M University and an Associate Research Engineer with the Texas Transportation Institute. His research focuses on understanding traveler behavior, primarily in response to changes in the cost of travel. He has studied traveler behavior on HOV lanes, HOT lanes and Managed Lanes in depth.

Kevin Lipnicky is an Engineer in Training with Halff Associates. Prior to joining Halff Associates, he was a research assistant for the Texas Transportation Institute studying traveler behavior and mode choice with respect to HOV, HOT, and Managed Lanes. Since joining Halff Associates, he has helped design roadway facilities, including a Managed Lane project.

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