Policy Options Evaluation Tool for 

Managed Lanes (POET-ML) Users 

Guide and Methodology Description 

Federal Highway Administration 

HOV Lane Performance

DTFH61-06-D-00006 

Submitted By: 

Under Contract To: 

December, 2008 
Notice 

This document is disseminated under the sponsorship of the Department of Transportation in the 

interest of information exchange. The United States Government assumes no liability for its 

contents or use thereof. This document does not constitute a standard, specification, or 

regulation. 

The United States Government does not endorse projects or manufacturers. Trade and manufacturers’ names appear in this document only because they are considered essential to the 

object of this document. 
Technical Report Documentation Page 

1. Report No. 

FHWA-HOP-09-031 

2. Government Accession No. 3. Recipient's Catalog 

No.

4. Title and Subtitle 

Policy Options Evaluation Tool for Managed 

Lanes (POET-ML) Users Guide and 

Methodology Description 

5. Report Date 

December 2008 

6. Performing 

Organization Code 

7. Author(s) 

Andrew Smith, HNTB Claudia Bilotto, HNTB; 

Mark Chang, Booz Allen Hamilton Inc. 

8. Performing Organization Report No. 

9. Performing Organization Name and Address 

Booz Allen Hamilton Inc. 

8283 Greensboro Drive 

McLean, Virginia 22102 

HNTB Corporation 

3715 Northside Pkwy NW 

Atlanta, GA 30327

10. Work Unit No. 

 

11. Contract or Grant 

No. 

 

12. Sponsoring Agency Name and Address 

Federal Highway Administration 

1200 New Jersey Avenue SE 

Washington DC 20590 

13. Type of Report and Period 

Covered 

Final Report 

14. Sponsoring Agency 

Code 

HOTM, FHWA 

15. Supplementary Notes 

Patrick DeCorla-Souza, FHWA Office of Operations, Office of Transportation Management, 

Contracting Officer's Technical Representative (COTR). 

The research was performed under contract to Booz Allen Hamilton. 

16. Abstract 

User’s guide for a sketch planning tool for exploring policy alternatives. It is intended for an 

audience of transportation professionals responsible for planning, designing, funding, operating, 

enforcing, monitoring, and managing HOV and HOT lanes, and considering conversion of HOV to 

HOT lanes. This material is also included in FHWA-HOP-09-29, 

17. Key Words 

High Occupancy Vehicle, HOV Lane, HOV Enforcement, 

HOV Facility Enforcement, HOV Facility Design, HOV 

Planning, HOV Operations, HOT Lane, HOT Lane Policy, 

HOT Lane Use, HOT Lane Exemptions, HOT Facility 

Design, HOT Facility Tolling, HOT Lane Planning, HOT 

Lane Policy, POET-ML 

18. Distribution Statement 

No restrictions. 

19. Security Classif. (of this report) 

Unclassified. 

20. Security Classif. (of 

this page) 

Unclassified. 

21. No. of Pages 

28 

22. Price 

Form DOT F 1700.7 (8-72) Reproduction of completed page authorized
FHWA POET-ML Methodology 

HOV Lane Performance Project 

Introduction 

High-occupancy vehicle (HOV) lanes are reserved for vehicles with a driver and one or more 

passengers. These lanes, which often run parallel to general purpose (GP) highway lanes, have 

been implemented in over 30 U.S. cities since they first appeared in the late 1960s and early 

1970s. HOV lanes were originally conceived as a means to encourage carpooling and therefore 

increase person throughput in the transportation system. The restrictions in these lanes limit 

traffic demand, which can provide travel time savings along a corridor when compared to 

adjacent general purpose lanes. This travel time advantage is an incentive to drivers to form 

carpools in order to bypass congestion. 

HOV lanes are one possible solution to increase transportation system efficiency. However, in 

practice these lanes do not always provide the advantages they advertise. Because occupancy 

restrictions are discrete (2+, 3+, etc.), it is difficult to achieve utilization balance in these lanes. 

Ideally, HOV lanes would carry between 1,500-1,800 vehicles per hour per lane, which roughly 

corresponds to Level of Service C conditions and operating speeds of 45 mph or higher. This 

level of flow would ensure a high degree of vehicle throughput, and greater overall system 

efficiency. However, in some areas around the country, occupancy guidelines prove to be too 

restrictive, resulting in empty lane syndrome, where HOV lanes experience very light demand. In 

other areas, peak period demand from eligible carpools can actually overwhelm the HOV lanes, 

leading to congested conditions. Situations with too many, or too few, vehicles using the facilities 

have left several HOV operators seeking solutions to improve the performance of their HOV 

lanes. 

Fortunately, a number of policy solutions do exist to improve utilization rates in HOV lanes. The 

planning team has developed the Policy Options Evaluation Tool for Managed Lanes (POET-ML) 

to evaluate potential changes to existing HOV facilities. This tool is intended for use by HOV 

owners who are considering changes to their current operating policies. It will allow them to see 

the impacts of various alternatives and to compare these alternatives to one another. The 

methodology behind POET-ML is outlined in this paper. 

POET-ML Purpose 

Every HOV lane is unique in its demand composition and operational characteristics. These 

characteristics are often difficult to quantify, so it is challenging for HOV operators to know exactly 

how well their HOV lanes are operating. Likewise, the impacts of any policy changes to their 

HOV facilities are also difficult to quantify, and would create additional uncertainty concerning 

future HOV performance. So before making any changes, it is critical to understand: (1) the 

current operating conditions of the existing HOV facility; (2) what impacts on the operational 

performance of the HOV facility can be expected with policy shifts; and (3) whether policy shifts 

will help the operator meet the goals and objectives established in the study region. 

Travel demand modeling is one approach commonly used to evaluate current and future 

conditions in transportation systems. These models can be used to estimate the potential 

impacts of policy shifts, including changes in HOV lane policies. However, the traditional 

modeling process tends to be complex and requires extensive time and budget to implement, 

rendering it ineffective for quick-response analysis. 

POET-ML is one feasible alternative to travel demand modeling. The tool makes it possible for 

HOV operators to complete a current HOV system condition assessment, quantify the impacts of 

HOV lane policy shifts on operational performance and financial feasibility, and ultimately 

prioritize the most appropriate HOV policy changes, or combination of HOV policy changes, to 

best align with their system goals and performance objectives. This will all be accomplished 

through a simple user interface that does not require extensive modeling know-how. Users 

 -4- December, 2008 
FHWA POET-ML Methodology 

HOV Lane Performance Project 

equipped with even limited input data will be able to apply what they know to get sketch-level 

planning output and suggestions for HOV policy modification. 

Specifically, POET-ML has been structured to help HOV operators answer the following 

questions: 

• How effective are HOV facilities in my region? How well are these lanes utilized? 

• What HOV policy changes are necessary to address locations where my HOV facility 

appears to be underutilized, or where excess capacity on the HOV facility exists during 

the peak period and off-peak period? What HOV policy changes are optional to address 

these concerns? 

• What HOV policy changes are necessary to address locations where my HOV facility is 

congested during the peak period? What HOV policy changes are optional to address 

these concerns? 

• What are the advantages or disadvantages associated with each HOV policy change? 

• How is HOV system performance impacted as a result of each policy change or 

combination of policy changes? 

• Will the changes in HOV policy meet the system goals and performance objectives? 

Which policy changes are recommended to meet those goals and performance 

objectives? 

 -5- December, 2008 
FHWA POET-ML Methodology 

HOV Lane Performance Project 

POET-ML Framework 

Figure 1 illustrates the analytical process used in POET-ML. 

Figure 1: POET-ML Framework

Step 1: Operational Assessment of Existing HOV Facility 

The initial step in the model process is an assessment of the operational effectiveness of the 

existing HOV facility. This assessment considers both physical and operational characteristics 

including number of lanes, length, separation, eligibility, and demand, among others. 

In this step, the user can select a specific HOV facility from the FHWA Highway HOV Facility 

Inventory database that includes information on HOV policy details and physical characteristics. 

 -6- December, 2008 
FHWA POET-ML Methodology 

HOV Lane Performance Project 

The user is then required to enter the number of HOV lanes and GP lanes in each direction 

during peak hour operations as well as the corresponding volumes in these lanes (records 

highlighted in red). Other information, such as public transportation vehicles (no. of buses per 

hour); percentage of motorcycles; percentage of taxi and percentage of low emission and/or 

energy efficient vehicles, is optional. Once valid values are entered for these items, the user can 

continue with the analysis. 

It is also possible to create a new record and store a specific profile for future use by modifying 

the text for one or more of the input data field records. This can be done by clicking “Store Profile” 

in the input page. The tool has the capacity for storing 50 new profiles in addition to the HOV 

facilities stored in FHWA’s HOV Facility Inventory database. If all profiles are in use, the user can 

delete the profiles that are no longer needed by clicking the button of “Manage Profiles” and save 

the latest record. 

Table 1 outlines the set of information to be populated either from the FHWA Highway HOV 

Facility Inventory database or by the user. The data was grouped into four major categories. 

General information, physical characteristics, and HOV policies should be readily available to 

nearly any user familiar with the HOV system under consideration. However, travel demand and 

operational performance could be more difficult to obtain. 

Table 1: User Inputs

Data 

Category Data Requirement Data Sources Requirement 

State / Province Required 

City / County Required 

Urban Area Required 

Road name Required 

General 

Information 

Segment (from/to) 

FHWA Highway HOV 

Facility Inventory 

Required 

Route Miles FHWA Highway HOV 

Facility Inventory Required 

No of HOV Lanes Per Direction User Input Required 

No of General Purpose Lanes Per 

Direction User Input Required 

Type Optional 

Intermediate Access Optional 

Physical 

Characteristics 

Separation 

FHWA Highway HOV 

Facility Inventory Optional 

Eligibility HOV Required 

Eligibility Toll Optional 

Eligibility Motorcycle Optional 

Eligibility Taxi Optional 

Eligibility Special Fuel Optional 

Eligibility Others Optional 

HOV Policies 

Hours of Operation 

FHWA Highway HOV 

Facility Inventory 

Optional 

HOV Lane Volume (Peak Hour) in 

Peak Direction* Required 

GP Lane Volume (Peak Hour) in Peak 

Direction* Required 

Public transportation vehicles (no. of 

buses per hour) Optional 

Percentage Motorcycles Optional 

Percentage Taxi Optional 

Travel 

Demand and 

Operational 

Performance 

Percentage Low emission and/or 

energy efficient vehicles 

User Input 

Optional 

 -7- December, 2008 
FHWA POET-ML Methodology 

HOV Lane Performance Project 

* Volumes represent demand for the corridor by lane type. 

REQUIRED 

USER INPUTS 

Figure 2: Model Input Page

User input, as outlined in 

Table 1, will supply the information necessary to assign the HOV facility to one of two categories 

based on the established performance thresholds, such as volume-to-capacity ratios or service 

flow rate (pc/h/ln). These categories describe the general performance of the facility in terms of 

utilization. During step 2 of this process, the user will be presented with a set of policy 

adjustments based on the specific category to which the facility is assigned. Table 2 outlines the 

two categories and corresponding performance thresholds by default. 

Table 2: HOV Facility Performance Thresholds 

Categories Volume-to-Capacity 

Ratios

Service Flow Rate 

(pc/h/ln) 

HOV facility that has excess capacity 

during both peak and off-peak periods; 

 Peak Hour V/C Ratio 

<0.75 

Peak Hour Service Flow 

Rate < 1650 pc/h/ln 

HOV facility that is congested during the 

peak period and has excess capacity 

during the off-peak period. 

Peak Hour V/C Ratio 

>=0.75 

Peak Hour Service Flow 

Rate >= 1650 pc/h/ln 

It is important to note that the default threshold values of V/C ratio (0.75) and service flow rate 

(1650 pc/h/ln) were established based on aggregated national survey results, and they are 

consistent with the assumptions in FHWA’s Spreadsheet Model for Induced Travel Estimation - 

Managed Lanes (SMITE-ML). The default values of V/C ratio and service flow rate are stored in 

the POET-ML parameters page and remain interactive and transparent to the user. Users are 

allowed to adjust these values to reflect the unique characteristics of facilities in their region. To 

review and/or modify the default model parameters navigate to the Potential Impacts page and 

select “Adjust Parameters”. 

 -8- December, 2008 
FHWA POET-ML Methodology 

HOV Lane Performance Project 

The Parameters page provides significant flexibility for regional customization. The user can 

hange a number of highway capacity assumptions including free-flow speeds, hourly and daily 

uality 

f the final model output depends entirely on the user’s ability to supply as much needed 

Step 2: Iden changes 

rocess, based on 

the assessment from step 1. If it is determined that the HOV facility has excess capacity in both 

uring peak periods. However, the potential adjustments will be more restrictive, rather than less 

c

freeway lane capacity, and the percentage of lane capacity used under LOS C conditions. Bus 

occupancy and passenger car equivalent information can also be updated to represent local 

conditions. HOV splits were assumed to be 85%, 10%, and 5% for HOV2, HOV3, and HOV4, 

respectively. These default values can also be modified by the user. Information used to 

calculate congested speeds and conditions is also customizable. V/C ratios, maximum flow 

rates, and minimum speeds for all LOS conditions are included in the Parameters page. Finally, 

the user can update vehicle emissions assumptions based on local fleet characteristics. The 

hourly emissions of carbon monoxide, various oxides of nitrogen, volatile organic compounds, 

carbon dioxide, and gallons of fuel are all included in this page and are available to update. 

The precision of the analysis will depend on the availability of data from the user, and the q

o

information as possible. 

Example:

le corridor is I-85 in Atlanta, GA from I-75 north to SR316. This 24 mile facility has 

 single HOV lane in each direction with HOV2+ occupancy policy. Key information was 

= 1 

o. of General Lanes Per Direction = 5 

 Peak Direct n 00 

k irection ,250 

The examp

a

loaded from the HOV data base. User input included: 

No. of HOV Lanes Per Direction 

N

HOV Lane Volume (Peak Hour) in io = 2,2

GP Lane Volume (Peak Hour) in Pea D = 11

tification of the required and/or optional HOV policy

A set of applicable policy adjustments are introduced in step 2 of the model p

the peak and off-peak periods, the user will be shown a number of policy change options related 

to vehicle occupancy, vehicle eligibility, and pricing. In order to fill unused HOV capacity, and 

avoid empty lane syndrome, the user could choose to lower the occupancy restrictions (e.g. from 

HOV3+ to HOV2+) or to allow additional free vehicles (e.g. public transportation vehicles, taxis, 

motorcycles, hybrid vehicles, etc.). Additionally, the user could convert the lanes from HOV to 

high-occupancy toll (HOT) lanes, and sell excess capacity to users not permitted in the lanes but 

who would be willing to pay for the travel time savings these lanes provide. These policy 

changes could also be bundled together in some combination that both achieves the utilization 

targets and meets the goals of the region. Table 3 shows the options to be presented to the user. 

The same set of policy change options applies for HOV facilities determined to be congested 

d

restrictive, as was the case for the excess-capacity scenario. For example, one option to address 

congested HOV lanes is to increase the occupancy requirements (e.g. from HOV2+ to HOV3+). 

Likewise, non-carpools that are currently eligible to use the HOV lanes could be prohibited (e.g. 

disallow motorcycles, transit vehicles, etc.). Pricing of non-eligible vehicles can also be 

implemented on congested HOV lanes, but it must be bundled with some other policy shift. Once 

demand in these lanes is brought down below capacity through more restrictive policies, any 

remaining capacity could be sold to ineligible vehicles (i.e. those not meeting the current 

occupancy/eligibility policy) that are willing to pay for access. In addition to policy change options 

related to vehicle occupancy, vehicle eligibility, and pricing, the user can also explore the impacts 

of adding an additional managed lane. This option is only available for HOV facilities that are 

 -9- December, 2008 
FHWA 

HOV Lan

POET-ML Methodology 

e Performance Project 

 -10- December, 2008 

congested during peak period. This could either be an additional lane in each direction, or an 

additional reversible lane, depending on the facility. By adding additional capacity, it provides 

increased flexibility for HOV operators and eliminates the need for immediate occupancy policy 

changes. Table 4 shows the options for the congested peak period condition. 

If HOV demand is deemed to be on target during peak periods (i.e. neithe

ested during peak period. This could either be an additional lane in each direction, or an 

additional reversible lane, depending on the facility. By adding additional capacity, it provides 

increased flexibility for HOV operators and eliminates the need for immediate occupancy policy 

changes. Table 4 shows the options for the congested peak period condition. 

If HOV demand is deemed to be on target during peak periods (i.e. neither underutilized nor 

congested), there are still opportunities for policy adjustment. Future demand may eventually 

lead to congestion in lanes that are operating well today, and proactive steps could ensure 

efficient operation for years to come. Pricing is always an option that provides flexibility for HOV 

operators to manage demand in these lanes in order to achieve more efficient use. Occupancy 

and eligibility policy changes alone, offer only discrete solutions that may tip the utilization 

balance too far in one direction. 

lead to congestion in lanes that are operating well today, and proactive steps could ensure 

efficient operation for years to come. Pricing is always an option that provides flexibility for HOV 

operators to manage demand in these lanes in order to achieve more efficient use. Occupancy 

and eligibility policy changes alone, offer only discrete solutions that may tip the utilization 

balance too far in one direction. 

Example:

e volumes in the corridor, both the HOV and general purpose lanes operate at 

ndesirable levels, LOS E and F for the HOV and GP lanes respectively. 

Based on th

u

Mobility Impacts in HOV Lanes and General Purpose Lanes During Peak Hours 

With Existing HOV Policy Mobility Impacts HOV Lane GP Lane 

Peak Hour V/C 1.00 1.02

Peak Hour Speed (mph) 34.2 33.1

Level of Service E F

Corridor Travel Time (minutes) - Congested Condition 41.9 43.3

Total Vehicle Travel Delays (hours) 728 3,983

Total Vehicle Delay * VOT of $/hr 18,200 99,575

 

Mobility Impacts in HOV Lanes and General Purpose Lanes Daily 

With Existing HOV Policy Mobility Impacts HOV Lane GP Lane 

Daily V/C 0.75 0.77

Daily Speed (mph) 47.2 46.0

Daily Level of Service C D

Corridor Travel Time (minutes) - Congested Condition 30.4 31.2

Total Vehicle Travel Delays (hours) 3,614 20,520

Total Vehicle Delay * VOT of $/hr 90,350 513,000

Travel Efficiency (Speed * Volume) 1,235,939 6,209,440

Potential policy adjustments include: 

1. Increase vehicle occupancy from HOV2+ to HOV3+ or HOV 4+ 

ty such as transit, motorcycles, taxis or low emission 

 the only vehicle types 

3. 

 increased occupancy or additional capacity for example). 

2. Further restrict vehicle eligibili

vehicles. In this example, motorcycles and transit vehicles are

with eligibility. 

Allow pricing of non-eligible vehicles (this requires an initial policy shift to free-up 

capacity to sell,

4. Add an additional managed lane in each direction. 
FHWA 

HOV Lane Performance Project 

Table 3: Potential Policy Adjustments for Facilities with Excess Capacity Condition (Empty Lane Syndrome)

Operating Element Direction of 

Change 

Details Policy Ch

Option

Vehicle Occupancy (HOV) 

Decrease 

By relaxing the vehicle occupancy restrictions, more vehicles could gain access to HOV lanes, filling unused capacity in the currently underutilized lanes. 

Vehicle Occupa(HOV) Public transporvehicles (no. of hour) Motorcycles Taxi 

Free Vehicle Eligibility 

Less Restriction By allowing vehicles that don't meet the existing 

vehicle eligibility policy (e.g. low emission and energy-efficient vehicles) to use the HOV lanes, more vehicles could gain access to these lanes, filling unused capacity. 

Low emission aenergy efficient 

Pricing 

Allow Paying 

Vehicles 

For the existing HOV lanes which are underutilized, allowing vehicles that don't meet passenger occupancy or vehicle eligibility requirements to gain access to HOV lanes by paying a toll provides the opportunity to fill unused capacity and also provides transportation choice for those willing to pay. By pricing those previously ineligible vehicles, new revenue is generated that could, if authorized, be utilized for transportation improvements. 

Paying vehicles 

-11- 
FHWA 

HOV Lan

e Performance Project 

-12- 

Table 4: Potential Policy Adjustments for Facilities with Congested Peak Period Conditions 

Operating Element Direction of 

Change 

Details Policy Ch

Option

Vehicle Occupancy (HOV) 

Increase 

By increasing the vehicle occupancy requirement, some currently eligible HOVs are diverted from the lanes, providing additional capacity in currently overutilized HOV lanes. 

Vehicle Occupa(HOV) Public transporvehicles (no. of hour) Motorcycles Taxi 

Free Vehicle Eligibility 

More Restrictions 

By disallowing some currently eligible vehicles, additional capacity is freed up in the overutilized HOV lanes. 

Low emission aenergy efficient 

Pricing 

Allow Paying 

Vehicles 

Pricing needs to be bundled with a vehicle occupancy change, (free) vehicle eligibility change, and/or capacity change for the facility that is overutilized. By pricing those previously ineligible vehicles, new revenue is generated that, if authorized, could be utilized for transportation improvements. 

Paying vehicles 

Additional Capacity Add a Managed Lane 

Building additional capacity provides increased flexibility for HOV operators facing peak period congestion. Additional capacity eliminates the need for immediate policy changes. 

Capacity 
FHWA POET-ML Methodology 

HOV Lane Performance Project 

Step 3: Evaluation of Potential Impacts 

The third step in the process is to assess the impacts of the HOV lane policy change or 

combination of policy changes that were selected in step 2. The tool will track four key measures 

of effectiveness: travel demand impacts, mobility impacts, environmental impacts, and financial 

feasibility. 

Travel Demand Impacts 

Both vehicle and person travel demand will be examined over daily and peak hour 

periods in the HOV/HOT and general-purpose (GP) lanes. Travel will be broken down 

into carpools, transit, motorcycles, special fuel vehicles, taxis, and paying vehicles. At a 

minimum, the user will be required to supply information on peak hour vehicle trips for 

each vehicle type under the current HOV policies. Relationships coded into the tool will 

be used to calculate peak hour person trips and daily vehicle and person trips. 

Travel demand impact calculations will depend heavily on which of the two conditions 

(excess capacity or congested) applies to the facility under evaluation. If pricing is 

selected as a policy change, the level of travel demand in priced lanes will be maintained 

at Level of Service C during the peak hour, by default, i.e., about 75% of absolute 

capacity. Paying vehicle volumes in priced lanes during the peak hour are estimated to 

be equal to the spare vehicle capacity that would be available on the lanes at a Level of 

Service C. The balance of the volume is occupied by non-paying vehicles. 

A number of combinations exist between existing conditions and subsequent policy 

adjustments. The algorithms in place to determine final volumes for both HOV/HOT 

lanes and general purpose (GP) lanes are different based on the combination under 

consideration. Following are four potential scenarios, meant to outline the different 

calculation processes executed by POET-ML. Each scenario description includes a table 

with example output data and a figure showing general travel conditions in the corridor. 

Following these scenarios is a detailed description of the calculations for mobility and 

environmental impacts, along with financial feasibility. 

Scenario 1: HOV & GP Lanes Both Under Capacity 

Many corridors with HOV lanes are uncongested in peak periods. Under these 

conditions, no changes are required to bring HOV operating speeds back to acceptable 

levels. However, the HOV operator may be interested in seeing the impact of 

implementing pricing, or of allowing additional vehicles into the HOV lanes through 

occupancy or eligibility changes. Figure 3 illustrates the potential impact of allowing 

priced vehicles into the HOV lanes. The colored arrows represent the flow conditions for 

each lane in the corridor. Table 5 shows an example calculation for this scenario. 

Of the 1,100 peak hour HOV trips in the existing condition, 1,004 of them are carpools. 

The rest are other eligible vehicles. These other vehicles generally make up a small 

proportion of total HOV demand, and therefore changes to eligibility restrictions could 

have little direct impact on HOV and GP lane performance. 

Initially, this uncongested corridor experiences LOS C conditions in the GP lanes and 

LOS A/B conditions in the HOV lane. Allowing priced vehicles in the HOV lane will attract 

additional users because of the time savings relative to the GP lanes. POET-ML pulls 

these users from two different places: the GP lanes and parallel facilities. The percent 

split from these sources depends on the conditions in the GP lanes. As the V/C ratio in 

-13- July 17, 2009 
FHWA POET-ML Methodology 

HOV Lane Performance Project 

the GP lanes rises, the contribution of vehicles from these lanes to the HOV/HOT lane 

also rises. When GP conditions are near LOS A/B, a larger portion of vehicles are 

diverted from parallel facilities to the HOV/HOT lane. The tool assumes that existing 

HOVs do not break up to form paying SOVs. Also, no transit riders are diverted to paying 

SOVs. In order to explore scenarios like these, the user must first implement a policy 

change and then return to the Parameters page to adjust the assumptions for HOV split, 

transit ridership, etc., as appropriate. This two step process allows the user to 

understand mode split changes resulting from HOV lane policy updates. 

The final volumes in the HOT lane under this condition are no higher than the maximum 

LOS C volume defined in the Tool. Nor are they larger than 1⁄4 the total corridor volume 

(1⁄4 because the facility has 4 total lanes, with one HOV lane). This is to ensure that HOT 

operating speeds do not fall below GP speeds, which is a possible, but an unlikely 

scenario. For these reasons, revenue is likely to be minimal under this condition. 

Obviously, few motorists would be willing to pay a toll to use the HOT lane when only 

minimal time savings can be realized. 

Indeed, Table 5 shows just 300 paying vehicles after the policy change, bringing the peak 

hour total in the HOV/HOT lane to 1,400. Volumes decrease from a total of 4,500 on the 

GP lanes to 4,380. With a per lane capacity of 2,200 vehicles per hour, the GP lanes 

have a similar V/C ratio to that of the HOV/HOT lane, which is the reason for the low 

demand from paying vehicles in that lane. 

Table 5: Scenario 1 Lane Condition Data 

Existing HOV Policy With Policy Changes 

Travel Demand Impacts – Scenario 1 HOV (1) 

Lane 

GP (3) 

Lane 

HOV (1) 

Lane 

GP (3) 

Lane 

Total Peak Hour Vehicle Trips (with PCE factor) 1,100 4,500 1,400 4,380 

Peak Hour Carpools (Free) 1,004 N/A 1,004 N/A 

Peak Hour Others (Transit) 10 N/A 10 N/A 

Peak Hour Motorcycle 17 N/A 17 N/A 

Peak Hour Taxi 17 N/A 17 N/A 

Peak Hour Special Fuel 33 N/A 33 N/A 

Peak Hour Tolling 0 N/A 300 N/A 

-14- July 17, 2009 
FHWA POET-ML Methodology 

HOV Lane Performance Project 

 A/B C D E/F

Level of Service (LOS)

HOV Lane

GP Lane

GP LaneGP Lane

HOT Lane

GP Lane

GP LaneGP Lane

 Initial Condition Condition after Change 

Figure 3: Scenario 1 Lane Condition Diagrams 

Scenario 2: HOV Lane Under Capacity & GP Lanes Congested 

Another common scenario involves congested GP lanes adjacent to an HOV facility that 

operates well below capacity. Again, the operator is not required to make policy changes 

in order to maintain an acceptable LOS in the HOV lane, but there may be interest in 

achieving greater utilization in this lane. Options for increasing HOV volumes include 

relaxing occupancy and eligibility restrictions in the lanes, as well as allowing previously 

ineligible vehicles (e.g. single-occupant vehicles) to pay a toll in order to use the lane. 

These options would have different impacts on lane volume, however, and caution needs 

to be observed to avoid creating congested HOV conditions. For example, lowering the 

occupancy restriction from 3+ to 2+, if applicable, could potentially allow too many 

vehicles into the HOV lane, degrading performance below acceptable levels. 

In this example, a congested corridor has an underutilized HOV lane. This condition is 

commonly referred to as “empty lane syndrome”, and is one key motivator for HOV policy 

change. Allowing priced vehicles access to the HOV lane can lead to improvements in 

the GP lanes and better use of the HOV lane. One likely outcome of this change can be 

seen in Figure . Here, LOS improves from ‘E/F’ to ‘D’ on the GP lanes, while LOS 

degrades slightly on the HOV/HOT lane from ‘A/B’ to ’C’. In POET-ML, most of the 

priced vehicles in the HOT lanes come from the GP lanes under these conditions, with a 

small contribution from parallel facilities. As a result, total corridor throughput increases 

slightly under this scenario. As noted previously, vehicle contribution from these two 

sources is determined based on a sliding scale with a 70/30 split between parallel 

facilities and GP lanes when the GP lanes operate at LOS A. This split changes to 60/40 

under LOS B, 50/50 under LOS C, 40/60 under LOS D, and 30/70 under LOS E/F 

conditions. This distribution is included in the parameters page, and can be modified by 

the user. 

HOT lane volumes are capped at the LOS C capacity, which is accomplished in practice 

through demand-responsive, variable tolling. If pricing is not a viable alternative, an HOV 

operator could still achieve greater corridor throughput by increasing the types of eligible 

vehicles in the HOV lane. Allowing hybrids or special fuel vehicles, taxis, or additional 

-15- July 17, 2009 
FHWA POET-ML Methodology 

HOV Lane Performance Project 

transit vehicles can provide a degree of relief to the GP lanes while increasing utilization 

of the HOV lane. However, as discussed previously, relaxing eligibility restrictions may 

not impact many vehicles, and therefore conditions may not change much in the corridor. 

Table 6 shows example output from this scenario. Here, HOV volume is brought to 

capacity after pricing is allowed, and GP lane conditions improve with decreases of more 

than 100 vehicles per lane. Again, total corridor throughput increases over the existing 

case. Pricing allows for more efficient movement in these 4 lanes. 

Table 6: Scenario 2 Lane Condition Data 

Existing HOV Policy With Policy Changes 

Travel Demand Impacts – Scenario 2 HOV (1) 

Lane 

GP (3) 

Lane 

HOV (1) 

Lane 

GP (3) 

Lane 

Total Peak Hour Vehicle Trips (with PCE factor) 1,100 6,700 1,650 6,315 

Peak Hour Carpools (Free) 1,004 N/A 1,004 N/A 

Peak Hour Others (Transit) 10 N/A 10 N/A 

Peak Hour Motorcycle 17 N/A 17 N/A 

Peak Hour Taxi 17 N/A 17 N/A 

Peak Hour Special Fuel 33 N/A 33 N/A 

Peak Hour Tolling 0 N/A 550 N/A HOV Lane GP Lane GP Lane GP Lane

 A/B C D E/F

Level of Service (LOS) HOT Lane

GP Lane

GP LaneGP Lane

 Initial Condition Condition after Change 

Figure 4: Scenario 2 Lane Condition Diagrams 

Scenario 3: HOV Lane & GP Lanes Over Capacity (Increased Restrictions) 

Some HOV facilities are congested during peak periods and require policy adjustment in 

order to maintain federally mandated performance standards. Low cost strategies for 

decreasing HOV lane volume include increasing occupancy restrictions and 

implementing more exclusive eligibility criteria. However, efforts to divert vehicles from 

the HOV lanes can lead to increased congestion on GP lanes. And if HOV lane rules are 

made too restrictive, traffic could fall well below LOS C conditions, leading to empty lane 

-16- July 17, 2009 
FHWA POET-ML Methodology 

HOV Lane Performance Project 

syndrome. For example, in many urban areas, the vast majority of HOVs have just 2 

occupants, with only a small percentage of 3+ occupant vehicles. If the HOV operator 

increases the occupancy restriction from 2+ to 3+, many of the vehicles in the lane will no 

longer be eligible, and will be diverted to the GP lanes and parallel facilities. The 

methodology for this diversion of vehicles out of the managed lane mirrors that for the 

diversion into the managed lane described previously. The diversion of the traffic into the 

GP lanes and parallel facilities depends on the congestion level of GP lane facilities. The 

splits of ineligible HOV vehicles are coded as parameters in the Tool. If the user desires 

to vary the diversion of ineligible HOV vehicles, he or she has the flexibility to adjust 

these parameters. 

POET-ML takes into account the overcapacity scenario described above. Once it is 

determined that the HOV lane is congested in peak periods, the user is presented with a 

list of potential policy changes designed to achieve improved HOV operating conditions. 

The greatest impact usually comes from increased occupancy restrictions. Figure shows 

an example of the impact of first increasing this restriction from 2+ to 3+, followed by 

allowing priced vehicles in the lane. 

The first selection shifts a large number of vehicles from the HOV lane to the GP lanes 

and parallel facilities. Of course, the number of vehicles diverted will vary by facility, 

according to the regionally-specific HOV mix (i.e. the relative number of HOV2, HOV3, 

HOV 4+, etc.). This split is coded as a parameter in the Tool, and it can be updated by 

the user as desired. If the user changes only the occupancy restriction, total corridor 

volume will remain constant, and GP lane conditions will likely become even more 

congested. In addition, it is possible that the HOV lane may exhibit LOS A/B conditions, 

which is suboptimal utilization. If the user follows this selection by allowing pricing in the 

HOV lane, however, vehicles return to the lane and fill the unused capacity. POET-ML 

pulls most of the priced vehicles from the GP lanes, and a smaller portion from parallel 

facilities. This split is also coded as a parameter in the Tool, and if the user desires to 

vary the source of priced vehicles, he or she has that flexibility. Once both decisions are 

executed, conditions are likely to appear as they do on the right of Figure . 

Table 7 shows the extent to which the GP lanes become more congested in this 

scenario. Of course, the HOV lane is maintained at the LOS C capacity, and most of 

these vehicles are tolled. HOV3+ vehicles, along with other eligible free vehicles, 

comprise the balance of the lane volume. The HOV2 vehicles, which were pushed to the 

GP lanes and parallel facilities in response to the occupancy policy change, are 

responsible for the increased GP lane congestion. 

Table 7: Scenario 3 Lane Condition Data 

Existing HOV Policy With Policy Changes 

Travel Demand Impacts – Scenario 3 HOV (1) 

Lane 

GP (3) 

Lane 

HOV (1) 

Lane 

GP (3) 

Lane 

Total Peak Hour Vehicle Trips (with PCE factor) 2,200 6,700 1,650 7,085 

Peak Hour Carpools (Free) 2,104 N/A 316 N/A 

Peak Hour Others (Transit) 10 N/A 10 N/A 

Peak Hour Motorcycle 17 N/A 17 N/A 

Peak Hour Taxi 17 N/A 17 N/A 

Peak Hour Special Fuel 33 N/A 33 N/A 

Peak Hour Tolling 0 N/A 1,238 N/A 

-17- July 17, 2009 
FHWA POET-ML Methodology 

HOV Lane Performance Project HOV Lane GP Lane GP Lane

GP Lane

 A/B C D E/F

Level of Service (LOS) HOT Lane

GP Lane

GP LaneGP Lane

 Initial Condition Condition after Changes 

Figure 5: Scenario 3 Lane Condition Diagrams 

Scenario 4: HOV Lane & GP Lanes Over Capacity (Additional Capacity) 

Another option for addressing a corridor with congested HOV and GP lanes is to add 

HOV/HOT capacity. In locations where available right of way affords such an investment, 

this option can provide a high degree of flexibility for HOV operators. Additional HOV 

capacity allows greater opportunities for efficient corridor flow and can eliminate the need 

for occupancy and/or eligibility policy change. In the scenario highlighted in Figure 5, the 

user has opted to add HOV capacity and implement tolling in these lanes. In doing so, 

corridor conditions are improved for both the managed and GP lanes. In addition, total 

corridor volume increased, which means the facility can serve more vehicles, more 

efficiently than before. And all of this is possible while maintaining occupancy restrictions 

of 2+. This last point is important, because raising occupancy restrictions can be 

controversial. Those that have formed 2 person carpools to use the HOV lanes will likely 

object to any change in policy that forces them out of the lanes. Additional capacity can 

help avoid this situation. 

In the scenario highlighted in Figure , the user has chosen to address corridor congestion 

by maintaining the existing HOV policy, adding a lane of HOV/HOT capacity, and 

implementing pricing on both lanes. POET-ML is equipped to respond to each of these 

decisions and to calculate the final conditions on the managed and GP lanes. The 

additional HOV lane doubles the capacity for qualifying vehicles. These vehicles are 

spread evenly over the two lanes, which likely eliminates peak period congestion. 

Allowing priced vehicles fills unused capacity in these lanes while helping to improve 

conditions in the GP lanes. Again, the majority of paying vehicles are taken from the GP 

lanes, with a smaller percentage diverted from parallel facilities. 

The number of free vehicles in the HOV lanes remains the same both before and after 

the capacity addition. This allows for a 1,100 paying vehicles to enter the HOV/HOT 

lanes, bringing both lanes to their LOS C capacity. Since many of those paying vehicles 

come from the GP lanes, total GP volume decreases from 6,700 to 5,930, resulting in 

improved LOS on these lanes as well. 

-18- July 17, 2009 
FHWA POET-ML Methodology 

HOV Lane Performance Project 

Table 8: Scenario 4 Lane Condition Data 

Existing HOV Policy With Policy Changes 

Travel Demand Impacts – Scenario 4 HOV (1) 

Lane 

GP (3) 

Lane 

HOV (2) 

Lanes 

GP (3) 

Lane 

Total Peak Hour Vehicle Trips (with PCE factor) 2,200 6,700 3,300 5,930 

Peak Hour Carpools (Free) 2,104 N/A 2,104 N/A 

Peak Hour Others (Transit) 10 N/A 10 N/A 

Peak Hour Motorcycle 17 N/A 17 N/A 

Peak Hour Taxi 17 N/A 17 N/A 

Peak Hour Special Fuel 33 N/A 33 N/A 

Peak Hour Tolling 0 N/A 1,100 N/A HOV Lane GP Lane GP Lane GP Lane

 A/B C D E/F

Level of Service (LOS) HOT Lane

HOT Lane

GP Lane

GP LaneGP Lane

 Initial Condition Condition after Changes 

Figure 6: Scenario 4 Lane Condition Diagrams 

Additionally, POET-ML analyzes the peak hour person trips based on occupancy rate for different 

vehicle types. It also analyzes the total daily vehicle trips and total daily person trips based on 

Peak Hour vehicle/person trips and the daily to Peak Hour Conversion factor. Table 9 outlines 

those travel demand impacts and its corresponding calculation methodology. The table also 

includes values from the earlier example. 

Table 9: Daily Conversion Formulas 

With Existing HOV 

Policy With Selected Policy Changes Travel Demand 

Impacts 

HOV Lane GP Lane HOV Lane GP Lane 

Total Peak Hour 

Person Trips

HOV Lane: 

Persons Vehicles) Paying Fuel Special

 Buses (Free) (Carpools Hour Peak Taxi s Motocycle

+

+ + + +

-19- July 17, 2009 
FHWA POET-ML Methodology 

HOV Lane Performance Project 

GP Lane: 

Occupancy Auto Average* Trips Vehicle Lane GP Hour Peak Rate

HOV Lane: 

=4,665 + 200 + 0 + 36 + 36 + 0 = 4,938

GP Lane: 

=11,250 * 1.1 = 12,375 

Peak Hour Carpool 

Persons (Free) 

Occupancy Carpool* Trips Vehicles (Free) Carpools Hour Peak Rate

=2,121 * 2.2 = 4,665

Peak Hour Others 

(Transit) 

Occupancy Bus* Trips Vehicles Bus Hour Peak Rate

=10 * 20 = 200

Peak Hour Motorcycle Hour Peak Occupancy Auto Average * Trips Motorcycle Rate

=0 * 1.1 = 0

Peak Hour Taxi Occupancy Auto Average* Trips Taxi Hour Peak Rate

=17 * 2.1 = 36

Peak Hour Special Fuel Hour Peak Occupancy Auto Average*Trips Vehicle Fuel Special Rate

=33 * 1.1 = 36

Peak Hour Tolling Occupancy Auto Average* Trips Tolling Hour Peak Rate

=0

Daily 

Total Daily Vehicle 

Trips 

HOV Lane: 

Daily + + + Vehicles) Paying Others Buses (Free) (Carpools

GP Lane: 

Factor Conversion Hour Peak toDaily 

* Trips Vehicle Lane GP Hour Peak

HOV Lane: 

=25,452 + 120 + 600 + 0 = 26,172

GP Lane: 

=11,250 * 12 = 135,000

Daily Carpools (Free) in 

HOV Lane* 

Factor Conversion Hour Peak toDaily * (Free) Carpools Hour Peak

=2,121 * 12 = 25,452

Daily Buses in HOV 

Lane* 

Factor Conversion Hour Peak toDaily * Buses Hour Peak

=10 * 12 = 120

Daily Others in HOV 

Lane* Hour Peak Daily to Factor Conversion

Hour Peak (Motorcycl + + * Vehicles) Fuel Special Taxi es

=(0 + 17 + 33) * 12 = 600 

Daily Paying Vehicles 

in HOV Lane* Factor Conversion Hour Peak toDaily 

* (Free) Vehicles Paying Hour Peak

=0

-20- July 17, 2009 
FHWA POET-ML Methodology 

HOV Lane Performance Project 

Total Daily Person 

Trips 

HOV Lane: 

Daily + + + Vehicles) Paying Others Buses (Free) (Carpools

GP Lane: 

Factor Conversion Hour Peak toDaily 

* Trips Vehicle Lane GP Hour Peak

HOV Lane: 

=55,980 + 2,400 + 864 + 0 = 59,244 

GP Lane: 

=12,375 * 12 = 148,500

Daily Carpool Persons 

(Free) in HOV Lane* 

Daily Conversion Hour Peak toDaily * (Free) Persons Carpools Factor 

=4,665 * 12 = 55,980

Daily Bus Passengers 

in HOV Lane* 

Factor Conversion Hour Peak toDaily * Passengers BusesDaily 

=200 * 12 = 2,400

Daily Other Persons in 

HOV Lane* Hour Peak Daily to Factor Conversion

Hour Peak (Motorcycl + + Persons) Fuel Special Persons Taxi Persons es *

=(0 + 36 + 36) * 12 = 864

Daily Paying Persons in 

HOV Lane* Factor Conversion Hour Peak toDaily 

* (Free) Persons Paying Hour Peak

=0

*Only applies to HOV Lane. 

Mobility Impacts 

The travel demand impacts will then be used to determine the facility operating 

conditions, including the volume-to-capacity ratio, operating speed, level of service, 

facility travel time, total vehicle travel delay, etc. Again, these impacts will be examined 

over daily and peak hour periods for both the HOV and GP lanes. 

To calculate each of the mobility impacts, a number of assumptions are embedded into 

the calculations of these impacts. Examples of values the user may wish to update 

include Hourly Freeway Capacity per Lane (vph), Free Flow Speed (mph), the values for 

“alpha” and “beta" used in the Bureau of Public Roads equation for computing congested 

Peak Hour and Daily Travel Speeds, V/C thresholds for Level of Service, etc. All these 

assumptions are stored in the POET-ML Parameters Page. The user will have explicit 

access to change these assumptions if desired to better fit the characteristics of specific 

facilities and areas. 

The calculations for mobility impacts are built with flexibility in mind, allowing the user to 

customize the assumptions to a specific region, if the data supports it, and if there is a 

desire for greater precision in the results. If not, the user can work with the set of 

assumptions that emerged out of the model calibration effort, which will be based on 

nationwide averages. 

Table 10 outlines the information to be included for these mobility impacts and the 

detailed calculation methodology of those mobility impacts. The table also fills in these 

formulas, under the existing conditions, with values from the example cited earlier. 

-21- July 17, 2009 
FHWA POET-ML Methodology 

HOV Lane Performance Project 

Table 10: Matrix of Mobility Impacts

With Existing HOV 

Policy 

With Selected Policy 

Changes Mobility Impacts 

HOV Lane GP Lane HOV Lane GP Lane 

Peak Hour 

Peak Hour V/C Hourly *Lanesof# Freeway Capacity (vph) Lane Per

PCE) (With Trips Vehicle Hour Peak Total

=2,200 / (1 * 2,200) = 1.0 

Peak Hour Travel Speed 

(mph) ( )beta alpha1

SpeedFlowFree

+ C/VHourPeak

=65 / (1+0.9 * (1)^3) = 34.2

Peak Hour Level of Service 

(LOS) 

Peak Hour V/C<=0.3, LOS = A 

0.3< Peak Hour V/C<=0.5, LOS = B 

0.5< Peak Hour V/C<=0.75, LOS = C 

0.75< Peak Hour V/C<=0.9, LOS = D 

0.9< Peak Hour V/C<=1.0, LOS = E 

Peak Hour V/C>1.0, LOS = F 

0.9< Peak Hour V/C<=1.0, LOS = E

Peak Hour Corridor Travel 

Time (minutes) - 

Congested Condition )mph(SpeedHourPeak

60*MilesRoute

=(23.9 * 60) / 34.2 = 41.9

Peak Hour Total Vehicle 

Travel Delay (hours) Trips Vehicle Hour Peak Total*

)

)mph(SpeedFlowFree

MilesRoute

)mph(SpeedHourPeak

MilesRoute

( −

(23.9 / 34.2 – 23.9 / 65) * 2200 = 728

Peak Hour Total Vehicle 

Travel Delay * Cost of 

Vehicle Delay ($/hr) 

($/Hr) VOT* Delay Travel Vehicle Total Hour Peak

=728 * 25 = 18,200

Peak Hour Travel Efficiency 

(Speed * Volume) 

Hour Peak Total * SpeedHour Peak Trips Vehicle

=34.2 * 2,200 = 75,240

Daily 

Daily V/C Daily *Lanesof# Freeway Capacity (vph) Lane Per

PCE) (With Trips Vehicle Daily Total

=26,160 / (1 * 35,000) = 0.75

Daily Travel Speed (mph) ( )beta alpha1

SpeedFlowFree

+ C/VDaily

=65 / (1 + 0.9 * (0.75)^3) = 47.2

Daily Level of Service 

Daily V/C<=0.3, LOS = A 

0.3< Daily V/C <=0.5, LOS = B 

0.5< Daily V/C <=0.75, LOS = C 

-22- July 17, 2009 
FHWA POET-ML Methodology 

HOV Lane Performance Project 

0.75< Daily V/C <=0.9, LOS = D 

0.9< Daily V/C <=1.0, LOS = E 

Daily V/C >1.0, LOS = F 

0.5< Daily V/C <=0.75, LOS = C

Daily Corridor Travel Time 

(minutes) - Congested 

Condition )mph(SpeedDaily

60*MilesRoute

=(23.9 * 60) / 47.2 = 30.4

Daily Total Vehicle Travel 

Delay (hours) Daily Total* Trips Vehicle

)

)mph(SpeedFlowFree

MilesRoute

)mph(SpeedDaily

MilesRoute

( −

(23.9 / 47.2 – 23.9 / 65) * 26,160 = 3,614

Daily Total Vehicle Travel 

Delay * Cost of Vehicle 

Delay ($/hr) 

Daily Total ($/Hr) VOT* Delay Travel Vehicle

=3,614 * 25 = 90,350

Daily Travel Efficiency 

(Speed * Volume) 

Daily Total * Speed TravelDaily Trips Vehicle

=47.2 * 26,160 = 1,234,752

Environmental Impacts 

POET-ML will use the traffic volume estimates and mobility impact estimates to evaluate 

the environmental effects of the HOV facility under consideration. Two key 

environmental indicators will be examined, including air quality performance and carbon 

dioxide. 

The quantity of gasoline conserved can directly relate to reduced vehicular emissions. 

Gasoline savings were based on numbers derived using Texas Transportation Institute 

assumptions of 0.68 gallons of fuel per hour of delay. The evaluation will consider 

changes in total vehicle delay as a result of the policy adjustment package selected by 

the user and estimate the fuel-based emissions based on gas savings and estimated 

vehicular emission rates per gallon. Due to the difficulty in determining advancement in 

emissions technology, the values used in POET-ML reflect modern day estimated 

emission rates, as illustrated in Table 11. 

Table 11: Matrix of Environmental Impacts1

Air Quality - Pollutant 

Passenger Car 

Average Emissions 

CO (kg/gallon) 14.44 

NOx (kg/gallon) 1.27 

VOC (kg/gallon) 1.91 

Carbon Dioxide (kg/gallon) 8.79 

For example, if the model results predict a reduction in total vehicle travel delay as a 

result of a conversion form HOV lanes to HOT lanes, POET-ML will model changes in air 

quality and carbon dioxide emissions based the above average rates. The user will be 

able to see the impact of any delay reduction in environmental terms. 

 

1

 Source: Environmental Protection Agency 

-23- July 17, 2009 
FHWA POET-ML Methodology 

HOV Lane Performance Project 

Table 12 outlines the information to be included for the environmental impacts and the 

calculation methodology of these two performance measures. The table also fills in these 

formulas, under the existing conditions, with values from the example cited earlier. 

Table 12: Environmental Impacts Formulas 

With Existing HOV 

Policy 

With Selected Policy 

Environmental Changes 

Impacts 

HOV Lane GP Lane HOV Lane GP Lane 

Peak Hour 

Air Quality (kg) ) X Passenger * Car NO (CO ofEmission Average

 /HourFuel of Gallons *Delay Travel Vehicle TotalHour Peak 

++ VOC

=728 * 0.68 * (14.44 + 1.27 + 1.91) = 8,723

Carbon Dioxide 

(kg) Passenger * Car Carbon ofEmission Average Dioxide

 /HourFuel of Gallons *Delay Travel Vehicle TotalHour Peak 

=728 * 0.68 * 8.79 = 4,351

Daily 

Air Quality (kg) ) X Passenger * Car NO (CO ofEmission Average

 /HourFuel of Gallons *Delay Travel Vehicle TotalDaily 

++ VOC

=3,614 * 0.68 * (14.44 + 1.27 + 1.91) = 43,302

Carbon Dioxide 

(kg) Passenger * Car Carbon ofEmission Average Dioxide

 /HourFuel of Gallons *Delay Travel Vehicle TotalDaily 

=3,614 * 0.68 * 8.79 = 21,602

Again, the user will have access to adjust all the estimated emission rates for CO 

(kg/gallon), NOx (kg/gallon), VOC (kg/gallon) and Carbon Dioxide (kg/gallon) in the 

POET-ML Parameter page if desired. 

Financial Feasibility 

The final measure of effectiveness is financial feasibility. The set of policy adjustments 

includes pricing of existing HOV lanes, and if pricing is selected, it will trigger additional 

analysis in the model. Again, the user will have access to assumptions behind these 

calculations, including value of time estimates, weekend/weekday revenue ratios, and 

per-transaction tolling operation costs. The model incorporates national averages for 

these inputs and uses the results from the mobility impact analysis to perform this 

financial evaluation. 

Output for this step includes the number of tolled vehicles, daily and annual revenue, and 

annual toll operation costs. Also bundled with this output is a set of transportation 

benefits calculated in monetary terms. These are presented as travel time and fuel 

savings as well as daily user benefits resulting from policy changes implemented on the 

-24- July 17, 2009 
FHWA POET-ML Methodology 

HOV Lane Performance Project 

HOV facility. Specific measures of financial feasibility and its corresponding calculation 

methodology are listed in Table 13. The table also presents example calculations 

assuming that occupancy restrictions were changed from 2+ to 3+ in the example cited 

previously. 

Table 13: Matrix of Financial Feasibility Output 

Financial With Selected Policy Changes 

Feasibility HOV Lane GP Lane 

Toll Revenue and Toll O&M Cost 

(Only apply to Scenario with Policy Change of Pricing on Existing HOV Lanes) 

Number of 

vehicles paying a 

toll in peak hours 

Impacts) Demand Travel (from

Trips Vehicle Tolling Hour Peak

=1,252

Number of 

vehicles paying a 

toll in other Daily 

Periods 

Impacts) Demand Travel (from

Daily Trips Vehicle Tolling

=15,029

Total Daily 

Revenue 60/ Time of Value Minimum*

Daily Total Time TravelDaily HOT * Trips Vehicle Tolling Savings)

Time Travel Hour Peak HOT * Trips Vehicle Tolling Hour Peak (Total Savings

+

=1,241 * (49-30.4) + 15,029 * (33.6-25.5) * 25/60 = 60,341

Total Daily 

Revenue per Mile 

Daily Total MileRoute / Revenue

=60,341 / 23.9 = 2,525

Number of 

Working Days 

per Year 

Page) Parameter (from 250

Gross Annual 

Revenue Weekday and Revneue Weekend of Ratio Revenue

Daily Total *Year) Per Days Working of Number -(365* Revenue

Daily Total Year Per Days Working of Number* Revenue +

=60,341 * 250 + 60,341 * (365-250) * 0.25 = 16,820,054

Annual Toll 

Operation Costs 

Transactio Per Cost Operation Toll Annual* n)

Weekday and Revneue Weekend of Ratio* Revenue))

Year) Per Days Working of Number -(365 Year Per Days Working of ((Number

Daily Trips Vehicle Tolling Hour (Peak * Trips) Vehicle Tolling

+

+

=(1,252 + 15,029) * (250 + (365-250) * .25) * .15) = 610,557

Travel Benefits 

(Categorized by Lane Type: HOV Lanes and GP Lanes when compared to Existing HOV Policy Scenario) 

Daily User 

Mobility Benefits 

(Travel Time 

Savings * VOT of 

$/hr) (With Policy ($/Hr) VOT * Policy) Existing v.sChange

Delay Travel Vehicle TotalDaily on Difference

(With Policy ($/Hr) VOT * Policy) Existing v.sChange

Delay Travel Vehicle TotalHour Peak on Difference

+

=(18,200 – 5,750) + (90,350 – 28,275) = 74,525

-25- July 17, 2009 
FHWA POET-ML Methodology 

HOV Lane Performance Project 

Fuel Cost 

Savings (Gallons) 

(With Policy Change v.s Existing Policy)*Gallons of Fuel /Hour

Difference on Daily Total Vehicle Travel Delay 

(With Policy Change v.s Existing Policy)*Gallons of Fuel /Hour 

Difference on Peak Hour Total Vehicle Travel Delay 

+

=(728 - 230) + (3,614 – 1,131) = 2,027

Note: 

HOT Peak Hour Travel Time Saving = GP Lane Peak Hour Travel Time - HOT Lane Peak Hour Travel Time 

HOT Daily Travel Time Saving = GP Lane Daily Travel Time - HOT Lane Daily Travel Time

Step 4: Evaluation of Goals and Objectives 

The analysis does not end with step 3. Recognizing that regional goals largely dictate

transportation policy decisions, POET-ML includes an evaluation of selected policy adjustments 

in order to understand their ability to address common goals. The tool will employ a simple matrix 

that relates policy changes with common goal statements. This matrix will be populated with

values that reflect the relative strength of each policy in addressing each goal. Example goals 

include the following: 

• Protect Mobility 

• Maximize person throughput 

• Provide an option for travel time savings and trip reliability 

• Encourage carpooling in peak periods 

• Support transit service and reliability 

• Manage congestion by improving system efficiency

• Improve air quality 

• Provide a Financially Viable System 

The user will then be able to refine the selected policy adjustment package based on this 

evaluation and return to the quantitative steps in the tool to reevaluate this selection. In this way, 

the user will be able to strike an appropriate balance between quantitative and qualitative policy 

acceptability. 

Conclusion 

POET-ML employs both quantitative analyses and qualitative reality checks so that HOV 

operators can select the best set of policy adjustments for their region. This tool is designed to

be flexible enough to allow a user with little information to gain a comprehensive understanding of

the current operational effectiveness of a specific HOV facility and to evaluate the impacts of 

potential policy changes. HOV operators with more extensive input data, and a motivation for 

more customized results, will be granted access to a number of model assumptions in order to

account for regional variation. However, the ultimate goal of this tool is to provide a sketch-level

planning analysis of proposed HOV policy adjustments. More detailed analysis is recommended

prior to implementation of any of the policy changes seen in this tool. 

-26- July 17, 2009
Contact Information 

For additional information on this report, contact: 

Federal Highway Administration 

Ms. Jessie Yung 

(202) 366-4672 

jessie.yung@fhwa.dot.gov 

Andrew Smith 

HNTB Corporation 

3715 Northside Pkwy NW 

Atlanta, GA 30327 

404-946-5708 

asmith@HNTB.com

Claudia Bilotto 

HNTB Corporation 

3715 Northside Pkwy NW 

Atlanta, GA 30327 

404-946-5747 

cbilotto@HNTB.com

Mark Chang

Booz Allen Hamilton Inc. 

8283 Greensboro Drive 

McLean, Virginia 22102

415-263-3752 

chang_mark@bah.com

Federal Highway Administration 

1200 New Jersey Avenue SE 

Washington, DC 20590 

December 2008 

Publication # FHWA-HOP-09-031