# Presentation: 057601

## Data Requirements to Support Road Pricing Analyses

**Johanna Zmud, Ph.D.**
**NuStats Partners, LP**
**Expert Forum on Road Pricing and Travel Demand Modeling, Sponsored by the Office of the Assistant Secretary for Transportation Policy, U.S. Department of Transportation**
**November 14, 2005**

## Introduction 

**Travel Demand Modeling**

**Survey Science**

**Policy and Planning**

- Title of Presentation

**Notes:**
Pleased to be part of Forum
Opportune time to bring the modeling community together with those involved in road pricing for mutual benefit – how to incorporate road pricing into travel demand modeling as well as converse, how to incorporate travel demand modeling into road pricing.
Because of the art and science involved in modeling road pricing, to me, this Forum represents the proverbial three-legged stool.
First leg – outside of my area of expertise but well represented at this forum are methods and analytical techniques underlying travel demand modeling.
Second leg – and one with which I have much experience is survey science – scientific methods of design and implementation to collect the data that feed the models.
And, the third leg – policy and planning applications is one that I have become quite familiar with over the past three years as a mobility authority board of director member in my home state of Texas – a leading state in the road pricing arena.  Part of my responsibility is the acquisition of consulting services for demand forecasts.
My charge is to discuss data requirements for pricing analyses.  As I hope to communicate in the first part of my presentation this morning, pricing analyses are complex and wide-ranging – so data requirements are not simple or unidimensional either.   

## Historical Context

**1790s, Lancaster Turnpike, PA**
**Mid-19****th**** century, tolling peaked**
**20****th**** century, roads operated by state highway depts**
**1920s, federal legislation banned tolling on roads that received federal funding**
**1950s, federal interstate highway program**
**1990s, congestion management**
**Late 1990s, transportation infrastructure funding shortage**
**“****Innovative financing” tools and programs**

- Title of Presentation

**Notes:**
Given the political battles being fought over tolling, such as those in my home town of Austin, it’s easy to overlook that tolling is not new and was once widespread.   To put contemporary road pricing in context, here is a rough timeline.
Direct user fees for road capacity and services to travelers have been in existence in US for over 200 years.  
Private toll roads peaked in mid-19th century – competition from other modes (canals, railroads) impacted demand.  By 1920s, private toll roads had faded.
Pay-as-you-go funding hand-in-hand with federal interstate highway program – starting the 1950s.
In 1990s, road pricing of interest for congestion management – 
In late 1900s, road pricing of interest to fund increasing demand for new infrastructure as financial resources dwindling.  
Congestion pricing projects implemented or planned in California, Minnesota, Texas, Georgia, Maryland, Virginia, Colorado, Washington, among other states.  New toll roads – either constructed and operated by state and location authorities, like the RMA’s in Texas, quasi-public authorities, and public-private initiatives – are common in many states including Texas, Virginia, and Florida.

## Road Pricing:  Two Purposes

**Congestion Management**
- Shift travel to other routes, modes and times desirable
- Reduce peak-period vehicle traffic
- Keep free-flow conditions in priced lane/ road
**Fund Infrastructure**
- Generate funds
- Set rates to maximize revenues or recover specific costs
- Shift to other routes and modes not desired (because this reduces revenues)

- Title of Presentation

**Notes:**
My assignment was to author a paper on data requirements for road pricing analyses.  
A complicated task because data requirements are dependent upon the particular research questions or purpose that the analyses are intended to address.  
Road pricing – direct user fees to use roads -- is intended to achieve either of two objectives:  Raising revenue to fund transportation infrastructure and / or manage congestion. 
Success in each case is defined somewhat differently –

## Current Road Pricing Strategies 

| Type | Description | Policy Objective |
| --- | --- | --- |
| Road Tolls | Fixed fee for driving a road | Fund infrastructure, generate revenue |
| Distance-based Fees | Fee for vehicle use | Fund infrastructure, generate revenue  |
| HOT Lanes (Managed) | Drivers pay fee to use HOV lane | Optimize road capacity, generate revenue |
| Cordon (Area) Tolls | Fees charged to enter a particular area | Reduce congestion in urban centers |
| Congestion Pricing | Direct time of travel charges for road use | Discourage trip making, shift travel routes, times, modes  |

- Title of Presentation

**Notes:**
Even when considering the two distinct purposes for implementing a road pricing project – there are several different types of road pricing in use – 

Road tolls and distance-based fees achieve the policy objectives of infrastructure funding and revenue generation -- excess revenue is used ]to build new or improve existing roads.

HOT Lanes (managed) used to generate revenue as well as to maximize use of roadway capacity.  With HOT lanes, excess revenue is used to fund all types of transportation improvements including transit. 

Cordon (area) tolls and congestion pricing relate to pure congestion management strategies to limit or manage demand.  
Another congestion management strategy is credit-based congestion pricing – which is a revenue neutral credit based system to reduce peak period tolls.  Unlike the other road pricing strategies – there are no current credit based pricing projects in operation.  
Not only are there different manifestations of road pricing – there different ways in which tolls can be collected – passes, toll booths, electronic tolling, optical vehicle recognitions, and GPS, among others.   

## Road Pricing Analyses 

**Increased pressure for:**
- Political acceptance
- Social equity
- Financially success.
**Dependent on:**
- Numerous operational factors,
- Contextual considerations,
- External variables.

|  |
| --- |

- Title of Presentation

**Notes:**

So what about road pricing analyses...where do they fit in to all of this.  Current policy interest in road pricing goes hand-in-hand with increased pressure for politically acceptable, socially equitable, and financially successful road pricing projects. 
The predictability of market demand for road use and / or the likely impacts of road pricing strategies are dependent upon numerous factors – Type and magnitude of fees, where they are applied, what alternative routes and modes are available, operations or toll collection technologies employed; enforcement strategies.  
Analyses rely on considerations that pertain to the context in which road pricing will take place (political, land use, economic development, transportation network).
Also, external variables not easily anticipated or measured must be considered (gas prices, economic swings, demographic shifts).

## Audiences for Road Pricing Analyses

**Financial community**
**Private consortia**
**Federal agencies**
**State agencies**
**Toll road authorities**

- What to price?
- When to price?
- Who to price?
- How to price?
- How much to price?

- Title of Presentation

**Notes:**
In addition to the different types of road pricing, there are different audiences for road pricing analyses.
These different audiences all have a common need for data on which to base pricing decisions:
While increasing interest...few agencies have initiated projects that rely on pricing as an alternative.
Public’s knowledge and awareness of pricing and financing strategies even more limited
Situation places great burden on policy makers and planners to provide reliable, credible information on pricing options in terms of their policy implications, impacts, and performance expectations

## Need Accurate, Reliable data

**Increasing scrutiny of reliability of traffic and revenue forecasts as well as accuracy of evaluations of project performance**
**Situation improved with standards or guidance for project performance measures and “total program” forecasts and assessment**
**Need for:**
- More data, more information
- Enhanced peer review of forecasts
- Increased competition within the community of demand forecasters

- Title of Presentation

**Notes:**
Upswing in interest and implementation of road pricing coming at a time when there is also greater scrutiny of Growing concerns about the accuracy and reliability of traffic and revenue forecasts, particularly for start-up projects (will be growing in number) – T & R studies feed credit evaluations for the bond market or private concessions.
Critics have called for more data (before and after studies, market segment variables, accounting for full range of costs (enforcement)), enhanced peer review of forecasts, and increased competition within the community of demand forecasters.  Belief that only forecasts from a small number of firms are acceptable to achieve investment-grade ratings.  Lead to higher costs, little incentive for methods improvement, and “black box analyses”. –

## Empirical Investigation of Data Quality Issues 

- Title of Presentation

**Notes:**
There is the belief that the final forecasts result from “art” as well as “science”. need to get modeling procedures and current data-gathering procedures for road pricing out in the open.   Slide not far from truth.

## Issues associated with Data Quality

**Before and after studies**
**Incomplete or dated information**
**Manipulation of estimates**
**Better estimation of VOT **
**Reliance on borrowed data**
**Lack of sufficient rigor in data collection (particularly in statistical sampling)**

- Title of Presentation

**Notes:**
Given the importance of accuracy in demand forecasts, surprisingly little empirical investigation of data quality issues
On the private sector side – by credit ratings agencies (Standard and Poors and Fitch).  
Academic side – one of few studies – Flyvbjerb – identified three main issues – lack of sufficient before and after studies, reliance on incomplete or dated information and manipulation of estimates to “get project started.”  
Other factors associated with data quality – 
Miscalculation of users’ willingness to pay based on VOT is a key error in forecast failures, esp. in recognizing that not everyone has the same VOT.  There is a distribution of VOT that can be derived from disaggregate models.
VOT often single average .. That is  borrowed from other projects, other geographies
To improve the capture of VOT and other important data – need for survey research capability and guidance, particularly in the area of statistical sampling.
Opportune time for constructive discussion and debate on how best to improve data quality – as travel demand modeling is advancing to “next generation” forecast models.  Models that go beyond conventional ones to understand travel behavior determinants at a disaggregate level and more realistically represent and forecast behavior.  
You’ll hear much more about these models, their characteristics and components, and application for road pricing later today and tomorrow.  I’ll move on to data requirements.

## Data Requirements for Pricing Analyses 

**Policy Level**
- Data to measure potential effects
- Macro-level analyses
- Longest time horizon
**Strategic Level**
- Data for implementation
- Shortest time horizon
**Tactical Level**
- Data for funding
- Micro-level analyses
- Both short- and long-time horizons

- Title of Presentation

**Notes:**
Given that this forum focuses on the intersection of road pricing and travel demand forecasting – I have focused my discussion of data requirements on this intersection as well. Road Pricing Analysis could also include economic impact analysis, cost-benefit analysis, risk analysis, social equity analysis and others.  Couldn’t do justice to the expanded lists of data requirements that all such analyses would entail.  
Even so as I’ve hoped to show --  road pricing analyses even within this focus are a very broad topic. 
To conceptualize a data framework to address this topic, I’ve organized data requirements into three types of pricing analyses:  political, strategic, and tactical.  
Types distinguished by purpose and objectives, level of analysis, and time horizon.

## Policy Level:  Data Requirements

**Demand side variables**
- Public sentiment
- Traveler characteristics
- Traveler’s travel activity levels
- Traffic flows
**Supply side variables**
- Road network information
- Congestion effects

- Title of Presentation

**Notes:**
at the policy level, key questions deal with the ability of road pricing to meet its objectives of reducing congestion, efficient use existing roadway capacity, and finance needed infrastructure within the constraints of political feasibility, social equity, and project solvency.
Data requirements demand side and supply side – reliable models that can forecast the “real world” impacts of road pricing are needed now.

## Strategic Level: Data Requirements

**Traffic data**
**Revealed preference data **
**Stated preference data (VOT, mode choice)**
**Socioeconomic variables**
**Demographic variables**
**Attitudes and values**
**Project revenues and expenditures**
**Roadway performance **

- Title of Presentation

**Notes:**
Perhaps the most critical road pricing analyses are strategic.
Focus on  decisions regarding implementation of road pricing strategies or on understanding the potential impacts of strategic decisions on the toll facility performance.  Questions regarding: Who pays?  How will they pay?  Who will use?  How facilities operated to enhance overall system performance.
Analyses dependent upon discrete choice models estimated to calculate the probabilities that travelers will pay to use the toll facilities under different travel times savings and toll scenarios as well as on data about users and non-users current travel behavior, attitudes, and values.
Attitudes and values important – and often overlooked.

## Strategic Level: Panel Data

**Mode split**
**RP data**
**Road user / non-user characteristics **
**Attitudes**
**Perceptions of roadway performance**
** **

- Title of Presentation

**Notes:**
At the strategic level – there is the need to measure longer term dynamics.
Road pricing has potential to alter travel behavior in significant and lasting ways.  May further influence decisions about tripmaking, auto ownership, location choice.  Need for panel data sets with lags and leads in behavior to determine cause and effect relationships.
Before and after measures should include:
Mode split to asses if pricing encourages or discourages certain modes
Time of day, frequency of travel length of toll trips, route of travel
Characteristics of road users and non-users
Attitudes toward pricing, including acceptance, equity, perceptions of success;
Perceptions of roadway performance – esp  in terms of reliability.
So important – consider incorporating pricing data into the standard data elements for household travel surveys either at the MPO,state, or national levels – as pricing strategies become mainstream alternatives in in future MPO long-range transportation plans, MPO models must be ready to evaluate these alternatives.

## Tactical Level

**Land use, demographic assumptions of population and employment**
**Alternative or competing routes or feeding projects**
**Weekday versus weekend traffic**
**Review of travel demand parameter assumptions**
**Trip making characteristics (i.e., revealed preference)**
**Value of time (probability of potential drivers paying to use the facility)**
**Market segments **
**Trip purpose**
**Vehicle class**
**Time of day**
**Toll rates**
**Economic and political risks**

- Title of Presentation

**Notes:**
At the tactical level, analyses are focused on project financing, securing a credit rating and obtaining revenue bond financing.  
Data requirements include the specific information that feed comprehensive demand studies of traffic and revenue to support debt repayment.  What is needed is both short-term (base case) and long-term forecasts. 
Data requirements include:
Issues outside of the specific data requirements include:the need for forecasts to incorporate the likelihood of multiple possible outcomes given the potential for changing conditions (economic, political, demographic); 
the need to develop a standard way to define the “accuracy” of the forecast (specification of an acceptable confidence interval and criteria for interpretation of the possible outcomes within the confidence interval; 
The need to accommodate irrational or heuristic decision-making and how this influences travel behavior;
The need to incorporate the “constraints” that govern people’s activity and travel patterns (mode availability, household obligations).  

## Conclusions

**Data framework  (consistency in data) regardless of analysis type**
**Criteria for designing framework**
- Relevance
- Appropriateness
- Reliability
- Affordability

- Title of Presentation

**Notes:**
What do we conclude from all this?
Given the complexity of road and financing analyses, there is a need for standards and consistency in the data regardless if the analyses are for political, strategic, or tactical objectives.  Otherwise, how can one compare and contrast the results of one set of analyses with another or the impact of one road pricing project with another? 
The following criteria should be considered when designing the data system for road pricing analyses ):
Relevance in terms of having a direct influence on the required output
Appropriateness both to the stage of planning and management process, and to the decision-maker’s capability to undertake the required data collection
Reliability in terms of accuracy, coverage, completeness and correctness, and 
Affordability in both financial and staff requirement terms.

## Recommendations

**Explicit standards for pricing analyses**
**Empirical meta-analysis of forecasting accuracy**
**Greater prominence and importance given to peer reviews.**
**Standard, valid, reliable data and methods of analysis needed to create informed pricing options**

- Title of Presentation

**Notes:**
To be effective, the data framework needs to be developed in the context of explicit standards for pricing analysis.  Since much of traffic and revenue forecasting is conducted in an analytical “black box” there is not a standard methodology for developing forecasts and interpreting their results. This practice will begin to build a knowledge base of information that will help ensure the success of future projects or quickly identify problematic start-up projects.
Another conclusion is the fact that few assessments of the accuracy and reliability of the road pricing analyses have been done by social scientists or transportation researchers.  As noted previously, credit ratings agencies have primarily conducted such examination.  This creates a further here need for a TRB-sponsored or DOT-sponsored empirical meta-analysis of forecasting accuracy for both forecasting traffic and revenue as well as impacts of road pricing.  Empirical investigation should focus on the factors or variables that impact different forecasting outcomes.
greater prominence and importance need to be given to the peer reviews that are part of traffic and revenue forecasts.  The peer reviews are often an afterthought and rely on either data different from that used in forecasts or with incomplete data.  The peer review data and analytical requirements o needs to be standardized.  In addition, a systematic study of peer review versus original forecasts should be undertaken , both in term of factors associated with different outcomes, the processes involved in conducted each, and the implications of differences (i.e., what happens when there are differences). 
he transportation needs in most states far exceed available funding, and increasing traffic congestion is adversely affecting the livability of our nation’s metropolitan areas.  Road pricing has been suggested as a solution for both these critical issues.  Standard, valid, and reliable data and methods of analysis are needed for creating informed pricing options with regard to the policy implications, impacts, and performance expectations.