Priced managed lanes share some aspects of both HOV lanes and toll roads. Like HOV lanes, they provide priority treatment for high-occupant vehicles. Like tolled roads, they provide premium service for paying motorists.
One of the unique aspects of planning for priced managed lanes is that planners must forecast demand levels for both high-occupant and single-occupant vehicles that buy in under a variety of pricing
and occupancy requirement scenarios. This exercise serves a dual purpose: First, it allows to the project sponsor to determine the combination of pricing and occupancy requirements that maximizes transportation benefits for all motorists traveling in the priced managed lane corridor. Second, it allows the project sponsor to forecast revenue streams and then evaluate financing approaches.
Forecasting demand on the priced managed lane corridor is accomplished by using a travel demand forecasting model. Travel demand models are mathematical tools that estimate roadway and transit travel based on projected population levels, land use trends, and expected roadway and transit characteristics such as cost and travel time. A travel demand model will forecast the level of demand for the toll facility, the impacts of tolling and pricing on corridor and regional travel, and the impacts of tolling on different groups of travelers.
Forecasting travel demand for priced managed lanes is challenging because traditional travel models use simplified representations of pricing and have limited capabilities for predicting how travelers would change mode, route, departure time, destination, or trip frequency in response to pricing. In addition, forecasting demand for priced managed lanes is very sensitive to future conditions, such as land use, population growth, characteristics of alternative road and transit modes, and even macro-economic cycles. The complexity of the forecast is compounded by the sensitivity of demand for priced managed lanes to travel conditions in the general-purpose lane and to the extent to which multiple-occupant vehicle trips are made in the corridor. At a minimum, demand assessments must consider the difference between travel times in the priced managed lanes to those in the general-purpose lanes, because motorists will chose the priced manage lane only if the value of the time savings value exceeds the cost of the toll. They should also consider the value of time savings afforded by the HOT lane, as it is likely that motorists will chose the HOT lane if the time savings value exceeds the out-of-pocket cost required to achieve the savings. The array of factors affecting travel demand for HOT lanes and priced managed lanes is provided in Table 2-2.
Table 2-2: Managed lane deMand FacTOrs
Categories Demand Factors
Price of HOT
lane Service • Toll or pricing structure as a function of time of day, vehicle occupancy, payment method, prevailing traffic levels on alternative facilities, etc. – affects all usage decisions including route choice, mode choice / carpooling attractiveness, time-of-day
• Expected HOT lane travel time
• “Membership cost” – the out-of-pocket, inconvenience, and/or opportunity cost of making the user eligible to use the facility (includes transponder, account deposit, setup fees, etc.) Cost of
Alternative “Free” Service
• Expected travel time on the parallel or alternate “free” route
• Additional time cost associated with the congestion-related uncertainty of using a parallel free facility (inconvenience and frustration arising from the variation between the expected travel time before use and the actual “true” travel time after use)
Travel
Characteristics • Trip purpose, situational context – affects value of time, and thus willingness to pay out-of-pocket costs • Vehicle occupancy – affects willingness to pay via the net time savings value for the vehicle,
and may impact the HOT lane price for the vehicle
• Trip frequency – may affect willingness to buy into the HOT lane concept (obtain an account and automated vehicle identification equipment or becoming a HOT lane “member”)
User
Characteristics • Risk profile of users (risk averse / risk neutral / risk receptive) – relates to willingness to pay for travel-time reliability • Income and other demographic user characteristics – affects value of time and risk aversion
in both observable and un-observable ways
Desirable Travel Demand Model Characteristics
How well the model predicts demand for the priced managed lane and the resulting revenues depends on the structure of the model, how well it is calibrated and validated, and how it is applied to quantify the uncertainty inherent in any forecast of future economic activity. In the case of priced managed lanes, three model structural characteristics are most important: representation of relevant travel choice decisions, representation of travel costs, and representation of travelers’ willingness to pay.
Priced Managed Lane Guide
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Relevant Travel Choices
The following first-order choices are the direct or immediate reactions that travelers may have to the presence of a priced managed lane:
• route Choice: Switching from the general-purpose lane into the priced managed lane or switching from other roadways into the priced managed lane corridor.
• Mode Choice: Forming carpools to take advantage of the free facility, dissolving carpools because of the new ability to buy into better travel conditions, or switching to or from competitive transit modes. • Travel Time Choice: Changing the time of travel in response to variable toll prices.
The travel demand model should explicitly capture all of these first-order responses.
Longer-term or second-order responses tend to be of a smaller magnitude, but over time they can also be significant. They include changing destinations, trip frequency, and even residential choice, among others. Travel demand models vary in their ability to represent both first-order and second- order responses to priced managed lanes. This effort should be commensurate with the level of analysis sought. A focus on a subset of first-order responses may be sufficient for preliminary feasibility studies, while an investment-grade study requires a better understanding of all choices and how they may vary under different land use, and economic assumptions.
Representation of Travel Costs
The cost of using priced managed lanes or alternate routes or modes includes not just the cost of the toll, but also the perceived value of travel time, fuel, vehicle maintenance and depreciation costs. The perception of these costs can also vary, depending on the length of the trip and the type of user. Traditional travel demand models employ relatively simple approach, while more advanced models take into account traveler perceptions based partly on studies conducted with managed lane users. 3
Accurate representation of the out-of-pocket cost of using the priced managed lane is an important property of the travel demand model. The model must be able to distinguish the price differential across types of vehicles, vehicle occupancy, time-of-day, congestion levels, method of payment, entry and exit locations, and other relevant attributes. Of these attributes, accounting for the differences in toll prices inherent in a dynamic, variably priced system are by far the most challenging, due both to the frequency at which tolls change (as frequent as every 5 minutes) and value of the toll. Models that are not designed to handle these types of pricing schemes rarely have the ability to represent them accurately, and
therefore require substantial modifications to the trip assignment step, as well as feedback to upper level models such as mode and destination choice.
Willingness to Pay
Willingness to pay refers to the tradeoff that travelers make between time and money, and it is a critical factor for priced managed lane applications. For the price of the toll, travelers are “buying” travel time savings or travel-time reliability or some other trip-related improvement. The value of time (VOT) can be thought of as the “price” of travel-time savings. The value of reliability (VOR) has a similar interpretation,
3 Transportation Research Board (2012). Improving our Understanding of How Highway Congestion and Pricing Affect Travel
but it measures willingness to pay for increased travel-time reliability for a given trip. Travelers have different VOTs and VORs, partly as a function of measurable personal, household, and trip characteristics (such as income, gender, worker status, trip purpose, etc.), and partly as a function of situational
variables and other attributes that the forecaster cannot observe.
To reflect the diversity of VOT and VOR, travel demand models subdivide the population into groups with similar VOT and/or VOR. The greater the VOT (or VOR) stratification, the more accurate is the response of the model to tolls. The more advanced travel demand models use continuous VOT functions that reflect both observed and unobserved variability within the travel population. Appropriate market segmentation attributes include trip purpose, time of travel, household income, auto ownership, traveler gender, ownership of a transponder or toll pass, among others. The choice of market stratification variable is typically dictated by data availability; however, at a minimum a model used for managed lane analysis should be segmented by trip purpose, time of day and household income.