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1 NCHRP Report 722: Assessing Highway Tolling and Pricing Options and Impacts, Volume 1 and Volume 2, together, con- stitute the body of the final report. Each volume, however, is a self-contained document that can be independently reviewed and understood by the reader. The purpose of Volume 2 is to survey forecasting tools applied at different stages of pric- ing projects, synthesize the best practices, provide practical recommendations for possible short-term improvements, identify the main gaps, and outline the major directions for principal long-term improvements. Several of the suggested improvements are further tested in the pilot studies. 1.1 Need for Solid Traffic and Revenue (T&R) Forecasts Decisions about toll roads involve structural and technical details. The accuracy of T&R forecasts is crucial; in addition to the usual planning aspect there is generally a political aspect involved (intertwined with public relation/intervention), as well as private investors. As a result, T&R forecasts are closely reviewed by many parties and the level of scrutiny of expected performance is much greater than for non-tolled highway projects. In particular, issues that relate to environmental justice will be especially scrutinized during the NEPA pro- cess. T&R projections will be scrutinized by the project spon- sors and specifically by the rating agencies at the Investment Grade stage. If the T&R forecasts are not reasonable and if modeling tools that were applied do not satisfy the criteria established in the profession, the project may fail during any stage of devel- opment. In addition, even if the project is accepted, financial conditions may be much worse than they could have been under different forecasts as the result of a low credit rating. Even if the pricing project is successful within the formal terms of the Environmental Impact Analysis and is graded high with respect to the revenue versus cost, computerized travel models and related tools can help tremendously with building consensus among the general public and potential stakeholders, as well as at the political level. Travel model results should be presented in a form that are accessible to and can be convincing for a wide audience of non-technical people. In this regard, the following products of the T&R forecasts can be seen as important: ⢠Demonstration of the congestion relief and improved highway throughput using visual traffic simulation tools. ⢠Mapping of travel time savings and showing accessibility improvements [like isochrones of travel time needed to reach the central business district (CBD) area]. ⢠Detailed equity analysis focusing on specific population seg- ments (like low income people in the area directly affected by pricing) to illustrate how each segment is affected, by a proposed pricing action and alternative transportation options. Pricing affects travel demand in many ways. In general, pricing affects travel demand negatively in the sense that travelers will attempt to avoid pricing by switching to other (free) roads, transit, or other periods of the day, depending on available options. With pricing, however, travel times on the priced facility improve significantly. Thus, toll versus saved time ultimately represents the major trade-off from the perspective of highway users. This trade-off is resolved differently by different population segments and also varies by different geographic segments. As a result, total aggregated user benefits and/or revenue cannot tell the whole story, and structural details are important to assess potential winners and losers of the pricing project. Ultimate winners are travelers for whom travel time sav- ings (as well as companion travel time reliability improve- ments) are valued more than the toll. Losers represent travelers for whom travel time savings are valued less than the toll. Some will continue using the facility while others will switch to alternative options (free roads, transit, other time of C h a p t e r 1 Summary
2day, other destination, etc.). The comparison of travel time savings to tolls is embedded in each decision-making process and is generally formalized by value of time (VOT), which represents traveler willingness to pay for one saved hour (and measured in dollars per hour). From a practical perspective, travel demand models rep- resent tools for modeling traveler responses to pricing and identifying winners and losers, their benefits and costs, with a desired level of accuracy for multiple travel segments com- prised of different population groups for different geographic areas. The necessary level of detail requested by the National Environmental Policy Act (NEPA) process, as well as by the rating agencies, includes four to five major travel purposes, three to four income groups, three to four time-of-day peri- ods, and (normally) thousands of zones (trip origins and des- tinations). All these details are important since they can have large impacts on traveler willingness to pay, calculation of travel time savings, and identification of alternative options available for each traveler. As a result, it is generally impos- sible to implement all related calculations with the necessary level of detail using simplified spreadsheet-based methods. 1.2 State of the Practice and Challenges in T&R Forecasting 1.2.1 Main Factors Affecting Reasonability of T&R Forecast A reliable and creditable T&R forecast is a function of many factors. The following major components of the fore- casting process can be identified as important: ⢠Travel forecasting model structure, its soundness from the analytical point of view, and its ability to realistically por- tray the behavioral response of travelers to congestion and pricing. ⢠Quality and comprehensiveness of the base year data used for model validation and calibration including a House- hold Travel Survey and other complementary surveys, traffic counts, etc. ⢠Reasonableness of the assumptions regarding the future growth of population and employment in the regions, as well as the development of the transportation network. These assumptions represent key inputs to the T&R fore- casting procedure and their impact on the final result is as substantial as the quality of the model itself. The reported criticism of T&R forecasts implemented in the past in many regions is, to a large degree, attributable to prob- lems with these input assumptions. Different from the first two factors, where concrete recommendations and well-defined technical procedures can be stated, substan- tiation of future socio-economic and transportation net- work scenarios is a very open issue that resides more in the planning domain, rather than in field of modeling. In this regard, the current report only summarizes certain basic rules (like conservatism and comparison of the future sce- narios to the observed trends), reporting the most frequent pitfalls and concerns. 1.2.2 State of the Practice and Major Gaps Identified The extensive analysis done in this research of travel mod- els and network simulation tools applied in practice for T&R studies has revealed a highly diverse picture, with a large pro- portion of applications with simplified methods as well as a growing number of applications of more advanced modeling tools. The following main conclusions can be made regard- ing the general tendencies and specific important methods observed, along with the identification of gaps where improve- ments are needed: ⢠There is a great deal of variation in approaches. In most cases, the model applied for the highway pricing project was essentially a modification of the existing regional model available for the study. Thus, limitations and defi- ciencies of the existing regional model were inevitably adopted for the study. ⢠In most cases, only route itinerary (assignment) and binary route type choice (toll versus non-toll) models were employed for comparison and evaluation of pricing alter- natives. This achieves reasonable results under the assump- tion that pricing would not affect mode choice, time-of-day choice, trip distribution, and trip generation. While this simplification might be acceptable for some analyses of intercity highways, it is more difficult to defend for fore- casting most of the metropolitan and urban facilities. ⢠Pricing effects on trip distribution have been incorporated by using mode choice Logsums as the measure of acces- sibility in destination choice or gravity-type distribution models. The use of mode choice Logsums in gravity mod- els needs to be tested extensively; unlike destination choice frameworks, where appropriate elasticities to cost are expected when reasonable Logsum parameters are used, it is not clear that doubly-constrained gravity models behave appropriately to changes in level of service (LOS) variables such as the introduction of tolls. ⢠In some cases there is an inconsistency between the travel times and costs used for the trip distribution and mode choice models, in that the travel times reflect priced condi- tions while the toll cost itself does not enter the impedance function. This is the case when travel times are fed back from a generalized cost assignment into a distribution model that is a function of travel times only.
3 ⢠In a few cases utility functions in multinomial or nested logit mode choice models are miss-specified. Undesirable specifications include toll utilities that are a function of the toll alternative travel time and travel time savings with respect to the free alternative. This type of specification may result in counterintuitive results when the LOS attri- butes on either the toll or the free routes change. Another potentially problematic specification is the use of thresh- olds, such as making the toll alternative available only if it meets a pre-defined minimum time savings goal. ⢠There is no consensus on whether road pricing costs should be shared among vehicle occupants and, if so, how. Most models either assume that the full toll cost is either borne by all occupants or that it is equally shared among the occupants. ⢠In some regional modeling systems that were specifically modified for congestion pricing projects, peak-spreading models were applied. Trip-based 4-step models are nor- mally based on time-of-day (peak) factors that are not sensitive to the relative congestion levels at different peri- ods of the day. AMBs can offer a better framework where peak-spreading effects are captured by time-of-day choice sub-model sensitive to the congestion level and pricing. ⢠Almost all models, including advanced activity-based mod- els (ABM) are characterized by a significant discrepancy between the user segmentation VOT in the demand model compared to network simulation. While at the demand modeling stage, segmentation normally includes several trip purposes, income groups, car occupancy, and time- of-day periods; network simulations are characterized by more limited segmentation. Traffic assignments are imple- mented by periods of the day and for multiple vehicle classes that typically include vehicle type and occupancy. However, trip purposes and income groups are blended together before assignment, creating strong aggregation biases with respect to VOT. ⢠Most models break down the network simulation into four broad time periods, typically AM Peak (2 to 4 hours long), Midday, PM Peak (2 to 4 hours long), and Night, and are therefore able to compute LOS differences by time of day only at this level of aggregation. Only one of the regional models reviewed performs the network simulation at a finer time-of-day disaggregation. 1.2.3 Recommended Short-Term Improvements Although the major strategic directions to improve models are strongly associated with a new generation of advanced ABMs and network simulation tools like DTA, there are many practical steps that can be taken to improve 4-step models (and simple ABMs) to better prepare them for T&R forecast- ing and to ensure reasonable model sensitivities for different pricing projects and policies. The following improvements can be made: ⢠A travel model that is going to be applied for a highway pric- ing study should comply with a minimal set of structural requirements. These include a reasonable model sensitivity to toll across all travel dimensions that can be affected by pricing, including route choice, mode (and car occupancy) choice, trip distribution, and time-of-day choice, etc. Across all these choices, a reasonable level of segmentation and correct estimates of VOT (with the necessary aggregations) should be applied. ⢠The demand model should be segmented by at least 4â5 travel purposes and 3â4 income groups with VOT specific for each combined segment. Additional useful steps that can be taken are to apply differential travel time coeffi- cients by segments in the network assignment step, as well as by congestion levels, representing in part a simple proxy for highway the effect of congestion on reliability. ⢠A revision of the network procedures to incorporate dif- ferential tolls and vehicle categories relevant to the pricing study is necessary. The traffic assignment should incorpo- rate and distinguish relevant vehicle classes (auto, commer- cial vehicles, trucks, taxis, etc.) with the appropriate average VOT per class. The technique of multi-class assignment is supported in all major transportation software packages (TransCAD, EMME, and Cube) and can be further applied to differentiate between VOT groups within the same vehi- cle class. If tolls or vehicle eligibility are differentiated by vehicle occupancy (HOV/HOT lanes) the auto vehicle class should be additionally segmented by the relevant occu- pancy categories (SOV, HOV2, HOV3, etc). ⢠It is highly recommended (though it is not absolutely essential in the early stages of pricing studies) to incorpo- rate a binary route type choice model (toll versus non-toll facility), either as a lower-level sub-nest in mode choice or as a pre-assignment procedure. This sub-model allows for capturing a toll bias associated with the perception of the generally improved reliability and safety of the toll facility, as well as provides for better (non-linear) specifications of the tradeoffs between travel time savings and extra costs. ⢠It is essential for congestion pricing studies to include an improved time-of-day choice (peak-spreading) model sensitive to congestion level and pricing. Although the trip-base structure is very limited in addressing time-of- day choice factors, it can incorporate a time-of-day choice model with a fine level of temporal resolution (one hour or less) that would roughly correspond to the outbound and inbound components of the tour-based time-of-day choice model applied separately for each trip segment. ⢠There are a growing number of applications where mode and/or occupancy choices were included. In several cases,
4mode, occupancy, and binary route type choices were combined in one multi-level nested logit choice model structure where occupancy and route type choice served as lower-level sub-choices. These improvements can be implemented and are equally relevant for both 4-step models and ABMs. ⢠It is essential to equilibrate the demand model (at least mode choice and route type choice) and the highway assignment to ensure that the results correspond to (or at least approxi- mate) a stable equilibrium solution. It is more difficult to include the trip distribution (and other sub-models like time-of-day choice or trip generation) in the global equilib- rium, which might require multiple iterations and special averaging algorithms. However, it is essential to eventually ensure a reasonable level of convergence of the entire model system. Recent experience with the New York ABM has shown that effective strategies of equilibration based on a parallel averaging of trip tables and LOS skims can achieve a reasonable level of convergence in three to four global iterations, even in one of the largest and most congested regional networks. ⢠Network simulations should be carefully validated and cal- ibrated to replicate period-specific traffic volumes, as well as period-specific LOS attributes. In this regard, the pre- vailing practice of model validation by daily traffic counts has to be replaced with more extensive and elaborate vali- dation or calibration by four to five time-of-day periods. ⢠There are many reserves for improvements that relate to a better understanding and incorporation of rules of the financial world. Many of them relate to the way a model is used, rather than to its structure. They include more thorough procedures for assessing non-modeled days (weekends and holidays) and time-of-day periods (if the model does not cover an entire weekday), as well as explicit consideration of possible ramp-up dynamics for the first several years of a project. The model structure and out- put should be made to produce the necessary inputs to the financial plan. Of special importance is the issue of quan- tification of risk factors. Risk analysis essentially represents an important strategic direction with many aspects that have yet to be explored by travel forecasters. Some simpli- fied procedures, however, based on the possible scenarios for main input factors can be applied even with a simple travel model. 1.2.4 Recommended Long-Term Improvements and Strategic Directions The main avenues for improvement of modeling tools applied for pricing studies are seen to be associated with the advanced ABM framework on the demand side and DTA on the network simulation side. ABMs provide clear advantages over trip-based models in the analysis of pricing policies. In particular, limitations of trip-based models such as a lack of policy sensitivity and insufficient market segmentation can be overcome with more advanced models. The main advan- tages of ABM structure for modeling highway pricing sce- narios can be categorized according to the following model features: ⢠Tour-based structure that is essential for accounting for tolls applied by direction by time-of-day periods, in a consistent and coherent way. This is, however, condi- tional upon obtaining a level of temporal resolution that matches the details of pricing schedules. Since variable pricing schemes are frequently in the focus of pricing stud- ies, it is essential to have a large set of period-specific simu- lations, ideally, hourly assignments (or a full-day DTA) in order to address different pricing schedules. ⢠Microsimulation of individuals that allows for probabi- listic variation of individual parameters including VOT, car rationing by license plate, toll discounts associated with different payment types, and/or population groups. In addition to that, a fully disaggregate structure of the model output is extremely convenient for reporting, analysis, and evaluation of the pricing scenarios, in particular for the screening of winners and losers, and for equity analysis across different population groups. ⢠Entire day individual activity pattern that allows for a consistent modeling of non-trip pricing options, such as a daily area pricing fee. There are, however, a number of issues that remain to be addressed by ABMs in practice. First, most ABMs continue to rely on static equilibrium highway assignment algorithms. It is common knowledge that such techniques fail to adequately address congestion due to their lack of ability to reflect queu- ing. One of the advantages of priced facilities (particularly dynamically priced facilities) is that they offer more reliable travel times than competing congested facilities where the variability of travel time can be quite onerous. From this per- spective, the integration of an ABM and DTA in one coherent modeling framework represents one of the most important strategic directions for the field. The advanced and flexible microsimulation modeling paradigm embedded in ABM and DTA structures opens a constructive way to include many recent theoretical advances in applied operational models. The following main aspects and directions were identified in this research: ⢠Heterogeneity of road users with respect to their VOT and willingness to pay. This requires a consistent segmenta- tion throughout all of the demand modeling and network
5 simulation procedures to ensure compatibility of implied VOTs. In addition to an explicit segmentation, random coefficient choice models represent a promising tool for capturing heterogeneity. ⢠Proper incorporation of toll road choice in the general hierarchy of travel choices in the modeling system. Addi- tional travel dimensions (such as whether to pay a toll, car occupancy, and payment type/technology) and asso- ciated choice models should be properly integrated with the other sub-models in the model system. The impacts of pricing on long-term choices, such as vehicle ownership, workplace location, residential location, and firm location, need to be better understood. Most ABMs are based on cross-sectional data and are unable to fully capture long- term behavior associated with the introduction of pricing policies. Hopefully, as more policies become implemented, more longitudinal data will be available to improve this critical aspect of travel demand models. ⢠Accounting for reliability of travel time associated with toll roads requires the incorporation of travel time reliability in applied models with quantitative measures that can be modeled on both demand and supply sides. ⢠More comprehensive modeling of time-of-day choice based on the analysis of all constraints associated with changing individual daily schedules. ⢠More comprehensive modeling of car occupancy-related decisions, including differences in carpool types (planned intra-household, planned inter-household, and casual) and associated VOT impacts. ⢠Advanced traffic simulation procedures such as DTA and microsimulation, and better ways to integrate them with travel demand models. In this regard, future research needs to systematically incorporate features such as het- erogeneous users in response to dynamic tolls and to develop efficient heterogeneous intermodal shortest path algorithms. Many of these research topics are being addressed in ongoing NCHRP and SHRP 2 projects. Incorporation of the results of these studies in models applied for highway pricing studies in practice represents an important challenge for the transportation modeling profession. 1.3 Model Features Required for Different Pricing Studies 1.3.1 Model Features for Different Pricing Projects Based on an accumulated modeling experience with vari- ous pricing projects, we have first classified the required model features that stem from the range of planning needs associated with different project types. Further on, in the sub- section that follows, the same model features are arrayed by the four main stages of decision-making process defined in Volume 1. As shown in Table 1, there are some model features that are absolutely essential to pricing studies in the very begin- ning of analysis, while other more advanced features may be reserved for subsequent stages of project development (detailed feasibility and investment grade studies). The more advanced features, however, may become extremely relevant even early on, if a corresponding pricing strategy is included in the range of options included in the scope of the particular study, and a robust and consistent analysis of it is required to compare with other more easily modeled alternatives. Both essential and advanced modeling features may still belong in the category of short-term improvements, however, and are not explicitly distinguished here between 4-step and ABM frameworks in this classification. 1.3.2 Model Features for Different Stages of Decision Making The model improvement process and its desired features can be arrayed in parallel with the basic general stages of pric- ing studies. A framework of gradual corresponding improve- ments is outlined in Figure 1: four major stages of the project development (described in Volume 1, Chapter 4, in detail), and four broad stages of improvement of the forecasting tools. In general, having an advanced model from the very early stage would be an advantage; however, it is not always necessary. A pricing study could begin with a simplified model, while the data and modeling tools are improved in the process, subject to the specific pricing alternatives identi- fied at the earlier stages for further analysis. In the majority of cases reviewed where decision making about highway pricing was made in a systematic way, sup- ported by forecasting tools, the existing regional model (typi- cally that of the MPO) was employed. The development of a new regional model from scratch is of course a time con- suming and costly effort. Also, the timing of a major model improvement effort, often driven by periodic data availabil- ity, might not coincide well with the schedule of road pricing study. Consequently, in many cases the best available model, along with some short-term improvements, was applied. There is, however, a growing recognition of the importance of travel model improvements in view of the scrutiny by rating agencies and private investors of T&R forecasts, and many agencies have made substantial efforts to improve their mod- els for pricing studies. In many cases, the RFP issued by the interested agency for a T&R study explicitly included a model improvement task. Additional benefits of this effort, as per- ceived by MPOs, are that this study also can contribute to the
6general improvement of the regional model and spur addi- tional useful data collection, model validation, and testing. A wide range of cases was found in this research with respect to the t rigor of the methods and levels of sophistica- tion applied for the modeling in each of the decision-making stages. Notwithstanding possible deviations based on differ- ent project development frameworks and varying states of existing regional modeling capabilities, there are several clear patterns that can be generalized and used to characterize both prevailing and best practice. The following correspondence between the stage of decision making and appropriate mod- eling tools can be recommended. Stage 1: Exploratory General strategic go/no-go decisions about highway pric- ing possibilities are made in this stage. The existing regional model should be applied with at least a set of minimal short- term improvements that would normally include the follow- ing common steps (corresponding to the list of general model features essential for all pricing studies identified earlier). ⢠Coding of highway facilities with the corresponding pric- ing forms (flat, fixed variation by time-of-day, variable real-time, etc.) converted into travel time equivalents for highway assignments and skimming. ⢠Incorporation of tolls in the demand models currently developed; most frequently trip distribution and mode choice models are included. ⢠Proper implementation of network equilibrium and associ- ated feedbacks (at least between the assignment and mode choice models, with a subsequent consideration of the trip distribution model as well). ⢠Calibration effort (through proper adjustment of model coefficients, mode specific constants, and/or distributional K-factors) in order to reasonably match traffic counts in the base year, approximate travel times and speeds, in the relevant corridor or sub-area. Stage 2: Preliminary Feasibility Study Further improvements are recommended at this stage depending on the pricing project nature. These improve- Pricing Study Component Model Features Essential Advanced All types of pricing Toll facilities coded in the highway network with toll incorporated in the volume-delay functions Toll plazas and access ramps coded with realistic delay functions Segmented VOT by travel purpose and income group in demand model Perceived highway time by congestion levels / reliability Segmented VOT by vehicle class in traffic assignment Additional vehicle class stratification by VOT Route type (toll vs. non-toll) sub- choice Mode choice and assignment equilibration Inclusion of trip distribution in equilibration through mode choice logsum HOV/HOT lanes Car occupancy (SOV, HOV2, HOV3+) sub-choice in mode choice Additional vehicle class stratification by occupancy in assignment Area and other large-scale pricing schemes Trip generation sensitive to accessibility/generalized cost Accounting for trends in flexible / compressed work schedules and telecommuting Highway pricing in parallel with transit improvements Mode choice with developed transit nest Bus speeds linked to highway congestion Congestion pricing Peak spreading model Time-of-day choice model Accounting for trends in flexible / compressed work schedules and telecommuting Dynamic (real-time) pricing Special network / toll equilibration procedure Highway pricing in parallel with parking policies Parking cost inclusion in mode choice Parking choice model for auto and drive-to-transit trips with parking constraints Equity analysis Model segmentation and reporting of user benefits (time savings and extra cost) by 3-4 income groups Table 1. Model features needed for different pricing studies.
7 ments would mostly include better model segmentation and differentiation of the model coefficients related to VOT. At least two additional improvements are generally needed: ⢠Mode choice (and trip distribution if technically possible) segmentation by travel purpose and income group (that have a strong impact on the VOT). ⢠Multi-class assignment procedure that would distin- guish traffic by vehicle types (auto, commercial vehicle, heavy truck, taxi, etc.) and auto occupancy (SOV, HOV2, HOV3+, etc) that directly relate to the pricing policy dif- ferentiation and eligibility. Stage 3: Environment Impact Statement (EIS) This stage is associated with full T&R studies. The model structure should be improved in order to incorporate addi- tional important sub-models. The following improvements are generally warranted at this stage: ⢠Introduction of a binary route type (toll versus non-toll) choice model as part of the mode choice model (at the lower level of mode hierarchy). Even in cases where mode choice may not play a significant role, such as intercity highways with perhaps no transit alternatives and a high percentage of trucks, this binary choice model explicitly represents the usersâ perception of tolls and related decision-making. It is also essential in order to be able to incorporate a sensitivity of demand, beyond just travel time savings, to the addi- tional measures of travel quality and reliability typically associated with toll roads. ⢠Introduction of a time-of-day choice and/or an incremen- tal peak-spreading model that is essential for urban toll roads and congestion pricing variable pricing analysis. General improvement of regional model Sp ec ific im pr ov em en ts fo r p ric in g st ud y by d ec isi on -m ak in g st ag e Exploratory / Deciding to study Preliminary / Initial Feasibility EIS, T&R Feasibility Study Investment Grade Study w/ risk analysis Existing model Improved segmentation & parameters Improved structure & estimation Advanced model ⢠Ne tw or k co ding of pr ic in g ⢠To ll in co rp or at io n in mo de ch oi ce & ot he r mo de ls ⢠Eq u ili br iu m / f eed ba ck ⢠Ca li br at io n to ma tc h tr af fi c co un ts & ti me s ⢠Mu lt i- cl as s a ssi gnme nt an d VO T es ti ma te s ⢠Mo de ch oi ce an d ot he r mode l se gm en ta ti on by pu rp os e / in co me ⢠Bi na ry pr e- rout e ch oi ce mo de l as pa rt of mode ch oi ce ⢠Ti me -o f- da y ch oi ce (p ea k sp re ad ing) mode l ⢠Mo de ch oi ce Logsum in de st in at io n ch oi ce (t ri p di st ri bu ti on ) ⢠Tr an si t in co rp or at io n ⢠Es ti ma ti on by av ai la bl e so ur ce s ⢠De ma nd mi cr os im ul at io n (a ct iv it y- ba se d to ur - ba se d st ru ct ur e) ⢠Tr af fi c mi cr os imul at io n / DT A ⢠VO T di st ri bu ti on ⢠In co rp or at io n of re li ab ili ty me as ur es ⢠Ri sk an al ys is ⢠Ne w su rv ey s Figure 1. Forecasting tools by stage of project development.
8⢠Including a proper linkage between mode choice and des- tination choice (trip distribution) models through the logsum accessibility measure, essential to ensure logical sensitivities of the model when multiple pricing alterna- tives are compared. ⢠Implementing this linkage may also require model (re) estimation efforts based on the existing household travel survey and other available sources, or the collection of new survey data in the corridor and possibly with a stated pref- erence component. Stage 4: Investment Grade Study The model improvement process will ideally lead to a complete or gradual transition toward an advanced model structure that would fully support specific requirements of the Investment Grade Study, including comprehensive risk analysis across different relevant factors. The following fea- tures of such an advanced model are especially relevant for highway pricing projects: ⢠Individual (household/person) microsimulation of the travel demand choices within an ABM tour-based structure. ⢠Individual (vehicle) microsimulation of traffic using DTA. ⢠Detailed analysis of travel markets and associated proba- bilistic VOT distributions, essential for capturing impor- tant factors such as situational variation in VOT. ⢠Explicit incorporation of travel time reliability measures and willingness to pay for reliability improvements, along with average travel time savings. ⢠Integration of the T&R forecasting and financial risk analy- sis stages through a set of well designed sensitivity tests and an analytical representation of risk factors in multivariate simulations. ⢠Implementation of multiple model runs with different toll values for the purpose of toll optimization, with toll opti- mization estimated with respect to the revenue, network conditions (measured by minimal speed, maximum V/C ratio, or maximum throughput), or by social welfare (util- ity) function. ⢠Conducting and using new RP household travel surveys, with supplementary SP components, designed for and applied in the estimation of advanced models. 1.3.3 Specific Requirements for Forecasting Tools for Investment Grade Studies Rating agencies put travel forecasting procedures under a high level of scrutiny that is different from the model evaluation/ validation criteria applied in the public sector. Investment Grade studies are characterized by more stringent requirements on T&R forecasts, added levels of scrutiny with regard to model structure and calibration, and the need for a number of addi- tional post-modeling steps compared to the preliminary financial feasibility studies. Analysis of the existing models done to date, as well as the tracking history of model applications and associated (well- published) criticism from the rating agencies, have clearly shown that some principal improvements in modeling tools are needed to ensure credibility of T&R forecasts, as well as to better integrate the transportation modeling culture with the culture of the investment analysis community. It should be understood that the quality of forecasts can directly affect the project bond rating (i.e., the possibility to obtain the necessary loans and the interest rate associ- ated with them). The three major rating agencies (i.e., Fitch Ratings, Moodyâs, and Standard & Poorâs) have developed demanding tests for T&R forecasts (especially those pro- duced by public agencies) and examine variations in many input parameters, as well as the model structure itself. For these reasons, investment grade studies require an advanced and well calibrated travel model integrated with network simulation. There are several important techni- cal specifics of an Investment Grade study compared to a T&R forecast produced for feasibility studies that should be addressed that are not necessarily included even in advanced ABMs. These relate to the model structure and calibration, model application, and a number of post-modeling steps that convert the model outputs into the inputs needed for a proj- ect financial plan. Model Structure and Calibration. The following aspects relate to the model structure and calibration: ⢠Presence of all three major relevant travel choice dimen- sions (route, mode, and time-of-day) that represent first- order responses of the travelers. ⢠More elaborate time-of-day choice or peak-spreading model distinguishing between the peak hour and âshoul- dersâ within each broad period. ⢠Flexible trip generation model sensitive to accessibility improvements. ⢠Flexible trip distribution model fundamentally linked to the mode and route type choice model by mode-choice inclusive values (Logsums) as impedance measures. ⢠User segmentation by VOT across travel purposes, income groups, times of day, vehicle type and occupancy. ⢠Extensive newly collected data and more rigorous model calibration is normally assumed. It should be understood that even a well-calibrated regional model might have cer- tain discrepancies compared to traffic counts and/or speed surveys for a particular corridor or facility. Consequently,
9 it is essential to recalibrate the model based on the most recently collected data, including traffic counts, special surveys (e.g., users of a particular toll facility), and speed measurements in the relevant corridor. Model Application. The following aspects relate to the model application: ⢠Toll rate optimization and multiple sensitivity tests with different toll and toll escalation scenarios. ⢠Risk analysis and risk mitigation measures. This includes identification and quantification of risk factors. It should be understood that the culture of the investment world is based on a probabilistic view of the model outcome, in contrast to the conventional travel forecasting culture based on a deterministic interpretation of the model out- come. A theoretically consistent incorporation of proba- bilistic risk analysis in T&R forecasting procedures is an important avenue for bringing these two worlds together and is essential for the current synthesis. Risk Factors. The following general concepts and risk factors are considered by rating agencies: ⢠Start-up toll facilities are considered the most risky and are put under a stress test, especially if the forecast was imple- mented by a public agency. ⢠Accurate traffic and revenue forecasting in dense urban areas will always lie at the opposite end of a reliability spectrum from, for example, a river crossing with a clear competitive advantage over limited alternatives. ⢠Traffic patterns associated with well-defined, strong radial corridors appear to be more reliable. ⢠Forecasts prepared by project sponsors and bidders (inter- ested parties) are generally higher than prepared by investors/ bankers; this âoptimism biasâ is estimated at 20% or more. More aggressive forecasts can be accepted for public private partnerships (PPP) that do not need rating. ⢠VOT miscalculation and improper aggregation across different income groups/travel markets are problematic (thatâs why a proper model segmentation is essential). ⢠Recession/economic downturn (GDP growth is correlated with traffic growth with some lags). ⢠Slower future-year land-use development along the cor- ridor. Reconsideration of population, employment, and income growth forecasts prepared by the MPO or DOT for the region/corridor is one of the frequent requests. ⢠Possibility for actual lower time savings than the modeled ones. ⢠Improvements to competing free roads or other alternatives. ⢠Considerably lower usage of toll roads and managed lanes by trucks. ⢠Lower off-peak/weekend traffic (40-50% of weekday) than is normally assumed (70-75% of weekday). ⢠Specific risk factors for trucking market that are essential if trucks constitute a significant share in the traffic. In par- ticular, less reliability should be placed on forecast if the trucking market is composed of a large number of small, owner-driver general haulers. Post-Modeling. The following important aspects relate to the post-modeling steps: ⢠Annualization of revenues including modeling of or assumptions about weekend and holiday revenues, sea- sonality, within-week variability, etc. The factors may vary from corridor to corridor, and the best way for estab- lished facilities is to develop individual factors based on the observed patterns. It is also important to consider that weekend VOTs are generally lower, due to a greater mix of purposes and schedule flexibility than on the modeled typical weekday. ⢠The yearly T&R stream needed for the Financial Plan is normally calculated by interpolating and extrapolating between and beyond modeled years for long periods (40â 50 years and longer). Capacity constraints and adverse effects of congestion when traffic volume approaches capacity should be taken into account for deep forecasts if they are not directly simulated in the model. ⢠Detailed consideration of a ramp-up period. If it is not modeled as a dynamic behavioral response in the model (which is unfortunately the case with even the most advance AB models), certain assumptions are made based on the past experience with similar projects. Specific ramp-up considerations are associated with electronic toll collection (ETC), especially if no cash payment option is provided. In this case, the ramp-up period is almost none for routine users and commuters, but might be significant for occasional users and visitors. ⢠Detailed consideration of bulk discounts, person/vehicle type discounts, toll evasion (if any), and other revenue loss factors such as accidents or incidents, extreme weather, or special events, among others. ⢠Accounting for toll rates escalation (CPI, GDP, floor, ceil- ing) versus population income (and VOT) growth over a long period of time. ⢠The model output needs to be processed in formats suit- able for the subsequent analysis and preparation of the Financial Plan. It is important to provide a transparency of the results and identify the key factors that have the most significant impact on the forecasts (Origin-Destination pairs with the largest number of trips, core travel markets) for which data and calculations can be demonstrated for practitioners and reviewers.
10 1.4 Organization of Volume 2 Volume 2 is organized in six subsequent major sections: ⢠Chapter 2: State of the Practice in Forecasting Methods represents a survey of the existing practices. It provides an in-depth analysis of models applied for highway pricing studies, including both 4-step trip-based models and tour- based ABMs. It concludes with a list of identified common features, gaps and critical issues that should be addressed. ⢠Chapter 3: Survey Methods to Support Pricing Studies is devoted to an overview of survey techniques that support the development of models and decision-making regarding pricing options. It includes Revealed Preference and Stated Preference survey types, as well as the ways to integrate them in an effective model estimation process. ⢠Chapter 4: Critical Issues and Directions for Short-Term Improvements covers short-term improvements and asso- ciated critical issues; most improvements in this section are those that can be implemented with trip-based, 4-step models. This section also identifies a core list of model fea- tures needed for different pricing studies and associated critical issues, relating to demand modeling and network simulation. The model improvements are put in the con- text of the different phases of the project development, from preliminary feasibility to investment grade studies. Special attention is paid to the issues associated with using a travel model output for evaluation of pricing projects. ⢠Chapter 5: Strategic Directions for Improvement out- lines long-term improvements that can expected to yield major breakthroughs. Many of them are oriented toward emerging advanced-practice-age models, such as ABMs on the travel demand side, and DTA and traffic microsimu- lation tools on the network supply side. Specific model improvements are suggested where technical break- throughs can be reasonably expected, such as those in the direction of user segmentation by VOT, incorporation of reliability measures, inclusion of additional travel choice dimensions (like acquisition of transponder), time-of-day choice models with fine temporal resolution, accounting for different carpool formation mechanisms, and better integration of demand and dynamic network simulation models. Special attention is paid to a constructive coordi- nation of the current NCHRP project and SHRP 2 Project C04, âImproving Our Understanding of How Highway Congestion and Pricing Affect Travel Demand.â ⢠Chapter 6: Pilot Studies for Demonstration of Improved Tools describes four Pilot Studies that demonstrate the advantages of the suggested improvements to travel models applied for highway pricing projects. It includes application of advanced ABMs for various pricing studies in the San Francisco and New York Regions, application of DTA in the Baltimore-Washington, D.C., corridor, and a technical overview of the enhancements for a trip-based 4-step model prepared for pricing studies in the Los Angeles Region. ⢠Chapter 7: Conclusions and Recommendations for Future Research distills the information presented in the previous sections and pilot studies. It summarizes the main conclusions and presents important directions for future research that were identified, but could not be fully addressed as part of the current synthesis. The report also contains a list of sources and an appendix that provides additional technical details on particular topics.
