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Suggested Citation:"Chapter 8 - Adapting Strategic Models to Address CAVs." National Academies of Sciences, Engineering, and Medicine. 2018. Updating Regional Transportation Planning and Modeling Tools to Address Impacts of Connected and Automated Vehicles, Volume 2: Guidance. Washington, DC: The National Academies Press. doi: 10.17226/25332.
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Suggested Citation:"Chapter 8 - Adapting Strategic Models to Address CAVs." National Academies of Sciences, Engineering, and Medicine. 2018. Updating Regional Transportation Planning and Modeling Tools to Address Impacts of Connected and Automated Vehicles, Volume 2: Guidance. Washington, DC: The National Academies Press. doi: 10.17226/25332.
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Suggested Citation:"Chapter 8 - Adapting Strategic Models to Address CAVs." National Academies of Sciences, Engineering, and Medicine. 2018. Updating Regional Transportation Planning and Modeling Tools to Address Impacts of Connected and Automated Vehicles, Volume 2: Guidance. Washington, DC: The National Academies Press. doi: 10.17226/25332.
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Suggested Citation:"Chapter 8 - Adapting Strategic Models to Address CAVs." National Academies of Sciences, Engineering, and Medicine. 2018. Updating Regional Transportation Planning and Modeling Tools to Address Impacts of Connected and Automated Vehicles, Volume 2: Guidance. Washington, DC: The National Academies Press. doi: 10.17226/25332.
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Suggested Citation:"Chapter 8 - Adapting Strategic Models to Address CAVs." National Academies of Sciences, Engineering, and Medicine. 2018. Updating Regional Transportation Planning and Modeling Tools to Address Impacts of Connected and Automated Vehicles, Volume 2: Guidance. Washington, DC: The National Academies Press. doi: 10.17226/25332.
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Suggested Citation:"Chapter 8 - Adapting Strategic Models to Address CAVs." National Academies of Sciences, Engineering, and Medicine. 2018. Updating Regional Transportation Planning and Modeling Tools to Address Impacts of Connected and Automated Vehicles, Volume 2: Guidance. Washington, DC: The National Academies Press. doi: 10.17226/25332.
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Suggested Citation:"Chapter 8 - Adapting Strategic Models to Address CAVs." National Academies of Sciences, Engineering, and Medicine. 2018. Updating Regional Transportation Planning and Modeling Tools to Address Impacts of Connected and Automated Vehicles, Volume 2: Guidance. Washington, DC: The National Academies Press. doi: 10.17226/25332.
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Suggested Citation:"Chapter 8 - Adapting Strategic Models to Address CAVs." National Academies of Sciences, Engineering, and Medicine. 2018. Updating Regional Transportation Planning and Modeling Tools to Address Impacts of Connected and Automated Vehicles, Volume 2: Guidance. Washington, DC: The National Academies Press. doi: 10.17226/25332.
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Suggested Citation:"Chapter 8 - Adapting Strategic Models to Address CAVs." National Academies of Sciences, Engineering, and Medicine. 2018. Updating Regional Transportation Planning and Modeling Tools to Address Impacts of Connected and Automated Vehicles, Volume 2: Guidance. Washington, DC: The National Academies Press. doi: 10.17226/25332.
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Suggested Citation:"Chapter 8 - Adapting Strategic Models to Address CAVs." National Academies of Sciences, Engineering, and Medicine. 2018. Updating Regional Transportation Planning and Modeling Tools to Address Impacts of Connected and Automated Vehicles, Volume 2: Guidance. Washington, DC: The National Academies Press. doi: 10.17226/25332.
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64 Overview Strategic models for planning have existed in various forms for a long time. Several forms of strategic models for transportation planning have been developed in recent years to address a gap in the technical understanding of an uncertain future. Scenario planning has received additional attention among transportation planners as an appropriate means of evaluating these uncertain futures. Strategic planning models for transportation have been developed to provide more robust statisti- cal evaluation of impacts for transportation scenarios. These models are intended for use as visioning tools, specifically to help guide transporta- tion policies and investments, so planners have adopted a revised name, “strategic visioning frameworks,” to emphasize this purpose. Current strategic visioning frameworks have been developed to address specific transportation policies, such as greenhouse gas reduc- tion strategies or smart growth policies. These resources bridge the gap between regional planning visioning exercises and transportation plans. FHWA, along with several state DOTs, is sponsoring a new pooled fund effort to develop an open-source framework to consolidate these tools and evaluate a broader range of strategies in a consistent modeling sys- tem (NCHRP n.d.) TRB also sponsored development of a sociodemo- graphic strategic planning tool (Zmud et al. 2014). The current strategic visioning frameworks were designed to be faster, allowing for extensive scenario testing. The processing speed is accelerated by not including detailed multimodal transport networks and instead describing the built environment and transportation supply by using aggregate measures. These models are developed and applied as disaggregate models maintaining detailed personal, household, and firm characteristics that influence travel demand, combined with aggregate land use and transport supply measures. The models allow for many (even hundreds of) scenarios to be pro- cessed quickly, after which visualizers can help interpret the scenarios interactively to provide a thorough understanding of the impacts derived from various combinations of policies and investments. Another important feature of strategic visioning frameworks is ensuring that the interac- tion between different policies or future scenarios is integrated so that population, land use, employment, transport supply, and travel behavior are linked. These linkages are important to understanding how the combination of policies or transport supply or demographics on travel C H A P T E R 8 Adapting Strategic Models to Address CAVs Chapter Highlights • Provides high-level guidance on accounting for CAVs in the strategic models developed to supplement more sophisticated modeling efforts as screening tools for evaluating policies. • Depicts the typical strategic model components. • Identifies potential modeling changes. • Discusses the contexts and approaches for modeling – Sociodemographics, – Built environment, – Mobility, – Accessibility, – Pricing, – Travel demand, – Mode choice, and – Truck and commercial vehicles.

Adapting Strategic Models to Address CAVs 65 demand can influence each other (and not be double-counted). Sometimes transport policies target similar demographic populations and are more or less effective in combination with other policies. The land use and transport interactions are used to quantify induced demand for travel, which is a critical aspect of uncertain futures. Model System Like AB models, strategic models are structured to be able to represent AV policy analysis as well but also require some modifications depending on the questions being asked of the model. Strategic models are currently sensitive to many of the behavioral impacts required and can be adapted to represent changing behaviors. Figure 7 illustrates a typical set of strategic model components. VisionEval (http://visioneval.org) is an example of a strategic modeling framework that has been used to represent emerging travel modes. Table 8 summarizes the model improvements for various components within strategic models. Strategic models have several components that are relevant to vehicle ownership and availability, including representing vehicle ownership costs (purchase, insurance, operating), bike ownership, vehicle age, and fuel efficiency. In addition, these components can be easily adapted to include carshare and bikeshare membership. Stra- tegic models also incorporate household budgeting, allowing the introduction of AVs into the household budget. Urban form is currently represented in strategic models as a combination of development and area type (e.g., transit-oriented development in a residential area). These models can be adjusted to incorporate place types that are important for AVs (e.g., parking-constrained areas). Life-cycle variables that define modality choice (e.g., young single adults, families, retirees) can also be incorporated into the urban form and VMT models. Most strategic models estimate VMT directly, and adjustments for different policies are pro- vided through empirical research. These adjustments can be included for households that opti- mize their travel with AVs or adjust their trip chaining because the AV can be used to pick up and deliver multiple household members. A new model component for estimating the VMT associated with deadheading (i.e., cars with no occupants that are repositioned or sent to pick up another passenger) could be developed for both privately owned AVs and for-hire AVs. Strategic models also currently evaluate induced demand, but this impact may need to be evaluated once empirical evidence on AV use is documented. Strategic models do not typically include a traditional mode choice model. Instead, modal shares of interest are directly modeled on the basis of characteristics of supply and demograph- ics. Models can be constructed to directly estimate VMT from for-hire services (e.g., carshare, bikeshare, or TNCs) or other modes that may emerge. Sociodemographics Context Strategic visioning frameworks often begin the process with a population synthesizer such as those developed for AB models and a firm synthesizer similar to those developed for supply chain or tour-based freight models. Characteristics of households, persons, firms, or establish- ments can vary depending on the model but typically include number of persons and workers by age, life cycle, income for households, and number of employees and industry for firms or establishments. For regional analysis, population synthesis is often controlled by county and Strategic visioning frameworks include more complex behav- ioral representations of travel behavior than do sketch planning tools but are less complicated than integrated land use and travel demand forecasting models.

66 Updating Regional Transportation Planning and Modeling Tools to Address Impacts of Connected and Automated Vehicles state control totals. The population is controlled for by household income and age of persons in the household. Per-capita and household income are calculated for each forecast year. A house- hold age model is used to identify persons by age in each household. Firm synthesis is controlled by regional, county, and state control totals. The County Busi- ness Patterns data collected by the U.S. Census Bureau are used to allocate firms to counties by size and type. Other data sources, including Woods & Poole, InfoGroup, and state-produced Figure 7. Typical strategic model components.

Adapting Strategic Models to Address CAVs 67 economic forecasts can be used to supplement the data, support forecasts, or provide control totals. Input–output data from the Bureau of Economic Analysis are used to describe what each industry produces and consumes. These relationships are known as make-and-use tables. When multiple commodities are made or used, then the data represent a proportional value. These data tables are used to assign production and consumption categories to the firms synthesized with the County Business Patterns data. Approach One distinct advantage of the population and firm synthesizers is that additional characteristics of the population or employment base can be added when data become available to control for these characteristics. Model Component Strategic Model Improvements Sociodemographics Population synthesizer Add smartphone ownership and education level Built Environment Urban form Adjust urban form Urban form Estimate area type, development type Mobility Vehicle ownership Add household vehicle ownership costs for CAVs Vehicle age model Represent higher turnover for buying CAVs Vehicle choice Add household AV choice model for vehicle use MaaS Add carsharing, ride-hailing, bikesharing memberships Accessibility Parking supply Add parking supply Modal accessibility Add walking and biking accessibility Pricing Household budgets Incorporate all aspects of cost for CAVs and MaaS Parking costs Segment parking cost Fuel cost savings Increase fuel efficiency for CVs and AVs Car service cost Model SAV cost Travel Demand VMT model by vehicle type Adjust VMT for households owning CAVs VMT model by vehicle type Add VMT for fleet-owned CAVs Feedback for congestion Separate VMT models for AVs and SAVs Feedback for congestion Separate VMT models for CAVs Feedback for induced demand Add VMT adjustment for induced demand Household VMT model Adjust VMT for mobility-limited populations Mode Choice VMT by mode Add CAVs and TNCs on basis of cost per mile Truck and Commercial Vehicles Mode choice–long haul Add choice models for current modes and CAVs Vehicle type–long haul Add choice model for medium/heavy trucks and CAVs Vehicle type–short haul Add choice model for light/medium/heavy trucks and AVs/drones CV VMT model Add feedback for congestion Table 8. Summary of model improvements for strategic visioning models. An advantage of the population and firm synthesizers is that additional characteristics can be added when data become available.

68 Updating Regional Transportation Planning and Modeling Tools to Address Impacts of Connected and Automated Vehicles One example is the strong relationship between smartphone ownership and other new technolo- gies, such as AVs. The population synthesizer could be adapted to identify smartphone own- ership for households, and then that information could be used as an indicator of household adoption of AV technology. Another personal characteristic that influences smartphone owner- ship (and, possibly, adoption of new technologies) is education level, so this characteristic could also be added to the population synthesizer. Built Environment Context Currently, strategic visioning frameworks include an urban form model that allocates future households and employment to different types of built environment. These data are not located geographically but are based on different area types (urban core, close-in community, subur- ban, and rural) and different development types (residential, commercial, mixed use, transit oriented, greenfield), as noted in Table 9. These projections can be estimated from available data sets such as the Environmental Protection Agency’s Smart Location Database. Approach The current urban form models do not include accessibility, so these models are not sensitive to travel time or cost. The household models are based on several demographic characteristics, and the employment models allocate randomly because no data were available to estimate model coefficients. The value of these models for evaluating the impacts of CAVs lies in their ability to provide input on the assessment of on-demand service (e.g., TNCs), in which level of service depends on the area type or the density of an area. This information may also influence adoption rates for CAVs and thus could be used as input to vehicle ownership models. In the long term, there may be evidence that CAV adoption or MaaS could influence residential or employment locations. If data exist to support this assumption, then new urban form models that include CAV adoption or MaaS can be estimated. These models would then be sensitive to changes in CAV adoption or MaaS, and residential and employment locations would vary depending on these assumptions. Strategic visioning frameworks currently address the influence that changes in parking cost have on travel demand but not on urban form. Parking capacity or access and egress time do not currently influence urban form or travel demand. CAVs are expected to reduce demand for parking in high-density areas, but the cost may increase as parking capacity is repurposed for other land uses. The relationship between parking cost and travel demand will need to be reestimated for CAVs to account for the trade-off between parking cost and operating cost related to repositioning the CAV outside parking charge areas. Development Type Area Type Urban Core Close-in Community Suburban Rural Residential Employment Mixed use Transit oriented Rural/greenfield Table 9. Place types, by area type and development type.

Adapting Strategic Models to Address CAVs 69 Mobility Context Mobility models simulate household-level choices to provide mobility options for all persons in the household. These models are primarily focused on household vehicles but also identify AVs, car service subscriptions, and bicycle ownership. These characteristics could be expanded to include transit pass ownership, ride-hailing service participation, or bikeshare subscriptions. Like trip-based and AB models, the mobility models currently built for strategic visioning frameworks identify the number of household vehicles. Then the household vehicle models assign vehicle type, vehicle age, and powertrain to each vehicle in the household (going beyond what current AB models predict). Powertrains are currently categorized as internal combustion engine, hybrid electric vehicle, plug-in hybrid electric vehicle, and electric vehicle. The model addresses autonomous and conventional carsharing by comparing the cost of using these ser- vices with the marginal cost of vehicle ownership to determine which households would use the services and, consequently, how many fewer cars they would own. The marginal cost approach is used because households make choices at the margin (to own one more car or one less car) and because vehicle travel and ownership costs do not scale uniformly (i.e., the second car owned does not double the miles traveled). Current vehicle ownership models estimate the ratio of household vehicles per driving-age person according to categories of no vehicles, less than one vehicle per driving-age person, one vehicle per driving-age person, and more than one vehicle per driving-age person. The vehicle models are further affected by elderly populations. Approach Current vehicle ownership models estimate AV adoption on the basis of the assumed costs of owning or using an AV as compared with a conventional vehicle and a household budget. Adoption rates can then be estimated following application of the vehicle ownership model or can be adjusted by revising the input cost and discount parameters. Another option would be to incorporate an adoption parameter representing a household’s preferences for buying an AV. The cost approach to identifying households who will buy or use AVs is sensitive to the assumed cost of purchasing an AV, the depreciation and interest rates for financing the vehicle, the insurance cost (including a discount for AVs), the registration cost, and a reduced parking cost. These costs are used to estimate a per-mile cost for owning a vehicle and are compared with the same costs for using a car service (both conventional and AV) plus the overhead and cleaning costs and the service life of the vehicle. Again, these costs are translated to a per-mile cost for using these services. One additional feature of current strategic positioning models is the assessment of greenhouse gases and other particulates on the basis of the vehicle powertrain, age, and type. AVs will likely be more fuel efficient on average, as they will likely constitute a greener vehicle fleet and be more fuel efficient, owing to the built-in optimal driving behavior. CVs will likely be even more fuel efficient under sufficiently saturated conditions, owing to the improved driving behavior afforded by vehicles that communicate with one another. These fuel efficiencies can be directly attributed to the CAVs owned by households and those operated as fleets for carsharing or ride-hailing. MaaS can be separately represented in strategic visioning frameworks as an additional service to the household. Some TNCs have tried certain types of subscription services, but typically there is no charge to sign up. Nonetheless, typical demographics exist for households that use

70 Updating Regional Transportation Planning and Modeling Tools to Address Impacts of Connected and Automated Vehicles TNCs, and with new data on these types of households, mobility models can represent house- holds signing up to use MaaS. Bikeshare is another option to consider as a mobility model, parallel to the bike ownership model. In general, this new mode may not have enough of an impact to warrant the extra effort to track it separately, but in cities with significant bikesharing, this may be worthwhile. Accessibility Context In strategic visioning frameworks, accessibility is measured on the basis of transport supply and demographics. Transport supply is currently freeway lane miles and transit revenue miles for the base and future years. These measures allow for calculating freeway and transit miles per capita as well as calculating the interaction of freeway and transit miles with household income, population density, elderly populations, and urban form (area type and development type). Accessibility measures are then used to inform the travel demand models. Approach The calculation of accessibility should be updated as new modes come online so that these mea- sures can reflect observed behavior for new mobility options and add detail on existing mobility options. Current accessibility measures are based on an individual’s per- ception of the value of time spent traveling, which is likely to change sig- nificantly if drivers are no longer required to drive and can use this time to do other things (e.g., working, reading, watching a show). Passengers will likely also adjust their perception of travel time if the driver is now available to engage in other things with the passenger (e.g., playing a game, sharing photos, making travel plans). The parameter estimate for these accessibility measures will reflect these changes in perception. Parking cost and inventory are aspects of accessibility that have not been incorporated in the current strategic visioning frameworks. These aspects could be included directly or indirectly as additional accessibil- ity measures for specific place types. Parking will potentially change as CAVs allow for parking remotely, and this may result in higher costs for parking in dense urban areas as a result of lower demand. Accessibility for nonmotorized travel (walking and biking) could be incorporated by provid- ing the supply of nonmotorized facilities (base and future years) and then calculating accessibil- ity for nonmotorized travel. These accessibility measures could be used to influence biking or walking miles, bike ownership, or bikeshare subscriptions, or to reduce or increase household VMT on the basis of changes to nonmotorized supply. Pricing Context One of the benefits of strategic visioning frameworks is the use of a household budgeting model, which can account for the mix of long- and short-term decisions that intersect. The cost of owning a conventional vehicle or AV is typically not considered in travel demand models, but the trade-off between owning (and using) a household vehicle versus using alternative modes, TNCs, or carshare for mobility is becoming more important. Current household budgeting Accessibility measures should be updated as new modes come online so that these measures can reflect observed behavior for new mobility options.

Adapting Strategic Models to Address CAVs 71 components estimate a household’s budget for transport and then, on the basis of this budget, limit choices for owning. In addition, the full cost of driving a household vehicle can be directly incorporated into the mode choice component. Approach The elements of the cost function developed for the household budgeting model should be refined to accommodate all aspects of cost that travelers consider. The current household bud- geting model can be reestimated for each location and thereby provide locally specific param- eters. Research on the elements of the cost function and how to represent new modes like TNCs and carshares with autonomous technology is needed to understand sensitivities. Household budgets limit the amount each household can spend on transportation. Each mode has a cost associated with its use. Ownership costs include depreciation, financing, insurance, vehicle licensing or registration, and parking cost. Operating costs include fuel, tire, and maintenance costs. Travel Demand Context Strategic visioning frameworks predict travel demand on the basis of a variety of factors, directly from the household and firm characteristics as well as from the transport supply and policies specified. Current models identify household VMT and commercial VMT as a starting point and then adjust these measures on the basis of the influence of various transport policies (i.e., travel demand management programs, nonmotorized travel, ecodriving). The nonauto modes are addressed in this chapter under the topic of mode choice, and commercial vehicle travel is addressed under the topic of trucks and commercial vehicles. The remainder of this section focuses on auto VMT. Average daily VMT is predicted in strategic visioning frameworks by estimating total daily VMT and then allocating it to freeways and arterials. These allocations are then used to deter- mine congestion levels for freeways and arterials as well as average speeds for these facility types. Congestion levels are then fed back to travel demand models to allow for equilibration of daily VMT. Congestion that arises from local street grids owing to different types of development, is also accounted for. Currently, street grids are identified according to design: either a neotradi- tional (grid) street design or a conventional (public utility district) network design. Induced demand for auto travel is determined as a function of future changes in the trans- portation system, and adjustments to estimates of travel demand are made to reflect the effects of changes in the urban form of the region in the future. The sensitivity of the model to induced demand and urban form effects is based on work completed by Cervero (2003) for the Path Model. Induced demand is currently a result of changes to the transportation system supply. Approach The flexibility of strategic visioning frameworks offers advantages for modeling travel demand for different segments of the population or for different facilities (freeways, arterials, other). VMT is currently predicted for different vehicle powertrains and for households owning AVs. This VMT is then allocated to different vehicle types according to household vehicle owner- ship. Current research simulating VMT for household-owned AVs can inform these models and account for deadheading (e.g., empty vehicle trips) to reposition the vehicle for the next passenger (or household member).

72 Updating Regional Transportation Planning and Modeling Tools to Address Impacts of Connected and Automated Vehicles VMT will also likely change when enough CAVs are on the road to make better use of existing capacity and, as a result, increase speeds. Intersection delay may also be significantly reduced with sufficient CV market share. These changes may ultimately increase VMT, so changes to VMT by mode, vehicle type, and other relevant dimensions can be added to strategic visioning frameworks to account for these impacts. Another advantage of the direct demand approach is that AVs will improve mobility options for those who currently cannot drive because of age, physical disability, or income (Levinson 2015). VMT that results from these new populations choosing auto modes can be estimated and included. The question of how AVs will induce demand is a concern for many planners. Significant speculation about how AVs will both reduce and increase VMT exists. Current escorting travel (i.e., driving a child to school) could be reduced because these trips may be chained together to serve multiple passengers or purposes. New research from the Uni- versity of California, Berkeley (Harb et al. 2017) shows that VMT could increase more than 80% from travelers making additional trips, trav- eling farther, or sending the car to pick up deliveries. The calculation of induced demand in strategic models is estimated as an elasticity and applied within the feedback for congestion and transportation policy influences. The household budgeting model also constrains VMT, since there is a cost to each mile traveled. Mode Choice Context Strategic visioning frameworks typically predict miles traveled for each mode separately, rather than as a probability of choosing a certain mode for each trip. These are aggregate predictions of miles traveled for each mode and household. Currently, these models estimate walking trips and miles traveled for bikes/lightweight vehicles and autos. Bike and auto travel sum to total daily miles traveled. Walking trips are separated to evaluate the impacts of trans- portation and land use policies on walking travel. Bus and rail VMT is also calculated. Auto VMT is assigned to each household vehicle (manual and autonomous), and carsharing is an option for a portion of household miles traveled. Mode choice is handled in several different ways in strategic visioning frameworks. VMT is estimated directly for SOV travel and for total household vehicle travel. Levers for estimating VMT reduction from travel demand management policies or bicycles exist. VMT is further assigned to each household vehicle. Bus and rail miles traveled are estimated separately. Approach Recent changes were made to one of the strategic visioning frameworks to incorporate CAVs and ride-hailing services that TNCs provide. This model calculates a per-mile cost for each household vehicle that incorporates purchase price and operating cost. The car service option also has a per-mile cost to use the car service, and this cost is segmented for autonomous and conventional vehicles. Additionally, the model calculates a per-mile cost for CAVs that incorpo- rates purchase price and operating cost. Ride-hailing services (e.g., TNCs) can also be included with a per-mile cost, which can be adapted to represent two options: ride-hailing in a conven- tional household vehicle and ride-hailing in a shared conventional household vehicle. While it is possible to expand ride-hailing services according to whether they are shared, this expansion is likely too complex for current strategic visioning frameworks, given the uncertainty involved in these new services. New research offers insight into how AVs will induce demand, showing that VMT could increase more than 80% from additional trip making, traveling farther, and zero-occupant trips.

Adapting Strategic Models to Address CAVs 73 Strategic visioning frameworks rely on elasticities to estimate impacts for a model compo- nent, and these impacts are integrated so that other parts of the modeling system are influenced. Elasticities are typically derived from observed data but can also be derived from a range of assumptions. Strategic visioning frameworks are intended for use as scenario planning tools, and applying a range of assumptions to determine the range of impacts they produce is encouraged. Trucks and Commercial Vehicles Context The three primary elements to the truck and commercial vehicle models in current strategic visioning frameworks are • Commodity flows, • Mode choice for long distance, and • Vehicle type for short distance. Commodity flows are developed by identifying firms that buy or sell goods in each industry and matching firms that will trade goods on the basis of their distance apart and the size of each firm. Input–output tables provide direction for allocating goods demand to each buyer– supplier pair on the basis of the employment of the buyer firm. An estimate of consumption (of the commodity being consumed) by a buyer firm is calculated on the basis of the value (in dollars) consumed per employee, which is obtained with input–output economic tables. Commodity flows are then segmented by long-distance or interstate movements and short- distance or intrastate movements. Interstate trips are assigned to modes (air, rail, and truck). Mode choice is completed with a fixed allocation model with historical average mode propor- tions by commodity type found in the Freight Analysis Framework data for the Freight Analy- sis Framework zone pair and commodity in question. Intrastate trips are assigned to one of two truck types: heavy or medium. This assignment is based on commodity type and volume according to Vehicle Inventory and Use Survey data. Any unobserved heavy truck VMT in the model as compared with Highway Performance Management System data is presumed to reflect unmodeled through trips (e.g., empty truck movements or backhauls). Any unobserved medium truck VMT in the model as compared with Highway Performance Management System data is assigned to pick-up and delivery trips. Approach Current vehicle type models in strategic visioning frameworks apply fixed factors to predict medium and heavy trucks. A choice model would add sensitivity to new technologies. Drones could change the pick-up and delivery systems for medium trucks. Connected heavy trucks could reduce time and cost for long-distance movement of goods. AVs may avoid the problem of driver shortages, which have constrained growth in the trucking industry, and hours-of- service regulations may change if drivers are no longer needed as much or are not required to drive (but remain in the vehicle for pick-up and delivery purposes). New technologies will undoubtedly affect the choice that suppliers make about which vehicle type should be used to deliver goods. Current freight mode choice models represented in strategic visioning frameworks apply fixed factors. A choice model would add sensitivity to expected reductions in trucking costs with the CAV technology so that modal shares could reflect the resulting modal shifts. Data from the Commodity Flow Survey, in combination with national multimodal networks and assumptions about CAV mode shares, could be used to estimate a freight mode choice model.

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TRB’s National Cooperative Highway Research Program (NCHRP) Research Report 896: Updating Regional Transportation Planning and Modeling Tools to Address Impacts of Connected and Automated Vehicles, Volume 2: Guidance provides detailed information and guidelines for state departments of transportation (DOTs) and metropolitan planning organizations (MPOs) to help update their modeling and forecasting tools. These tools address expected impacts of connected and automated vehicles (CAVs) on transportation supply, road capacity, and travel demand components. CAVs are likely to influence all personal and goods movement level of demand, travel modes, planning and investment decisions, physical transportation infrastructure, and geographic areas.

DOTs and regional MPOs are required to have a multimodal transportation plan with a minimum time horizon of 20 years under the requirements of the Moving Ahead for Progress in the 21st Century Act (MAP-21) requirements. This report explores ways to develop new planning and modeling processes that include CAVs in the transportation environment. The volume provides the details to NCHRP Research Report 896: Updating Regional Transportation Planning and Modeling Tools to Address Impacts of Connected and Automated Vehicles, Volume 1.

The research report is accompanied by a PowerPoint presentation that can be adapted for presentations to agency decision makers.

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