Updating Regional Transportation Planning and Modeling Tools to Address Impacts of Connected and Automated Vehicles, Volume 1: Executive Summary (2018)

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11 Modeling Systems The CAV planning and modeling framework presented in Figure 1 uses three types of model- ing systems: • Trip-based models developed as aggregate models of population and employment in a region with disaggregate measures of transportation supply and an aggregate assignment process, • Activity-based (AB) and dynamic traffic assignment (DTA) models developed as disaggregate models of persons and firms in a region with disaggregate measures of transportation supply, and • Strategic models developed as disaggregate models of persons and firms in a region with aggregate measures of transportation supply. Strategic models are typically applied in a scenario planning context to evaluate the impacts of a variety of policies and investments. Trip-based and AB or DTA models are applied to explore detailed impacts of policies and investments on the transportation system. However, these mod- eling systems also could be applied in parallel to provide a range of potential options to address uncertainty in CAV planning and modeling. Adapting Trip-Based Models Trip-based models are long-range travel demand models that follow the conventional four-step process of trip generation, trip distribution, mode choice, and traffic assignment. These models have been cali- brated, validated, and tested throughout the world, and they are used extensively across most MPOs and state DOTs in the United States. Table 1 lists potential changes to the trip-based modeling system from CAV impacts, including • New modes or submodes: – CAVs, – SAVs, and – SAV access to transit (submode); • Additional submodels: – Auto availability models that reflect the level of market penetra- tion of CAVs and – Market penetration models to determine fleet composition changes over time; • New algorithms and processes: – Routing routines to model dynamic ridesharing (e.g., uberPOOL), – Coordinated multimodal mobility services modeling (e.g., MaaS), and – Network flow coordination (real-time speed governing and predicted arrival rates); and • New supply models to reflect CAV impacts on roadway space. NCHRP Research Report 896 discusses adapting the following components of trip-based models to account for CAVs: • Land use modeling, • Auto availability and mobility choices, • Trip generation, • Trip distribution, • Mode choice, and • Routing and traffic assignment.

12 Updating Regional Transportation Planning and Modeling Tools to Address Impacts of Connected and Automated Vehicles Table 1. Potential trip-based modeling changes. Model Component Trip-Based Model Improvement Sociodemographics Land use/demographic model Adjust accessibility measures Land use/demographic model Account for parking reuse Land use/demographic model Estimate levels of expanded mobile populations Market/Fleet Fleet composition models Estimate and forecast types of vehicles and technology Auto Ownership Auto ownership Estimate and forecast CAV or manual vehicle ownership Auto availability Estimate and forecast availability of SAVs and carsharing Trip Generation Trip rates Estimate and forecast rates for expanded mobile populations Trip rates Account for zero-occupant vehicle trip generation Trip rates Adjust rates within reason for improved accessibility Trip Distribution Impedance to travel Estimate network cost matrices reflecting CAVs Impedance to travel Estimate new friction factor matrices if CAVs affect trip lengths Mode Choice Mode choice model Design new nesting structure including CAVs, SAVs, and SAV access to transit Mode choice model Account for MaaS impacts on multimodal tour plans Value of time Account for improved value of time for CAV modes Network Assignment Supply models Estimate CAV-enhanced capacity on signalized arterial systems Network capacity Estimate CAV-enhanced capacity on grade-separated facilities Path costs; pricing and tolling Estimate value of time including discounts for CAV passengers Adapting Disaggregate/Dynamic Models The primary difference between AB methods and more traditional trip-based methods is that AB models incorporate a more disaggregated and detailed simulation of travel behavior. The travel of each individual household and person in the region is simulated across the course of a day. Trips are simulated as parts of home-based trip chains (tours), and tours are scheduled within the time available during the day. Travel decisions are simulated as discrete choices based on the model prob- abilities. Using disaggregate discrete choices (rather than multiplying aggregate probabilities, as is done in trip-based models) tends to make the model structure more flexible and able to incorporate several dif- ferent levels and types of choice behavior. The flexibility is valuable in incorporating new aspects of travel behavior that may be associated with CAVs. Disaggregate modeling systems such as AB are well suited to evaluat- ing CAVs, with some modifications. The structure of the disaggregate system (as shown in Figure 2) focuses on individual characteristics of the people and vehicles in the system. In DTA methods, the trend is toward microscopic dynamic assign- ment that simulates each vehicle’s trajectory and each driver’s behavior on the network. Rather than use fixed lane capacities and speed–flow NCHRP Research Report 896 discusses adapting the following components of AB and DTA models to account for CAVs: • Sociodemographics, • Land use/built environment, • Auto ownership/mobility models, • Activity generation and scheduling, • Destination/location choice, • Mode choice, • Routing and traffic assignment, • Pricing, and • Truck and commercial vehicles.

Modeling Systems 13 relationships, DTA reveals traffic congestion levels and effective capacities through the simulation of how vehicles navigate the roads and intersections. Because no observed data exist on how the introduction of CAVs will affect aggregate speed–flow relationships, the use of a simulation method that can represent detailed differences in the ways that human drivers and AVs will navigate road networks may be the most promising approach for learning how CAVs will influence traffic capac- ity and congestion levels. Use of DTA methods for region-wide long-range forecasting is still in the initial implementation stages, but combining DTA with AB demand models may become more widespread in the future as the methods and software mature and network data become more plentiful and accessible. Table 2 summarizes model improvements for AB and DTA methods. Adapting Strategic Models 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. These models are intended for use as visioning tools, specifically to help guide transportation policies and investments, so plan- ners 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 GHG reduction strategies or smart growth policies. These resources bridge the gap between regional planning visioning exercises and transportation plans. The current strategic visioning frameworks were designed to be faster, allowing for exten- sive scenario testing. The processing speed is accelerated by not including detailed multimodal transport networks and instead describing the built environment and transportation supply Synthetic Population Long-Term Choices Mobility Choices Daily Activity Patterns Tour and Trip Details Trip Assignment Dynamic Traffic Assignment Figure 2. Typical disaggregate AB and DTA model components.

14 Updating Regional Transportation Planning and Modeling Tools to Address Impacts of Connected and Automated Vehicles Table 2. Summary of model improvements for AB and DTA models. Model Component Disaggregate AB/DTA Model Improvements Sociodemographics Population synthesizer Control for age and income Population synthesizer Add smartphone ownership and education level Built Environment Urban form Set place type by area type and development type Mobility Vehicle ownership Add CAVs as an option for households to own Vehicle ownership Add purchase cost, incentive policies, parking cost, or accessibility variables to distinguish vehicle type MaaS Add carsharing, ride-hailing, bikesharing memberships Activity Generation and Scheduling Activity generation Lift age restrictions for CAVs, add constraints for persons with disabilities and seniors using conventional vehicles Activity generation Adjust value of travel time (VOT) and review induced demand Activity generation Add representation of empty car trips Destination/Location Choice Work/school locations Integrate with land use model to provide sensitivity Mode Choice Mode choice Add new modes (CAVs, TNCs, shared modes, microtransit) Mode choice Adjust VOT for CAVs Access/egress Add access and egress modes (TNCs, shared modes, microtransit) Mode choice Add dynamic pricing for new modes, adjust parking costs for CAVs Mode choice Adjust age and disability restrictions for CAVs Parking choice Add parking choice model to include off-site parking Routing and Traffic Assignment Dynamic assignment Add vehicle-following and speed characteristics for CAVs Vehicle operations Parameterize vehicle operating characteristics Vehicle operations Track empty vehicles and their travel characteristics Dynamic assignment Simulate different levels of CVs in mixed traffic Dynamic assignment Simulate nonrecurring congestion with/without CAVs Pricing Cost models Determine cost per mile for each new mode by time period Parking costs Adjust parking cost as demand shifts away from high-cost areas Truck and Commercial Vehicles Supply chain Adjust cost and time for CAVs Truck touring Adjust driver hours of service for CAVs Truck touring Add pick-up and delivery services by TNC 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 processed 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

Modeling Systems 15 to understanding how the combination of policies or transport sup- ply or demographics on travel demand can influence each other (and not be double-counted). Sometimes transport policies target similar demographic populations and are more or less effective in combina- tion 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. Figure 3 illustrates a typical set of strategic model components. Table 3 summarizes the model improvements for various components within strategic models. Figure 3. Typical strategic model components. NCHRP Research Report 896 discusses adapting the following components of strategic models to account for CAVs: • Sociodemographics, • Built environment, • Mobility, • Accessibility, • Travel demand, • Mode choice, • Pricing, and • Truck and commercial vehicles.

16 Updating Regional Transportation Planning and Modeling Tools to Address Impacts of Connected and Automated Vehicles 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 Note: VMT = vehicle miles traveled. Table 3. Summary of model improvements for strategic visioning models.