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Suggested Citation:"Chapter 3 - Uncertainties Associated with 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 3 - Uncertainties Associated with 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 3 - Uncertainties Associated with 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 3 - Uncertainties Associated with 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 3 - Uncertainties Associated with 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 3 - Uncertainties Associated with 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 3 - Uncertainties Associated with 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 3 - Uncertainties Associated with 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 3 - Uncertainties Associated with 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 3 - Uncertainties Associated with 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 3 - Uncertainties Associated with 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 3 - Uncertainties Associated with 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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11 The transition from travel by horse and buggy to mass adoption and use of motor vehicles was a major socioeconomic transforma- tion of the 20th century. This transformation helped to produce huge gains in economic productivity and quality of life but also spawned negative externalities of vehicle use such as congestion, crashes, and inequalities in access to jobs. Likewise, the transition from travel by conventional motor vehicles (e.g., automobiles, trucks, and public transit) to adoption and use of CAVs will be a defining mobility trans- formation of the 21st century. Huge positive changes are possible in the economy, environment, and society, but only if the transition is managed effectively by DOTs and MPOs. Uncertain CAV Adoption Timelines When CAV adoption timelines are being considered, it is important to separate the hype from the reality. Many reports have been made through the blogosphere about the potential roll-out dates of CAVs. Each manufacturer comments about the release of its first products, many of which appear to be speculative and aggressive. As noted in the previous chapter, while OEMs may be publicizing information about product releases to gain market share, other conditions surrounding the technology remain even more uncertain. Conditions related to the market penetration and consumer adoption of CAV technology include the following: • The cost of the technology will certainly drive the rates of adoption. • Whether the technology is used in privately held vehicles or through private corporations supplying fleet services will drive the rate of market penetration. • On-road testing of CAVs continues, but actual usage safety statistics and experience will drive public attitudes about the technology. • Comfort and convenience, in addition to cost, will drive consumer preferences regarding AVs. • Roadway and parking infrastructure will need to be adapted to CAVs. • Government policy and traffic laws, including tests of liability in the court system, will undoubtedly drive market penetration scenarios. • Finally, the technology will certainly advance and change, and features will be added or subtracted on the basis of cost effectiveness in the market. Several studies have focused on deployment and adoption timelines and scenarios. These range from scenario-based assumptions defining “evolutionary” to “revolutionary” development and C H A P T E R 3 Uncertainties Associated with CAVs Chapter Highlights • Describes the uncertainties associated with adoption of CAV technologies. • Presents a framework of three phases of adoption: – Testing and early deployments, – Consumer initial adoption, and – System-level organization as CAVs become predominant. • Discusses potential impacts related to safety, congestion, and land development. • Examines critical considerations for planning and modeling in five areas of impact: transportation costs, trans- portation safety, vehicle operations, electrification (fuel), and personal mobility.

12 Updating Regional Transportation Planning and Modeling Tools to Address Impacts of Connected and Automated Vehicles market penetration (Zmud et al. 2015), to modeling approaches based on existing vehicle adop- tion and turnover rates (Fagnant et al. 2015). Because of the high uncertainty in published deploy- ment scenarios, the only thing that can be said for sure is that deployment will occur in three eras: 1. CAVs are developed and tested. 2. Consumers begin to adopt CAVs. 3. CAVs become the primary means of transport. The industry does not have enough information to provide exact timing and details for the start and end of these eras. The highest levels of uncertainty pertain to the gray area between the second and third eras. The transition from a human-driven world to a world with only CAVs will not happen overnight. There will be a long period of time (perhaps three to four decades or more) with a mix of human-driven vehicles and CAVs on the roadways. This intervening period will be challenging, with many safety, secu- rity, and privacy issues to be resolved. Governments and transportation agencies need to plan ahead, anticipate potentially unintended conse- quences, and formulate policies to facilitate the movement toward a new way of traveling and to reduce the potentially offsetting effects of CAVs. For example, cyber security vulnerabilities associated with CVs could compromise safety. Energy use and suburban sprawl could increase with the proliferation of AVs as driving becomes less onerous and persons without a driver license have more opportunities for travel. Combining AVs and CVs with the practice of sharing vehicles could modify these effects. Both the expected development path for these technologies and their potential impacts are uncertain, but AV and CV technologies will clearly influence the transportation system and travel demand going forward. For the purposes of this report on planning and modeling tools, three general phases or categories of adoption were assumed. While the information below suggests evolutionary growth of CAVs, the authors acknowledge that this is not a consensus view. • Testing and early deployments: – Currently, most vehicles on the road are at Level 1. The transition to Level 2 or Level 3 vehicles will be influenced by fleet turnover rates. With people keeping their vehicles on average for about 7 years, and with an average age of vehicles on the road of 11 years, it will take decades to obtain saturation of Level 4 or 5 vehicles. – Automation will be vehicle specific (i.e., AVs) with limited V2V connectivity (due to absence of mandate) and no systematic enhancement. – Lidar sensors are still too expensive to be used in mass-produced vehicles and will be costly for private ownership. This cost of technology is considered less of a barrier for fleet vehicles because they generate revenue throughout the day to cover the expense, whereas the typical privately owned vehicle is used for a small fraction of a day. – Regulation will limit usage to specific geographies. Early stage deployments will need to be near perfect in operations to engender trust among the public and policy makers. Testing on controlled roadways so that these technologies are as foolproof as possible is important before their introduction on public roadways. – SAV services will be introduced first in limited geographies, following the current models of TNCs such as Uber and Lyft or small shuttles such as Drive.ai and Navya vehicles. They can also take the form of carsharing services, such as Zipcar and car2go. • Consumer initial adoption: – Growth in Level 4+ to 50% or more of the overall vehicle fleet will take time. Level 4–5 vehicles entail self-driving operations. Road operators need to implement coordinated rules There will be three to four decades (or more) of a mix of human-driven vehicles and CAVs on the roadways. This intervening period will be challenging, with many safety, security, and privacy issues. Transportation agencies need to be thinking and planning ahead.

Uncertainties Associated with CAVs 13 of the road for their safe operation. An owner of a private vehicle may not want to pay a high purchase price for a vehicle that is initially geographically constrained in its sphere of operations. – With expansion of operating geographies, adoption will increase for suburban and com- muter usage. – Shared automated services will continue to grow in denser core urban areas of metropolitan regions. – Some systematic organization of flow and automated route optimization will occur, but overall, optimization will remain limited because of the substantial number of non-CAVs still on the roads. • System-level organization as CAVs become predominant on the road: – Traffic will be predominantly Level 4+ CAVs. Usage will be widespread enough to achieve systematic route and flow optimization, practically eliminating delay due to congestion. – Shared AVs may become the predominant mode, mostly because of operating cost. If high- occupancy SAVs predominate, passenger miles traveled (PMT) may become a more impor- tant measure of performance than vehicle miles traveled (VMT). – It is also possible, however, that privately owned CAVs will predominate, particularly in less urbanized areas. – The eventual mix of private and shared CAVs is currently unpredictable because it will depend on consumer preferences, on pricing and supply decisions by OEMs and TNCs, and on future regulation and pricing of vehicle ownership and insurance. On the basis of this paradigm of market adoption, modeling and plan- ning tools can be developed to address the short-, mid-, and long-term impacts on travel behavior that each of these conditions promulgates: • In the short term, many existing planning and modeling tools will suffice, as travel behavior changes will not be significant, other than increasing use of new modes, such as TNCs, and perhaps new types of access and egress options for public transportation systems. • In the mid-term, the operational characteristics of CAVs will become more widespread, and non-AVs will be either organically minimized in the fleet (by natural attrition) or regulated in such a way that their usefulness and attractiveness to buyers and riders are limited. The existence of non-AVs in the fleet becomes a problem for system operations because AVs can be controlled by route and operational functions while competing for roadway maneuvering space with manual vehicles that are unpredictable in their behavior. Modeling and planning tools will need to address this important phase of market penetration and must be able to present the problems related to having mixed fleets of CAVs and non-CAVs. • In the longer term, the technology will be pervasive and require a complete set of new assumptions about urban form, land use, parking requirements, and other indirect impacts in addition to the direct impacts on travel behavior and choice. Planning tools and the models that support them will need to be based on scenario assumptions for this longer- range timeframe. Monitoring and Surveying AV and CV Adoption SAVs could soon be freely operating on public roads, so it is important to examine creative approaches for assessing their potential impacts on the transportation system. Transport and land use impacts will vary significantly, depending on extent to which AVs are used as pri- vately owned vehicles, sequential ride-hailing fleets, or pooled ridesharing fleets. Policy makers, public road operators, and transportation service providers need empirical data (not modeled Because of adoption timeline uncertainty, modeling and planning tools should be developed to address the short-, mid-, and long-term impacts on travel behavior.

14 Updating Regional Transportation Planning and Modeling Tools to Address Impacts of Connected and Automated Vehicles simulations) on potential behavioral responses. However, capturing accurate answers to what people might do in the future is tricky; preferences change as policies, society, and technol- ogy mature. Research participants today are in a situation vastly different from the one people will be in years from now, when the technology has become widespread. For example, asking an 18-year-old today about his or her likely use of AVs is wildly different from asking a future 18-year-old who has grown up with highly automated technologies available since birth. Perhaps the best researchers can do in the short term is to track and monitor. Researchers need to better understand current trends in vehicle ownership and vehicle usage, and, through such insights, better forecast likely impacts. However, such understanding has to be based on empiri- cally derived data, not on arbitrary assumptions and mechanical simulations. True insight will be achieved by research focused on better understanding behavior through attitudes, lifestyle issues, adoption behaviors, situational influences, and foundational activity and travel pattern choices. Ultimately, planners and modelers need to begin to answer the following question: How might behavioral trends change when the driver is removed? Uncertain Benefits and Risks of AVs and CVs AVs and CVs are potentially transformative technologies with benefits and risks that are still highly uncertain. While great promise for substantial benefits exists, the technologies are still in the development and testing stages, and the rules under which they should be safely operated are yet to be fully defined, so the possibility of harm or damage exists. This section highlights benefits and risks in three key areas: safety, congestion/pollution, and land use. Safety CV Applications When individuals drive a vehicle, they increase not only their own risk of a crash and related costs, but also crash risks and costs for other motorists as well as pedestrians, cyclists, and society in general. V2V safety applications can enhance safety by addressing a majority of vehicle crash types if the V2V communication is successfully interpreted and acted upon (Najm et al. 2010). This outcome necessitates that CV applications are demonstrably effective and widely used and that the driver–vehicle interface performs well. More testing is necessary to reach a satisfactory level of certainty in effectiveness and usage. Research has indicated that a marginal increase in benefit can be obtained through V2I safety applications, depending on the extent to which V2I infrastructure exists widely (Eccles et al. 2012). Highly Automated Vehicles Even without V2V and V2I, AVs can reduce a majority of driver-related errors, which account for 94% of traffic crashes according to NHTSA (2015). To achieve this outcome, certain mech- anisms need to be in place. As more of the driving task is switched to AVs (as is the case with Levels 3–5), many technologies (i.e., sensors, motion control, trajectory planning, driving strat- egy, situational awareness) need to operate effectively so that the vehicle performs at least as well as a human driver (Trimble et al. 2014). The Casualty Actuarial Society’s Automated Vehicles Task Force (2014) reevaluated the results of the National Motor Vehicle Crash Causation Survey in the context of an AV world. This reevaluation found that about half of all accidents could be addressed by AVs. The study concluded that driverless cars may be safer than human drivers, but that flawed hardware or software could cause accidents, and liability could then fall on manufacturers or installers. In such cases, the insurance pricing would fall to product liability actuaries for coverage. Recent

Uncertainties Associated with CAVs 15 fatal crashes in 2018 involving ADSs reflect the uncertainties that exist in the readiness of such vehicles to operate on public roads [e.g., Tesla Autopilot in Florida and California (Levin 2017) and Uber vehicle in Arizona (Griggs and Wakabayashi 2018)]. Vehicle errors could be intro- duced because hardware or software could be insufficiently tested, prematurely released, or inadequately maintained by owners or manufacturers, resulting in decreased safety benefits. Safety benefits are enhanced through widespread use of AVs and concomitant reduction of human errors. However, a factor limiting the safety benefits of AVs is that AV applications may only operate under specific conditions, and these conditions can be constrained by vehicle loca- tion, speed, or dynamics (Smith et al. 2015). On the basis of these constraints, AVs may only address certain precrash scenarios. For instance, Smith and colleagues identified GM’s Cadillac CTS Super Cruise technology (Level 3 automation for motorway environment) as working well in both bumper-to-bumper traffic and on long road trips in light traffic but cautioned that more complicated driving conditions might be challenging. Staying centered in a lane on a highway is much less demanding than staying centered on a road in a crowded city where lane markings can be less visible, other vehicles may block a camera’s view of them, and bicyclists and pedestrians travel alongside cars and trucks. A related uncertainty pertains to whether AV technology can match the learning while driving of a human driver, who exercises the aggregated wisdom of predictive knowledge from many drivers. The ADS is learning from the driving it experiences as an iterative process, so the vehicle is learning from itself. Thus, the automation system may not know how to behave in unknown situations, and in some cases, the vehicle’s response may lead to a crash situation (Sivak and Schoettle 2015). For example, the system may fail to respond to a hazard. Conversely, it may respond inappropriately to a nonhazard (e.g., braking hard for a piece of paper in the road). Cybersecurity Cybersecurity issues are another potential source of safety error. Cybersecurity, in the context of vehicle systems, refers to security protections for systems in the vehicle that actively commu- nicate with other systems or other vehicles (Garcia et al. 2015). While cybersecurity issues are a challenge for CVs, security becomes a bigger concern with Level 4–5 vehicles, in which software and connectivity play a much bigger and more critical role for the safe driving of vehicles. Unlike traditional vehicles, AVs may be vulnerable to cyberattacks that can spread from V2V. Hackers could potentially stop a fleet of AVs, halting the transportation system and reducing safety (even though no real case of malicious car hacking has yet been reported). Congestion The true implications of AVs on congestion and pollution may not be known for a long time. However, on the basis of past studies, assumptions about possible impacts of this new technology are possible. AVs and CVs are likely to affect factors that contribute to congestion—potentially in both positive and negative ways—resulting in an uncertain and likely mixed net overall effect. System Efficiency CAVs could potentially drive with greater precision and control than humans (Smith 2012). Various V2V- and V2I-enabled mobility-focused applications could increase the efficiency of the vehicle system (U.S. DOT 2015). For example, dynamic speed harmonization and coopera- tive adaptive cruise control are two applications that could increase system efficiency by enabling vehicles to coordinate their actions in certain circumstances. This ability could plausibly enable infrastructure operators to redesign aspects of their facilities to accommodate more traffic in various ways. By reducing lane size and shoulder width, an agency could restripe a road and add

16 Updating Regional Transportation Planning and Modeling Tools to Address Impacts of Connected and Automated Vehicles a lane, thus effectively increasing the supply of roads. If this were to occur, it would likely be over the long term, as it would require all (or nearly all) vehicles to be capable of driving with a high level of control. Because the vehicle fleet turns over slowly, even in optimistic projections, this is likely a distant proposition, but it is possible that fleet services and privately owned vehicles may turn over faster in the future. Additionally, new lanes may only be possible in areas with sufficient spacing, so roads that are already lane dense may benefit less than locales with existing excess space. Rural areas, and less-dense areas in general, would likely benefit more than dense urban areas. Vehicle Occupancy Congestion impacts are dependent on the future demand for and supply of public trans- portation. AVs combine the advantages of public transportation (e.g., not having to pay atten- tion to the driving task) with those of traditional private vehicles (e.g., flexibility, comfort, and convenience). Much research has focused on whether the use of ride-hailing services has led to increased congestion and reduced use of public transportation in some urban areas (Hughes- Cromwick 2018). If AVs were to be made available as autonomous ride-hailing fleets, public transportation ridership would likely suffer and congestion would increase, particularly in urban areas. Regulations (or lack thereof) will undoubtedly have a large effect on the potential out- comes by encouraging travelers to choose higher-occupancy mode choices. In such conditions, AVs might offer benefits for congestion by providing first-mile/last-mile linkages to mass transit systems. Induced Demand Pricing will be a critical component in how travelers will choose new modes or new technolo- gies. AVs and CVs could decrease the cost of driving, thus inducing additional VMT (Anderson et al. 2014). CAVs are likely to reduce the costs (both direct and indirect) associated with driving, namely the opportunity cost of a motorist’s time, fuel costs, and crash-related costs. Oppor- tunity costs are related to factors of convenience and flexibility as well. For example, demand may increase because traveling to downtown is more convenient when driving and parking are automated. When the cost for an activity decreases, all things being equal, demand for that activity will increase. It is unclear how much or how quickly the cost of driving will decrease, or how much a change in price will change the demand for driving. When the costs associ- ated with driving changed in recent years, motorists were relatively unresponsive in the short term, indicating that large changes in prices (or a long time horizon) may be required to alter consumer driving behavior. The U.S. Energy Information Administration found, for example, that large changes in gasoline prices created minimal change in VMT (Morris 2014). This evidence indicates that short-term changes in the cost of driving will likely have minimal effect on VMT; how changes from AVs and CVs over the longer term will affect VMT is less clear. Congestion outcomes are also related to the fact that SAE Level 5 AVs could alter demand by enabling persons who were previously unable to drive to do so (Smith 2012). Persons under the legal driving age and those who are unable to drive because of disabilities are two potential sources of increased demand. If these populations were legally and otherwise empowered to independently operate a motor vehicle, they could dramatically increase VMT. It is unclear exactly how many people in these groups would choose to take advantage of increased mobility services or options, or how much they would drive given the opportunity, but this could rep- resent a large share of the U.S. population. For example, the U.S. Census Bureau estimates that one in five people in the United States has a disability, and more than half of those have a severe disability (U.S. Census Bureau 2012). Stated differently, about 56.7 million people have a dis- ability, and more than 23.4 million have a severe disability. These groups are much more likely to be unemployed than the general population, and they are likely to have a lower income as

Uncertainties Associated with CAVs 17 well. Moreover, the U.S. Census Bureau (2017) estimates that about 26% of the U.S. population (or about 83 million people) is less than 16 years of age. If this population were capable of riding unescorted in personal vehicles, they could add significantly to VMT as well. Traffic Incidents AVs and CVs are likely to decrease the frequency of crashes, which should result in decreased congestion from nonrecurring sources of congestion. Yet, how or whether AVs will alleviate or contribute to congestion resulting from work zones is still unclear. Thus far, AV designers have already given construction zone navigation careful thought. Some image recognition systems are capable of identifying warning signs or cones, understanding that these symbols connote a work zone, and acting on this information to drive more cautiously to navigate a changed road configuration (Amadeo 2014). How these behaviors will change over time, and what impact— if any—automated driving will have on work zone–related congestion, is unclear. There will be a period during which AVs will operate on roads alongside conventional vehi- cles. Traffic crashes will likely result from the interaction of human-driven cars and AVs as they share the road. Several V2V- and V2I-enabled CV applications are also envisioned to address driving in or near work zones or in inclement weather conditions. These applications and their associated warnings focus on safety and would likely decrease crashes, but whether they will decrease con- gestion related to inclement weather is unclear. How well AVs will be able to drive in poor weather is also unclear. According to media reports, some current automated systems are inca- pable of driving in inclement weather conditions, such as snowstorms (Trudell 2015). Under such conditions, these vehicle systems will often cede control to the human driver. Pollution Congestion and air pollution are inextricably related. Automobiles emit local air pollutants (e.g., particulate matter, hydrocarbons, nitrogen oxides, and carbon monoxide) and global air pollutants (greenhouse gases) when they combust fuels, primarily fossil fuels. Thus, when people drive a vehicle, they reduce the air quality of the surrounding area and impose the costs of climate change—a global effect—on everyone. Vehicles are also loud. When people drive, they add to the noise pollution of those who live and work in the area. Noise and air pollution are related to vehicle factors (e.g., type of vehicle), travel factors (e.g., number of trips), driver behavior (e.g., driving style), and infrastructure (e.g., operation of transportation infrastruc- ture). CAVs have the potential to affect each of these categories in uncertain ways. Land Development Urban land development has always been influenced by transportation technologies. As U.S. cities expanded to provide housing for a growing population in the 20th century, the intro- duction and proliferation of the personal automobile reduced transportation costs and facili- tated the spreading out of urban populations, resulting in what is commonly termed “sprawl.” Investments in transportation infrastructure that increased transportation capacity and con- sequently reduced travel times (or, more broadly, the disutility of travel), have largely been met with an increasing tendency for low-density land use development, with both population and employment moving out of central cities and into suburban locations where land is less expensive and more plentiful. While automobile travel has enabled the rapid growth of cities and their economies, it may have distorted the market for land to produce development patterns with unintended exter- nal consequences. Land development is a complex process: the effect of automobile use on

18 Updating Regional Transportation Planning and Modeling Tools to Address Impacts of Connected and Automated Vehicles development patterns is complicated by many market and policy factors. A cyclical relationship exists between current development patterns and automobile use, such that each may reinforce the other. This relationship is still highly debated in academic literature (Burchell et al. 2002; Glaeser and Kahn 2003; Ewing and Hamidi 2015). Yet, current development patterns in the United States undeniably allocate a large portion of land for automobile use in the form of high- ways, streets, and parking. Economic Factors In terms of market forces, transportation costs (both monetary and nonmonetary) currently moderate the distance one is willing to travel to access lower-priced land for development. Auto- mobile availability has greatly increased mobility and improved accessibility outside of the cen- tral city core (Glaeser and Kahn 2003). As with the introduction of the automobile, AVs and CVs have the potential to decrease the nonmonetary costs of driving. AVs and CVs could increase safety and the convenience of vehicle travel, thereby lowering transportation costs. Consumers might travel more miles and take more trips to access lower-priced land and rural locations. With fully automated Level 5 AVs, time and other nonmonetary costs of vehicle travel would be further diminished. Owners could send vehicles on pick-ups, to accomplish errands, or to drop off a passenger without having to devote time or energy to the trip. An AV equipped and allowed to drive unoccupied and return home after each trip may more easily allow shared use among household members, which could lead to a decrease in the number of vehicles per household. Sivak and Schoettle (2015) estimated that this shared use could reduce average vehicle owner- ship rates by 43%. However, the same authors also concluded that travel per vehicle would increase by 75% (Schoettle and Sivak 2015). Thus, individual vehicle costs may decrease, but the related impact on land development is uncertain. Parking effects will be experienced differently in urban and rural areas. In urban areas, AVs may reduce the need for parking adjacent to destinations. AVs and CVs may be able to park in smaller spaces with more precision than human drivers, and higher-level AVs are expected to have the ability to drive and park at home or in remote parking areas. This capability would allow for more cars to fit in less space and in nonadjacent locations to free up centrally located land for other uses. Changes to parking needs will only occur with high levels of CAV adoption and will require changes to parking requirements, which currently mandate parking minimums for new development. In the long term, this may stimulate infill development as existing parking infrastructure in high-rent areas is no longer needed. If vast expanses of central city land devoted to parking can be reclaimed for housing and other uses, then a move to urban centers may be accelerated because housing in central cities may be more affordable and expansive than it is today. In contrast, in rural areas, the unbundling of parking adjacent to activity centers could lead to the construction of parking on cheaper, undeveloped land, following the same patterns seen with previous sprawl development. Lifestyle Factors Other land use impacts remain largely unknown in the context of an AV future. For example, would parents feel comfortable sending their kids alone in an AV? If yes, then it is plausible for households to live farther away in more sprawled settings because chauffeuring children to and from school is no longer a major constraint. However, if parents do not have such trust in the technology, then households may be more restricted in their location choices as they strive to remain within a reasonable travel time and distance of good schools and recreational and after-school activities for their children. Recent trends have seen many older households move into urban centers to access opportunities more easily. Would the introduction of AVs slow down this trend, with older households comfortable residing in suburbs well past retirement age because automated urban mobility service fleets can easily transport them to and from activity destinations?

Uncertainties Associated with CAVs 19 Regulatory Factors Policy and regulatory frameworks will play a major role in shaping future land use develop- ment patterns and residential and work location choices. Land use policies and zoning regula- tions strongly affect various location choices, and the extent to which regulatory authorities and city councils will alter policies and relax or tighten zoning restrictions in response to the introduction of AVs in the marketplace remains unclear. Another key question in this context is the extent to which different stakeholders and players will wield influence in shaping land use and location decisions. How will real estate developers, financiers, city councils and policy makers, and consumers interact, and what will be their relative influence in shaping future urban spaces? However, the main question is whether the changes brought about by AVs will be structural (highly disruptive) in nature or whether they will merely magnify or reduce effects that have already been observed over the past several decades? A nonstructural change may simply lead to a modest increase or decrease in the rate of sprawl, for example, while a structural change may either dramatically increase the rate of sprawl or kill the suburbs and promote significant densification in urban centers. Critical Considerations for Planning and Modeling The prior discussion of uncertainties is only important insofar as it provides context for areas of impact by CAVs on travel behavior, and, by extension, on modeling and planning tools. These areas of impact can be categorized as follows: • Transportation cost, • Transportation safety, • Vehicle operation, • Electrification (fuel), and • Personal mobility and convenience (including shared, owned, or rented vehicles). Table 2 summarizes the potential of these impact categories to influence travel behavior and choice. Transportation cost is a very uncertain impact area. Costs of vehicles that include highly automated technology will need to be recouped by OEMs, so the cost per vehicle is likely to increase. However, the cost per trip may decline if fleet services of driverless vehicles prevail in the market. Thus, the overall transportation cost to the consumer is uncertain and is most likely tied to vehicle ownership versus distributed vehicle ownership, vehicle club membership, or ridesharing. The safety impacts of CAVs were discussed earlier in this chapter. A reduction in crashes would improve the reliability of travel times and reduce property damage, injuries, and fatali- ties. Improved reliability would increase the utility of AVs, which would increase their market share. Improved reliability would also increase the utility of the network performance itself by encouraging users to travel farther as trip and tour planning becomes more consistent. Impacts of CAV operational characteristics are perhaps the most discussed in the industry to date. Much research has focused on the impact of connecting vehicles through DSRC into pla- toons of vehicles, which would dramatically shorten headway space and thereby improve coor- dinated acceleration and vehicle throughput. The overall impact would be to increase capacity, with most estimates arriving at a doubling of existing roadway capacities. However, because platooning requires increased space, the prospect of increased capacity where formation and dissolution of platoons is frequent may be diminished. In the longer term, the prospects of positive impacts from vehicle operations of an automated fleet are expected to be impressive. While groups of platooned vehicles may or may not improve Will the changes in CAV influence be structural (highly disruptive) in nature or simply a continuance of effects that have already been observed over past decades?

Transportation Cost Uncertain impact. Transportation cost in CAVs could rise for some and decrease for others, depending on the prices for privately owned vehicles and TNC services and the relative use of those options. For private AVs, prices could be high initially and then come down if market penetration increases. Transportation Safety Crashes are expected to decrease, saving personal operation cost and public agency cost. Incident delay would be reduced, increasing travel time reliability. Vehicle Operations CAV is expected to increase capacity by reducing headway, particularly when AV-only facilities begin operation. Coordinated flow (through V2V and V2I communications) could further increase effective capacity and reduce congestion. Fuel Type Fleet could be electrified and refueling automated, reducing personal time requirements and commercial fuel delivery. Electrification could reduce vehicle operation cost. Personal Mobility and Convenience SAVs (and private AVs) could increase independence of young, old, and limited- mobility populations by increasing their access to auto and auto-to-transit modes. Reduced disutility of travel time could reduce perceived auto in-vehicle times and have an effect on choices similar to that of reducing actual travel times. Category Modeling Element If Cost Increases . . . If Cost Decreases . . . Crash Avoidance Increases Travel Time Reliability Crash Avoidance Increases Personal Safety Capacity Increases from Reduced Headways Coordinated Flow Reduces Congestion Fleet Is Increasing in Electrification Greater Mobility for Current Nondrivers Relief of Driving Task (Reduced Disutility of Travel Time) Land use Work, housing, retail location Shifts growth in housing/work/retail location choice to denser areas (increase in densification) Shifts growth in housing/work/retail location choice to less-dense areas (increase in sprawl) Increase in sprawl na Increase in sprawl (possibly offset by urban parking land being available for other uses; see below) Increase in sprawl (possibly offset by urban parking land being available for other uses; see below) Increase in sprawl na Increase in sprawl Land use Parking land use needs na na na na Zero-occupancy trips reduce need for central parking (on- street and garage) Automated/stacked parking reduces parking space needed per vehicle na na na Trip generation Trip and tour making Fewer trips, but possibly more home-based tours with fewer stops per tour More induced trips, but possibly fewer tours because of more trip chaining More induced trips, but possibly fewer tours because of more trip chaining na More induced trips, but possibly fewer tours because of more trip chaining More induced trips, but possibly fewer tours because of more trip chaining More induced trips, but possibly fewer tours because of more trip chaining na More induced trips, but possibly fewer tours because of more trip chaining Trip time/length Trip distance (VMT) Shorter trips Longer trips Longer trips na Longer trips Longer trips Longer trips Longer trips Transit Use of scheduled transit (bus, rail, Transit use increases Transit use decreases (perhaps offset by TNCs Transit use decreases Transit use decreases (relative safety Transit use decreases Transit use decreases Transit use decreases Transit use decreases (fewer Transit use decreases bus rapid transit) offering better first- and last-mile connections to transit) effect) captive riders) Table 2. Critical considerations for travel behavior impacts of CAVs.

Transit Auto access to transit and multimodal tours Access to transit by auto decreases Access to transit by auto increases (use of longer- distance commuter bus and rail could increase) Access to transit by auto increases na Access to transit by auto increases Access to transit by auto increases Access to transit by auto increases Access to transit by auto increases Access to transit by auto increases Time choice Variability in choice of time of day to travel Peak spreading is reduced Peak spreading is increased Uncertain—peak demand and reliability both increase na Uncertain—peak demand and capacity both increase Uncertain—peak demand and reliability both increase Peak spreading is increased na Uncertain— peak demand increases but sensitivity to delays decreases Vehicle occupancy Carpool formation Increase in shared ride Increase in riding/ driving alone na Increase in riding/driving alone (fewer people afraid of using auto) na na na Increase in riding/driving alone (fewer people dependent on others for rides) Increase in riding/driving alone (less stress/ disutility of using auto alone) Vehicle ownership/ availability Access to private or shared vehicles; number of autos per household; zero-car households Lower in general—mix depends on relative costs of private vehicles and TNCs Higher in general—mix depends on relative costs of private vehicles and TNCs Safety may be a motivator to purchasing a CAV na na na Hybrid/electric may affect buying behavior choices Increased availability of both na Intercity travel Intercity work and recreational trip generation Intercity trips decrease Intercity trips increase Intercity trips increase Intercity trips increase Intercity trips increase Intercity trips increase na Intercity trips increase Intercity trips increase Intercity travel Intercity work and recreational trip distance Intercity trip distance decreases Intercity trip distance increases Intercity trip distance increases Intercity trip distance increases Intercity trip distance increases Intercity trip distance increases Depends on vehicle range and recharging time Intercity trip distance increases Intercity trip distance increases Freight and commercial Long-haul freight Fewer and shorter trips More and longer trips More and longer trips, more during peak times More and longer trips More and longer trips, more during peak times More and longer trips, more during peak times Cost of fuel is significant na More and longer trips (platoons with driverless vehicles lower costs) Freight and commercial Residential and commercial delivery Reduced home/commercial delivery trips Increased home/commercial delivery trips Increased home/commercial delivery trips Increased home/commercial delivery trips Increased home/commercial delivery trips Increased home/commercial delivery trips na na Increased home/ commercial delivery trips (use of driver- less vehicles could lower costs) Freight and commercial Residential and commercial service calls Reduced home/commercial service trips Increased home/commercial service trips na na Increased home/commercial service trips Increased home/commercial service trips na na na Note: na = not applicable.

22 Updating Regional Transportation Planning and Modeling Tools to Address Impacts of Connected and Automated Vehicles intersection and freeway operations, the coordination of flow through the concept of synchro- nized arrivals and reserved time and space may indeed prove to reduce queuing and congestion. This type of coordinated fleet would require a significant saturation of AVs communicating and adjusting speed to optimize flow across the roadway network. For a typical commuter or shop- per, this type of system would practically guarantee reliability. Electrification is also discussed frequently in conjunction with vehicle automation. If shared fleet services are used, the user would not need to be concerned with refueling the vehicle because the fleet owner/operator would use optimizing algorithms to reduce cost. As the need for a personal vehicle that performs both intercity travel for hundreds of miles and daily short trips diminishes, the use of electricity directly may remain the most economical fuel choice. This would have an enormous impact on the petroleum industry, but also on fuel delivery services, gas station land use, and the need for strategically located electric refueling stations. Impacts on personal mobility and convenience are perhaps the most uncertain aspects of CAVs. If shared-use fleet services prevail in the marketplace as the population of the United States ages, the prospect of older adults, young teens, and persons who currently struggle with independent mobility gaining greater transportation freedom could greatly improve. However, these individuals could also benefit in a scenario of privately owned CAVs if the cost of the vehicles is not prohibitive. The impact on the (dis)utility and (in)convenience of travel time—a key component of the value of travel time—from CAV technology is also somewhat uncertain. A key modeling issue is how the perception of travel time will change as drivers become passengers. It is expected that being relieved of the driving task will allow users to perform productive activities in the car, thereby reducing the disutility of in-vehicle travel time and, thus, decreasing the value of travel time savings. The extent to which this will be true and how it might vary according to journey duration, trip purpose, lifestyle, and other factors remain uncertain. It is also conceivable that there will be a novelty effect of riding in a CAV that will diminish over time, although this may be positive or negative. Some riders may be wary of the technology at first and gradually become used to it, while others may find the ability to perform other activities in the vehicle exciting at first but more commonplace over time. Finally, robotics may help produce a new age of a sharing economy in place of an owning economy. This would have an enormous impact on the auto finance industry because the need for personal loans would decline. However, from a transportation point of view, a complete replacement of the entire owned fleet with a shared fleet would have a dramatic impact.

Next: Chapter 4 - Framework for Planning and Modeling CAVs »
Updating Regional Transportation Planning and Modeling Tools to Address Impacts of Connected and Automated Vehicles, Volume 2: Guidance Get This Book
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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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