Autonomous Vehicles

Autonomous Vehicles: changing the surface landscape of communities through increased green infrastructure adoption and implementation to help US cities combat stormwater runoff

by Kelsey L. Schmidt B.S. Environmental Science Lourdes University, May 2015

A Thesis Submitted to the Graduate School of the University of Cincinnati in Partial Fulfillment of the Requirements for the Degree of Master of Community Planning in the School of Planning, College of Design, Architecture, Art, and Planning

Committee Chair: David Edelman, Ph.D., School of Planning Committee Member: Leah Hollstein, Ph.D., School of Planning Committee Member: Travis Miller, MCP, OKI

March 2018

Abstract

Today many communities are trying to find different solutions for mitigating the negative impacts of growth, impervious surfaces, and stormwater runoff on the environment. Sustainable stormwater management is a challenge for cities but there is also opportunity. The purpose of this research was to explore an environmentally positive scenario to how Autonomous Vehicles will impact communities. The research attempted to gain insight about Autonomous Vehicles and their impact on the built environment, trees, and stormwater. For this report three methods of research were used: background experience, four case studies, and a site selected scenario case study. With the idea that Autonomous Vehicle adoption is going to occur in the next 10-30 years this is going to change not only the way we travel but also create changes to the built environment. Autonomous Vehicles can have positive implications to communities by allowing new ways to incorporate trees as green infrastructure and to reduce impervious surface leading to stormwater problems. Autonomous Vehicle technology has the potential to create available spaces in our communities. The built environment changes would most affect street design width and surface parking lots. The study revealed new areas of analysis to be researched in terms of stormwater and Autonomous Vehicles. Green infrastructure implementation, particularly tree planting, can be used to mitigate stormwater runoff in cities due to changes to the built environment resulting from the adoption of Autonomous Vehicles.

Keywords: Impervious Surface, Stormwater, Autonomous Vehicles, Green Infrastructure,

Trees, Community Planning, Built Environment, Surface Parking Lots, Depaving, Road Diet,

Sustainability, Resiliency

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Acknowledgements

This thesis is the result of many sleepless nights, hours staring at a computer screen, too much coffee, and piles of reports. But the great things in life do not come easy. This thesis would have not been possible without the extended support from various people.

I would first like to thank my three committee members Dr. David Edelman, Dr. Leah

Hollstein, and OKI Regional Manager Travis Miller. Their guidance helped me get through this report and provided me with the knowledge needed to understand Autonomous Vehicle potential in cities. Whenever a new report came out they made sure to send it my way for analysis. In addition, I would also like to thank OKI Environmental/Water Quality Senior Planner David

Rutter, who was willing to help read this report and give his expertise. His knowledge of stormwater problems throughout Hamilton County helped to increase my knowledge of what

Autonomous Vehicles could really do in future stormwater management and green infrastructure.

The OKI staff was also important in providing completed staff projects that had additional data needed to conduct an analysis of Hamilton County and the suitability of the site for Autonomous

Vehicles.

I would also like to thank two Green Umbrella Action Teams: Greenspace and Green

Infrastructure. These groups helped me to formulate my idea, provided me with Hamilton

County stormwater information, and informed me about the potential of green infrastructure in this county. These groups have many environmental leaders that were able to provide their professional expertise. Last, but very much not least, I would like to send my greatest thanks to

Christopher M. Cooper, my family, and my friends. All were there for me when I was ready to give up. They made sure to let me know how proud they were and how important this research will be to my future.

Table of Contents Abstract ii Acknowledgements iv List of Figures viii List of Tables x Chapter I 1 What is the Relationship Between Growth and the Environment? 1 Problems with Parking 2 Cost of Parking 3 Problems with Roadways 5 Cost of Roadways 6 Stormwater Runoff Problems in U.S. Communities 7 Using Green Infrastructure as a Stormwater Management Solution 9 The use of Trees in Green Infrastructure Stormwater Management 12 Stormwater and the Relationship with Climate Change 14 Problem Statement 16 Research Hypothesis 17 Significance of the Study 17 Thesis Structure 17 Chapter II 18 Literature Review 18 Autonomous Vehicles Definition 18 Levels of Autonomy 20 Autonomous Vehicle Timeline 22 Market Timeline 22 Policy Timeline 24 Impacts of Autonomous Vehicles 25 Parking Impacts 27 Roadway Impacts 31 Environmental Impacts 39 Green Infrastructure: Trees 40 Private or Shared Ownership of Autonomous Vehicles 42 v

Chapter III 45 Methodology 45 Methodology Framework 45 Background Experience 46 Case Study 47 Site Selection 47 Chapter IV 49 Case Study Analysis 49 Executive Summary 49 New Mobility Street Design: A Case Study of Autonomous Vehicles 49 in San Francisco Hacienda Avenue Green Street Improvement Project 51 The Depave Organization 52 The Philadelphia Water Department’s Waterways Restoration Team Project 53 Results 55 Chapter V 56 Discussion 56 Restated Research Questions 56 Findings 57 Recommendations 59 Changes 60 Further Research 61 Limitations 61 Chapter VI 63 Site-Selection Scenario Case Study 63 Hamilton County, Ohio 63 Background of Hamilton County 64 Built Environment 65 Natural Environment 68 Stormwater Management 69 Hamilton County and Autonomous Vehicles 71 Parking 71 vi

Clusters 73 Site Analysis 75 Suitability Analysis 86 Stormwater Reduction Potential 93 Roads 94 Chapter VII 100 Conclusion 100 Next Steps 100 Appendix I 102 Appendix II 104 References 106

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List of Figures

Figure 1-1. Land required per parking space (Victoria Transport Policy Institute). 2 Figure 1-2. Parking facility costs (Litman, 2014). 4 Figure 1-3. Extreme one-day precipitation events in the contiguous 48 states, 14 1910-2015 (EPA, 2016). Figure 2-1. Levels of autonomy (Union of Concerned Scientists, 2018). 22 Figure 2-2. When will automakers release an autonomous car (Keeney, 2014). 23 Figure 2-3. When will you be able to buy a driverless car (MojoMotors & Dia, 2015). 24 Figure 2-4. States with enacted Autonomous Vehicle legislation 25 (National Conference of State Legislatures, 2018). Figure 2-5. Existing parking and a conceptual site plan of parking redevelopment 28 opportunities for the University of South Florida campus (Chapin, et al., 2016). Figure 2-6. Urban arterial street design (Schlossberg, Riggs, Millard-Ball, & Shay, 2018). 33 Figure 2-7. Decreasing width in all lanes (Author). 34 Figure 2-8. The removal of one on-street parking lane and reduced width lanes (Author). 34 Figure 2-9. The removal of two on-street parking lanes and reduced width lanes (Author).35 Figure 2-10. The removal of one travel lane and reduced width lanes (Author). 35 Figure 2-11. Rethinking radically #1 (Author). 36 Figure 2-12. Rethinking radically #2 (Author). 37 Figure 2-13. Typical residential street cross-section 37 (Schlossberg, Riggs, Millard-Ball, & Shay, 2018). Figure 2-14. Reducing the width of all the lanes (Author). 38 Figure 2-15. Removing street parking and reduced width lanes (Author). 38 Figure 2-16. Removing another driving lane and reduced width lanes (Author). Figure 2-17. Rethinking radically #3 (Author). 39 Figure 4-1. Revolutionary scenario future plan View 50 (Baumgardner, Ruhl, & Tiemey, 2017). Figure 4-2. Revolutionary scenario future cross section 50 (Baumgardner, Ruhl, & Tiemey, 2017). Figure 4-3. Hacienda Avenue green street (City of Campbell, 2013). 52 Figure 4-4. New street design (City of Campbell, 2013). 52 Figure 4-5. Depaving project Eadom Street Philadelphia 54 (Philadelphia Water Department, 2011). Figure 6-1. OKI region (OKI, 2008). 64 Figure 6-2. Hamilton County, OH (Academic , 2018). 64 Figure 6-3. Hamilton County urban boundary (OKI, ArcGIS). 65 Figure 6-4. Hamilton County impervious surface (OKI, ArcGIS). 66 Figure 6-5. Hamilton County existing land use (OKI, ArcGIS). 67 Figure 6-6. Hamilton County tree canopy (OKI, ArcGIS). 68 Figure 6-7. Total monthly precipitation (World Media Group, LLC, 2018). 69 Figure 6-8. Sewer system map (Metropolitan Sewer District of Greater Cincinnati 70 Figure 6-9. Hamilton County parking lot clusters (Green Umbrella Water Action 72 Team, 2014). Figure 6-10. Kenwood Towne Centre cluster (Author, ArcGIS). 73 Figure 6-11. Anderson Township cluster (Author, ArcGIS). 73 viii

Figure 6-12. Downtown Cincinnati cluster (Author, ArcGIS). 74 Figure 6-13. Cincinnati State (ArcGIS). 77 Figure 6-14. Surface parking Cincinnati State campus (Author, ArcGIS). 78 Figure 6-15. Xavier University (ArcGIS). 79 Figure 6-16. Surface parking Xavier University campus (Author, ArcGIS). 80 Figure 6-17. Anderson Towne Center (ArcGIS). 81 Figure 6-18. Surface parking Anderson Towne Center (Author, ArcGIS). 82 Figure 6-19. Hyde Park Plaza (ArcGIS). 83 Figure 6-20. Surface parking Hyde Park Plaza (Author, ArcGIS). 84 Figure 6-21. Rookwood Commons (ArcGIS). 85 Figure 6-22. Surface parking Rookwood Commons (Author, ArcGIS). 86 Figure 6-23. Cincinnati’s Central Business District (ArcGIS). 87 Figure 6-24. Surface parking Cincinnati’s Central Business District (Author, ArcGIS). 88 Figure 6-25. Cincinnati State suitability analysis (Author, ArcGIS). 92 Figure 6-26. Xavier University suitability analysis (Author, ArcGIS). 94 Figure 6-27. Cincinnati Central Business District suitability analysis (Author, ArcGIS). 95 Figure 6-28. Hamilton County roads map (OKI, ArcGIS). 98 Figure 6-29. ODOT roadway projects (ODOT, 2017). 99 Figure AI-1. Suitability analysis rendering of parking lot to urban forest canopy 102 (Author, SketchUp). Figure AI-2. Suitability analysis rendering of parking lot to new development and 103 tree canopy (Author, SketchUp).

List of Tables

Table 1-1. Parking structure construction costs (Victoria Transport Policy Institute, 2017). 5 Table 2-1. The impact of autonomous vehicles on parking (Holmes, 2017). 43 Table 6-1. Parking lot suitability analysis criteria (Author) 90 Table AI-1. Downtown parking lot analysis (Author, Cincinnati, 2017). 104

Chapter I

What is the Relationship Between Growth and the Environment?

Technological advances in areas such as medicine, agriculture, transportation, and industry have helped foster a rise in world population (Pottage, 2003). With the rise in population comes increased demand for economic growth. As our communities grow, we notice many visible changes, including housing developments, road networks, and the expansion of services

(Donaldson, 2004). Greater population has necessarily brought about greater consumption of

natural resources, as more people demand food, shelter, and material goods (Pottage, 2003).

Population increase, and economic growth have not only caused the depletion of natural

resources, but they have also impacted the environment (Pottage, 2003). Urbanization of land

has caused an increase in land modification in recent years, with impervious surface increase

being the most prevalent. According to the 2006 National Land Cover Database, in the

conterminous U.S., roadways, rooftops, parking lots, and other impervious surfaces that prevent

runoff from infiltrating the soil cover more than 25.6 million acres—an area nearly the size of

Ohio (Water Environment Federation, 2015). The central core of cities like Houston, TX, Little

Rock, AR, and Washington, D.C. have devoted more than half their land area to highways,

streets, and parking areas (Lewis, 2017). Suburbanization has also led to an increase in impervious surfaces. Low-density residential suburbs and office parks might not seem to create much impervious surface, but they are served by roads, shopping centers, recreational centers, schools, utilities and their associated parking lots, which together add up to increased impervious surfaces (EPA, 2017). The US has created communities that are habitats for cars as much as for they are for the people living there (The Economist, 2018).

Problems with Parking

The U.S. alone has a billion parking spaces and together they take up the size of Puerto

Rico (Green, 2015). Parking lots are real estate that only performs at half of its potential while

the other half it is sitting vacant. They are large, open expanses of asphalt with minimal to non-

existent landscape, creating voids and dead space in our communities (Fichter, 2018). A typical

parking space is between 8-10 ft. wide and 18-20 ft. long (Victoria Transport Policy Institute,

2017). The problem that arises is that it is not just about that one 300-sq. ft. space in the

driveway but also the 300-sq. ft. needed at your job, the supermarket, school, the coffee shop,

etc. (Goodyear, 2014). When you add all those spaces it constitutes more than 1,000-sq. ft. of

impervious surface, and that is just for one person. Imagine this same scenario for a whole

community. Figure 1-1 below outlines the land required per parking space,

Figure 2-1. Land required per parking space (Victoria Transport Policy Institute, 2017).

According to a study conducted by the Victory Transport Policy Institute, on-street parking requires about 150 sq. ft. of parking per space: Compact, urban off-street parking requires the same amount of parking per space as on-street; however, these types of spaces normally also include an access lane and driveway which adds an additional amount of impervious surface.

Full-size, urban, off-street parking requires about 200-sq. ft. per space but with the additional access lane and driveway it can total to approximately 350-sq. ft. of asphalt. The last type of development setting that was looked at was full-size, suburban, off-street parking. These types of lots can require about 500-sq. ft. of asphalt per parking space. As a result, the average parking space can need anywhere from 150-500-sq. ft. of asphalt per space (Victoria Transport Policy

Institute, 2017).

People have become accustomed to driving and finding a parking space as close to their destination as possible. It has led to surface parking lots hollowing out our urban cities and dividing our neighborhoods (Goodyear, 2014). But it is not just a problem for urban communities. Suburbanization hailed by the previous century brought major damage laying waste to entire neighborhoods and fine grain commercial districts in favor of single purpose towers surrounded by convenient parking (Lewis, 2017). Office park designs are also open- ended with a large proportion of real estate traditionally devoted to parking (Eddy, 2014).

Cost of Parking

The cost that communities pay for both parking garages and surface lots is not a small expense.

Land costs range from thousands of dollars in rural areas to millions of dollars per acre in a

Central Business District (Victoria Transport Policy Institute, 2017). Figure 1-2 below, outlines different types of parking and the annualized cost of each,

Figure 1-2. Parking facility costs (Litman, 2014).

From Figure 1-2 you can note that on average on-street parking can cost from $700-$3,250 per space depending on location. Off-street lots range from $1,000-$2,000 per space, also depending on location. According to Todd Litman, Director of Victoria Transport Policy Institute, a typical structured, parking garage can cost around $30,000-$60,000 to construct. In urban areas the annualized costs a parking space can range from $500-$1,500.

Different US cities have different costs for parking spaces. Table 1-1 below, has calculated costs for parking spaces in various US cities. The lowest cost for both cost per space and cost per sq. ft. is in Charlotte, SC. The average cost per space is around $15, 915 and the cost per sq. ft. is $47.65. The highest cost is in New York City. There the price per space is about $25, 957 and the price per sq. ft. is $74. 72. The national average was also considered. The average cost per space is $19, 037 and the average cost per sq. ft. is $56.99.

Table 1-1. Parking structure construction costs (Victoria Transport Policy Institute, 2017).

When you start to think about communities and the on-street and off-street parking throughout, these numbers can cost developers and communities large amounts of money. Parking lots are not the only impervious surface that influence the landscape and communities. Roadways are

also a large proportion of impervious surface throughout the US.

Problems with Roadways

There are 4.09 million miles of roadway in the U.S; when accounting for highway travel the number increases by 4 million to 8.61 million miles (Elswick, 2016). In metropolitan areas streets account for 25-35% of a city’s land area (Sisson, 2017), about a third or more of land in cities (NACTO, 2017). Two prominent issues exist with roadways. One issue is that many metropolitan area highways are not keeping up with the demand from population growth. The increase in vehicle miles traveled have led to physically expanding existing highways by adding

additional lanes which leads to an increase in environmental degradation (Bamonte, 2013). The second issue is that the roadway is largely designed to accommodate human error with wide streets, guardrails, and medians (Abbott, 2017). To be automobile dependent communities, with road and parking supply sufficient to keep traffic congestion to the level typical in US cities, a city must devote 2,000-4,000 sq. ft. of land to roads and off-street parking per automobile, which is far more land than most urban neighborhoods devote for parks (Litman, 2014). Large

US cities average 4.7 road-miles per 1,000 residents; assuming a 50 ft. road width, this in turn equals about 1,240 sq. ft. of road area per capita (Litman, 2014). This 50 ft. of road width maybe an underestimation; in suburban areas large intersections with separate-dedicated turn lanes, nine lanes in total, requires about 140 ft. of width (Abbott, 2017).

Cost of Roadways

Though the cost of roadways varies by location due to terrain, cost of land, number of lanes, width of the road, etc., cost models have been produced that have calculated costs to build a mile of road. Data was collected and produced in an article titled, How Much Does It Cost to Build a

Mile of Road. A 2-lane, undivided road in a rural community can cost around $2-3 million per mile; a 2-lane undivided road in an urban or suburban area increases to $3-5 million per mile. A wider 4-lane highway in a rural or suburban area ranges from $4-6 million, while in an urban area the same would cost around $8-10 million per mile. Six-lane interstates are the most-costly, with the price being around $7 million per mile in rural areas, and $11-plus million in urban areas. This does not include the annual cost to maintain these roads for either resurfacing or expansion projects, which alone can range from $1.25 million per mile to $4 million per mile

(Elswick, 2016).

Stormwater Runoff Problems in U.S. Communities

The increase in impervious surface has caused one of the largest sources of pollution to our waterways, stormwater runoff (EPA, 2016). Stormwater runoff is defined by the Environmental

Protection Agency (EPA), as water from rain or snow storms that cannot be absorbed into the ground due to hard impervious surfaces, and instead flows over streets, parking lots, sidewalks, and roofs into our water bodies and storm drains (EPA, 2017). Stormwater is a major contributor to urban nonpoint source water pollution. Nonpoint source water pollution is rainfall or snowmelt moving over and through roads, bridges, highways, urban areas, and farmland, picking up natural and human-made pollutants such as oil, grease, salt, fertilizers, etc.; nonpoint source pollution is not pollution from industrial and sewage waste (EPA, 2017). Just 0.1 inch of accumulation can overload the system and force out contaminated water, while a storm can drop around 2 inches an hour (Briggs, 2016). Around the globe, ten trillion gallons of stormwater pollution wash over the city streets into lakes, rivers and oceans (Holland, 2016). Of the globally available fresh water only 2% is in the form of surface water, making it a vitally important resource to protect (Donaldson, 2004). These are also the same bodies of water that people are using for swimming, fishing, and drinking (EPA, 2013). When stormwater runoff flows over impervious surfaces it collects debris and particles. This can include natural and human-made pollutants including oil, grease, metals, bacteria from pet wastes, failing septic systems, soil from construction sites, detergents from cars and equipment washing, accidental spills, or any other substance that ends up on the ground (EPA, 2013). Runoff from commercial land uses such as shopping centers, business districts, office parks, and parking lots may contain high hydrocarbon and metal concentrations (Indiana Government, 2007). Roads, highways, and

bridges also contribute to high stormwater pollutant sources from activities such as maintenance, construction, fueling operations, and littering (Indiana Government, 2007).

The impacts of impervious surfaces not only affect how a community may look but they also affect the hydrologic cycle and threaten our water resources (Donaldson, 2004). The hydrological cycle, also known as the water cycle, is the exchange of water between land, water bodies, and the atmosphere (Ruby, 2017). Impervious surfaces inhibit the natural infiltration of rainwater into the ground, which causes an increase in stormwater runoff leading to stormwater peak flows (Beckwith, et al., 2007). Peak flows, or peak discharges, occur when there is a larger and faster flowing volume of runoff due to impervious surfaces in areas (Ruby, 2017), decreasing the amount of time it takes for stormwater runoff to move from remote areas of the watershed to the receiving water body, causing flooding and habitat damage (Indiana

Government, 2007).

Communities should be concerned because stormwater runoff not only affects health, but can cause an increase in economic, social, and environmental costs. The stormwater, instead of being recycled back into the hydrologic system at a natural rate, flows quickly into nearby streams, rivers, and lakes causing unnaturally large and sudden flows depending on the precipitation rate (CRD, 2017). During heavy rainfalls there is nothing to stop the flow of stormwater. This has led to significant property damage, loss of aquatic habitat, and floodplain connectivity (EPA, 2013). Other potential impacts include increased stream bank erosion, an increase in sediment concentration in the water, and the degradation in water quality

(Government, 2007). Aging stormwater infrastructure, along with pressures to construct new facilities, adds billions of dollars to future municipal, state, and federal fiscal needs; the EPA estimates that U.S. communities are facing a total of $106 billion in needed stormwater

management and combined sewer correction improvements (Federation, American,

ECONorthwest, & American Rivers, 2012). A study conducted by the Donald Bren School of

Environmental Science and Management at the University of California, Santa Barbara, predicted runoff characteristics and impervious surface coverage reduction; model results in all scenarios indicated that total stormwater flow decreased and stormwater peak flow decreased as impervious surface area decreased (Melack, 2007).

Today many communities are trying to find different solutions for mitigating the negative impacts of growth, impervious surfaces, and stormwater runoff on the environment. Common stormwater management goals have been to improve the quality of local waterways, minimize flooding and erosion caused by stormwater runoff, reduce volume of stormwater runoff, slow the velocity of stormwater runoff, and filter pollutants from stormwater runoff (Association,

2009). One method that has been adopted in communities is using a low impact, cost-effective, and natural approach: green infrastructure. Incorporating green infrastructure practices in efforts to control stormwater, communities and property developers can diminish impacts of flooding, reduce infrastructure costs, and restore the natural process of the hydrologic cycle to better serve the community and environment (Federation, American, ECONorthwest, & American Rivers,

2012).

Using Green Infrastructure as a Stormwater Management Solution

Stormwater management has historically been aided by grey infrastructure in the forms of drains, gutters, and pipes. For years the federal government’s advice to cities was simply to build bigger, costlier pipes to solve the problem (Briggs, 2016). This type of infrastructure is not only aging but it is also not fully addressing stormwater runoff issues. Grey infrastructure does not deal with water problems on site. Too often large rain events cause the storm drains, or

pipes, to overflow causing sewage and stormwater to mix. This leaves communities having to pay large sums to fix both flooding and water quality concerns. It also does not have any other benefit besides stormwater management. Green infrastructure systems use, or mimic, natural processes to infiltrate, evapotranspire, or reuse stormwater runoff on the site where it is generated; these practices keep rainwater out of the sewer system, thus preventing sewer overflows and reducing the amount of untreated runoff discharged to surface waters (Copeland,

2016). Examples of green infrastructure include reforestation, grass and riparian buffers, green roofs, porous pavement, urban trees, constructed wetlands, stream restoration, and best- management practices for agriculture and forestry (Gray, Yonavjak, Talberth, & Gertner, 2013).

After researching various community comprehensive plans, including, Connecting Cleveland

(Cleveland, OH), Indy 2020 (Indianapolis, IN), Toledo by Choice (Toledo, OH), and NYC

Vision 2020 (New York, NY), some keywords that have been constant throughout each are stormwater management, sustainability, green infrastructure, community health and happiness, and vibrant communities. Many American communities have realized considerable financial and water quality gains by adding green infrastructure to their approaches to reducing and managing stormwater (Federation, American, ECONorthwest, & American Rivers, 2012). One interest in green infrastructure from government officials, city planners, developers, and conservationists is that investments in green infrastructure can be less expensive than using grey infrastructure.

New York City recently evaluated two different plans: One used green infrastructure to manage its stormwater flows, and the other only used grey infrastructure. The results proved that the green infrastructure option presented a cost savings of more than $1.5 billion, while providing the same type of service as grey infrastructure (Talberth & Hanson, 2012). In Cleveland, the

Northeast Ohio Regional Sewer District (NEORSD) saw an opportunity to address their water quality issues caused by stormwater runoff and overflows by using green infrastructure. The

whole project would have cost $182 million using only grey infrastructure methods. Instead, the

NEORSD used $42 million toward green infrastructure solutions and $53 million on partially upsizing current grey infrastructure, all while generating a cost savings of $87 million (Terraza,

2013). It has been estimated that Norther Carolina could minimize stormwater runoff for $0.47 cents per thousand gallons treated using green infrastructure; using conventional stormwater methods such as grey infrastructure the number jumps to $3.24 per thousand gallons (Gray,

Yonavjak, Talberth, & Gertner, 2013).

In addition, green infrastructure approaches provide multiple ecosystem services including improving air quality, increasing habitat and green space, enhancing human health, and reducing flooding, which have their own monetary and health benefits for communities (Federation,

American, ECONorthwest, & American Rivers, 2012). In a report titled: Banking on Green: A look at How Green Infrastructure can Save Municipalities Money and Provide Economic

Benefits Community-Wide, economic impacts caused by pollution and how different local, state, and national organizations viewed the benefits of Green Infrastructure were analyzed

(Federation, American, ECONorthwest, & American Rivers, 2012). Green infrastructure can increase energy efficiency and reduce energy costs if green roofs, street trees, and increased urban green spaces are implemented. It also protects public health and can lead to the reduction in illness and illness-related costs by reducing harmful pollutants into drinking water. The EPA estimates that sewer overflows cause at least 5,576 illness every year from pollutant exposure

(Federation, American, ECONorthwest, & American Rivers, 2012). The Urban Ecosystem

Analysis of Washington, DC reported that tree cover and green roofs in the city not only saved

$4.7 billion in stormwater storage costs but also $49.8 million in air quality savings annually by the removal of 20 million pounds of pollutants from the cities air each year (Federation,

American, ECONorthwest, & American Rivers, 2012). Sustainable Green Infrastructure is the 11

future for communities being able to properly manage wet weather and address resiliency challenges in a cost-effective manner, in a way that truly improves the quality of life for residents (Kirk, 2014). Green Infrastructure has an important role to play in how communities reduce local water impacts. By shifting from traditional grey infrastructure to green infrastructure approaches for stormwater runoff management, communities may reap additional economic and community benefits creating healthier and more livable communities, while addressing pressing water quality needs (Federation, American, ECONorthwest, & American

Rivers, 2012). Development is our friend; every acre of green development saves us a million gallons a year (Briggs, 2016).

The use of Trees in Green Infrastructure Stormwater Management

A variety of measures are taken by cities to manage stormwater runoff; however, most overlook the stormwater utility benefits that trees provide. Trees have long been recognized for their contributions to clean air, heat island mitigation, property value increases, and energy reduction; their ability to absorb and divert rainfall has, however, been underutilized. In many cases trees are generally, and falsely, considered to be solely a landscape value rather than a green stormwater management plan; planting a tree just for aesthetic purposes is not taking advantage of the benefits it can provide (EPA, 2013).

Trees are a form of green infrastructure that are often overlooked, even though they are one of the most effective stormwater management solutions. Trees can reduce the amount of stormwater runoff by increasing runoff storage potential in three primary ways: interception occurs when rainfall lands on the leaves, branches, or trunks and is absorbed and stored; transpiration is the transfer of water from the soil through the tree and released as a gas; and infiltration occurs when the roots of trees take up soil moisture (Stone Environmental Inc.,

2014). There have been studies done to determine the quantity of stormwater runoff that trees can reduce. Depending on size and species, a single tree may store 100 or more gallons of water until it reaches saturation, which occurs after one or two inches of rainfall (Fazio, 2010). One study showed that a typical street tree can intercept anywhere from 760-4,000 gallons of water, per tree, annually (Charles River Watershed Association, 2009). Another study indicated that a medium-sized tree can intercept as many as 2,380 gallons of rainfall per year (Research, 2002).

Trees on the UC San Diego Campus filter 140 million gallons of stormwater runoff annually; each tree in New York filters 1,525 gallons annually (Trees, 2011). A USDA Forest Service study found that New York City's street trees, in total, reduced stormwater runoff annually by 890 million gallons (Cotrone, 2018). Research has also calculated that 65 percent of stormwater runoff can be reduced in residential developments when trees are combined with other natural landscaping (Fazio, 2010).

There have also been studies done to quantify the dollar value of urban trees in ten megacities. At SUNY’s College of Environmental Studies and Forestry, research led by

Theodore Endreny, shows an annual median payoff of tree infrastructure is $505 million.

Endreny and company argue that rededicating parking lots and other available surfaces to trees could nearly double the benefits that existing trees currently provide (Hester, 2017). In 2010, the

State of Indiana Department of Natural Resources performed a statewide street tree benefit study which showed that the stormwater management conducted by the states trees saved the state around $24.1 million annually (EPA, 2013). The Alliance for Community Trees also compiled a list of benefits of trees and urban forests. The annual economic benefits in dollar amount that urban forests provide include $2.3 billion in Chicago, $2.7 billion in New York, $3.25 million in

Berkeley, and $15.7 million in Minnesota (Trees, 2011).

Extreme weather and climate events have increased in recent decades, and from current data new and stronger evidence does confirm that these storm increases are related to human activities (NCA, 2014). Storms are responsible for most of the annual pollutant loading of receiving waters, and trees are the most effective method for intercepting stormwater during these events (Research, 2002). Trees give us an opportunity to solve stormwater runoff impacts that communities across the U.S are facing.

Stormwater and the Relationship with Climate Change

Communities across the United States have already started experiencing the effects of

climate change. The heaviest rainfall events have become heavier and more frequent, and the

amount of rain falling on the heaviest rain days has also increased, with the Northeast, Midwest

and the Great Plain regions experiencing the impacts the most (NCA, 2014). The National

Oceanic and Atmospheric Administration (NOAA) conducted a study to determine how

precipitation has changed over a century (EPA, 2016). Their results are shown in Figure 1-3 below,

Figure 1-3. Extreme one-day precipitation events in the contiguous 48 states, 1910-2015 (EPA, 2016).

As shown in Figure 1-3, during the past century there has been a larger percentage of precipitation that has come in the form of intense single-day activities, and the percentage of land area that experienced these events has increased (EPA, 2016). The study confirmed that by

2020, unless action is taken, there will be a greater number of people affected by climate change effects (EPA, 2016). Stormwater runoff and flooding will be intensified because of climate change on precipitation amounts, intensities, and frequencies. More than 750 U.S. cities have sewage systems that overflow during large storms sending an estimated 10 trillion gallons of untreated runoff into our waterways each year (Thompson, 2016). Combined sewer systems in more than 700 municipalities spanning 31 states and the District of Columbia estimated receiving more than 850 billion gallons of stormwater each year (Zahmatkesh, Karamouz,

Goharian, & Burain, 2015). In Los Angeles, a one-inch rain event can generate more than 10 billion gallons of stormwater runoff (NRDC, 2014).

Extreme weather such as hurricanes and floods have already been seen more frequently in recent decades (NCA, 2014). In 2005 Hurricane Katrina hit the Gulf of Mexico becoming one of the most destructive, costliest, and deadliest hurricanes in U.S history. In 2012 Super Storm

Sandy hit the East Coast. Sandy became the second costliest storm in U.S history, second to

Hurricane Katrina. More recently in 2017 Hurricane Harvey slammed into the Southern U.S and the Caribbean, becoming the costliest natural disaster in US History. The weather and climate events in 2017 alone produced losses exceeding $1 billion dollars across the U.S. (Lam, 2017).

Human-induced climate change has already increased the number and strength of some of these extreme events (Assessment, 2014). Kevin Trenberth, a scientist at the National Center for

Atmospheric Research in Boulder, Colorado stated, “storms like Harvey are still rare, however the probability of them occurring has increased substantially because of climate change: what used to be a 500-year event has now become a 50-or 100-year event” (Popovich & O'Neill, 2017 15

pp.1). The warming world brings about uncertainty as to what lies in the coming future. The expected increased frequency and intensity of storm events requires rethinking the strategy towards stormwater (Steen, Butterworth, & Langenbach, 2006). But with these challenges lie new opportunities to build systems that improve the vibrancy and climate resiliency of our urban areas (O'Neill, 2015).

New technological advancements could provide these opportunities. But how important is technology in the fight against climate change? According to Edward A. Parson, the faculty Co-

Director of the Emmett Institute on Climate Change and the Environment at the University of

California, Los Angeles, and a consultant for the White House Office of Science and

Technology Policy, technology is fundamental to combating global warming, it’s absolutely decisive, you can start to understand climate change as a mostly technical problem to which there is a mostly technical solution (Beres, 2015). Just as automobiles led to problems associated with impervious surface and stormwater runoff that we see in our cities today, they also give us an opportunity to use them as a solution. The Autonomous Vehicle technology gives us an opportunity to innovatively solve the stormwater management problem that is occurring in communities across the United States. The battle is to create a space in which the question is not

‘how we get ready for self-driving vehicles’ but rather ‘what kind of city we want’

(Baumgardner, 2016).

Problem Statement

Communities are currently reacting to stormwater runoff problems instead of producing proactive solutions; there is a lack of focus on resiliency. Stormwater runoff has created a burden, not only on the people living in said city or community, but also on the environment.

Communities in the past have been treating stormwater as a waste product but it needs to be seen instead as a valuable resource. Protecting vital water sources and improving water quality is often difficult because there is limited space suitable for the implementation of stormwater runoff treatments (Indiana Government, 2007). Sustainable stormwater management is a challenge for cities but there is also opportunity. Autonomous Vehicle technology has the potential to create available spaces in our communities. The purpose of this research is to explore an environmentally positive scenario to how Autonomous Vehicles will impact communities.

Research Hypothesis

This research used a qualitative research approach to answer the question that frames this study. The hypothesis is that green infrastructure implementation, particularly tree planting, can be used to mitigate stormwater runoff in cities due to changes to the built environment resulting from the adoption of Autonomous Vehicles. The following are critical research questions that arose from the hypothesis:

1. What are the current and future issues that communities will face resulting from

stormwater?

2. How widespread will Autonomous Vehicle adoption be over the next 10-30

years?

3. Due to widespread Autonomous Vehicle adoption, what changes will take place

in the built environment?

4. How can green infrastructure implementation, particularly tree planting, address

stormwater issues in an Autonomous Vehicle era?

Significance of the Study

There is potential for Autonomous Vehicles to improve the sustainability of communities. Some of the focus points throughout local community comprehensive plans have been the reduction of stormwater runoff, the increase of green infrastructure, the improvement of community health, and the building of vibrant cities. The significance of this research is to:

1. Contribute knowledge and provide information to decision-makers regarding the

potential that Autonomous Vehicles could bring to tree implementation and

stormwater management;

2. Explore a new idea and provide a solution to a problem;

3. Encourage communities to adopt an Autonomous Vehicle Tree Program; and

4. Produce a replicable product that could help other communities when they begin

to explore Autonomous Vehicle opportunities and legislation.

Thesis Structure

Chapter 2: Literature Review

The literature review contains an organized evaluation of Autonomous Vehicle articles

and reports that have attempted to bridge the gap of the research questions that have been

identified in this paper. It contains what is already known about Autonomous Vehicles

as related to changes to the built environment.

Chapter 3: Methodology

The methodology section details how this study was conducted by identifying how the

data was collected and how it was analyzed. For this report three methods of research

were used here: background experience, case studies, and a site selected scenario cases

study.

Chapter 4: Case Study Analysis

This chapter includes the multiple case study analysis. The problem that occurred,

solutions implemented, and suggested recommendations were identified for each study.

New understandings and contributing knowledge were noted.

Chapter 5: Discussion

The discussion chapter focuses on the findings of this multiple case-study analysis and

addresses the hypothesis and research questions stated in chapter one.

Chapter 6: Site-Selection Scenario Case Study

The purpose of this chapter was to identify characteristics of an additional site that was

chosen for analysis. Using the findings from the multiple case-study analysis, certain

sites were chosen in Hamilton County that may see potential changes with the

implementation of Autonomous Vehicles.

Chapter 7: Conclusion and future directions

Some additional research will still need to be conducted for cities to understand the

implications of Autonomous Vehicles. The conclusion chapter focuses on potential

research that could be conducted and policy recommendations.

Chapter II

Literature Review

Autonomous Vehicles Definition

Some refer to them as driverless cars, robot cars, or even self-driving cars. Daniel Fagnant and Kara Kockerlman in their article Preparing a nation for autonomous vehicles: opportunities, barriers and policy recommendations, describe an autonomous vehicle as a vehicle that can guide itself without human conduction (Fagnant & Kockelman, 2013). The Union of Concerned

Scientists define Autonomous Vehicles as vehicles in which human drivers are never required to take control to safely operate the vehicle; Autonomous Vehicles combine sensors and software to control, navigate, and drive the vehicle (Scientists, 2017). Automation and vehicle connectivity technology represent the largest transportation sector disruption in decades

(Alexander-Kearns, Peterson, & Cassady, 2016).

Levels of Autonomy

There are different levels of autonomy that describe the type of system from complete driver to complete autonomy. They have been used by automakers to determine the timeline for when fully-autonomous vehicles will be introduced, as shown in Figure 2-1. In Pollands article,

What Are the Autonomous Car Levels? Levels 1 to 5 of Driverless Vehicle Tech Explained, he gives a brief explanation as to what makes the car more autonomous at each level, and what level of Autonomous Vehicles are currently on the market. Level One indicates vehicles that have a single aspect that is automated: One element of the driving process is taken over, but the driver is still in charge. Some examples of the single-automated element are cruise-control and the lane keeping assistant. Level Two are vehicles that will have chips that control two or more elements.

This is the level that we are at currently and that are mainly on the roads. The computer takes over multiple functions from the driver. Examples of level two elements include the additions of lane-change modes, self-parking, and automatic braking. Level Three vehicles will include boss safety-critical functions. According to Polland, we are not far off from this type of vehicle.

Level Three vehicles include features where all the driving is done for you; however, the driver must be on hand to respond to a request. Audi will be introducing a Level Three version of

Autonomous Vehicle the A8 in 2018. Level Four vehicles will be fully autonomous in controlled areas, like metropolitan cities that have geo-fenced Autonomous Vehicle

Infrastructure. This means they will only be able to operate in the area defined by the infrastructure. Level Four Autonomous Vehicles will hit the market in the next decade. Level

Five will also be fully-autonomous with the driver being optional. The only difference from level four to level five is that the car will not be confined to metropolitan cities but will be able to drive anywhere. Autonomous Vehicle infrastructure will be more widespread. This level is said to happen around 2035 (Pollard, 2014). The Union of Concerned Scientists includes a level

0 in Figure 2-1 below, which is no vehicle autonomy; however, that has been bypassed as of

2014.

Figure 2-1. Levels of autonomy (Union of Concerned Scientists, 2018).

Autonomous Vehicle Timeline

Market Timeline

According to the article, Autonomous Vehicles Through the Ages, the idea of

Autonomous Vehicles has been around since their exposure at the World Fair in 1939. This is where automakers envisioned abundant sunshine, fresh air, and fine green parkways upon which cars would drive themselves (Vanderbilt, 2012). Instead of this idea being hypothetical, scientists and engineers at various universities, auto companies, and the military continued exploring this technology well into present day. Today automakers are racing to create vehicles with the advanced automation and connectivity technology needed to stake a claim in the emerging driverless car market (Alexander-Kearns, Peterson, & Cassady, 2016). The trailblazers in the Autonomous Vehicle industry are Telsa, Google, and Uber, who are already conducting

tests in real world conditions, while traditional automakers, like General Motors and BMW, are buying out start-up companies, or partnering with established tech companies (Silkin, 2017).

Car companies from Mercedes to Uber say they will have Autonomous Vehicles on the road in the next few years and according to a study by the Boston Consulting Group, more than

12 million fully Autonomous Vehicles are expected to be sold per year, globally, by 2035

(Snow, 2017). In the San Francisco Bay Area, ride-share car company Lyft just confirmed that a self-driving car will soon be a part of the options available to riders (Somerville, 2017).

Figure 2-2. When will automakers release an autonomous car (Keeney, 2014).

Figure 2-2 (above) indicates the timeline for when ten of the top car companies will be producing, selling, and operating fully or semi-autonomous vehicles (Keeney, 2014). Mercedes-

Benz and MojoMotors also created a timeline, Figure 2-3 below, that was featured in the article,

When Will You be Able to Buy a Driverless Car (MojoMotors & Dia, 2015). The timeline reiterates Figure 2-2, with some slight year differences. In this timeline Google is to start selling their Autonomous Vehicles in 2018, while Volvo is anticipated to start marketing in 2020.

BMW and Nissan were similar with 2020 being the year of selling a line of Autonomous

Vehicles. Ford was mentioned in Figure 2-3 with the year being 2025 when the car company

will start to sell their line of Autonomous Vehicles. Adam Jones at Morgan Stanley thinks that in 2026, 100% of the cars being sold will be autonomous; IHS research analysis suggests that the United States will only allow the sale of Autonomous Vehicles by 2030 (MojoMotors &

Dia, 2015).

Figure 2-3. When will you be able to buy a driverless car (MojoMotors & Dia, 2015).

Policy Timeline

According to a study released by the National League of Cities only 6% of cities have the potential effects of Autonomous Vehicles integrated into their long-term transportation plans

(Dupuis, Martin, & Rainwater, 2016). The National Conference of State Legislatures recently produced an updated database showing Autonomous Vehicle legislation that has been introduced (National Conference of State Legislatures, 2018). According to Figure 2-4, twenty- one states have passed Autonomous Vehicle legislation.

Figure 2-4. States with enacted Autonomous Vehicle legislation (National Conference of State Legislatures, 2018).

Some examples of enacted Autonomous Vehicle legislation in the United States: the state of

Florida has permitted operation of Autonomous Vehicles on public roads by individuals with a

valid driver license; Illinois preempts local authorities from enacting or enforcing ordinances

that prohibit the use of vehicles equipped with Automated Driving Systems; and Michigan

allows for the creation of mobility research centers where Autonomous Technology can be

tested (National Conference of State Legislatures, 2018). In 2017, California also became the

first state to allow sale of Autonomous Vehicles (MojoMotors & Dia, 2015).

Impacts of Autonomous Vehicles

As planners, we will need the knowledge to address these unavoidable changes associated with Autonomous Vehicles so that we can respond correctly. Ultimately cities play a critical role in maximizing the benefits of Autonomous Vehicle technology and are in the unique position of understanding how and where these new technologies will work best (Cities, 2017).

Being able to plan and determine what impacts Autonomous Vehicles will have on communities

will be more beneficial than if we ignore potential impacts. But what are the infrastructure changes that will be occurring in our urban, suburbs, and rural areas?

Autonomous Vehicles have the potential to change the current transportation infrastructure, how people move about their community, how green infrastructure is implemented, how space is allocated, where new businesses are located, and current building infrastructure. This technology promises to radically remake the very fabric of our cities

(Baumgardner, 2016) and the urban form as we know it will be radically transformed by self- driving technology (Madia, 2017). The biggest difference cities will see when Autonomous

Vehicles replace human drivers is the new utilization of roads and parking spaces (Snow, 2017).

The possibilities of saving space is named as an essential argument for the use of Autonomous

Vehicles (Heinrichs, 2016).Vast amounts of impervious surface cover the landscape of our communities. Carlo Ratti, Director of Sensible City Labs at MIT predicts that Autonomous

Vehicles will require 80% fewer cars (Baumgardner, 2016). Fewer cars would mean opportunities for investment in surface infrastructure. Erik Guerra, Assistant Professor of City and Regional Planning at the University of Pennsylvania thinks that Autonomous Vehicles will impact the urban environment the most giving options for urban spaces to be used in a more positive way (Baumgardner, 2016). There are many similar opinions as to the possible changes that are going to be seen to the built environment in cities and suburbs resulting from

Autonomous Vehicles. Madia claims that driverless technology will impact parking the most

(Madia, 2017).

Parking Impacts

Will we need to allocate space for parking in the near future? Drivers spend about 30% of their time looking for a perfect parking space (Green, 2015). With cars driving themselves and parking on their own in places far less convenient, the need for parking within 100 feet of the front door will be obsolete (Lewis, 2017). Parking will become an unnecessary form of infrastructure. This is already being seen without Autonomous Vehicles on the road. Retail stores like Macy’s have reported a decline in people going into the store. By the year 2019, 20% of all total U.S sales will be attributed to e-commerce, with Amazon alone being responsible for

60% of those ecommerce sales (Carlson & Larco, 2017). According to some analysts 33% of US malls will be closed within the next few years; Macys has closed 90% of their department stores in the last five years while Sears has closed 200 department stores since 2014 (Carlson & Larco,

2017). Blaine Leonard, Director of Utah’s Department of Transportation believes that most of our urban landscape is built assuming someone is going to drive and park (Snow, 2017). But with recent data these stores are now looking to re-imagine their unused lots. Universities and colleges are also seeing the potential impact from Autonomous Vehicles on their campuses.

They are beginning to discuss and consider where transportation is heading. Gary A. Brown, the

Director of Campus Planning at the University of Wisconsin-Madison, was reluctant to add any new parking spaces to the campus, saying “The Autonomous Vehicle thing came up in the middle of our discussion and at some point that is going to impact how much parking we need so we might not be building parking as fast or we are going to design it differently” (Prevost,

2017, p. 1). David Lieb, a consultant with Walker Parking Consultants, conducted a study at

Midwestern University in Illinois and stated, “Typically universities have more parking availability than they think they are just not using it efficiently; the study found that 26,000

spaces were empty at peak hours” (Prevost, 2017, pp. 2-3). Figure 2-5 below, shows existing parking on the University of South Florida’s Campus in 2016 (left), and indicates a conceptual site plan of parking development opportunities (right) with Autonomous Vehicle implementation.

Figure 2-5. Existing parking and a conceptual site plan of parking redevelopment opportunities for the University of South Florida campus (Chapin, et al., 2016).

If Autonomous Vehicles require less parking, then these surface lots can be converted and repurposed. Lyft co-founder John Zimmer wrote that “eventually we’ll be able to turn parking lots back into parks (Snow, 2017). Others believe these lots should be turned into housing or commercial businesses. Kinder Baumgardner, Managing Principle SWA, predicts that a reduction in cars will transform urban cores with entrepreneurs reimagining parking lots and spaces into housing, retail outlets, and public spaces (Baumgardner, 2016). Sara M.

Watson, affiliate with the Berkman Klein Center for Internet and Society, thinks that in a well- managed transition plan it gets used up and becomes valuable real estate (Snow, 2017).

Fleets of shared, self-driving vehicles could indeed remove 9 out of 10 vehicles, eliminating all on-street parking and 80% of off-street parking, according to the Organization for

Economic Co-operation and Development (OECD), a think tank tasked with looking into the urban mobility future (McKenna 2016). On-street parking pertains to metered, curb, and road shoulder lanes while off-street consists of surface lots (Chester, Horvath, & Madanat, 2010).

Restructuring will take place in areas that qualify as high-density, attractive designations, where costs of building parking is also high; this can include areas such as city and shopping centers, business districts, train hubs, and airports (Heinrich, 2016). In a recent article published by two

Michigan agencies, Public Sector Consultants (PSC) and the Center for Automotive Research

(CAR), different scenarios were considered to aid stakeholders with Autonomous Vehicle implementation. They considered that Autonomous Vehicles will be able to park themselves at the back of building lots, or park out of prime locations, eliminating the need for onsite and off- street parking. Reducing on-street parking may represent opportunity to convert road lanes dedicated to parking into other uses such as bike lanes, wider sidewalks, or green space such as parks (Public Sector Consultants + Center for Automotive Research, 2017).

Not every spot will need to be removed or eliminated. They could also be saved as parking spaces for pick-up and drop-off zones (Public Sector Consultants + Center for

Automotive Research, 2017). In urban areas, driverless taxis will dramatically reduce the demand for city-center parking but could also increase the need for on-street passenger drop-off and pick-up zones outside the building (Eddy, 2014). On-Street parking will continue to supply the physical separation needed between moving cars, bicyclists, and pedestrians (Giarratana,

2017). Curb space will become more valuable for drop-off areas (Rice & Tomer, 2017). We need to understand how to manage that curb space as it will be important for loading and unloading, cyclists, and transit (Sisson, 2017). Off-street parking could be relocated so it does

not take up valuable land. Large Autonomous Vehicle parking lots could be located outside dense areas such as the city center, and could be used during off-peak hours, for servicing and storage, while smaller parking areas could be located in the downtown area mainly for peak hours, charging, or refueling (Public Sector Consultants + Center for Automotive Research,

2017). Simply moving a parking space outside of the central business district may save a city nearly $2,000 in annualized costs, while moving one to a suburban location may save another

$1,000 (Fagnant & Kockelman, 2013). Autonomous vehicles not only allow for fewer spaces in garages but will also allow for fewer spaces in parking lots. If 10% of Autonomous Vehicles are publicly shared, $250 in parking savings is assumed per new Autonomous Vehicle (Fagnant &

Kockelman, 2013).

Parking garages are going to see a different transformation compared to on-street and off-street parking. In one scenario garages will resort to becoming the priority parking destinations for Autonomous Vehicles once they drop off their passengers. This scenario would require little to no reconstruction as the infrastructure is already well suited for the transformation. In the second scenario, the smart parking1 market is going to be initiated and influenced by Autonomous Vehicles. Currently in Boulder, CO there is a company called Park

Plus that is working on developing a fully automated parking garage (Citron, 2017). It is estimated that these types of parking garages, the smart park, designed for self-driving cars, with sensors and communication networks, will be able to take up 60% less space than traditional garages (Citron, 2017). This is due to extra space not being needed in-between cars: Four inches on either side of the space, designed for humans opening doors, equaling 21 sq. ft. total for each

1 Smart Parking is a Smart City technology that enhances passive car parks with the ability to sense occupancy and patronage. Invariably the results of this smart new sense are communicated and collated, either over the Internet or over a private network (Raftery, 2016). 30

spot (Newcomb, 2016). Now eliminate the elevators and some of the stairwells (maintenance will need to have access to floors) and four times as many cars could fit in the same amount of space (Citron, 2017). The city of Somerville, Massachusetts has collaborated with Audi’s Urban

Future Initiative and the Federal Realty Investment Trust on a design that will cut garage parking space by 62% and save $100 million dollars (Sisson, 2016). In Nashville, TN a developer is taking parking underground, creating a large underground garage in anticipation of

Autonomous Vehicles (Newcomb, 2016). This is again another opportunity for areas that currently have underground garages. Instead of constructing new garages, these could be revitalized as a smart park garage. In the second scenario parking garages will be used to create more space for residents and businesses in urban and high demand areas (Newcomb, 2016).

Parking garages could be repurposed for housing or low-rent commercial buildings, demolished for public space, or moved outside the urban center (Madia, 2017); they could become flexible spaces for apartments or even artist studios (Green, 2015). In Los Angeles AvalonBay

Communities Inc. has begun work on an apartment development in the city’s arts district with parking garages designed to be convertible. AvalonBay Communities Inc. is looking forward to a time when extra spaces won’t be needed and allows for an easier conversion of the garage

(Sisson, 2016).

Roadway Impacts

There have been many articles stating similar opinions as to what people will see in relation to roadways and Autonomous Vehicles. Autonomous Vehicles are predicted to be safer than a human-driven car. Currently a highway lane can move about 2,000 cars per hour; with autonomous vehicles those numbers could increase to 3,000 cars per lane per hour and reduce the need to build more roads (Snow, 2017). If driverless cars have the predicted

efficacies over current drivers (no human error), current traffic volumes could flow through a narrower roadway designed with both narrower lanes and fewer of them (Abbott, 2017).

Removing travel lanes from a roadway and utilizing the space for other uses and/or travel modes is what the Federal Highway Administration (FHA) calls a Road Diet (Administration, 2014).

Major opportunities exist for road diets to enhance walkability and bike use (Larco, 2017).

Higher vehicle density in relation to road area is to be expected (Heinrichs, 2016); driverless cars can drive closer together and travel faster more safely, allowing lanes to narrow (Eddy,

2014). Safety concerns such as accidents will be preventable using driverless-vehicle technology. According to data produced by the National Safety Council 38,300 people were killed on roadways in the U.S. in 2015 (Snow, 2017). Over 40% of fatal crashes involve some combination of alcohol, distraction, drugs involvement, and fatigue—self-driven vehicles would not fall prey to human failings like these, suggesting the potential for at least a 40% fatal crash- rate reduction rate if all other factors remain constant (Fagnant & Kockelman, 2013). This gives options as to what might happen to the street design of cities. Narrower lanes and removal of obstructions can make way for pedestrian-centric infill, including street trees, bike lanes, and public spaces (Madia, 2017). Twenty-two lane highways or ones even wider (With 26 lanes in certain parts Interstate 10, in Texas is the widest highway in the world) could be reduced to eight lanes and all those extra lanes on the sides of the now too-wide highways could be transformed into green corridors (Green, 2015).

The University of Oregon has produced an article called Rethinking the Streets in an Era of Driverless Cars that portrays some images of what streets could look like in the future with

Autonomous Vehicles. The researchers emphasized throughout the article that the goal was not about changing the layout of the street but changing how street space is allocated. The article suggests different street layouts. The first section looks at common urban street design while the 32

second section looks at common residential street design. Figure 2-6 below, shows the common street design for urban streets.

Figure 2-6. Urban arterial street design (Schlossberg, Riggs, Millard-Ball, & Shay, 2018).

In the current urban street design model each direction has two lanes for vehicle traffic, on-street parking, pedestrian sidewalks, and in some cases a center turn lane and street trees. The driving lanes are each usually 12 ft. in width and the parking lanes are 8 ft. in width. Using the research, each of the additional figures are modeled after figures from this article. The first step that was suggested for changing the urban street design was to decrease the width of all existing driving lanes from 12 ft. in width to 8 ft. in width. Autonomous Vehicles will be able to operate more efficiently than human drivers in these smaller spaces. Decreasing the travel lane width to 8 ft. for all four lanes would give 16 ft. of extra land to be utilized. Figure 2-7 below, indicates a design of thinner lanes in an urban setting,

Figure 2-7. Decreasing width in all lanes (Author).

With the lanes being thinner a bicycle lane could be added on one side and the sidewalk width could also increase, allowing more pedestrian access and additional vegetation. The second transformation that is possible with Autonomous Vehicles is the removal of either one or two on- street parking lanes. With the removal of one on-street parking lane an additional 8 ft. of asphalt can be removed. With the addition of all the travel lanes widths being reduced this could add an additional 24 ft. of right of way. Figures 2-8 and 2-9 below show two design strategies for these scenarios.

Figure 2-8. The removal of one on-street parking lane and reduced width lanes (Author).

With Autonomous Vehicles able to park themselves remotely in areas outside of the urban core, or continuously driving around, parking could be eliminated entirely in these areas. Reducing both on-street parking lanes would decrease the amount of asphalt again by 8 ft. In total with the

driving lane width reduction and both on-street parking lanes removed, 32 ft. of right-of-way could be added.

Figure 2-9. The removal of two on-street parking lanes and reduced width lanes (Author).

The third transformation that could occur to the urban street design due to Autonomous Vehicles

would be the removal of one travel lane. Autonomous Vehicles traveling either direction could

share the middle lane when space is available or when no other car is in that lane. This again

would allow for an additional 8 ft. of space to be gained. Figure 2-10 below, indicates what the

removal of one travel lane could look like for urban street design,

Figure 2-10. The removal of one travel lane and reduced width lanes (Author).

With only three lanes, optimal space is opened up. As shown above two bike lanes could be added, more vegetation, and pedestrian sidewalk space would be increased immensely.

The last urban street design scenario that could be possible with Autonomous Vehicles is a complete street transformation where only one travel lane exists. This travel lane could be used as both a travel lane and a drop-off and pick-up lane. With Autonomous Vehicles being able to communicate between each system there would be less likelihood of an accident between a vehicle dropping someone off/picking someone up and a vehicle continuously driving through the space. Figures 2-11 and 2-12 below show the radical transformation of a street with only one vehicle lane.

Figure 2-11. Rethinking radically #1 (Author).

People, instead of cars, would become the priority in these spaces. In addition, some of these spaces could be designated as transit only corridors as shown in Figure 2-12, below. Instead of bus lane requirements, Autonomous Vehicles could move out of the way with clearance technology to allow public transportation to take precedence in those lanes (Rice & Tomer,

2017). Transit in these spaces would become more efficient, enhancing quality and decreasing operation costs.

Figure 2-12. Rethinking radically #2 (Author).

The above scenarios would be the ideal cases for Autonomous Vehicles in urban settings. They

would provide the most opportunity to gain as much of the right-of-way as possible and allow for the beautification of street designs. According to the same University of Oregon study residential

street design could also see a transformation with the implementation of Autonomous Vehicles.

A typical residential street includes on-street parking on both sides of the street, two travel lanes

(one going in each direction) and a pedestrian sidewalk (Schlossberg, Riggs, Millard-Ball, &

Shay, 2018). Figure 2-13 below shows a typical residential street design,

Figure 2-13. Typical residential street cross-section (Schlossberg, Riggs, Millard-Ball, & Shay, 2018).

With the implementation of Autonomous Vehicles there will be potential to significantly change over-built residential infrastructure. The first design transformation that could be possible with

Autonomous Vehicles is similar to that of the urban street design. Reducing both travel lanes to

8 ft. and removing one on-street parking lane would allow for additional right-of-way space.

Again, this is possible due to Autonomous Vehicles being able to operate closer together in narrow street widths. Figure 2-14, below, shows what reducing the width of all travel lanes and removing one on-street parking space would look like for a residential street.

Figure 2-14. Reducing the width of all the lanes (Author).

The second transformation that could occur is the complete removal of all street parking shown in Figure 2-15, below. This design could recapture 26 ft. of right-of-way space. The travel lanes could again be used as a versatile drop-off/pick-up lane. This design would work best in residential areas that do not see a high volume of vehicle traffic.

Figure 2-15. Removing street parking and reduced width lanes (Author).

Since residential areas do see less volumes of traffic, one design could be the removal of another travel lane, shown in Figure 2-16, below. Streets would only contain one lane for vehicle traffic.

Autonomous Vehicles will be able to communicate and yield to oncoming traffic allowing for a smooth transition. These types of streets are already common in Europe and some historical U.S. cities.

Figure 2-16. Removing another driving lane and reduced width lanes (Author).

The last residential design transformation that could occur is the complete removal of both travel and parking lanes. Some residential streets are vastly underutilized or overbuilt. This gives an opportunity for reclaiming the land. Figure 2-17, below, is the author’s idea of starting over with residential streets.

Figure 2-17. Rethinking radically #3 (Author).

Environmental Impacts

Though the types of environmental benefits resulting from widespread Autonomous

Vehicle adoption do depend on what pattern of adoption takes place and policy implications, it is important to look at how Autonomous Vehicles can impact climate change. Could it lead to an improvement in environmental sustainability by mitigating carbon emissions, reducing air pollution, or decreasing our urban heat island effect (Sisson, 2017)? Key environmental questions surrounding Autonomous Vehicles are: How will it help or hinder the environment, what will self-driving cars mean for the environment (Roberts, 2017), and will Autonomous

Vehicles mitigate or accelerate carbon emissions (Rice & Tomer, 2017). Autonomous Vehicles could be more fuel efficient, leading to a reduction in Green House Gas (GHG) emissions

(Miller & Heard, 2016), if clean electric vehicle technology was used in collaboration with the

Autonomous Vehicle revolution (Rice & Tomer, 2017). Autonomous Vehicles are supposed to be lighter leading some to believe a 6-7% reduction in fuel consumption by allowing for the support and the space for clean vehicle technology (Barcham, 2014). In most cases this is already true. Driverless cars that are being driven and tested today are already fully electric, although they do not charge using clean energy (Andrew, 2017). Autonomous Vehicles being more efficient with braking and acceleration could lead to a 20-30% reduction in energy per vehicle (Barcham, 2014). Platooning is another support for reduction in energy consumption; platooning is the practice of running vehicles closer together, cutting down on air drag, leading some to believe a 10-20% energy reduction (Barcham, 2014). These scenarios depend on vehicle miles traveled and number of vehicles on the road. If everyone owns their own vehicle these numbers could be inaccurate. According to research done by the Department of Energy if total vehicle miles traveled increases they could increase energy consumption by more than

200%; however, if total vehicle miles decrease then they could reduce energy consumption by

90% (Worland, 2016).

Green Infrastructure: Trees

There is potential with Autonomous Vehicles to reclaim both private and public asphalt.

Revamped roadways and parking lots offer solutions to many environmental and health problems that challenges cities (Sisson, 2017). Autonomous Vehicles bring opportunity for communities to be designed with tree-filled urban cores and streets. They could bring back the idea of garden cities by replacing all the land that has been given to the car with landscape (The

Economist, 2018). With Autonomous Vehicles being safer (see above), the days of worrying about colliding with trees is gone. City planners will be able to use more aesthetically appealing development plans, including the incorporation of more trees, and will not have to anticipate human error (Giarratana, 2017). There will also be an elimination of currently needed roadside infrastructure, such as signs and lights, allowing for these areas to be replaced by trees or other greenery, making streets more attractive (Silkin, 2017).

Highways can once again become tree-lined, grand boulevards that beautify our environment and no longer divide neighborhoods (Lewis, 2017). Madia believes that because

Autonomous Vehicles will be quieter and cleaner, sound walls, rumble strips, and buffers will become obsolete; concrete laden highways can now be reimagined as tree-lined boulevards rich in vegetation (2017); an area filled with carpets of grass underneath a tree canopy (Sisson,

2017). In Barcelona a design is underway to transform mini superblocks repurposing them with greenery, such as trees, offering respite from car dominance resulting in pollution and noise

(Madia, 2017).

Private or shared ownership of Autonomous Vehicles

Consumer behavior is a big question mark and one that determines what our urban futures will look like. Studies indicate that it is unclear what the consumer market will choose in relation to private or shared Autonomous Vehicle ownership. “We don’t know if consumers will use a fully shared vehicle network, or if they’ll purchase privately owned self-driving cars, or share privately owned ones with other households,” stated Susan Shahenn, the Director of

Innovative Mobility Research at UC Berkeley’s Transportation Sustainability Research Center

(Moskowitz, 2017, pp. 3). If Autonomous Vehicles simply replace the volume of privately- owned, non-autonomous vehicles on highways and streets today, then the demand for parking may not change significantly. In a report published by Morgan Stanley, there are four car ownership views. The first is that there will be an increase in Autonomous Vehicle ownership due to people currently using public transit for convenience, cost, and safety choosing to have their own vehicle. The second is that car ownership will decrease as Autonomous Vehicles could serve more than one person in the household, making it unnecessary to own more than one vehicle. The third scenario, which is the most extreme, is zero car ownership and 100% vehicle sharing. The last scenario is something in-between, with most households having cars but fewer cars per household overall (Stanley, 2013). Holmes also gives a summary of the potential impacts of three types of Autonomous Vehicle ownership and the impacts on parking demand.

The summary is outlined in Table 2-1 below,

Impact Private Shared use, single Shared use, multiple occupancy

occupancy

Number of Equivalent to today, subject to Significantly lower than Scenario 1. parking lots whether vehicles can re-position Lower than Significantly fewer vehicles require themselves in different locations Scenario 1. Fewer parking. on the public road network vehicles require parking and duration of stay reduces.

Location Parking lots could Parking lots located at key destinations Basic autonomy will permit be in cheaper, out with high demand to provide spare drop-off and parking, lots still of town locations vehicles and servicing centers. need to be located near during periods of destination. Higher autonomy lower demand. will allow drop-off at destination and parking located elsewhere

Parking revenues Same as today or greater Reduced due to Significantly reduced due to less time in less time spent in parking lot and significantly fewer parked parking lots and vehicles. fewer parked vehicles.

Type of facility Same as today. Opportunity to Parking lots transformed to become service centers and waiting widen service offer areas until AV is requested by ‘user’

Operational Capacity optimized (more Significantly fewer parking spaces needed capacity vehicles, same space) Fewer spaces than Scenario 1 needed than Scenario 1

Rate of change/ Gradual implementation of AV Subject to local market conditions and implementation floors (e.g. one floor at a time) Big bang (i.e. once familiarity with ridesharing Uber decides to do this it will happen quickly)

Table 2-1. The impact of autonomous vehicles on parking (Holmes, 2017).

In addition to vehicle sharing, autonomy could open a new wave of ride sharing already being done by car companies such as Via, Uber Pool, and Lyft Line, allowing different people to

share the same ride, cutting operating costs and individual fares; autonomy could boost ride sharing even more because all trips could be managed online (Navy, 2017). If shared ownership and multiple occupancy takes off, then the impact on parking demand will be enormous

(Holmes, 2017). One thing that recent evidence has shown is that the millennial generation is less car-oriented and more inclined toward on-demand mobility, ride-sharing, and living in dense urban environments (Miller, 2016). This provides some proof that the market will be predominantly shared Autonomous Vehicles and that parking and roadways will see drastic transformations. In a report published by Nelson Nygaard and Perkins + Will since 1983 the rate of licensed drivers aged 18 has dropped from 80% to 60%, indicating that young people are becoming less likely to drive; car share membership has increased since 2016 and increases each year reducing 9-13 vehicles on the road for every car share available (Nygaard & Perkins, 2016).

Chapter III

Methodology

Methodology Framework

This research attempts to gain insight about Autonomous Vehicles and their impact on the built environment, trees, and stormwater. It is informed by two primary methodologies: background experience and multiple case studies. The methodologies contribute to answering the critical research questions:

1. Communities currently face, and will continue to face, issues resulting from

stormwater;

2. Autonomous Vehicles will have widespread adoption in the next 10-30 years and

different ownership scenarios may exist;

3. There will be changes to the built environment resulting from the widespread

adoption of Autonomous Vehicles; and

4. There is a correlation between green infrastructure implementation, particularly tree

plantings, and a decrease in stormwater issues within the Autonomous Vehicle era.

While conducting my Literature Review I noted that articles by authors such as Andrew (2017),

Barcham (2014), Miller & Heard (2016), Rice & Tomer (2016), Roberts (2017) and Worland

(2016) did include environmental impacts but were mainly focused around GHG emissions and

energy consumption. Autonomous Vehicles have the potential to solve additional environmental

issues, such as excess stormwater, that have plagued our communities by allowing green

infrastructure, such as trees, to be implemented in areas previously constructed for the car. The

hypothesis of my research is that green infrastructure implementation, particularly tree

plantings, can be used to mitigate stormwater runoff in cities due to changes to the built

environment resulting from the adoption of Autonomous Vehicles. I propose that the reduction

in surface parking and road width will be the two built environment changes resulting from the

widespread use of Autonomous Vehicles that have the most opportunity for tree implementation

to address stormwater runoff.

Background Experience

The first method used was activating and using prior knowledge and experience. Since starting work at Ohio-Kentucky-Indiana Regional Council of Governments (OKI) in November

2016, I have been able to sit in on multiple partner discussions. Part of OKI’s mission is to develop collaborative strategies to improve the quality of life and economic vitality of the region. Throughout the past year Autonomous Vehicles have been the focus of discussion. As a staff member I was given the privilege to sit in on partner meetings. From these meetings I was able to grasp the uncertainty surrounding Autonomous Vehicles and their impact with both the built and natural environments. The region is trying to prepare and understand the changes that

Autonomous Vehicles will bring to cities.

One of the larger OKI partners is Green Umbrella. Two groups I was a part of were the

Green Space Group and the Green Infrastructure Group. Some members of the Green Space group included staff from Cardinal Land Conservancy, Great Parks, OKI, Ohio Division of

Natural Resources, and Cincinnati Nature Center. The Green Space Group’s goals deal with trying to understand how to incorporate more greenspace into the region through conservation, restoration, and rehabilitation. The second group was the Green Infrastructure Group. Some members in this group included Taking Root, Metropolitan Sewer District, OKI, and Hamilton

County Planning. The main goal of this group is to provide ideas and identify areas where green infrastructure can be implemented throughout the region. The idea for my research paper grew

from the interactions with both Green Umbrella Groups, and through interactions with my bosses. I saw opportunity in using prior knowledge and building new knowledge to bridge this gap of uncertainty.

Case Study

Using a secondary research approach, I reviewed case studies previously done by other organizations. A case study is an empirical inquiry that investigates a contemporary phenomenon within its real-life context, especially when the boundaries between said phenomenon and context are not clear (Yin, 2014); case studies investigate a far-reaching range of topics and are used when researchers wish to obtain an in-depth understanding (Zach, 2006).

I will be following what Yin calls a multiple-case study type. This is different than a single-case study type in that multiple case studies are analyzed compared to just a single case study. A multiple case study design allows the researcher to explore the phenomena under study with a replication strategy (Yin, 2014). There are several categories of case study and Yin notes three categories, namely exploratory, descriptive and explanatory (Zainal, 2007). The case study used in this research is an exploratory case study. According to Yin this category is set to explore any phenomenon in the data which serves as a point of interest to the researcher in which general questions are meant to open the door for further examination of the phenomenon observed

(Zainal, 2007). Using the case study method, I hope to achieve credibility, transferability, dependability, and conformability (Zach, 2006).

Subject Selection

I chose to split my case study research reviews into two categories. The first category is

looking at street design analysis while the second category is parking lot analysis. Case Study

one included reviewing New Mobility Street Design: A Case Study of Autonomous Vehicles in

San Francisco. The site for this study was the 4th Street block in San Francisco. The study generated mode-share scenarios based on uptake of, and reaction to, Autonomous Vehicle: traffic, parking, and curbside calculations. This analysis was conducted by two independent

firms, ARUP and Perkins + Will. This was the only case study I found that was directly related to Autonomous Vehicles and green infrastructure implementation. The purpose of this study was to quantify and visualize the ways in which Autonomous Vehicles could change the street and how we could reclaim public right of way. These second case study I reviewed is this category did not directly correlate with Autonomous Vehicle implementation but could be used alongside Autonomous Vehicle research. Case study two is focused on a 1.8 mile stretch of

Hacienda Avenue located in the City of Campbell in northern California. This case study

provides a scenario when lane reduction is implemented.

The second category of case studies reviewed were related to parking lot analysis. These

case studies did not directly correlate with Autonomous Vehicle implementation but could be

used alongside Autonomous Vehicle research. The first case study reviewed in this category was

The Depave organization. Their mission is to promote the transformation of oversized surface

parking lots and to reconnect urban landscapes with nature. With the idea that paved surfaces

contribute to stormwater pollution, Depave works at freeing the soil and implementing green

infrastructure (Depave, 2018). Some projects include the removal of parking lot asphalt at

schools, hospitals, churches, local businesses, and apartment complexes. The second case study

reviewed in this category was a depaving project on Eadom and Bridge Streets in Philadelphia.

This was part of their Philadelphia Water Department Waterway Restoration project.

Chapter IV

Case Study Analysis

Executive Summary

The purpose of reviewing the four case studies was to determine the feasibility of reducing street width and parking for green infrastructure implementation in various cities throughout the

US. They were used to help answer the research questions for this paper identified in the introduction section. The goal was to find studies that reduced impervious surfaces due to stormwater runoff issues while also implementing green infrastructure, and if possible trees. The first case study, New Mobility Street Design: A Case Study of Autonomous Vehicles in San

Francisco, was the only one directly linked with Autonomous Vehicles and street design; however, the other three case studies did relate back to the Literature Review regarding the surface changes that will be affected by Autonomous Vehicle implementation and the potential for green stormwater infrastructure.

New Mobility Street Design: A Case Study of Autonomous Vehicles in San Francisco

This case study is a collaboration between ARUP and Perkins + Will. The report investigated how Autonomous Vehicles would influence street design by reclaiming the public right-of-way. Four main goals were identified in this study: increase space for green stormwater infrastructure, improve quality and character of public realm, increase space for pedestrians- wider sidewalks, and improve safety. This case study addressed questions such as: will it be possible to redesign streets and streetscapes and to narrow lanes for other opportunities such as green infrastructure implementation, and how do you design more multimodal streets with

Autonomous Vehicles?

They identified 4th Street, located in downtown San Francisco, as their study area. This street has potential to be redeveloped with Autonomous Vehicle introduction due to location and transportation connections. The study investigated what an auto-oriented, multi-modal street would potentially look like when Autonomous Vehicles are implemented. Figure 4-1 and 4-2 below, represent the new design that was created for a block of 4th street.

Figure 4-1. Revolutionary scenario future plan View (Baumgardner, Ruhl, & Tiemey, 2017).

Figure 4-2. Revolutionary scenario future cross section (Baumgardner, Ruhl, & Tiemey, 2017).

Mode share scenarios were generated based on research of potential uptake of, and reaction to, Autonomous Vehicle technologies which were applied to traffic, parking, and curbside calculations. There were two relevant findings from this study on how Autonomous Vehicles

will impact urban street design. First, in urban areas, the curb will be the center of increasing levels of conflict. As Autonomous Vehicles services escalate passenger loading demand, on- street parking will remain in high demand if private ownership vehicles remain in circulation.

Balancing passenger loading with on-street parking will be crucial. Additionally, building more high-quality bicycle facilities will remain critical to achieving safer, more sustainable communities. Though not directly in front of every business, ensuring curb space near destinations would allow for these zones to be used more often. The second finding was that

Autonomous Vehicles are likely to bring greater efficiencies to roadway operations. This presents an opportunity to repurpose the reclaimed road capacity. Lane reductions and complete street features will be more feasible.

Hacienda Avenue Green Street Improvement Project

The second case study is focused on the 1.8-mile road of Hacienda Avenue, located in the

City of Campbell in northern California. This case study provides a scenario anticipating results when Autonomous Vehicle related lane reduction is implemented. It was the city’s first road diet project in which street narrowing and green street elements were a focus. The goals of the project were to reconstruct and restore failed asphalt pavement, improve connectivity between neighborhoods, connect green features to parks and trails, and minimize long term environmental impacts through low impact development (LID) stormwater features. This was done by reducing impervious areas, adding vegetation, improving drainage, and increasing aesthetics. On-site stormwater infiltration and treatment were implemented by replacing impervious surfaces with pervious material and constructing bioswales and tree-lined parkways.

Below Figures 4-3 and 4-4 show the new street design that was created by the City of Campbell.

Figure 4-3. Hacienda Avenue green street (City of Campbell, 2013).

Figure 4-4. New street design (City of Campbell, 2013).

The results of the road diet and green features reduced the CO2 footprint for the road by

33%, added 42,000 sq. ft. of sidewalk area, and allowed for the treatment and control of 90% of annual rainfall. The amount of increased vegetation equaled about 1 acre (147% increase), which included street trees and non-invasive vegetation that were located in the bioswale.

The Depave Organization

The Depave Organization was created in 2008 in Portland, OR. The organization is tasked with transforming driveways and parking lots into vibrant, living spaces to empower

communities to change pavement habits. They advocate for urban green space that reduces stormwater runoff, improves water quality, and mitigates climate change. Since its inception in

2008 it has branched off into five sister organizations located in Canada, Cleveland, Tennessee,

Massachusetts, and Seattle-Tacoma, all with the same goal in mind. Projects have included removing impervious surface at schools, local businesses, and churches.

The problem identified in the various completed projects is that over-paved places are becoming a large concern for communities. These areas contribute to stormwater runoff pollution and cause a disconnection between people and the natural world. The Depave

Organization’s solution is to remove impervious surfaces, which will then reduce stormwater

pollution and increase land available for trees, native vegetation, and urban farming. Since its

inception in 2008 the Portland Depave Organization has removed 165,000 sq. ft. of asphalt,

creating 63 new green spaces. These new green spaces have used trees and other native

vegetation to divert 4,000,000 gallons of stormwater from storm drains, annually.

The Philadelphia Water Department’s Waterways Restoration Team Project

The Philadelphia Water Department created a Waterways Restoration Team with the goal to create Green City, Clean Waters, Philadelphia's plan to reduce stormwater pollution currently entering the city’s combined sewers using green infrastructure. As land development increases the urbanized landscape transforms from pervious areas to impervious surfaces. This has caused a concern as it affects the Philadelphia watersheds by impairing water quality and degrading stream habitats. The vision is to protect and enhance watersheds by managing stormwater runoff with innovative green stormwater infrastructure throughout the city.

One of the solutions that was adopted was depaving an area. The goal was to identify and replace an impervious area so that once depaved it would capture 1” of runoff. Eadom and

Bridge Streets located close to I-95 in Frankford had a large parking lot which was causing excessive stormwater runoff. The team chose to focus on the parking lot adjacent to Eadom

Street. It became known as the Eadom Street Depaving Project, which was the first depaving project in Philadelphia and the first green parking lot. It was initiated in the winter of 2011 and completed in April 2013.

The team chose a bio-retention feature with the addition of trees that would be replacing impervious surfaces as shown below in Figure 4-5.

Figure 4-5. Depaving project Eadom Street Philadelphia (Philadelphia Water Department, 2011).

This depaving project removed 187 parking spaces which equaled about 10,000 square feet of impervious concrete. The depaved area was then replaced with rain gardens and trees. It was calculated that approximately two acres will manage stormwater runoff in this newly designed parking lot. This project was only made possible by the collaboration of the local government, community partners, and neighborhood volunteers.

Results

The case studies provided similar results. All the studies indicated stormwater runoff pollution and water quality problems in conjunction with excessive impervious surfaces. The

four case studies show that there is a possibility for parking and lane reduction, or road diets.

Each study used the implementation of trees as green stormwater infrastructure. In the San

Francisco Autonomous Vehicle study and the Hacienda Avenue Lane study, lane width

reductions were possible due to excessive impervious surface that were causing environmental

impacts. The San Francisco study did show that with Autonomous Vehicles streets will not need

to be as wide due to the potential for greater efficiency within roadways. The Hacienda Avenue

study, though not directly correlated with Autonomous Vehicles, did show how a city realized

that some street designs have become outdated, underused, and inefficient. The Depave

Organization has made extensive changes to surface parking lots in Portland, Oregon and other

cities. This organization took initiative in the city’s stormwater runoff problem. By identifying

underutilized surface parking lots they were able to address areas within the lots where native

vegetation could be implemented to help the over-arching goals of the city’s stormwater

management plans. This allowed them to gain support and to educate community members.

Again, though not related to Autonomous Vehicles, the revamping that is being done currently

is only going to be beneficial to the area in future years. Philadelphia, though only having

implemented one depaving project, had similar goals to the Depave Organization. Stormwater

management is a growing concern for the city and realizing the potential to remove impervious

surface and implement green infrastructure has proved a success. With these case studies,

recommendations were also made that will be discussed further in the next chapter.

Chapter V

Discussion

The study revealed new areas of analysis to be researched in terms of stormwater and

Autonomous Vehicles. The purpose of this research was to identify how Autonomous Vehicles can have positive implications to communities by allowing new ways to incorporate trees as green infrastructure and to reduce impervious surface leading to stormwater problems. Climate change effects can already be seen in cities across the US. There is concern due to the potential for storms to increase and intensify. Stormwater runoff is already posing a large concern due to the cost and impact that it causing to cities. Billions of dollars are spent trying to enlarge outdated piping and to create more impervious grey infrastructure to solve the problem. With the idea that Autonomous Vehicle adoption is going to occur in the next 10-30 years this is going to change not only the way we travel but also create changes to the built environment.

The built environment changes would most affect street design width and surface parking lots.

With Autonomous Vehicles these spaces will not need to be as large as they currently are. This is due to Autonomous Vehicles being safer and limiting the need to have parking.

Restated Research Questions

5. What are the current and future issues that communities will face resulting from

stormwater?

6. How widespread will Autonomous Vehicle adoption be over the next 10-30

years?

7. Due to widespread Autonomous Vehicle adoption, what changes will take place

in the built environment?

8. How can green infrastructure implementation, particularly tree planting, address

stormwater issues in an Autonomous Vehicle era? 56

Findings

The Literature Review and Methodology sections determined that there is opportunity for impervious surfaces such as parking lots and roadways to be transformed using green infrastructure such as street trees. They also determined that Autonomous Vehicles do have the potential to decrease lane width and parking surfaces. With the research I was able to determine four key findings:

1. Stormwater runoff pollution and water quality problems are prevalent in cities in the

U.S. currently, but stormwater management and reduction could be more feasible

with the removal of impervious surfaces;

2. Autonomous Vehicles will be hitting the streets in the next 30 years or less; however,

there is still uncertainty concerning shared vs. private ownership;

3. There are excessive impervious surfaces and a possibility for parking lots to be

removed and road diets to be implemented with the adoption of Autonomous Vehicles

a. Parking in downtowns could be removed or re-allocated to non-prime

locations; Parking garages and larger outer parking lots may need to be kept

for future Autonomous Vehicle parking; Parking lots at schools, large

shopping centers, industrial plants, commercial businesses, and hospitals to

name a few could be smaller or removed; to provide access for drop off and

pick up deliveries, employees, or customers certain smaller lots and

driveways could be kept;

b. Some city street designs have become outdated, underutilized, and inefficient;

Some on-street parking will be needed for Autonomous Vehicle drop-off and

pick-up zones; the removal of 4, 5, and 6 lanes could be possible with

Autonomous Vehicles along arterial streets, highways, and interstates; most

roadways could see changes to travel lane width;

4. Street trees and urban forests could be the main stormwater management

infrastructure used by communities and could be implemented in areas currently not

well suited, due to increased Autonomous Vehicle safety and efficiency.

The downtown areas of cities contain a large percentage of impervious surfaces with a majority being surface parking lots. With the introduction of Autonomous Vehicles most of these lots will not be relevant. Even if Autonomous Vehicle ownership takes a more privately- owned track, from the research there are still excessive lots that are underutilized or vacant.

Autonomous Vehicles will also still need parking areas when they are not in use. Parking garages and outer, larger surface lots could be kept in their current state. Other areas that will see parking changes include schools, shopping centers, commercial and industrial businesses, and hospitals to name a few. Most of these types of infrastructure have larger lots than needed.

With Autonomous Vehicles the people currently using the lots will be able to be dropped off, allowing for significant space to be transformed. From the research, college campuses and large shopping centers were noted as a high priority area that could see dramatic change. With shopping being done mainly online and products being delivered to the home, most shopping centers do not see as much traffic and the lots are excessive in size. In addition, on-street parking in these locations, including downtown, may still be needed, although not all on-street parking will be needed. Some on-street parking can be turned into designated drop-off and pick- up zones, while other areas can remove on-street parking completely. I also noted that smaller

lots in the areas stated above could be kept along with the driveway access that can be used as drop-off and pick-up zones, not only employees but also for customers and potential deliveries.

Roadways are currently built to take into consideration the human error potential. If

Autonomous Vehicles do become safer than this area need not exist. The research suggested that even now some roads are wider than needed and do have potential to be transformed for better use. Autonomous Vehicles could change various types of roadway lanes from the current design. Road diets may become a more common practice with Autonomous Vehicles by allowing cities to reduce lane width and remove 4, 5, and 6 lane streets. The above findings indicate that with these transformations there is potential to rethink both parking and street design. Autonomous Vehicles could allow for green infrastructure of the future to be implemented in areas currently not well suited. Cities could also see a reduction in overall stormwater runoff with the reduction of parking and street impervious surface. Street trees and urban forests could be the main stormwater management infrastructure used by communities and could be implemented in areas not well suited now due to Autonomous Vehicle safety and efficiency features

Through my research and findings, I can conclude that green infrastructure implementation, particularly tree planting, can be used to mitigate stormwater runoff in cities due to changes to the built environment resulting from the adoption of Autonomous Vehicles.

Recommendations

Two types of recommendations were produced from reviewing the research. The first is recommendations for cities to create change in policy and agencies and the second is looking at potential research that could be conducted to help further understand Autonomous Vehicle impacts.

Changes

Built Environment

1. Create new development standards that require Green Infrastructure feature

implementation in design

2. Create civil codes limiting the amount of impervious surface being implemented from

beginning

Parking

1. Adopt more aggressive parking policies

a. Establishing parking maximums

b. Eliminate or lower parking minimums

2. Stop building new parking

3. Repurpose existing parking

4. Establish car free housing units

Roads

1. Create new street standards

2. Create some streets now that are car-free zones to ease residents into an Autonomous

Vehicle era

3. Consider appropriate locations for drop-off and pick-up areas

Agencies

1. Realize the importance of community partnerships in implementing projects and

pushing green stormwater infrastructure

2. Establish an agency or unit to guide the development and adoption of Autonomous

Vehicles

3. Create an organization or sub-organization such as Depave to help reduce currently

underutilized impervious surfaces

Further Research

There is additional research that needs to be conducted for communities. The first thing

would be to develop a framework for understanding Autonomous Vehicle pilot projects and

implementing them into cities. There will need to be research done to determine what

infrastructure is needed and will be necessary to support Autonomous Vehicle technology. To

have a better grasp on future ownership a study could be conducted pertaining to what the

current driving preference is now for people interested in Autonomous Vehicles.

To help with green infrastructure implementation a Parking Lot and Road analysis could

be completed. These two analyses could determine currently underutilized parking and roadways

that could be redesigned today. A tree canopy suitability and greenspace analysis could also be undertaken to provide a better understanding of where potential space could be used for these types of green infrastructure.

Limitations

Limitations to this research and the findings are due to two weaknesses. The first is the

uncertainty of not knowing what type of ownership is going to exist with Autonomous Vehicles

and what impact they are going to have on cities. All the research obtained is hypothetical and

could be proven wrong in the future.

The second was due to the limited amount of cases reviewed and due to the small number

of cases that address Autonomous Vehicles. Throughout my research I was only able to find, and

review, one case study done in San Francisco as a potential road diet transformation and green

infrastructure implementation; additional case studies I reviewed relate to tree canopy implementation without any direct relationship with Autonomous Vehicle implementation.

Chapter VI

Site-Selection Scenario Case Study

Hamilton County, Ohio

Most cities today are facing the challenges associated with impervious surface and stormwater runoff pollution. The problem is occurring now, and solutions need to be addressed and implemented. Autonomous Vehicles give opportunity to look at different solutions when dealing with impervious surfaces such as roads and parking lots. To gain more insight into the potential of Autonomous Vehicles I conducted a case study. The case study is used to explore how a community would analyze the potential use of trees as a stormwater management solution when Autonomous Vehicle adoption occurs. A case study that investigates a place suggests a subject of analysis that is unique or special in some way and can be used to build new understanding or knowledge about the research problem (USC, 2017). My study is limited to

Hamilton County, in Southwestern Ohio. There were a multitude of attributes that contributed to choosing Hamilton County as the case study. One was due to convenience. I am currently located in Cincinnati which is in Hamilton County, so I am easily able to acquire the needed data. Though convenience was a factor it was also relevant due to the well-documented knowledge of the site components of Hamilton County. Hamilton County is representative of other U.S. counties in the areas of impervious surface, stormwater runoff, and types of parking and roadway infrastructure. In addition, Cincinnati, which is in Hamilton County, has as a large role in potential future Autonomous Vehicle adoption.

Background of Hamilton County

Hamilton County is in Southwest Ohio. It is one of eight counties of the Ohio-Kentucky-

Indiana (OKI) Regional Council of Governments study area, Figure 6-1. It is about 413 sq. miles and includes the City of Cincinnati, Figure 6-2,

Figure 6-1. OKI region (OKI, 2008).

Figure 6-2. Hamilton County, OH (Academic , 2018).

Built Environment

Hamilton County is the third largest county in Ohio by population, with around 800,000 residents (USA, 2017), and is the third-fastest-growing economy in Ohio (Wetterich, 2016).

Using data created by staff members at OKI, various maps were produced. The map below,

Figure 6-3, includes the outline of what the Hamilton County Urban Area Boundary consists of.

Figure 6-3. Hamilton County urban boundary (OKI, ArcGIS).

Hamilton’s urban boundary indicates where there has been the most development and sprawl over the years. About 41.30% of Hamilton County is developed low intensity while 14.86% of

Hamilton County is developed high intensity: Together that is 56.16% of developed land (Ohio

Development Services Agency, 2018). This has led to a large percent of impervious surfaces throughout Hamilton County. From Figure 6-4, it can be noted that the majority of Hamilton

County has impervious surfaces. The lighter red, high impervious surface value, is the focus

from this research where there are larger surface parking lots and major arterial roadways. The

areas in the high impervious sections include the communities of: Sharonville, Evansdale,

Reading, Norwood, Blue Ash, and Cincinnati. Cincinnati Metropolitan Area is the largest urban

city in Hamilton County and contributes the largest percent of Hamilton County’s total

impervious surface area per acre. According to the OKI My Community’s Water Map Cincinnati

is 18,248.6 acres and about 36% of Cincinnati is covered by impervious surface (OKI, 2017).

Figure 6-4. Hamilton County impervious surface (OKI, ArcGIS).

With the increase in development and sprawl there has also been an increase in the types and intensity of land uses throughout the county. A land use map shows which areas are used for what purpose; it can help determine where major activities are taking place and how the land in

certain areas are being used. Hamilton Count has nine different land uses. The types of activities that have the most impact on the land of Hamilton County consist of residential (yellow), commercial/office (red), industrial (pink), and public/institutional (blue). Figure 6-5 below, is a recent Hamilton County existing land use map.

. Figure 6-5. Hamilton County existing land use (OKI, ArcGIS).

Residential development activity produces a large percent of land use activity in Hamilton

County. Though residential areas do produce many impervious surfaces such as driveways, and

house footprints, this type of land use activity was not a focus of this research and site analysis.

Due to the growth that Hamilton County has experienced in the last decade it has produced a large amount of impervious surface. Other activities such as commercial/office, industrial, and public/institutional shown in the red, pink, and dark blue were a focus. These land use activities contain most of the parking and the arterial roadways that produce large amounts of impervious surface area that affects stormwater runoff and water quality of Hamilton County.

Natural Environment

Hamilton County lies in a region of gentle hills and valleys formed by the slopes of the

Ohio River and its tributaries. The three major tributaries include the Great Miami River, the

Little Miami River, and the Mill Creek. In 2010 an urban tree canopy assessment was done for

both Cincinnati and Hamilton County. Per the results, trees covered 38.8% of the city of

Cincinnati and 43.4% of Hamilton County (Johnston, 2014). Figure 6-6 below, is a map of the

Hamilton County Tree Canopy. From the map there is a distinction that can be seen as to what jurisdictions have more acreage of impervious surfaces compared to tree canopy

Figure 6-6. Hamilton County tree canopy (OKI, ArcGIS).

Stormwater Management

According to Figure 6-7 below, Hamilton County, on average, receives between 2.64-5.6 inches of rain per month (Hamilton County Weather, 2017). According to the Metropolitan

Sewer District data, Hamilton County receives around 41 inches of rainfall annually. This is equivalent to 180 billion gallons of stormwater runoff (MSD, 2011). Of the 180 billion gallons of stormwater runoff, 14 billion gallons cause the combined sewer overflows that exist in the county (MSD, 2011). But with recent events such as the February 2018 storm surge, inches of rainfall have steadily been increasing on average every year. As stated in Chapter One, communities should be concerned because rainfall events will be increasing in frequency and intensity. With the large number of impervious surfaces located in Hamilton County, controlling the amount of stormwater runoff that occurs from these rain events is a problem.

Figure 6-7. Total monthly precipitation (World Media Group, LLC, 2018).

Knowledge of county-level problems associated with stormwater runoff have been documented. Hamilton County has stormwater runoff problems worse than others, due in part to its geography and hilly topography. According to the Hamilton County Soil and Water

Conservation District, Hamilton County’s landscape, being both rural and urban is greatly impacted by stormwater runoff (Stormwater, 2015). The Metropolitan Sewer District (MSD) provides wastewater collection and treatment for residents and businesses in Hamilton County, 69

Ohio (MSDGC, 2017). Figure 6-8 is a map that was created by MSD. It identifies where there are combined sewer overflows, indicated by star shapes, throughout the county. According to the data, Hamilton County has around 214 combined sewers that regularly overflow due to stormwater runoff.

Figure 6-8. Sewer system map (Metropolitan Sewer District of Greater Cincinnati, 2018).

Hamilton County has acknowledged their stormwater runoff problem and have

implemented a stormwater management plan. The plan addressed the need to have an

overarching organization and created the Hamilton County Stormwater District that represents

Hamilton County's commitment to storm water issues (Stormwater, 2015). The Hamilton

County Stormwater District was the County’s response to a new form of legislation that was

considered an extension of the 1972 Clean Water Act. The mission statement rests on the fact

that the waterways know no jurisdictional boundaries and to truly address water quality the

waterways must be viewed as a whole system (Hamilton County Storm Water District, 2007).

The agency is tasked with declaring and implementing stormwater management programs to

reduce the contamination of stormwater runoff. The District consists of jurisdictions throughout

Hamilton County, including all 12 of the townships, and 24 of the municipalities (Hamilton

County Storm Water District, 2007). Cincinnati has also recognized the stormwater runoff

problem and the implications it is having on the community. The “Green Cincinnati Plan” which

was readopted in 2013 describes Cincinnati’s water recommendations being that of reducing

combined and sanitary sewer overflows, water advocacy, watershed restoration, and smart water

management (Cincinnati, 2013).

Trees have been shown to significantly reduce the amount of stormwater runoff that has

been a persistent problem for communities around the US. Various organizations have realized

the potential of trees as a solution to the stormwater runoff problem and have worked on trying

to increase the number of trees throughout Hamilton County, including: Taking Root, Green

Umbrella, OKI, Great Parks of Hamilton County, Cincinnati Parks, and the Hamilton County

Soil and Water Conservation District.

Hamilton County and Autonomous Vehicles

Parking

The parking in Hamilton County ranges from on-street, off-street, large surface lots,

small compact surface lots, and parking garages. An analysis previously done by the Green

Umbrella Water Action Team identified the main parking lot clusters that impact Hamilton

County. The purpose of the analysis was to increase awareness about surface parking lots and the

problems they create in Hamilton County and to determine and map where problem areas were

(Green Umbrella Water Action Team, 2014). Figure 6-9 below, is the parking lot cluster map that was produced from the study.

Figure 6-9. Hamilton County parking lot clusters (Green Umbrella Water Action Team, 2014).

Using the above figure, I was able to produce additional figures of all the parking lots, both public and private, located in three of the Green Umbrella parking lot clusters. The three clusters

I chose to do a further analysis on were the Kenwood Towne Centre Cluster, the Anderson

Township Cluster, and the Downtown Cincinnati Cluster. These figures are shown below in

Figures 6-10, 6-11, and 6-12.

Clusters

Figure 6-10. Kenwood Towne Centre cluster (Author, ArcGIS).

Figure 6-11. Anderson Township cluster (Author, ArcGIS).

Figure 6-12. Downtown Cincinnati cluster (Author, ArcGIS).

Site Analysis

This research concluded that with Autonomous Vehicles, parking lots can be located outside prime locations, they do not need to be as large, and their number may decrease. College

Campuses, shopping areas, and downtowns were sites that were mentioned in the Literature

Review that could see changes to the overall size and quantity of parking. The next part of this

research looks at analyzing different sites contained in the Hamilton County parking clusters.

From these sites the total number of parking spaces was calculated to determine the total square

feet of asphalt dedicated to parking lots on each site. The sites chosen have parking lots that have

become underutilized. These underutilized lots, and the amount of impervious asphalt located on

them, leads to a large amount of stormwater runoff being diverted off site and entering the sewer

systems. The next step of this analysis was looking at what a 100% surface parking lot removal

would look like. This is shown below, in a new map of the site with green polygons over the

parking lots, both public and private, to indicate what the site could look like with 100% parking

lot removal and tree canopy implementation. If Autonomous Vehicles take more of the Morgan

Stanley study 100% shared vehicle ownership scenario, from the Literature Review, this could

be a potential outlook for parking lots. In addition, using the United States Geographic Survey

(USGS) stormwater runoff calculator the gallon amount that could be diverted from storm drains

with 100% surface parking lot removal was also calculated. Since a 100% parking lot removal is

deemed unlikely, due to the potential to have more of a mix of shared and private Autonomous

Vehicle ownership, additional suitability analyses were created for three of the sites shown in the

next section below, suitability analysis.

Hamilton County is home to three colleges, Cincinnati State, Xavier University, and the

University of Cincinnati. For the first analysis I chose to look at Cincinnati State and Xavier

University. Both schools have large surface lots and various small parking lots. The University of Cincinnati Main Campus has only one surface lot and various parking garages, which from the analysis may still be used for parking Autonomous Vehicles so that campus was not analyzed.

1. Cincinnati State

The college campus has four large surface parking lots, two parking garages, compact parking lots, and off-street parking. Due to Cincinnati State being on a hill, most of the rainfall collects at basins on either side of the campus and run directly into storm drains. Through meetings with the Green Infrastructure Group there is an interest at the school for additional green infrastructure implementation and the reduction of the school’s environmental impact.

Currently some of the large surface lots are underutilized has shown in Figure 6-13.

Figure 6-13. Cincinnati State (ArcGIS).

Some analysis was done with the parking spaces on the Cincinnati State campus. There are 425 parking spaces in the four larger surface lots combined, while the other lots located closer to the school have around 227 parking spaces combined. That is a total of 652 surface spaces located on campus. Using the Hamilton County parking and loading regulations the required parking space must be no less than 8.5 ft. in width and contain 160 sq. ft. in area (CAGIS, 2018).

If we use 8.5 ft. x 19 ft. (161.5 sq. ft.) as the standard size and take the total amount of parking spaces, Cincinnati State has designated 105,298 sq. ft. of asphalt to surface parking lots. Figure

6-14 below, indicates a 100% removal of full-sized lot surface parking, and compact lot surface parking for the implementation of green infrastructure such as tree canopies.

Figure 6-14. Surface parking Cincinnati State campus (Author, ArcGIS)

According to the USGS website one inch of rain falling on one acre of ground is equal to about

27,154 gallons. Using the USGS rainfall calculator the amount of stormwater runoff to be

diverted was calculated for 100% removal of all surface parking. From the data above Cincinnati

State has 105,298 sq. ft. of asphalt to surface parking lot which is equal to about 2.42 acres. If all

these acres were turned into greenspace with tree canopies, 65,712 gallons of stormwater could

be captured and diverted from storm drains per one inch of rainfall. Since 100% removal of all

spaces may be unlikely an additional analysis for all Cincinnati State was completed and is below.

2. Xavier University

The campus of Xavier has mostly large surface lots and smaller compact surface lots. Only

one lot has the addition of green medians with trees while the other lots are strictly asphalt. In

2008 the University signed into the American College & University President’s Climate

Commitment and according to their Sustainability Plan there is effort to increase green space on

campus (Xavier University, 2018). Figure 6-15 below, shows the campus currently, and as noted

with Cincinnati State, some of the surface lots are underutilized.

Figure 6-15. Xavier University (ArcGIS).

Xavier’s website indicates that there are 3,200 parking spaces located directly on campus. The

University also purchased the old Kroger store parking lot located on the back of Norwood Plaza

which added an additional 1,100 parking spaces (Xavier University, 2008). In total this gives

Xavier University 4,300 parking spaces. If using the 8.5 ft. x 19 ft. size parking space this is

equivalent to 694,450 sq. ft. of area designated to asphalt and parking. Figure 6-16 below, indicates a 100% removal of both full-sized lot surface parking, and compact lot surface parking for the implementation of green infrastructure such as tree canopies.

Figure 6-16. Surface parking Xavier University campus (Author, ArcGIS).

Using the USGS rainfall calculator the amount of stormwater runoff to be diverted was calculated for 100% removal of all surface parking. From the data above Xavier University has

694,450 sq. ft. of asphalt to surface parking lot which is equal to about 15.94 acres. If all these acres were turned into green space with tree canopies, 432,834 gallons of stormwater could be captured and diverted from storm drains per one inch of rainfall. With no parking garages located on campus a 100% removal is unlikely. Autonomous Vehicles will still need areas to drop-off and pick-up riders if shared ownership prevails and parking will still need to be relevant if private ownership is the outcome. An additional analysis for all Xavier University was completed and is below.

Large shopping districts were also noted as having a potential to change with Autonomous

Vehicle implementation. As noted in the Literature Review Chapter shopping districts are becoming a thing of the past. People are shopping more online and having materials delivered.

This has led to a decrease in the number of people leaving their home and shopping in the store and less cars in the parking lots. Throughout Hamilton County large shopping areas are common. 80

These shopping districts contain enormous, underutilized surface and compact parking lots. With

Autonomous Vehicles some of these parking lots could be removed and replaced with tree canopies. This analysis will touch on some of the more common and larger shopping centers that could see change with Autonomous Vehicle implementation.

3) Anderson Towne Center

Anderson Towne Center is in Anderson Township off Beechmont Avenue and Five Mile

Road on the east side of Hamilton County. The parking lots located in the plaza are full-size surface lots and smaller compact parking lots. Figure 6-17 below, shows the Anderson Towne

Center.

Figure 6-17. Anderson Towne Center (ArcGIS).

The Anderson Towne Center has an estimated 2,669 parking spaces (MallsDB, 2018). Using the same parking space size as in the college campus analysis, 431,043 sq. ft. is designated to asphalt for the parking lots. Figure 6-18 below, shows a map of the site with green polygons over the

parking lots to indicate what the site could look like with 100% parking lot removal and tree

canopy implementation.

Figure 6-18. Surface parking Anderson Towne Center (Author, ArcGIS).

Using the USGS rainfall calculator the amount of stormwater runoff to be diverted was

calculated for 100% removal of all surface parking. From the data above Anderson Towne

Center has 431,043 sq. ft. of asphalt to surface parking lot which is equal to about 9.89 acres. If

all these acres were turned into greenspace with tree canopies, 268,553 gallons of stormwater

could be captured and diverted from storm drains per one inch of rainfall.

4) Hyde Park Plaza

Hyde Park Plaza is located off Paxton Avenue in Cincinnati, Ohio. This plaza has full-sized

surface parking lots and smaller compact parking lots. Figure 6-19 below, shows the Hyde Park

Plaza.

Figure 6-19. Hyde Park Plaza (ArcGIS).

The estimated parking spaces for the Hyde Park Plaza was calculated to about 1,572 spaces. In addition, other buildings located on site such as Richards Industries, Queen City Clay, and

Shauer Brand Brookwood group parking lots located in the far-right corner were estimated at

190 parking spaces. This total site has an estimated 1,762 parking spaces. Using the same parking space size of 8.5 ft. x 19 ft. the total amount of asphalt for the parking lots was calculated at 284,563 sq. ft. Figure 6-20 below, shows a map of the site with green polygons over the parking lots to indicate what the site could look like with 100% parking lot removal and tree canopy implementation.

Figure 6-20. Surface parking Hyde Park Plaza (Author, ArcGIS).

Using the USGS rainfall calculator the amount of stormwater runoff to be diverted was

calculated for 100% removal of all surface parking. From the data above Hyde Park Plaza has

284,563 sq. ft. of asphalt to surface parking lot which is equal to about 6.53 acres. If all these

acres were turned into greenspace with tree canopies, 177,315 gallons of stormwater could be

captured and diverted from storm drains per once inch of rainfall.

5) Rookwood Commons

Rookwood Commons is located northeast of downtown Cincinnati, OH on the corners of

Edmondson, Edwards and Madison Roads. This plaza has full-sized surface parking lots and smaller compact parking lots. Figure 6-21 below, shows Rookwood Commons.

Figure 6-21. Rookwood Commons (ArcGIS).

The estimated parking spaces for Rookwood Commons was calculated to about 3,620 spaces.

Using the same parking space size of 8.5 ft. x 19 ft. the total amount of asphalt for the parking lots was calculated at 584,630 sq. ft. Figure 6-22 below, shows a map of the site with green polygons over the parking lots to indicate what the site could look like with 100% parking lot removal and tree canopy implementation.

Figure 6-22. Surface parking Rookwood Commons (Author, ArcGIS).

Using the USGS rainfall calculator the amount of stormwater runoff to be diverted was calculated for 100% removal of all surface parking. From the data above Hyde Park Plaza has

584,630 sq. ft. of asphalt to surface parking lot which is equal to about 13.42 acres. If all these acres were turned into greenspace with tree canopies, 364,406 gallons of stormwater could be captured and diverted from storm drains per once inch of rainfall.

With the idea that more e-commerce is going to be prevalent in the future these shopping lots could see a larger percent, compared to the campuses, of the parking turned into something new.

As stated in the Literature Review companies such as Macys and Sears are already closing their doors. Kroger has started using ClickList and Amazon now delivers groceries, which allow for the customer to not have to set foot into the store. Eventually, these buildings could be reutilized for mixed development, while the parking lots have the potential to become greenspace with tree canopies.

The Literature Review also noted changes to central business districts. Parking lots will not

need to be in prime locations directly next to buildings but instead can be located outside the city

center in less prime locations if more private Autonomous Vehicle ownership is more common.

However, shared Autonomous Vehicles ownership could see additional parking lots removed.

Below is an analysis done for the Cincinnati Central Business District.

6) Cincinnati Central Business District

Downtown Cincinnati is located along the Ohio River and contains Cincinnati’s central

business district. Parking lots include full-sized surface lots, on-street parking, compact surface lots, and parking garages. Figure 6-23 below, shows all Cincinnati’s Central Business District.

Figure 6-23. Cincinnati’s Central Business District (ArcGIS).

Using an analysis previously done pertaining to Downtown Cincinnati parking availability there

were 65 surface lots that were identified with a total of 16,228 parking spaces. The largest lot

had 1,600 spaces while the smallest lot had only 14 spaces (City of Cincinnati, 2017). The total

area designated for surface parking lots in Cincinnati’s Central Business District equaled

2,620,808 sq. ft. Figure 6-24 below, shows a map of the site with green polygons over the

parking lots to indicate what the site could look like with 100% parking lot removal and tree canopy implementation.

Figure 6-24. Surface parking Cincinnati’s Central Business District (Author, ArcGIS)

. Using the USGS rainfall calculator the amount of stormwater runoff to be diverted was calculated for 100% removal of all surface parking. From the data above the Cincinnati Central

Business District has 2,620,808 sq. ft. of asphalt to surface parking lot which is equal to about

60.17 acres. If all these acres were turned into greenspace with tree canopies, 1,633,856 gallons of stormwater could be captured and diverted from storm drains per one inch of rainfall.

Downtown areas such as the Cincinnati Central Business District would likely not see a 100% transformation due to the location and value of the land, so an additional suitability analysis was done and is shown below.

Suitability Analysis

Due to 100% transformation of parking lots being unlikely a suitability analysis was

created and can be located below. The changes that occur to surface parking lots is going to depend on whether private vehicle ownership or shared vehicle ownership occurs with

Autonomous Vehicles. Using the Morgan Stanley scenario in the Literature Review that included a combined shared and private Autonomous Vehicle ownership scenario I was able to create my own example of potential site-specific suitability analysis, although parking has a potential to be reduced it will still be needed. I chose to do a suitability analysis for all of Cincinnati State, all of

Xavier University, and all the Cincinnati Central Business District.

Each analysis focused on tree canopy implementation if certain parking lots were no

longer needed. In each analysis green indicated most suitable, yellow was potential suitability,

and red was not suitable for potential transformation with Autonomous Vehicles. Table 6-1

below, further explains the criteria for the suitability analysis. Using Google Earth, the total

acreage of each color set was calculated. This analysis also included renderings of what green

and yellow sites could potentially look like with greenspace and tree canopy which is located in

Appendix I.

Xavier Cincinnati Central Business Cincinnati State University District (CBD) Underutilized Underutilized Small underutilized surface full-sized surface surface lots parking lots, possibly not lots. Most parking around campus viable for new development is done in the and student and larger surface lots near garage and in the housing areas. Ohio River that could be parking lots Parking sections implemented with tree canopies closer to the to the North to mitigate flooding events. buildings. These could be full-sized lots also connected with produce large already intact Green (Most amounts of tree canopy. Suitable) runoff. These areas have These areas have The larger parking lots in the potential to potential to CBD will most likely be used include tree include tree for new development but could canopy, however canopy, however include some tree canopy on they can also be can also be kept the site. Larger parking lots kept for parking for parking or outside the urban core can be or drop-off and drop-off and the new parking areas if more pick-up areas or pick-up areas or private ownership. Yellow (Potential new development. new Suitability) development. Parking garages Since no parking All the parking garages can be can be kept for garages exist kept for parking if more private parking if more smaller lots could ownership or as areas where private ownership be used as the car can go during none and some drop- parking areas if prime pick-up times. off and pick-up more private zones will also be ownership and needed which I additional drop- located closer to off and pick-up the school areas closer to entrance. the school will be needed to help with traffic Red (Not Suitable) capacity. Table 6-1. Parking lot suitability analysis criteria (Author)

1. Cincinnati State Suitability Analysis

Cincinnati State currently has four large full-sized lots that are not utilized entirely. Most of the students choose to park in the garage or take public-transit. In this analysis the large full- sized lots would be where tree canopies could be implemented shown in green. The yellow lot at the top of the campus contains smaller compact surface lots. This area has potential to see some additional tree canopy but could also include additional development. The red areas include smaller compact lots and the two parking garages. These could be left for future parking areas or drop-off and pick-up locations with Autonomous Vehicles. The green areas have a total acreage of 3.24, the yellow areas have a total acreage of 1.46, and the red areas have a total acreage of

2.12. Figure 6-25 below, shows the analysis.

Figure 6-25. Cincinnati State suitability analysis (Author, ArcGIS).

2. Xavier University Suitability Analysis

Due to the number of students, staff, and faculty, Xavier’s campus has larger full-sized surface lots, compact surface lots, and on-street parking. Some of the larger surface lots and smaller compact lots are only fully utilized during sporting events. In this analysis I chose four of the full-sized surface lots to be transformed into tree canopies. The three at the top are close to

an already existing forest which would help extend this feature. The other lot is the newly purchased Kroger parking lot located on the far right of the picture. This area has minimal trees and could help combat stormwater runoff from the existing shopping center located next to it.

The other green lots were lots that looked underutilized and with so much parking already these were chosen to increase green infrastructure features. The yellow lots are ones that could have tree canopies located on the site but could also be used for additional development or as parking if needed. Some of them included the student housing located in the far-right corner and a portion of the parking near the sports area. All the red spaces were mostly smaller compact lots and off-street parking. These areas could be used for drop-off and pick-up areas. With the amount of people on this campus daily I kept added additional red areas to combat the potential traffic that could arise in these locations. The total acreage of the green areas was 15.17, the total acreage of the yellow areas was 14.14, and the total acreage of the red areas was 3.49. Figure 6-

26 below, is the Xavier University analysis.

Figure 6-26. Xavier University suitability analysis (Author, ArcGIS).

3. Cincinnati Central Business District Suitability Analysis

The Central Business District of Cincinnati includes small compact surface lots, on-street parking, parking garages, and full-sized surface lots. From the Literature Review Central

Business Districts will not need to have parking located directly next buildings. The first thing I did with this analysis was change all the parking located along the Ohio River to tree canopy green space. This will help with reducing flooding impacts. The second was changing some of the smaller compact lots to tree canopy. I felt that due to the amount of parking in downtown these areas could see the greatest change at becoming tree canopies compared with larger sites that may be used for development. The yellow sites include parking lots on the outskirts of the

Central Business District that may be utilized for parking. Most of these areas are located under

overpasses and tree implementation may be unlikely due to lack of sunlight. In addition, as stated above the larger parking lots have potential to include tree canopies but may be used as new development due to the size and location. The red sites are all the current parking garages in the Central Business District of Cincinnati and some on-street parking areas. These would be kept as additional parking and as drop-off and pick-up areas. The total acreage of the green areas was 21.82, the total acreage of the yellow areas was 32.71, and the total acreage of the red areas was 13.84. Figure 6-27 below, shows this analysis.

Figure 6-27. Cincinnati Central Business District suitability analysis (Author, ArcGIS).

Stormwater reduction potential

According to the United States Geological Survey (USGS) website one inch of rain falling on one acre of ground is equal to about 27,154 gallons. Using the USGS rainfall calculator the amount of stormwater runoff to be diverted was calculated for both the green most suitable parking lots for tree implementation and the yellow potential suitability parking lots for tree implementation.

The four green lots identified in the suitability analysis for Cincinnati State have a total acreage of 3.24. If all lots were converted to greenspace with a tree canopy the amount of stormwater runoff that could be captured would equal 87,978 gallons of stormwater runoff per inch of rainfall. The yellow potential parking lot has a total acreage of 1.46. Due to this area being an area that has potential tree canopy in some locations the .46 acreage portion was used to gain a more realistic calculation. I chose to reduce the number in the thought that some of these lots could contain tree canopy. If .46 acres of this area was turned into greenspace with tree canopies an additional 12,490 gallons of stormwater could be captured per one inch of rainfall.

The nine green lots identified in the Xavier University suitability analysis have a total acreage of 15.17. If all lots were converted to greenspace with a tree canopy the amount of stormwater runoff that could be captured would equal 411,926 gallons of stormwater runoff per inch of rainfall. The fourteen yellow potential parking lots have a total acreage of 14.14. Due to this area being an area that has potential tree canopy in some locations this number was reduced to gain a more realistic calculation. I chose to reduce the number by half in the thought that some of these lots could contain tree canopy. If 7.07 acres of this area was turned into greenspace with tree canopy an additional 191,978 gallons of stormwater could be captured per one inch of rainfall.

There were thirty-three green lots identified for the Cincinnati Central Business District

suitability analysis with a total acreage of 21.82. If all the green lots were converted from

parking lots to urban forests the amount of stormwater that could be captured would equal

592,500 gallons of stormwater runoff per inch of rainfall. There were thirty-four yellow colored

potential suitable sites for tree canopy implementation in the Cincinnati Central Business District

with an acreage of 32.71. Due to these areas being potential tree canopy implementation sites the

number was reduced to gain a more realistic calculation. I chose to reduce the number by half in

the thought that some of these lots could contain tree canopy. Using half of the 32.71 acreage a

new acreage of 16.36 was used. If 16.36 acres of this area was turned into greenspace with tree

canopy an additional 444,239 gallons of stormwater could be captured per one inch of rainfall.

Roads

The Ohio Development Services Agency identified 2,968 total roadway miles in

Hamilton County which consisted of county, township, and municipal road miles (ODSA, 2016).

Figure 6-28 below, is a map of the all the current streets in light grey and the highways in a brown color, located throughout Hamilton County.

Figure 6-28. Hamilton County roads map (OKI, ArcGIS).

In the year 2017, the Ohio Department of Transportation (ODOT) received funding to complete

thirty projects within Hamilton County. Of these projects three were construction to widen major

roadways equally about thirty-three million dollars. Figure 6-29 below, shows the total roadway

construction projects by ODOT. Autonomous Vehicles have the potential to change city

roadways by allowing for road diets to occur. Hamilton County has roads ranging from 2-lane to

8-lane. Two major interstates that run through Hamilton County are I-71 and I-75. I-71 and I-75 are both 8-lane highways with a concrete median dividing the travel lanes. These interstates could be reduced to 4-lane highways with no concrete median in the center. The majority of downtown Cincinnati streets have 4-lanes, 2 travel lanes and 2 lanes for on-street parking. As noted from the Literature Review the potential to reduce these areas to a total of two or one lane

streets is possible with Autonomous Vehicles. This would limit the need to spend billions of dollars trying to widen roads and instead that money could be used elsewhere in communities.

Figure 6-29. ODOT roadway projects (ODOT, 2017).

Chapter VII

Conclusion

Autonomous Vehicles are already on the streets and rolling out at a rapid pace: Cities

should start planning now because they play a critical role in maximizing the benefits of this

technology by understanding how and where Autonomous Vehicles will work best (National

League of Cities, 2017). The impacts that Autonomous Vehicles will have on communities is

going to depend on legal policy, technological process, and the urban planning response

(Millard-Ball, 2018).

Cities need to start taking a more proactive approach. Cities need developers with

foresight as to the future, who can build infrastructure that can be easily converted if current use

is not ideal for the future city. Autonomous Vehicles will have a large impact to play

environmentally, socially, and economically. For major effects in cities and society at large

Autonomous Vehicles are not simply a transportation issue but more precisely they have the

potential to affect land use, land variation, sprawl, social equity, labor, and urban vitality (Larco,

Tierney, & Riggs, 2017). Sustainable stormwater management is a challenge for cities but there

is opportunity. Autonomous Vehicle technology has the potential to create available spaces in

our communities. Green infrastructure implementation, particularly tree planting, can be used to

mitigate stormwater runoff in cities due to changes to the built environment resulting from the

adoption of Autonomous Vehicles.

Next Steps

One point was made that the environmental research community must better engage social sciences and urban planning communities to study this critical yet unexplored research area (Miller, 2016). At the American Planning Association National Planning Conference held

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this year attendees agreed that if cities do not set the parameters of when and where

Autonomous Vehicles operate, and leave Autonomous Vehicle rollout strictly to market forces, the future urban landscape may only intensify many of the economic, social, and environmental challengers we already face today (Rice & Tomer, 2017). Driverless vehicle innovators are not waiting for transportation authorities; they are introducing driverless vehicles to the existing infrastructure environment, prompting infrastructure providers to change roadway architecture that will optimize such vehicles (Bamonte, 2013).

People and the environment need to be put first while technology needs to be put second.

There needs to be a better understanding of commuter preference with Autonomous Vehicles by

determining their behavior standards and what people would like to see. Policy makers, urban

planners, government officials, and staff need to advocate for Autonomous Vehicles. There is

still speculation surrounding Autonomous Vehicles that makes people uneasy about what the

future may hold and where they will fit in. Leadership is going to play a large role in how

Autonomous Vehicles are implemented and how future cities will look. Poor leadership and

decision making may make it harder for some cities, leading to more cost and concern. Cities

need to start investing in the future and planning ahead.

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Appendix I

Figure AI-1 and AI-2 below, are renderings that were completed to show what surface

parking lots, if transformed to greenspace with tree canopies in an Autonomous Vehicle era,

could potentially look like. Figure AI-1 is a rendering of a site in the green color (most

potential) above, in the suitability analysis. These surface parking lot sites would be completely

transformed to an urban forest canopy.

Figure AI-1. Suitability analysis rendering of parking lot to urban forest canopy (Author, SketchUp).

Figure AI-2 below, is a rendering of a site in the yellow color (potential tree canopy) above, in the suitability analysis. These areas would potentially see new development on the lots due to lot size and location, however, trees could still be incorporated in the design.

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Figure AI-2. Suitability analysis rendering of parking lot with new development and tree canopy (Author, SketchUP).

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Appendix II

A complete analysis was done for all of Cincinnati’s Central Business District as shown in Figure AII-1 below in order to complete the parking space and square feet analysis. It helped calculate spaces and area in square feet. Using the square feet, the total acres were calculated to determine potential stormwater runoff capture in both the site and suitability analysis sections.

Location Address Spaces Area (Ft^2) NE (North of Sixth Street and East of Vine Street) 326 East 8th Street 326 East 8th Street 14 2,261 318 East 8th Street 318 East 8th Street 20 3,230 Court St. Lot 14 219 E. Court Street 24 3,876 9th Street Lot 315 East Ninth Street 29 4,683 Lot #1634 209 E. Seventh Street 30 4,845 T-Bone Lot 8th & Vine 39 6,298 Wessel Lot 616 Main 43 6,944 Lot #1645 209 E. Court Street 44 7,106 Parkway Lot Central Parkway and Main 47 7,590 Ann Lot 123 East 9th Street 47 7,590 Lot #1623 121 E. Eighth Street 54 8,721 CAS Lot 830 Main Street 54 8,721 PCA Lot #2 NW Corner 9th and Main 55 8,882 Lot #1647 Court and Walnut 61 9,851 Lot #1627 Ninth Street & Sycamore 61 9,851 Legal Aid Lot 215 E. Ninth Street 63 10,174 Lot #1656 9th and Walnut 78 12,597 Lot #1649 8th and Walnut 84 13,566 St. Xavier Lot 7th & Sycamore 150 24,225 Lot #1636 7th Street/Main and Sycamore 239 38,598 Lot #10 419 East Court Street 335 54,102 Center at 600 Vine 600 Vine Street 452 72,998 Total 2023 326709

NW (North of Sixth Street and West of Vine Street) Lot #1639 9th Street and Race 25 4,037 Lot #1650 NW Corner Race and Garfield 28 4,522 Lot #1635 SE Corner of Garfield and Race 29 4,683 Blue Chip Parking Lower 20 W. Court Street 30 4,845 PCA Lot #19 908 Plum Street 44 7,106 Lot #1626 222 W. Eighth Street 55 8,882 Blue Chip Parking 109-120 West Court 60 9,690 PCA Lot #22 Central Parkway and Charles 66 10,659 PCA Lot #16 7th and Plum Street 83 13,404 PCA Lot #20 327 W. Court Street 89 14,373 PCA Lot #1 Court and Elm Street 97 15,665 Lot #1815 224 W. Ninth Street 100 16,150 PCA Lot #21 Court and Plum Street 278 44,897

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84.51 Degrees NW Corner 5th and Race Street 1087 175,550 Total 2071 334463

Over the Rhine (North of Central Parkway) PCA Lot #3 1014 Elm Street 31 5,006 Walnut Lot 1107 Walnut street 36 5,814 Music Hall 1239 Elm Street 115 18,572 Hild Lot 12th and Sycamore 152 24,548 Mercer Commons 5 Mercer Street 340 54,910 Total 674 108850

Riverfront (South of Third Street) The Dock 603 W. Pete Rose Way & Smith 65 10,497 Eggleston Lot SE Eggleston btw 4th & 5th 184 29,716 537 Lot 537 E. Pete Rose Way 188 30,362 Lot BH 601 E. Pete Rose Way 225 36,337 Paul Brown Stadium Paul Brown Stadium 238 38,437 Lot A Third & Plum 239 38,598 PCA Lot #33 Third & Gest Streets 650 W. 3rd 268 43,282 Lot B Pete Rose Way 350 56,525 Lot E Mehring Way at PBS 394 63,631 Sawyer Point 801 E. Pete Rose Way 400 64,600 Crosset/Smith Lot SW Corner of 3rd & Central 903 145,834 Longworth Hall 700 W. Pete Rose Way 1000 161,500 Lot D Btw Suspension Bridge & PBS 1276 206,074 Kenton Co Park and Ride 220 Madison Avenue 1600 258,400 Total 7330 1183793

SE (South of 6th & East of Vine Street) Third & Eggelston Lot Third & Eggleston 94 15,181 Lot #753 E. Pete Rose Way across Purple Bridge 164 26,486 PCA Lot #31 5th & Eggleston 226 36,499 Lot #1644 Third & Main 300 48,450 Fountain Square South 416 Vine 408 65,892 Lot #1648 580 Walnut Street 450 72,675 Scripps 312 Walnut Street 590 95,285 Total 2232 360468

SW (South of 6th & West of Vine Street) Drury Lot #771 NE Corner of 3rd & Race 25 4,037 Lot #25 W. Third Under Brent Spence Bridge 35 5,652 PCA Lot #35 325 John St. 50 8,075 Lot #758 250 W. Fifth Street 260 41,990 Enquire Building 212 W. Fourth Street 753 121,609 Mabley Place 42 W. Fourth Street 775 125,162 Total 1898 306525 Table AI-1. Downtown parking lot analysis (Author, Cincinnati, 2017).

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