system appendpdf cover-forpdf · 9 state and local governments are required by law to provide and...

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Automated Compliance Analysis of Pedestrian Facilities with

Accessibility Requirements

Journal: Canadian Journal of Civil Engineering

Manuscript ID cjce-2016-0598.R1

Manuscript Type: Article

Date Submitted by the Author: 15-Nov-2017

Complete List of Authors: Halabya, Ayman; University of Illinois at Urbana-Champaign, Civil and Environmental Engineering; El-Rayes, Khaled; University of Illinois, Civil and Environmental Engineering

Is the invited manuscript for consideration in a Special

Issue? :

N/A

Keyword: Compliance assessment, highways < Transportation, ADA Self-Evaluation and Transition Plan, PROWAG, Sidewalks

https://mc06.manuscriptcentral.com/cjce-pubs

Canadian Journal of Civil Engineering

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Compliance Analysis of Pedestrian Facilities with Accessibility Requirements 1

Ayman Halabya1 and Khaled El-Rayes

2 2

1 Research assistant (corresponding author), Dept. of Civil and Environmental Engineering, University of 3 Illinois at Urbana-Champaign, Urbana, IL 61801. E-mail: [email protected] 4

2 Professor, Dept. of Civil and Environmental Engineering, University of Illinois at Urbana-Champaign, 5 Urbana, IL 61801. E-mail: [email protected] 6 ____________________________________________________________________________________ 7

ABSTRACT 8

State and local governments are required by law to provide and maintain accessibility on 9

their pedestrian facilities. They need to conduct, document, and update self-evaluations to 10

identify non-compliant pedestrian facilities. This paper presents the development of a novel 11

model for analysing the compliance of pedestrian facilities with accessibility requirements. The 12

model provides original and unique capabilities that enable decision makers to: (1) quantify the 13

degree of non-compliance of all types of pedestrian facilities including transit stops, on-street 14

parking, and passenger loading zones; (2) estimate cost and labour-hours needed to achieve 15

compliance; (3) prioritize upgrade projects for pedestrian facility types; (4) rank pedestrian 16

facilities upgrade projects in multiple geographical regions based on their collective degree of 17

non-compliance; and (5) classify pedestrian facilities based on the type of required upgrade. A 18

case study that includes 1327 pedestrian facilities is analysed to evaluate the performance of the 19

developed model and illustrate its capabilities. 20

Keywords: Assessment, ADA Transition Plan, Self-Evaluation, Pedestrian Facilities, Public 21

Right-of-Way, PROWAG, ADA Compliance, Curb Ramp, Sidewalk. 22

INTRODUCTION 23

The U.S. Census Bureau reported in 2012 that people with disabilities represent 18.7% of 24

the United States population (US Census Bureau 2012). Providing accessible facilities for this 25

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significant portion of the population is essential to avoid discrimination against people with 26

disabilities and ensure their active participation in society. This prompted the United States 27

Congress to enact several laws to prohibit discrimination against people with disabilities, 28

including (1) the Architectural Barriers Act (ABA) in 1968 (42 U.S.C. §§4151 et seq., 1968), (2) 29

the Rehabilitation Act in 1973 (29 U.S.C. § 701 et seq., 1973), and (3) the Americans with 30

Disabilities Act (ADA) in 1990 (42 U.S.C. § 12101 et seq., 1990). These federal laws require 31

public agencies to provide accessibility to people with disabilities on their sidewalks and 32

pedestrian facilities (Anderson et al. 1995). 33

The inability of a number of public agencies to comply with the aforementioned 34

accessibility laws has resulted in costly settlements in recent years, including $1.4 billion 35

settlement by the City of Los Angeles in 2015 (“Willits v. City of L.A.” 2016), $1.1 billion 36

settlement by California Department of Transportation (Caltrans) in 2010 (“CDR v. Caltrans” 37

2010), and $50 million settlement by the City of Chicago in 2007. The aforementioned 38

settlements highlight the pressing need for compliance with the latest state and federal 39

accessibility laws and regulations in order to provide and maintain accessibility for people with 40

disabilities on sidewalks and pedestrian facilities and avoid costly non-compliance penalties. 41

To achieve compliance with federal accessibility laws, state and local governments need 42

to perform a Self-Evaluation process to assess their policies, practices, and structures in order to 43

identify any barriers that deny or limit the participation of individuals with disabilities in their 44

programs, services, or activities (U.S. Department of Justice 1994). This self-evaluation process 45

must identify non-compliant sidewalks and pedestrian facilities (“Barden v. Sacramento” 2002) 46

and its results must be documented, kept on record, and updated regularly (U.S. Department of 47

Justice 1994). 48

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To perform the aforementioned self-evaluation process, state and local governments are 49

required to measure and record the conditions, dimensions, and geometry of existing sidewalks 50

and pedestrian facilities and evaluate their compliance with accessibility requirements (U.S. 51

Department of Justice 1994). Current practices for conducting self-evaluations often require 52

manual data processing using paper forms and spread sheets to compare existing conditions of 53

sidewalks and pedestrian facilities to accessibility requirements in order to assess their 54

compliance (Axelson et al. 1999). These manual practices for conducting self-evaluations are 55

labour intensive, time consuming, and error prone, which limits the ability of public agencies to 56

efficiently and comprehensively perform and update these required self-evaluations. To 57

overcome this limitation, there is a pressing need to develop a novel methodology that is capable 58

of evaluating the degree of non-compliance of pedestrian facilities with accessibility 59

requirements. 60

A number of related research studies were conducted to support state and local 61

governments in (1) identifying the required degree of accessibility for sidewalks and pedestrian 62

facilities, and (2) evaluating the compliance of these facilities with accessibility requirements. 63

The first group of studies that focused on identifying accessibility requirements utilized field 64

experiments and surveys to study the impact of varying dimensions of pedestrian facilities on the 65

mobility, safety, and comfort of people with disabilities. For example, Kockelman et al. (2002) 66

conducted a field study to identify critical sidewalk cross slopes that place pedestrians into 67

unacceptable levels of effort or discomfort and reported that the maximum sidewalk cross slope 68

that provides accessibility can range from 5.5% to 6.0%. In this study, people using cane, crutch, 69

or brace and manual wheelchair users who were up to 80 years old were required to traverse 70

13.75 meters (45 feet) long sidewalk sections with 5% running slope and varying cross slopes 71

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while monitoring their heart rates and level of discomfort. In another field study that was 72

conducted to evaluate the degree to which users of wheelchairs perceive differences in the 73

running and cross slope of ramps, Vredenburgh et al. (2009) reported that a ramp should not 74

exceed a maximum cross slope of 5% or a maximum running slope of 7%. 75

The second group of studies developed varying procedures to assess the compliance of 76

existing conditions of pedestrian facilities with accessibility requirements. These assessment 77

procedures included: (a) Sidewalk Assessment Process (SWAP) that was developed in a Federal 78

Highway Administration (FHWA) study that was conducted to evaluate the accessibility of 79

sidewalks, curb ramps, medians, refuge islands, and driveway crossings (Axelson et al. 1999; 80

FHWA 2001); (b) GIS-based pedestrian audit tool that was developed by Schlossberg et al. 81

(2007) that required surveyors to provide their subjective assessment on whether pedestrian 82

facilities comply with accessibility requirements or not; and (c) Ultra-Light Inertial Profiler ADA 83

system (ULIP-ADA) that was developed in an FHWA pilot program that utilized laser scanners 84

to capture, process, and record sidewalk dimensions and geometry in a GIS geodatabase format 85

and required the subjective judgement of assessors on the compliance of pedestrian facilities 86

with accessibility requirements (City of Bellevue 2008; Loewenherz 2010). 87

Despite the significant contributions of the aforementioned studies, they are incapable of: 88

(1) quantifying the degree of non-compliance of transit shelters, on-street parking, and passenger 89

loading zones with accessibility requirements; (2) estimating the cost and labour-hours needed to 90

upgrade non-compliant pedestrian facilities in order to achieve compliance; (3) generating a non-91

compliance index for each type of pedestrian facilities such as sidewalks and curb ramps; and (4) 92

identifying a non-compliance index for a specific geographical location that represents the 93

overall compliance of all pedestrian facilities in that location. To overcome these limitations, this 94

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paper presents the development of a novel model for assessing the degree of non-compliance of 95

pedestrian facilities with accessibility requirements. 96

OBJECTIVE 97

The research objective of this study is to develop a novel model for assessing the degree 98

of non-compliance of pedestrian facilities with accessibility requirements to enable public 99

agencies to maximize their compliance with the ADA self-evaluation requirement. The model is 100

designed to support decision makers in state and local governments in identifying non-compliant 101

sidewalks and pedestrian facilities in their public right-of-way and evaluating the degree of non-102

compliance of each of these facilities with accessibility requirements. The model provides the 103

capabilities of (1) efficiently quantifying the degree of non-compliance of all types of pedestrian 104

facilities in the public right-of-way with accessibility requirements including transit shelters, on-105

street parking spaces, and passenger loading zones; (2) estimating the cost and labour-hours 106

needed to upgrade non-compliant pedestrian facilities; (3) generating a pedestrian type non-107

compliance index for each type of pedestrian facility to enable decision makers to rank these 108

facility types based on their degree of non-compliance with accessibility requirements; and (4) 109

identifying a region non-compliance index for a specific geographical region that represents the 110

overall degree of non-compliance of all pedestrian facilities in that region to enable decision 111

makers to prioritize future upgrade projects by comparing the degree of non-compliance of these 112

regions. These original and unique capabilities of the developed model are designed to support 113

decision makers in improving their efficiency and effectiveness in conducting the 114

aforementioned federally-mandated self-evaluations and in prioritizing their planned upgrade 115

projects to maximize compliance with accessibility requirements. 116

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The model is developed in five main phases: (i) accessibility requirements analysis phase 117

that identifies pedestrian facility types and their related accessibility requirements; (ii) non-118

compliance assessment phase that develops a Non-Compliance Index (NCI) to quantify the 119

degree of non-compliance of each type of pedestrian facility in the public right-of-way with 120

accessibility requirements; (iii) cost and labour-hours estimation phase that calculates the cost 121

and labour-hours needed to upgrade non-compliant pedestrian facilities; (iv) collective non-122

compliance phase that aggregates the individual non-compliance indices of a group of 123

pedestrian facilities based on their type and/or geographical location to enable their ranking and 124

prioritization for upgrades; and (v) performance evaluation phase that analyses a case study to 125

illustrate the use of the developed model and demonstrate its novel capabilities. The following 126

sections provide a concise description of these five model development phases, as shown in 127

Figure 1. 128

ACCESSIBILITY REQUIREMENTS ANALYSIS 129

The purpose of this phase is to analyse all accessibility requirements and identify all 130

relevant metrics that represent the degree of non-compliance of pedestrian facilities with 131

accessibility requirements. The degree of non-compliance of a specific pedestrian facility can be 132

determined by comparing the existing conditions and measurements of that facility with 133

accessibility requirements (El-Rayes et al. 2016). Accessibility requirements include (1) the 134

Proposed Accessibility Guidelines for Pedestrian Facilities in the Public Right-of-Way 135

(PROWAG), which focuses on all types of pedestrian facilities that are built for the purpose of 136

transportation of pedestrians in the public right-of-way (U.S. Access Board 2011); (2) Shared 137

Use Path Accessibility Guidelines (SUPAG), which focuses on facilities that are built for either 138

transportation or recreation of pedestrians and bicycles (U.S. Access Board 2011); and (3) Guide 139

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for the Development of Bicycle Facilities (GDBF), which focuses on facilities that are mainly 140

designed to be used by bicycles (AASHTO 1999). These three guidelines have similar 141

requirements for sidewalks and shared use paths with the exception of few minor differences 142

such as the path width requirement which is specified to a minimum of 1.2 meters in PROWAG, 143

1.5 meters in SUPAG, and 2.4 meters in GDBF for shred use paths. While the main focus of this 144

paper is pedestrian facilities as listed in PROWAG, the developed model enables decision 145

makers to specify which standards they need to comply with. For example, decision makers can 146

elect to comply with PROWAG only, SUPAG only, GDBF only, or the strictest of any 147

combination of the three. 148

When a pedestrian facility (e.g. a curb ramp or sidewalk) does not meet the minimum 149

accessibility requirements specified by state and federal laws and regulations, the pedestrian 150

facility is considered non-compliant with these laws and regulations (U.S. Access Board 2011a). 151

This binary classification of pedestrian facilities as either compliant or non-compliant was 152

reported to be ineffective for assessing the degree of non-compliance of sidewalks and pedestrian 153

facilities (CCRPC 2016; City of Clayton 2014). The cause of non-compliance for these non-154

compliant facilities can be due to major or minor deviations from the minimum accessibility 155

requirements. While major deviations from accessibility requirements often render facilities to be 156

fully inaccessible for people with disabilities, minor deviations often enable facilities to be 157

partially accessible. For example, a 0.6 meter wide and a 1.05 meter wide sidewalks are both 158

classified as non-compliant because their width is less than the required 1.2 meter minimum 159

width. In this example however, the 0.6 meter wide sidewalk is fully inaccessible while the 1.05 160

meter wide sidewalk can provide partial accessibility for pedestrians travelling in one direction 161

(PROWAAC 2007). 162

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To overcome the limitation of the aforementioned binary classification of compliant or 163

non-compliant, the present model is designed to distinguish between varying degrees of non-164

compliance by developing a novel non-compliance index (NCI). NCI represents the degree of 165

non-compliance for pedestrian facilities using a scale that ranges from 0.0% for fully compliant 166

facilities to 100.0% for fully non-compliant. NCI of a given pedestrian facility is calculated in 167

the present model by identifying the degree of deviation between its existing conditions, 168

measurements, and geometry and its specified accessibility requirements. The model is designed 169

to calculate NCI for all pedestrian facility types including (1) sidewalks, (2) curb ramps, (3) 170

crosswalks, (4) pedestrian signals, (5) refuge islands, (6) transit stops, (7) on-street parking 171

spaces, and (8) passenger loading zones (U.S. Access Board 2011a). Each of these pedestrian 172

facility types has accessibility requirements that must be satisfied in order to achieve compliance 173

with accessibility laws and regulations, as shown in Figure 2. 174

NON-COMPLIANCE ASSESSMENT 175

The purpose of this phase is to develop a novel non-compliance index �� that 176

represents the degree of non-compliance of each pedestrian facility type� with accessibility 177

requirements, as shown in equation (1). 178

�� =�� × �,��

�� (1)

Where, ��is non-compliance index for pedestrian facility of type� that is located in 179

geographical location �, � is pedestrian facility type (see Figure 2), � is geographical location of 180

pedestrian facility, � is accessibility requirement for each pedestrian facility type� (see Figure 181

2),� is total number of accessibility requirements for pedestrian facility type� (see Figure 2), 182

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�� is relative importance weight of accessibility requirement� for pedestrian facility type�, 183

and �,�� is non-compliance score of pedestrian facility type� with accessibility requirement � in 184

geographical location �. 185

The non-compliance index �� for each pedestrian facility type � represents the 186

weighted average of non-compliance scores including all its accessibility requirements. For 187

example, the non-compliance index for a sidewalk in location � (��) is determined by 188

calculating the weighted average of non-compliance scores of all the sidewalk accessibility 189

requirements (� = 1��7) including its width, running slope, cross slope, surface discontinuities, 190

change in level, bevel status, and protruding objects (U.S. Access Board 2011a). �� is 191

calculated using equation (1) based on the weight of each accessibility requirement �� and its 192

non-compliance score �,�� . These weights �� can be specified by decision makers to reflect the 193

relative importance of each accessibility requirement � (see example in Table 1), and the non-194

compliance score �,�� can be determined based on the existing condition of the sidewalk and the 195

specified ranges shown in Table 1. It should be noted that the weights �� and non-compliance 196

scores �,�� in Table 1 were identified based on the reported values in a number of existing reports 197

and best practices that are utilized by several state and local governments (CCRPC 2016; City of 198

Clayton 2014; City of Bellevue 2008). Similarly, a list of accessibility requirements and their 199

non-compliance score ranges were identified for each of the remaining pedestrian facility types 200

in Figure 2 (� = 2��8). 201

The non-compliance score �,�� is calculated in the present model to represent the degree 202

of non-compliance of pedestrian facility � in location� with accessibility requirement�. 203

Compliance with the aforementioned accessibility requirements can be verified using a wide 204

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range of techniques that vary in the type and frequency of measurements required for 205

verification. Each accessibility requirement can only be verified using one verification technique 206

that requires a specific set of measurements to be conducted and sometimes repeated with a 207

specific frequency depending on pedestrian facility type and the accessibility requirement under 208

verification. This model utilizes a novel verification methodology that conducted a 209

comprehensive analysis and categorization of all accessibility requirements that need to be 210

complied with for all pedestrian facility types. This resulted in identifying four main verification 211

categories: (1) continuous verification, (2) discrete verification, (3) single verification, and (4) 212

presence verification, as shown in Figure 2. Table 2 presents four examples of these accessibility 213

requirements for three pedestrian facility types along with their verification procedure and 214

technique. In Table 2, the sidewalk width requirement example can only be verified by 215

conducting “continuous measurements” of the sidewalk width along its entire length (U.S. 216

Department of Justice 1994), while the sidewalk surface discontinuities requirement example can 217

only be verified by taking multiple “discrete measurements” wherever these discontinuities are 218

encountered along the length of the sidewalk (U.S. Access Board 2011a). Similarly, the height of 219

pedestrian push-buttons requirement example can only be verified by taking a “single 220

measurement” of the button height at each pedestrian signal, while the detectable warning 221

surfaces (DWS) requirement example can only be verified by confirming the “presence” of DWS 222

at curb ramps or other locations. These four categories of verification techniques are used to (a) 223

classify the aforementioned accessibility requirements of all pedestrian facilities into four 224

categories as shown in Figure 2; and (b) calculate the non-compliance score �,�� for each of these 225

accessibility requirement categories as described in the following sections. 226

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Continuous verification 227

This compliance verification category requires collecting continuous measurements along 228

the entire length of the pedestrian facility to calculate its non-compliance score �,�� . As shown in 229

equation (2), �,�� is calculated in this category by dividing the pedestrian facility into several 230

segments, determining non-compliance score �,�,�� for each segment�, and calculating the 231

average of these scores to find �,�� for the entire pedestrian facility. For example, the non-232

compliance score �,�� for the sidewalk width located in geographical location � is calculated by 233

(i) dividing the entire length of the sidewalk into several segments � ∈ (1, 2, 3, … , !��) based on 234

their width, as shown in Figure 3; (ii) determining the sidewalk width non-compliance score 235

�,�,�� of each segment � based on its minimum width ("�#�ℎ�) and scoring criteria (see example 236

in Table 1); and (iii) calculating an average �,�� for the entire sidewalk. This segmentation of 237

the sidewalk example is often needed when the measurement of the requirement under 238

verification varies along the length of the sidewalk. For example, verifying the compliance of the 239

sidewalk width often requires dividing the entire sidewalk length into segments based on 240

variations in its width. The sidewalk in this example needs to be divided to include an additional 241

segment whenever there is a change in its width, as shown in Figure 3. 242

�,�� =∑ & '�('�� × �,�,�

� )*+,��!�� (2)

Where, �,�� is non-compliance score for the requirement � of pedestrian facility located in 243

geographical location �, � is segment of pedestrian facility,!�� is total number of segments in 244

pedestrian facility of type � located in geographical location �, '� is length of segment � of the 245

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pedestrian facility,('�� is total length of pedestrian facility, and �,�,�� is non-compliance score of 246

segment � of the pedestrian facility. 247

Discrete verification 248

This compliance verification category requires conducting discrete measurements along 249

the entire length of a pedestrian facility to calculate its non-compliance score �,�� . As shown in 250

equation (3), �,�� is calculated in this category by identifying points - ∈ (1, 2, 3, … , (��) where a 251

change in specific pedestrian facility dimensions occur, assigning a non-compliance score to 252

each of these points based on its dimensions, and calculating the average of these scores to find 253

�,�� for the entire pedestrian facility. For example, sidewalk surface discontinuity non-254

compliance score �,.� for a sidewalk in location � is calculated by (i) identifying surface 255

discontinuities - ∈ (1, 2, 3, … , (��) in sidewalk, as shown in Figure 4; (ii) determining a non-256

compliance score �,.,/� for each surface discontinuity based on its vertical change in level 0/ 257

and scoring criteria (see example in Table 1); and (iii) calculating an average sidewalk surface 258

discontinuity non-compliance score �,.� for the entire sidewalk. 259

�,�� = ∑ 1 �,�,/� 23+,/��(�� × ��,��,4�5� (3)

Where, �,�� is non-compliance index for requirement � of pedestrian facility located in 260

geographical location �, - is a point where a change in the pedestrian facility dimension is 261

recorded, (�� is total number of points - where a change in the pedestrian facility dimension is 262

recorded, �,�,/� is non-accessibility score of requirement � of point - in pedestrian facility, ��,�� is 263

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number of points - per meter in pedestrian facility, and ��,4�5� is the maximum number of points 264

- per meter for all pedestrian facilities of type � in the city. 265

Single verification 266

This compliance verification category requires conducting single measurements at each 267

pedestrian facility to identify its non-compliance score �,�� by evaluating the compliance of its 268

existing conditions with accessibility requirement�. For example, verifying the compliance of a 269

pedestrian push-buttons with the accessibility requirement for its height requires conducting a 270

single measurement of that height. This means that there is one value for existing conditions that 271

needs to be verified to ensure compliance with accessibility requirements, as shown in Table 3. 272

Other examples of accessibility requirements in this category include curb ramp width, curb 273

ramp running slope, clear space length, and clear space width, as shown in Figure 2. 274

Presence verification 275

This compliance verification category requires verifying if the required feature is present, 276

non-standard, or missing to identify its non-compliance score �,�� by evaluating the compliance 277

of its existing condition with accessibility requirement�, as shown in the curb ramp detectable 278

warning surface example in Table 4. 279

UPGRADE COST AND LABOR-HOURS ESTIMATION 280

The purpose of this phase is to estimate cost and labour-hours needed to ensure the 281

compliance of non-compliant pedestrian facilities with accessibility requirements. The upgrade 282

work includes installing, changing, or completely rebuilding elements of pedestrian facilities. 283

The scope of the upgrade can be determined based on the accessibility requirements that are not 284

met in the existing conditions of pedestrian facilities. For example, a curb ramp that is missing 285

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DWS can become compliant if a DWS panel is installed without the need to change or rebuild 286

the entire curb ramp. On the other hand, a curb ramp with a non-compliant width, length, or 287

slope will have to be completely demolished and rebuilt in order to achieve compliance. 288

Accordingly, the upgrade of pedestrian facilities in the present model is classified as (a) complete 289

upgrade that requires the complete demolition and reconstruction of pedestrian facilities, or (b) 290

partial upgrade that requires partial installation, alteration, or removal of specific non-compliant 291

elements of pedestrian facilities, as shown in Figure 2. These two categories were used to 292

calculate the estimated upgrade cost 6�� and labour-hours 6'7�� required to achieve 293

compliance with accessibility requirements, as described in the following sections. 294

Complete upgrade 295

This upgrade category requires the complete demolition and reconstruction of pedestrian 296

facilities in order to achieve compliance with a specific set of accessibility requirements (see 297

Figure 2). Upgrade cost and labour-hours for pedestrian facilities that do not meet accessibility 298

requirements in this category can be calculated using equations (4) and (5). 299

6�� = 8�� × 96�� (4)

Where, 6��is total upgrade cost for pedestrian facility of type� that is located in 300

geographical location � (see Figure 2), 8�� is a binary variable that equals “0” if the pedestrian 301

facility is compliant with all accessibility requirements � in this category1 �,�� = 02 and equals 302

“1” otherwise, and 96��,�� is user-specified total upgrade cost of pedestrian facility type� in 303

geographical location �. 304

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'7�� = 8�� × 9'7�� (5)

Where, '7��is total labor-hours for upgrading pedestrian facility of type� that is located 305

in geographical location �, 8�� is a binary variable that equals “0” if the pedestrian facility is 306

compliant with all accessibility requirements � in this category1 �,�� = 02 and equals “1” 307

otherwise, and 9'7�,�� is user-specified total labour-hours for upgrading pedestrian facility type� 308

in geographical location �. 309

In the present model, if the aforementioned complete upgrade is required there will be no 310

need to estimate or execute partial upgrades. Otherwise, the model will estimate the partial 311

upgrade cost and labour hours for non-compliant facilities, as described in the following section. 312

Partial upgrade 313

This upgrade category requires partial installation, alteration, or removal of non-314

compliant elements of pedestrian facilities in order to achieve compliance with a set of 315

accessibility requirements without rebuilding the entire pedestrian facility. Upgrade cost and 316

labour-hours for pedestrian facilities that do not meet accessibility requirements in this category 317

can be calculated using equations (6) and (7). 318

6�� =8�,��

��× ;6��,�� (6)

Where, 6��is total upgrade cost for pedestrian facility of type� that is located in 319

geographical location �, � is accessibility requirement for each pedestrian facility type� (see 320

Figure 2),� is total number of accessibility requirements in this category for pedestrian facility 321

type� (see Figure 2), 8�,�� is a binary variable that equals “0” if the pedestrian facility is 322

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compliant with accessibility requirement � 1 �,�� = 02 and equals “1” otherwise, and ;6��,�� is 323

user-specified partial upgrade cost of pedestrian facility type� with respect to accessibility 324

requirement � in geographical location �. 325

'7�� =8�,��

��× ;'7�,�� (7)

Where, '7��is total labour-hours required for upgrading pedestrian facility of type� that 326

is located in geographical location �, � is accessibility requirement for each pedestrian facility 327

type� (see Figure 2),� is total number of accessibility requirements in this category for 328

pedestrian facility type� (see Figure 2), 8�,�� is a binary variable that equals “0” if the pedestrian 329

facility is compliant with accessibility requirement � 1 �,�� = 02 and equals “1” otherwise, and 330

;'7�,�� is user-specified partial labour-hours required for upgrading pedestrian facility type� 331

with respect to accessibility requirement � in geographical location �. 332

COLLECTIVE NON-COMPLIANCE 333

The purpose of this phase is to aggregate the previously calculated individual non-334

compliance indices ��, upgrade costs 6��, and labour-hours '7�� of a group of pedestrian 335

facilities based on their type � and/or geographical region < to enable their ranking and 336

prioritization for upgrades. The two collective non-compliance indices calculated in this phase 337

are (a) pedestrian facility type non-compliance index��, and (b) region non-compliance 338

index��=. First, pedestrian facility type non-compliance index�� is calculated in the 339

present model by averaging all the previously calculated individual non-compliance indices 340

�� for each pedestrian facility type�. Second, region non-compliance index��= is 341

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calculated by (1) computing pedestrian facility type non-compliance indices��,= for the 342

subset of all pedestrian facility types� that are located in the user-specified geographical 343

region<; and (2) computing a weighted average of all ��,= calculated in the previous step to 344

identify a collective region non-compliance index��=, as shown in equation (8). 345

��= =��,= ×

>

��

(8)

Where, ��= is collective non-compliance index for all pedestrian facilities in region <, 346

��,=is collective non-compliance index for all pedestrian facilities of type� in the region <, 347

and � is relative importance weight of pedestrian facility type �. 348

The collective upgrade cost 6�=, and collective labour-hours '7= are calculated in the 349

present model by adding all individual upgrade costs and labour-hours needed to upgrade each 350

pedestrian facility in region<, as shown in equations (9) and (10). 351

6�= =6�?

@

?��

(9)

Where, 6�= is collective upgrade cost for all pedestrian facilities in region <, 6�? total 352

upgrade cost for pedestrian facility A in region <, and B is number of pedestrian facilities in 353

region <. 354

'7= ='7?

@

?��

(10)

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Where, '7= is collective upgrade cost for all pedestrian facilities in region <, '7? total 355

upgrade labour-hours for pedestrian facility A in region <, and B is number of pedestrian facilities 356

in region <. 357

PERFORMANCE EVALUATION PHASE 358

The purpose of this phase is to analyse a case study to illustrate the use of the model and 359

demonstrate its novel and unique capabilities. The case study requires assessing the degree of 360

non-compliance of 1327 pedestrian facilities in a small town that includes all pedestrian facility 361

types, as shown in Table 5. Decision makers need to assess the degree of non-compliance of 362

these pedestrian facilities in order to comply with the federal mandate to conduct self-evaluations 363

and prioritize these facilities for future upgrade projects. 364

For this case study, the required input data by the model can be grouped in two main 365

categories: (a) dimensions and slopes of all sidewalks and pedestrian facilities; and (b) the 366

geographical regions that decision makers need to calculate their collective non-compliance 367

index ��=. The first category of input data is readily available in most municipalities sidewalk 368

network inventory databases (CCRPC 2016; City of Bellevue 2008; City of Clayton 2014). This 369

data can be captured and recorded using several procedures and technologies, including (1) 370

manual measurement tools and techniques such as tape measures, traditional levels, and 371

measuring wheels; (2) computer vision and remote sensing techniques such as photogrammetry; 372

(3) terrestrial laser scanning; (4) LiDAR scanning technologies; and (5) drone based data 373

collection. It should be noted that the developed model is designed to provide decision makers 374

with the flexibility of assessing and analysing any type of existing conditions data regardless of 375

its capture procedures and technologies. The model utilizes this input data to calculate, (1) the 376

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non-compliance index �� for each of the 1327 pedestrian facilities in this case study; (2) the 377

collective non-compliance index �� for each pedestrian facility type �; (3) the collective non-378

compliance index ��= for each user-specified region < in the case study using the 379

aforementioned calculation procedure. 380

The generated results for this case study illustrate the novel and unique capabilities of 381

model that can be used by decision makers to (a) efficiently quantify the degree of non-382

compliance of all types of pedestrian facilities including transit shelters, on-street parking spaces, 383

and passenger loading zones; (b) calculate estimated total cost and labour-hours needed to 384

upgrade each pedestrian facility to achieve compliance; (c) assess the degree of non-compliance 385

of each pedestrian facility type to identify facility types that are in urgent need for upgrades; (d) 386

prioritize pedestrian facilities upgrade projects in multiple regions based on their region non-387

compliance index; and (e) classify pedestrian facilities based on the type of required upgrade to 388

achieve compliance with accessibility requirements. 389

The capability of the present model to efficiently quantify the degree of non-compliance 390

of pedestrian facilities can be illustrated by its ability to (a) analyse all types of pedestrian 391

facilities including transit shelters, on-street parking spaces, and passenger loading zones using 392

the aforementioned novel assessment methodology (see Figure 2); and (b) complete the 393

computational assessment of all the aforementioned 1327 pedestrian facilities in 2.053 seconds 394

with an average computational time of 0.0015 seconds per pedestrian facility. This 395

computational efficiency enables the model to perform compliance assessment and estimate 396

upgrade cost and labour-hours for all types of pedestrian facilities for large datasets that are often 397

encountered in larger cities. For example, the accessibility requirements for 48,334 pedestrian 398

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facilities that are included in a dataset for a city with a population of 204,897 residents (CCRPC 399

2016) can be assessed using the present model in approximately 75 seconds. 400

The model also provides the capability of assessing the collective degree of non-401

compliance for each pedestrian facility type to enable decision makers to prioritize the upgrade 402

of these different types of facilities. For example, the case study results illustrate that on-street 403

parking spaces suffer from the highest level of non-compliance and therefore they have the 404

greatest need for upgrades as shown in Figure 5. In addition, the model calculates the ranges of 405

non-compliance for each pedestrian facility type to highlight the deviation of individual facilities 406

form the collective index. For example, the results indicate that pedestrian signals exhibit 407

varying degrees of non-compliance ranging from 0.0% to 82.5%; while sidewalks exhibit a 408

narrower range of 0.0% to 33.8% (see Figure 5). 409

The model can also be used to prioritize pedestrian facilities upgrade projects in multiple 410

geographical regions based on their non-compliance index. This enables decision makers to rank 411

upgrade projects based on the overall non-compliance in each region. For example, the second 412

region in this case study has the highest collective non-compliance index of 39.1% and therefore 413

it has the greatest need for upgrade, as shown in Figure 6. This region contains 59 pedestrian 414

facilities including 37 sidewalks, 15 curb ramps, 2 crosswalk, 1 pedestrian signal, 1 refuge 415

island, 1 transit stop, 1 on street parking, and 1 passenger loading zone. 416

Additionally, the model is capable of calculating the estimated upgrade cost and labour-417

hours needed to bring all pedestrian facilities in a specified geographical region into compliance 418

with accessibility requirements. The model is capable of integrating cost and labour data from 419

several sources including DOT databases, construction companies, or construction manuals. For 420

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example, basic cost data form RSMeans manual was used to estimate upgrade costs and labour-421

hours for all regions in this case study (RSMeans 2016). The results show that the second region 422

requires an upgrade cost of $148,700 and 1186 labour-hours in order to achieve compliance with 423

accessibility requirements, as shown in Figure 7 and Figure 8. 424

Furthermore, the model enables decision makers to classify pedestrian facilities as (a) 425

complying, (b) non-compliant requiring partial upgrade, or (c) non-compliant requiring complete 426

upgrade. This capability assists decision makers in state and local governments in evaluating and 427

visualizing general conditions of each pedestrian facility type and determining the urgency of its 428

upgrade. For example, the model can be used to report the number of compliant facilities, non-429

compliant requiring partial upgrade, and non-compliant requiring complete upgrade for each 430

facility type in the case study, as shown in Figure 9. 431

SUMMARY AND CONCLUSIONS 432

This paper presented a novel model for assessing the degree of non-compliance of 433

pedestrian facilities with accessibility requirements. The model was developed in five main 434

phases: (1) accessibility requirements analysis phase that identified pedestrian facility types and 435

their related accessibility requirements, (2) non-compliance assessment phase that quantified the 436

degree of non-compliance of each pedestrian facility with accessibility requirements; (3) cost and 437

labour-hours estimation phase that calculates the estimated upgrade cost and labour-hours 438

needed to upgrade non-compliant pedestrian facilities; (4) collective non-compliance phase that 439

aggregated the individual non-compliance indices, upgrade cost, and labour-hours of a group of 440

pedestrian facilities based on their type and/or geographical region; and (5) performance 441

evaluation phase that analysed a case study to illustrate the use of the developed model and 442

demonstrate its novel capabilities. The analysis of the case study illustrated the novel capabilities 443

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of the model in (a) efficiently quantifying the degree of non-compliance of all types of pedestrian 444

facilities including transit shelters, on-street parking spaces, and passenger loading zones; (b) 445

calculating the estimated upgrade cost and labour-hours required to achieve compliance, (c) 446

assessing the degree of non-compliance of each pedestrian facility type to identify facility types 447

that are in urgent need for upgrades; (d) prioritizing pedestrian facilities upgrade projects in 448

multiple regions based on their non-compliance index; and (e) classifying pedestrian facilities 449

based on the type of required upgrade to achieve compliance with accessibility requirements. 450

The model was able to assess the degree of non-compliance with accessibility requirements for 451

1327 pedestrian facilities that cover all pedestrian facility types and calculate pedestrian facility 452

type �� and region��=. 453

ACKNOWLEDGMENTS 454

This material is based on a work supported by the Illinois Department of Transportation 455

(IDOT), research funded under Project No. R-27-136. Any opinions, findings, and conclusions 456

or recommendations expressed in this publication are those of the authors and do not necessarily 457

reflect the views of the Illinois Department of Transportation. 458

NOTATIONS 459

The following symbols are used in this paper: 460

!��=totalnumberofsegmentsinpedestrianfacilityoftype�ingeographicallocation�;461

�=segmentofpedestrianfacility;462

-=apointwhereachangeinthepedestrianfacilitydimensionisrecorded;463

(��=totalnumberofpoints-inpedestrianfacilityoftype�ingeographicallocation�;464

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('��=totallengthofpedestrianfacility;465

�=totalnumberofpedestrianfacilities;466

�=geographicallocationofpedestrianfacility;467

��X==totalnumberofpedestrianfacilitiesoftype�thatarelocatedintheregion<;468

'�=lengthofsegment�ofpedestrianfacility;469

'7��= total labor-hours for upgrading pedestrian facility of type� that is located in470

geographicallocation�;471

'7==collectiveupgradecostforallpedestrianfacilitiesinregion<;472

��=non-complianceindexforpedestrianfacilityoftype�ingeographicallocation�;473

��=collectivenon-complianceindexforpedestrianfacilitytype�;474

��==collectivenon-complianceindexforallpedestrianfacilitiesinregion<;475

��,== collective non-compliance index for all pedestrian facilities of type� in476

geographicalregion<;477

��,�� =numberofpoints-permeterinpedestrianfacility;478

��,4�5� =themaximumnumberofpoints-permeterforallpedestrianfacilitiesoftype�479

inthecity;480

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;6��,�� = partial upgrade cost of pedestrian facility type� with respect to accessibility481

requirement�ingeographicallocation�;482

;'7�,�� =partiallabour-hoursrequiredforupgradingpedestrianfacilitytype�withrespect483

toaccessibilityrequirement�ingeographicallocation�;484

�=pedestrianfacilitytype;485

�=totalnumberofaccessibilityrequirementsforpedestrianfacilitytype�;486

�=accessibilityrequirementofpedestrianfacilitytype�;487

�,�� =non-complianceindexforrequirement�ofpedestrianfacilitylocatedingeographical488

location�;489

�,�,�� =non-compliancescoreofsegment�ofpedestrianfacility;490

�,�,/� =non-accessibilityscoreofrequirement�ofpoint-inpedestrianfacility;491

9'7�,�� =totallabor-hoursforupgradingpedestrianfacilitytype�ingeographicallocation492

�;493

96��,�� =totalupgradecostofpedestrianfacilitytype�ingeographicallocation�; 494

6��= total upgrade cost for pedestrian facility of type� that is located in geographical495

location�; 496

6�==collectiveupgradecostforallpedestrianfacilitiesinregion<;497

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8��=avaluethatequals“0”if1 �,�� = 02forallaccessibilityrequirements�inthecomplete498

upgradecategoryand“1”otherwise; 499

8�,�� =avaluethatequals“0”if1 �,�� = 02and“1”otherwise;500

�=relativeimportanceweightofpedestrianfacilitytype�;501

�� = relative importance weight of accessibility requirement� for pedestrian facility502

type�.503

REFERENCES 504

AASHTO. 2012. Guide for the Development of Bicycle Facilities. In 4th Edition. American 505

Association of State Highway and Transportation Officials, Washington, D.C. Available from 506

https://bookstore.transportation.org/item_details.aspx?ID=1943. 507

Anderson, D., Rivers, R., Appleton, W., and Goel, M. 1995. Americans with Disabilities Act: 508

Issues for Design and Facility Managers. Journal of Management in Engineering, 118(7): 78. 509

Available from http://ascelibrary.org/doi/abs/10.1061/(ASCE)0742-510

597X(1995)11:1(38)#aHR0cDovL2FzY2VsaWJyYXJ5Lm9yZy9kb2kvcGRmLzEwLjEwNjEvKEFT511

Q0UpMDc0Mi01OTdYKDE5OTUpMTE6MSgzOClAQEAw. 512

Axelson, P., Wong, K., and Kirschbaum, J. 1999. Development of an Assessment Process To 513

Evaluate Sidewalk Accessibility. Transportation Research Record, 1671(99): 5–10. 514

doi:10.3141/1671-02. 515

Barden v. Sacramento. 2002. United States Court of Appeals, Ninth Circuit, San Francisco. 516

Available from 517

https://www.justice.gov/sites/default/files/crt/legacy/2010/12/14/barden.pdf. 518

CCRPC. 2016. Sidewalk Network Inventory and Assessment. Champaign, IL. Available from 519

http://www.ccrpc.org/wp-520

content/uploads/2016/02/SidewalkNetworkInventoryAssessment.pdf. 521

CDR v. Caltrans. 2010. United States District Court Northern District of California, San Francisco. 522

City of Bellevue. 2008. City of Bellevue , Washington Americans with Disabilities Act ( ADA ) 523

Sidewalk & Curb Ramp Inventory. City of Bellevue, Washington. Available from 524

http://www.ci.bellevue.wa.us/pdf/Transportation/ada_report_summary0708.pdf. 525

City of Clayton. 2014. City of Clayton ADA Transition Plan. Clayton, MO. Available from 526

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http://www.claytonmo.gov/Assets/Public+Works/ADA+Transition+Plan/Clayton+ADA+Tra527

nsition+Plan.pdf. 528

El-Rayes, K., Liu, L., Golparvar-fard, M., Elgohary, N., and Halabya, A. 2016. Development of 529

Public Right-of-Way Accessibility Guideline Resource Material. Available from 530

https://apps.ict.illinois.edu/projects/getfile.asp?id=4741. 531

FHWA. 2001. Designing Sidewalks and Trails for Access Part II of II : Best Practices Design Guide. 532

Available from 533

https://www.fhwa.dot.gov/environment/bicycle_pedestrian/publications/sidewalk2/pdf.c534

fm. 535

Kockelman, K., Heard, L., Kweon, Y.J., and Rioux, T.W. 2002. Sidewalk cross slope design: 536

Analysis of accessibility for persons with disability. Transportation Research Record, 537

1818(2): 108–118. 538

Loewenherz, F. 2010. Asset Management for ADA Compliance Using Advanced Technologies. In 539

Institute of Transportation Engineers annual meeting and exhibit 2010: Vancouver, BC, 540

Canada, 8-11. Vancouver, BC, Canada. p. 6S4-667. 541

Markesino, J., and Barlow, J. 2007. Accessible Public Rights-of-Way Planning and Designing for 542

Alteration. Washington, D.C. 543

RSMeans. 2016. RSMeans Building Construction Cost Data. In 74th edition. RSMeans, Rockland, 544

MA. Available from https://www.rsmeans.com/products/books/2016-cost-data-545

books/2016-building-construction-cost-data-book.aspx. 546

Schlossberg, M., Weinstein Agrawal, A., and Irvin, K. 2007. An Assessment of GIS-Enabled 547

Walkability Audits. Journal of Urban and Regional Information Systems Association, 19(1): 548

5–11. 549

U.S. Access Board. 2011a. Accessibility Guidelines for Pedestrian Facilities in the Public Right-of-550

Way. In 36 CFR Part 1190. Washington, D.C. Available from https://www.access-551

board.gov/guidelines-and-standards/streets-sidewalks/public-rights-of-way/proposed-552

rights-of-way-guidelines. 553

U.S. Access Board. 2011b. Shared Use Path Accessibility Guidelines. Code of Federal Register, 554

Unites States of America. Available from https://www.access-board.gov/guidelines-and-555

standards/streets-sidewalks/shared-use-paths/background/advance-notice. 556

U.S. Census Bureau. 2012. Americans with disabilities: 2010 household economic studies. In US 557

Census Bureau. Available from 558

http://www.census.gov/newsroom/cspan/disability/20120726_cspan_disability_slides.pdf559

%5Cnhttp://scholar.google.com/scholar?hl=en&btnG=Search&q=intitle:Americans+with+d560

isabilities:+2010+Household+Economic+Studies#2%5Cnhttp://scholar.google.com/scholar561

?hl=en. 562

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U.S. Congress. 1968. Architectural Barriers Act. 42 U.S.C. § 4151 et seq., U.S.A. 563

U.S. Congress. 1973. Rehabilitation Act. 29 U.S.C. § 701 et seq., U.S.A. 564

U.S. Congress. 1990. Americans With Disabilities Act. 42 U.S.C. § 12101 et seq., U.S.A. 565

U.S. Department of Justice. 1994. The Americans with Disabilities Act Title II Technical 566

Assistance Manual. Washington, D.C. Available from http://www.ada.gov/taman2.html. 567

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users’ perceived sidewalk and ramp slope: Effort and accessibility. Journal of Architectural 569

and Planning Research, 26(2): 145–158. 570

Willits v. City of L.A. 2016. United States District Court, C.D. California, Los Angeles. Available 571

from https://casetext.com/links/5wvcb7ctsa82b0u8plkvlfovh. 572

573

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Table 1. Example Weights and Non-Compliance Scores for Sidewalk Requirements

Sidewalk

Requirement � Weights

(��)%

Range of Existing Conditions Non-Compliance

Score (��,�� )%

Width 1 20

��ℎ < 0.9�� 100

0.9 ≤ ��ℎ < 1.0�� 80

1.0 ≤ ��ℎ < 1.05�� 60

1.05 ≤ ��ℎ < 1.15�� 40

1.15 ≤ ��ℎ < 1.2�� 20

��ℎ ≥ 1.2�� 0

Running Slope 2 20

�� !"� > 12.5% 100

12.5% ≥ �� !"� > 10% 50

10% ≥ �� !"� > 8.33% 10

8.33% ≥ �� !"� > 5% 5

�� !"� ≤ 5% 0

Cross Slope 3 20

'�!�� !"� > 8% 100

8% ≥ '�!�� !"� > 6% 50

6% ≥ '�!�� !"� > 4% 25

4% ≥ '�!�� !"� > 2% 5

'�!�� !"� ≤ 2% 0

Surface

Discontinuities 4 20

*��+, 'ℎ,��-�.� > 25.4�� 100

25.4 ≥ *��+, 'ℎ,��-�.� > 20�� 80

20 ≥ *��+, 'ℎ,��-�.� > 13�� 25

13 ≥ *��+, 'ℎ,��-�.� > 6.4�� 5

13≥ChangeinLevel>6.4��, 9�.� � 0

*��+, 'ℎ,��-�.� ≤ 6.4�� 0

Protruding

Objects 5 20

:�!��!�;��,�+� > 175�� 100

175�� ≥ :�!��!�;��,�+� > 125�� 50

125��≥:�!��!�;��,�+�>100�� 5

:�!��!�;��,�+�≤100�� 0

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Table 2. Compliance Verification Techniques for Example Pedestrian Facilities

Pedestrian

Facility Type

Accessibility

Requirement

Verification Procedure Verification

Technique

Sidewalks Width Collecting continuous measurements

along the entire length of the pedestrian

facility.

Continuous

Sidewalks Surface

Discontinuities

Conducting discrete measurements at

specific points along the length of

pedestrian facility.

Discrete

Pedestrian

Signals

Pedestrian

Pushbuttons Height

Taking a single measurement of one of

the dimensions of pedestrian facility.

Single

Curb Ramp Detectable Warning

Surfaces

Verifying if the required features such

as detectable warning surface is present,

non-standard, or missing.

Presence

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Table 3. Example Non-Compliance Scores for Pedestrian Pushbutton Height

Pedestrian Pushbutton Height Non-Compliance

Score��,=> (%)

?��ℎ� > 1.20m 100

1.20� ≥ ?��ℎ� > 1.05m 50

1.05� ≥ ?��ℎ� > 0.75m 0

0.75� ≥ ?��ℎ� > 0.50m 50

?��ℎ� < 0.50 100

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Table 4. Example Non-Compliance Scores for Curb Ramp Detectable Warning Surfaces

Curb Ramp DWS Non-Compliance

Score��,A> (%)

Missing 100

Non-Standard 50

Present 0

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Table 5. Pedestrian Facilities in the Case Study

B Pedestrian Facility Type Number of Facilities in

the Case Study

1 Sidewalks 864

2 Curb Ramps 384

3 Crosswalks 32

4 Pedestrian Signals 16

5 Refuge Islands 14

6 Transit Stops 10

7 On-Street Parking 4

8 Passenger Loading Zones 3

Total 1327

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List of Figures

Figure 1. Model development phases

Figure 2. Accessibility requirements for pedestrian facility types

Figure 3. Continuous verification along the entire sidewalk length

Figure 4. Longitudinal section of sidewalk showing surface discontinuities ��

Figure 5. Collective non-compliance indices for all pedestrian facility types

Figure 6. Collective non-compliance indices of regions in the case study

Figure 7. Estimated upgrade cost of regions in the case study

Figure 8. Estimated labour-hours needed to upgrade regions in the case study

Figure 9. Classification of pedestrian facilities in each type

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