List of Paper Abstracts Received for The 13th TRB National Transportation Planning Applications Conference

List of Abstracts11-100: Estimating the Residential Location of Non-Drivers at the Block Level using Census Data...1 11-101: A New Transportation Planning Paradigm: Constraints-Based Pla

TRB Transportation Planning Applications Committee

(ADB50)

List of Paper Abstracts Received

for

The 13th TRB National Transportation

Planning Applications Conference

Reno, Nevada May 8-12, 2011

September 21, 2010

List of Abstracts

11-100: Estimating the Residential Location of Non-Drivers at the Block Level using Census Data 1

11-101: A New Transportation Planning Paradigm: Constraints-Based Planning in Response to the Declining Transportation Funds and Growing Interest in Sustainable Communities and Climate Change 2

11-102: Sustainability on the Cheap Low cost retrofit strategies for making urban and suburban environments more resource efficient and livable 3

11-103: A Quantitative Approach to Estimating Congestion and Air Quality Benefits of LRT Starter Line on the Phoenix Metropolitan Region 4

11-104: Prioritization of Future Freight Infrastructure Projects within the Anchorage Metropolitan Area 5

11-105: Driving Smart: Car2Go in Austin and Beyond 6

11-106: Cell-based Data Collection 7

11-107: Anchorage Bike Plan: On a Shoestring Budget 8

11-108: Repurposing Turning Movement Counting Boards for PNR Counts 9

11-110: HUB-CAP, A-HOW-TOOL TO MEET LEGAL CHALLENGES FOR LANE RENTAL DURING CONSTRUCTION 10

11-111: Optimising the Application of Urban HOV Lanes 11

11-112: Evaluating and Communicating Model Results: Guidebook for Planners [NCHRP 08-36 (Research for the AASHTO Standing Committee on Planning)] 12

11-113: Tour-Based Model for a Small Area 14

11-114: Sustainable Transportation Solutions in Austin Texas 15

11-115: Feedback on Feedback: CAMPO’s Findings from Testing Various Feedback Approaches 16

11-116: Testing the Transferability of Activity Based Model Parameters 17

11-117: Defining a Travel Demand Model Sharing Protocol 18

11-118: OPTIMIZATION OF SATURATED ARTERIAL NETWORK 19

11-119: Assessing the Marginal Cost of Freeway Congestion for Vehicle Fleets Using Passive GPS Data 20

11-120: Non-Traditional Public Engagement: E-Surveys and Virtual Public Meetings at the NYSDOT 21

11-121: Impact of Crowding on Rail Ridership: Sydney Metro Experience and Forecasting Approach 22

11-122: Predicting the Impacts of Housing and Jobs Site Decisions on Work Travel in Connecticut: A Model using Census Journey-To-Work Data 23

11-123: Challenges and Findings Estimating Demand for Special-Event Transit, the “Train-to-the-Game” Example 24

11-124: Adapting a Four-Step MPO Travel Model for Wildfire Evacuation Planning: A Practical Application from Colorado Springs 25

11-125: An Innovative Approach to Mapping Vehicle Classification Data 27

11-126: Congestion Management Process (CMP): Lessons Learned and Ongoing Challenges for Connecting Long-Range Plans and Projects 28

11-127: The Highway 82 Corridor: Planning for Alternatives to Sprawl in Rural Areas 29

11-128: Some aspects of bush-based algorithms for the traffic assignment problem: turns handling, monotone volume/delay functions, and path analysis 30

11-129: INTEGRATING UNCONVENTIONAL ARTERIAL INTERSECTION DESIGNS INTO TRANSPORTATION PLANNING PROJECTS 31

11-130: Regional Bicycle Demand Model: In Use Today in Portland 32

11-131: A Simple Framework to Assess Potential Impact of Regional Toll System on Environmental Justice Population 33

11-132: Quantifying Non-Motorized Demand – A New Way of Understanding Walking and Biking Demand 34

11-133: A Comparison of Bluetooth vs Traditional Origin-Destination Survey Data Collection Methods 35

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11-134: Transportation Data Collection and Management Under Poor Driving Behaviour and Extreme

Weather Conditions 36

11-135: Select Bus service, New York city's experience and reduction in Greenhouse gas 37

11-136: Justifying the benefits of freeway corridor detour operations under non-recurrent congestion: A decision model and numerical analysis 38

11-137: You Only Need 9,214 Model Runs to Evaluate 12 Projects 39

11-138: Adaptation to Climate Change in the Long-Range Transportation Planning 40

11-139: Sensitivity Analysis on Dynameq: A View from Practice 41

11-140: Say It To My Facebook: Current Transportation EISs Using Social Media for Public Involvement 42

11-141: Complex Issues Associated with Urban Arterial Capacity Projects and TOD Corridors 43

11-142: Use of TRANSIMS to Analyze Large-Scale Land-Use Changes 44

11-143: ENLARGING THE CHOICES IN THE STRATEGY BASED TRANSIT ASSIGNMENT 45

11-144: GIS-Based Framework for Modeling Non-Motorized Transportation 46

11-145: A New Approach to Expand Metropolitan Travel Choices 47

11-146: Integrating MORPC's Tour Model with Dynamic Network Simulation 48

11-147: Adjustments to the Alameda County Travel Demand Model for HOT Lane Analysis 50

11-148: Adapting an Integrated Transportation and Land Use Model (MetroScope) to Simulate Travel and Land Use Impacts of Transit and Tolling Options for the Columbia River Crossing Project 51

11-149: GIS Application for Transit Access Data Development: 52

A Case Study of the Chicago Metropolitan Agency for Planning (CMAP) Travel Demand Model 52

11-150: Development of the National Travel Model of Slovenia 53

11-151: Analysis Tool for Model Output to Evaluate Express Lanes Feasibility 54

11-152: Incorporation of Pricing in the Time- of-Day Model 55

11-154: Developing a Regional GIS enterprise and decision support tool for transportation planning - Transportation Environmental and Land-Use Data Enterprise (TELUDE) 57

11-155: Development of a Dynamic Traffic Assignment for the Portland Metropolitan Region 58

11-156: Integrated Systems Planning: Corridor Concept and Application 59

11-157: Traffic flow data collection using inductive loop detectors at signalized intersections 60

11-158: COMPARISONS OF SYNTHETIC POPULATIONS GENERATED FROM CENSUS 2000 AND AMERICAN COMMUNITY SURVEY (ACS) PUBLIC USE MICRODATA SAMPLE (PUMS) 61

11-159: SPATIAL DISTRIBUTION AND VISUALIZATION OF DAILY POPOULATION WITHIN AN ACITIVITY-BASED MODELING FRAMEWORK 63

11-160: Houston-Galveston Area Council Regional Transit Framework Study 64

11-161: Successful Integration of a Demand Model with a Microsimulation Model 65

11-162: Develop a Standardized Indicator of Public Transportation Access using Block-Level Data- A Washington D.C Case Study 67

11-163: Translating policy to practice: an interdisciplinary investigation of transportation planning 68

11-164: Improving the modeling of time-of-day effects in the PSRC activity-based model: joint mode & time-of-day models and time-sensitive logsums 69

11-165: Estimating Mobile Source GHG Emissions in Los Angeles County: A Web-based Tool for Calculating the Greenhouse Gas Benefits of Transportation Improvement Projects 70

11-166: A Case Study of Assessing Applicability of Regional Travel Demand Model for Nexus Study 72

11-167: The impact of road network expansion on agricultural development: a review study of Al-Qassim region in Saudi Arabia 73

11-168: Applying DTA Model for Travel Demand Modeling 74

11-169: USE OF TRIP TABLE ESTIMATION TO IMPROVE PROJECT TRAFFIC FORECASTS 75

11-170: Development of Connectivity and Accessibility Measures for Atlanta Metropolitan Area 76

11-171: Developing GIS Databases for Transportation Planning in Kuwait 77

11-172: Application of Transportation Models for Integrated Public Transport System in Kuwait 80

11-173: Livability in Transportation: Lessons from the FHWA/FTA Guidebook 82

11-174: Application of Transportation Data Collection in a Rapidly Changing Society: Challenges and

Solutions – A Case Study from for Kuwait’s Planning Needs 83

11-175: Hybrid Mesoscopic-Microscopic Traffic Simulation Framework 85

11-176: Short Term Improvements for Pedestrians, Bicyclists and Transit Riders - The Jackson Heights Neighborhood Transportation Study 86

11-177: Effects of Household Life Cycle Changes on Travel Behavior: Evidence from Michigan Statewide Household Travel Surveys 87

11-178: Tri-level freight modeling: A simulation of trucks going near and far 89

11-179: Utilizing Data Collected from Consumer Navigation Devices: Case Studies with Validation 90

11-180: Surveying and Modeling Long Distance Trips 92

11-181: Changes in Household Vehicle Fleet Compositions and Policy Implications 94

11-182: Development of a Regional Transit Forecasting Model Based on Google Transit Feed 95

11-183: A Simple Methodology to Evaluate the Disparate Impacts of Fare and Service Change on Protected Populations in a Fixed Guideway System 96

11-184: After Evans: Working on an Approximation of a Combined Equilibrium Model Based on Quick-Precision Assignment 97

11-185: An Innovative Software Approach to Developing a Multi-Jurisdictional Congestion Mitigation Fee Program in Los Angeles County 99

11-186: Modeling parking capacity constraint without detailed parking user side info 100

11-187: An Improved GIS-based Master Network Solution for Modeling 101

11-188: A Tool for the Assessment of Local Land Use Decisions on VMT and Resulting Air Quality 102

11-189: Modeled Impacts of Land Use Intensification Near Transit Stations as an Alternative to Major Highway Improvements 103

11-190: Methodology for Estimating Greenhouse Gas Emissions and Assessing Mitigation Options for Project Level and Regional Level Applications for On-Road Mobile Sources 104

11-191: Travel Demand Modeling with R 105

11-192: Integrating a Capacity-constrained Station Choice Model into the Regional Travel Forecasting Model Set 106

11-193: Second Day Response Rates: Implications for CMAP’s Travel Tracker Survey 107

11-194: Converting Commodty Flow Databases For Use In MPO Travel Demand Models Through The Use Of Subarea Extraction 108

11-195: The Use of GPS Subscription Records to Develop Planning Data for Trucks 109

11-196: Early Report on a Regional Traffic Microsimulation Model for Planning and Operations Analysis 110 11-197: Calculating Emissions Within a Travel Demand Model Using MOVES Emissions Rates 111

11-198: Development of a Multi-modal Trip Table to Support a Mesoscopic Model for Manhattan, New York 112

11-199: MICROSCOPIC VS MESOSCOPIC NETWORK LOADINGS FOR SIMULATION-BASED DYNAMIC TRAFFIC ASSIGNMENT 114

11-200: Integrating Assessment of the Economic Benefits of Transportation Improvements in Project-Level Alternatives Analysis 115

11-201: A DEPLOYABLE REGIONAL DYNAMIC TRAFFIC ASSIGNMENT TOOL FOR PLANNERS 116

11-202: COMBINING URBAN MODELS IN THE VIRGINIA TIDEWATER REGION 117

11-203: Application of a Disaggregate Quasi-Dynamic Model of Park-and-Ride Lot Choice 118

11-204: Implementation of GIS Technology in Land Use Data Development and Data Quality Assurance 119 11-205: Corridor-Level Air Quality Analysis of Freight Movement 120

11-206: Buffer-based Area Type Determination for Travel Demand Model Network Links 121

11-207: Congestion Pricing in Oregon: First Steps in Implementing A Congestion Pricing Strategy in the Portland Metro Area 122

11-208: Empirical Results from a New Traffic Assignment Method 123

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11-209: DEPLOYMENT OF STATE OF THE PRACTICE PLANNING METHODS IN VICTORIA, BC 124 11-210: Can Multi-Resolution Dynamic Traffic Assignment live up to the Expectation of Reliable Analysis

of Incident Management Strategies? 125

11-211: Leveraging Web-Based GIS for Managing Transportation Network Information 126

11-212: Integration of an Activity Based Model, Traffic and Transit Simulation Model, and MOVES 127

11-213: Freight Data and Decision Makers: How they were introduced in Utah 128

11-214: Analyzing Transit Oriented Development (TOD) Indicators for Houston Metropolitan Area 130

11-215: Good Level of Service is Bad Service 131

11-216: State-of-the-Art Traffic Modeling for Cowboys Stadium; An Innovative Approach to Gameday Traffic Forecasting 132

11-217: Recent Practice in Modeling Non-Motorized Travel 133

11-218: Using a Statewide Model to Help Improve State Long-Range Plan Decision-Making: The Utah DOT Experience 135

11-219: An Integrated Travel Demand, Mesoscopic and Miroscopic Modeling Platform to Assess Traffic Operations for Manhattan, New York 137

11-220: I love LUCI: Practical Use of the San Francisco County Land Use and Champ Integrated model .139 11-221: Making Activity-Based Travel Demand Models Play Nice with Trip Rates 141

11-222: Easy Breezy Beautiful DTA Modeling of the Geary Boulevard Bus Rapid Transit Project – Was it Really That Simple? 143

11-223: Development of a Large Scale Traffic Simulation Model to Improve the Flow of Commercial Vehicles from Maquiladora Industry to International Border-Crossings 144

11-224: Cost Analysis for Reliability of Public Transit System 145

11-225: Effect of Bus Capacity on Transit Reliability in a Simulated Route 146

11-226: Abu Dhabi New Transport Demand Model 147

11-227: Collecting data for wasting time and service time for urban bus of public transportation 149

11-228: Long Range GIS Land Use Planning with UPlan; Plan and Projection 150

11-229: PIMS, an impressive tool in running Air Quality Conformity 152

11-230: Comparative Analysis of Queue Estimation Models at Traffic Signals 154

11-231: Managing Complexity with Multi-Scale Travel Forecasting Models 155

11-232: A Multi-attribute Framework for Statewide Transit Ridership Determination 156

11-233: Exploring the National Housedold Travel Surveys on Telecommuting Behavior 158

11-234: Integration of Dynamic Traffic Assignment in a Four-Step Model Framework – A Deployment Case Study in PSRC (Puget Sound Regional Council) Model 159

11-235: I-70 Dedicated Truck Lanes Feasibility Study: Modeling Methodology for a Four State Study Corridor 160

11-236: TCRP Report 95 Series: Chapter 19 –Employer and Institutional TDM Strategies 161

11-237: Dynamic Modeling Methods for Analyzing Interstate Corridor Operational Planning Projects 162

11-238: Davie Road and State Road 84 Transit Facility Demand Estimation 163

11-239: MID-TERM FOLLOW-UP ASSESSMENT OF A DISAGGREGATE LAND USE MODEL 165

11-240: Discrete Choice Models of the Preferences for Alternative Fuel Vehicles 166

11-241: Model Validation/Application for Significant Growth Areas in Florida 168

11-243: Transit On-Board Surveys – Using a Segment Approach 170

11-244: GreenSTEP: Applying Oregon’s Statewide Greenhouse Gas Emissions Model in Florida 171

11-245: Computational Challenges and Prospects in Advanced Travel Demand Modeling 172

11-246: Estimating the Transportation Effects of Smart Growth: Implementation of a 4D postprocessor for the Monterey Bay region 173

11-247: I-15 Multi-State Corridor Master Plan: Visioning for a Future without Borders 174

11-248: Looking for a Better Way to Smooth: Recent Applications in Texas and Utah 175

11-249: Development of a Park and Ride Lot Choice Model for Portland Metro 176

11-250: Development and Application of the Atlanta Activity-Based Model Visualization Dashboard 177

11-251: Implementing an EVMS/Cost Controls System for FAA's TAMR Program 178

11-252: An Open-Source Integration of Land-use and Travel Models: Challenges and Lessons Learnt in the Charlotte Metro Region 179

11-253: An Alternative Method to Develop and Apply Household Stratification Curves: An example from the 2010 Indianapolis MPO model update 180

11-254: Development of a Hybrid Freight Model from Truck Travel Surveys and Commodity Flow Data 181

11-255: The Use of Complex Disaggregate Models in a Planning Context 183

11-256: Quality of Life Improvement in NYC 184

11-257: Present Operational Data on High Occupancy Vehicles (HOV) Facilities to the General Public 186

11-258: Development of a Regional Special Events Model and Forecasting Special Events LRT Ridership 187

11-259: Developing a Subregional Model Tool for the Southern California Association of Governments 189

11-260: Multi-Urbanized Area Model Development and Application 190

11-261: Interactive Mapping Applications for LRTP/TIP Development 191

11-262: Travel Speed Surveys and Model Calibration 192

11-263: Linking Land Use and Transportation Models: 193

Transportation User Benefits and Site Values 193

11-264: The Evolution of Integrated Land Use and Transportation Modeling and Planning Tools in Greater Fredericksburg, Virginia 194

11-265: Building a Better Plan: 195

The Costs and Benefits of Transportation Alternatives 195

11-266: A Validation Methodology for an Integrated Mesoscopic and Microscopic Modeling Platform to Assess Traffic Operations for Manhattan, New York 196

11-267: Electronic Fare Card Data – A Transit On-Board Survey Application 198

11-268: Statewide Model Influence Areas: Turning Model Data into Clear and Understandable Results 199

11-269: 1996 Atlanta Olympics: Planning, Visioning, and Deriving Lessons 201

11-270: Planning Sustainable Community Strategies by Integrating Scenario Visioning Tools and Economic Urban Forecasting Models Using GIS 202

11-271: Southwest Georgia Interstate Study 203

11-272: Surface Transport Master Plan (STMP) 2030: Changing Vision to Reality 204

11-273: Developing a Land Use Forecast Model for Integrated Land Use/Transportation Modeling 205

11-274: Experiences and Lessons Learned Abu Dhabi Travel Surveys 2009 206

11-275: Origin - Destination On-Board Study Multi-Mode Data Collection Alternatives 207

11-276: New Findings from Application of Accelerated User Equilibrium Traffic Assignments 208

11-277: Travel Demand Modeling to Support Smart Growth and Climata Change Policies 209

11-278: Assessing Feasibility of Electric Buses in Small/Medium-Sized Communities 210

11-279: Does Public Transit Really Reduce Our Carbon Footprint? A Life Cycle Analysis of Different Transportation Modes 211

11-280: Comparing Aggregate Trip-Based and Disaggregate Tour-Based Travel Demand Models: Highway Results 213

11-281: Methods for Conducting a Large-Scale GPS-Only Survey of Households 215

11-282: Methods for Conducting a Large-Scale GPS-Only Survey of Households 217

11-283: Developing dynamic parameters of volume-delay function for transportation planning applications 219

11-284: Environmental Justice and the Public Involvement Process for a Successful NEPA Project in North Carolina 220

11-285: Emission Pricing for Sustainability of an Urban City Network in Uncertain Demand ConditionsBi-objective optimal capacity expansion model for minimizing emissions and travel time in uncertain demand conditions 221

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11-286: Effective Usage of Travel Demand Modeling and Traffic Analysis Tools for the Reconstruction of

Business 40 in Downtown Winston-Salem 223

11-287: Evaluating Small-Scale Results of Activity-Based Models 224

11-288: Integrated Modeling for Small and Medium Sized MPOs and Cities 226

11-289: Environmental Justice and the Public Involvement Process for a Succeful NEPA Project in North Carolina 227

11-290: Modeling Advanced Corridor Freight Management Strategies Using Dynamic Traffic Assignment 228

11-291: Innovative Interchange Concept Development 229

11-292: Planning a Regional Express Lanes Network - Lessons from the Bay Area 230

11-293: Destination choice model success stories 231

11-294: Discrete Choice Models and Behavioral Response to Congestion Pricing Strategies 232

11-295: A Mesoscopic Simulation and Dynamic Traffic Assignment Model for Planners 233

11-296: Congestion and Time-of-Day Forecast Outcomes of An Activity-Based Model 235

11-298: Generating Employment and Job XY Points in Disaggregate Models 236

11-300: Integrated Passenger & Commercial Vehicle Model for Assessing the Benefits of Dedicated Truck-only Lanes on the Freeways 237

11-301: Enhancing MOVES Transportation and Air Quality Analysis by Integrating with Simulation-Based Dynamic Traffic Assignment 238

11-302: USING PLANNING DATASETS TO DEVELOP DYNAMIC ORIGIN-DESTINATION MATRICES FOR TRAFFIC SIMULATION 240

11-303: Planning and Policy Impacts of Alternative Public Transport Modeling Methods 241

11-304: Evaluating Transportation Projects: An Analytical Hierarchy Approach 243

11-305: LONG-TERM DEMAND FORECASTING OF MANAGED LANES: Challenges in addressing key influential risk parameters 244

11-306: Modeling Active Traffic Management for the I-80 Integrated Corridor Mobility Project 245

11-307: Design and Specification of an Economic Land Use Forecasting System for the Twin Cities 246

11-308: Serving Suburbia with the STAR Line 247

11-310: Sketch Planning Application for a Regional Managed Lane System Access Concept of Operations 248

11-311: How Green is Your Dream: Implementing Quick-Response Tools to Evaluate Sustainability and Climate Change 249

11-312: Workplace Choice Model: Insights into Spatial Patterns of Commuting in Three Metropolitan Regions 250

11-313: Sample Size Requirements and Control Counts for Expansion of Transit Rider Survey Data 252

11-314: Subarea Friction Factors in a Regional Model 253

11-315: Building a Database for Estimation of an Advanced Activity-Based Travel Model from the NHTS 254

11-316: Investigating the Relationship of Service Headway to Wait Time in the Dallas-Fort Worth Metropolitan Area 256

11-317: A Logit Model for Transit Path Choice Behavior 257

11-318: Travel Characteristics of Zero-Vehicle Households in South Florida 258

11-319: Exploring Interactions between Transit Demand of Smart Card Users and Land Use using Intersection-level Origin-Destination Estimation 260

11-320: Sensitivity Testing of an Integrated Regional Travel Demand and Traffic Microsimulation Model 262 11-321: USING TRAFFIC SIMULATION TO PLAN THE ROADWAY NETWORK FOR A TRANSIT ORIENTED DEVELOPMENT 264

11-322: Travel Model Validation Manual Update (or Why Your Model is Wrong and What to Do About It) 265

11-323: The San Joaquin Valley Model Improvement Program: An Innovative and Collaborative Approach to

Addressing Climate Change in California 266

11-324: Integrated Dynamic Traffic and Transit Assignment Considering Traffic Congestion and a Schedule-Based Transit Network 268

11-325: VALIDATION AND SENSITIVITY CONSIDERATIONS FOR STATEWIDE MODELS 270

11-326: Automating the Process of Updating Regional Transit Network:: The SCAG Experience 271

11-327: Administrative Coordination for the California 2010-2012 Statewide Household Travel Survey 273

11-328: Impact of Congestion, Pricing, and Travel Time Reliability on Travel Demand: Summary of Model Specification Tests on Multiple US Data Sources 275

11-329: Integrated Modeling Approach for Studying Sustainable Development in Proposed Transit Corridors 276

11-330: GIS Based Fee Revenue and Growth Forecast Calculator for Congestion Mitigation Fee Program Nexus Study 277

11-331: Application of a Greenhouse Gas-Based Sustainable Return on Investment Analysis for San Mateo County Transit District 278

11-332: The Seaside, CA Parking Strategy and the Influence of Public Involvement 279

11-333: Sipping from a Firehose: Now that I Have all this Output, How Do I Make Sense of it All? 280

11-334: Understanding Ridership Patterns on Light Rail in Phoenix 281

11-335: Pedestrian Behavior in Urban Centers and its Impact on Vehicle Operation 283

11-336: Multimodal Corridor System Management – Incorporating Analysis of Transit, Demand Management Programs and Operational Strategies 285

11-337: Action Plans for Routes of Regional Significance: A Tool for Growth Management 286

11-338: Hedonic Regression of Local-level Attraction Influences on Housing Price: Evidence from California Statewide Rent Modifier Estimation 287

11-339: Travel Demand Forecasting Parameters and Techniques: A New Guidebook 288

11-340: Linking Transportation and Land Use to Create a Legacy 289

11-341: Improving Transit Accessibility Modeling for Regional Travel Demand Models Using GIS Technology 290

11-342: Modeling Travel Time Reliability for Managed Lane Travel Demand Forecast 291

11-343: TRAFFIC FORECASTING FOR I-95 HOT LANES IN VIRGINIA 292

11-344: ON-ROAD VEHICLE ACTIVITY GPS DATA AND PRIVACY 293

11-345: Risk Assessment & Sensitivity Analysis of Traffic and Revenue Projections for Toll facilities 294

11-346: HEAVY DUTY DIESEL VEHICLE MODAL EMISSIONS MODEL ESTIMATES USING GPS DATA 295

11-347: MONITORING VEHICLE OCCUPACNY ON HOV/HOT LANES 296

11-348: An automated approach for identifying type of non-signalized intersection and estimating delays in statewide model application 297

11-349: Congestion Pricing Triggers on Toll Roads 298

11-350: Development and Application of a Parcel Based Statewide Travel Demand Model for the Assessment of the Travel Impacts of Smart Growth strategies and Sidewalk Investments 299

11-351: Understanding and Modeling Transit Preferences in Portland, Oregon 300

11-352: Development of Congestion Management Process Using A Travel Demand Forecasting Model 301

11-353: Comparison of two approaches to integrate UrbanSim Landuse and travel demand model 302

11-354: Applying the SWIM2 Integrated Model in Freight Planning in Oregon 303

11-355: Impacts of Proxy Reporting in Household Surveys on Trip Rates 304

11-356: Comparative Analysis for an Urban Design-Build Construction Project using Dynamic Traffic Assignment 305

11-357: Changes in Young People's Travel 306

11-358: A Trip-Based Joint Departure and Arrival Time Choice Model 307

11-359: A Genetic Algorithm to Develop Truck Model Parameters from Local Truck Count Data 308

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11-360: Life-cycle Benefit-cost Analysis of Alternatives for Accommodating Heavy Truck Traffic in the Las Vegas Roadway Network 309 11-361: Integrated Model of the Urban Continuum – Location Choices, Activity-Travel Behavior, and Dynamic Traffic Patterns: Design, Development and Prototype Implementation 310 11-362: Setting Up Validation Data Targets for Assessing Interregional Travel in the California Statewide Travel Demand Modeling Framework 312 11-365: Analysis of a Multimodal Light Rail Corridor Using an Activity-Based Microsimulation Modeling Framework: An Application of TRANSIMS 313 11-366: Statewide Rest Area Prioritization, Project Planning and Implementation 315 11-367: Making and Measuring Progress: From Zonal to Parcel Level Integrated Land Use and

Transportation Models 316 11-368: Envisioning Sustainability: Integrating Scenario Planning, Modeling and 3D Visualization 317 11-369: Evaluating the Impact of Road Pricing on Traveler Behavior in the Seattle Region Using Household Travel Diary Surveys 318 11-370: Four-Step Models Deserve to Die By When? 319 11-371: Using Intelligent GPS Devices and Learning Algorithms for Collecting Multi-Week Activity-Travel Diary Data: Experiences in a Dutch Context 320 11-372: Sustainability Evaluation and Planning Guidance for Transportation 321 11-373: An Exploratory Analysis of PAS Characteristics in Solving the Static Deterministic User Equilibrium Traffic Assignment Problem on a Large Scale Urban Network 322 11-376: An Econometric Approach to Forecasting Ridership on the Staten Island Ferry 323

11-100: Estimating the Residential Location of Non-Drivers at the Block Level using Census Data

Topic Area: Measuring and Planning for Livability, Sustainability & Environmental Justice

Robert Case (Corresponding Author)

Principal Transportation Engineer

Hampton Roads Transportation Planning Organization

723 Woodlake Dr, Chesapeake, VA 23320, USA

in zero-vehicle households Local government and transit agencies can use this data when deciding where to promotethe development of activity locations and where to invest in transit, two factors which improve non-driver mobility Keywords: non-driver, transit, evacuation, mobility, block, census

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11-101: A New Transportation Planning Paradigm: Constraints-Based Planning in Response to the Declining Transportation Funds and Growing Interest in

Sustainable Communities and Climate Change

Topic Area: Integrated Transportation-Land Use Modeling and Planning for Smart Growth, Transit-Oriented-Design,and More

Ron Milam (Corresponding Author)

Principal

Fehr & Peers

2990 Lava Ridge Court, Suite 200, Roseville, CA 95661, US

r.milam@fehrandpeers.com

Tel: 916-773-1900

Abstract: Traditionally, local jurisdictions plan transportation facilities to provide uncongested traffic operations fordecades into the future Under the traditional planning paradigm, transportation projects are selected based on criterialike functional classification, design standards, and ability to provide acceptable operating conditions, as defined bymeasures such as level of service (LOS) , through a determined horizon year Once a design is developed to meetthese objectives, funding is obtained and the project is constructed

However, as funding for transportation projects becomes scarcer, more often than not, this traditional planningparadigm is unrealistic Funding availability to construct a project can no longer be assumed This has already beenwell established in regional transportation planning process, but has yet to take hold at the individual city and countylevel Moreover, with increasing congestion in urban areas, designing facilities that would meet target LOS thresholds

in the long-term is becoming cost prohibitive

Beyond funding shortfalls, the traditional planning paradigm is becoming outmoded for cities and counties astransportation professionals begin to recognize factors aside from automobile operations—including the experience ofnonmotorists, preservation of open space, and most recently, climate change—as important considerations in planningtransportation facilities

This paper promotes replacing the traditional transportation planning process with a constraints-based approach thataddresses new funding, environmental, and political realities The authors focus on widespread funding shortfalls asevidence that the traditional planning paradigm for cities and counties is failing To demonstrate how transportationplanning could better adapt to funding constraints, the authors present two case studies of local jurisdictions inCalifornia, where a new constraints-based approach is being applied (or could be) to make the most effective use oflimited public investment The authors also examine how concurrency requirements, such as those that exist inWashington State, dovetail with the new planning paradigm Lastly, the article highlights how the new paradigm can

be expanded to address issues of growing importance, including transportation system performance for non-automodes and climate change

Keywords: LOS, funding constraints, new paradigm, climate change

11-102: Sustainability on the Cheap Low cost retrofit strategies for making urban and suburban environments more resource efficient and livable

Topic Area: Measuring and Planning for Livability, Sustainability & Environmental Justice

Patricia Tice (Corresponding Author)

Associate Transportation Engineer

Keywords: sustainability, livability, planning, walkability, connectivity, comprehensive planning

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11-103: A Quantitative Approach to Estimating Congestion and Air Quality Benefits

of LRT Starter Line on the Phoenix Metropolitan Region

Topic Area: Air Quality and Climate Change – Modeling and Planning

Abhishek Dayal (Corresponding Author)

Planner II

Valley Metro Rail

101 N First Ave., Suite 1300, Phoenix, Arizona 85003, USA

The purpose of this research was to get a quantitative estimate of the amount of vehicular emissions reduced attributed

to the LRT starter line The research used a data sources such as the Household Travel survey conducted by MaricopaAssociation of Governments, the regional Metropolitan Planning Organization for Phoenix region, Light Rail interceptsurvey commissioned by METRO in April 2009, Monthly ridership reports, and emission factors provided byEnvironmental Planning Agency (EPA)

In 2001, MAG undertook a Regional Household Travel Survey to obtain information related to travel behaviorincluding average vehicle occupancy, trip generation, trip distribution, and mode choice Data was gleaned from thesurvey as well as from the Light Rail intercept survey, which was commissioned in April of 2009 to look at theridership characteristics of the LRT line

The following methodology was used to estimate vehicular emission savings attributed to the Light Rail project:

• Estimate the number of choice riders using Light Rail from the intercept survey by five trip purpose categories(Home-Based Work, Home-Based University, Home-Based Shopping, Home-Based Other and Non Home-Basedtrips)

• Using the household survey, derive the average auto occupancy by the five trip purpose categories andestimate the number of personal vehicles removed from the streets

• The intercept survey was used to estimate the average trip lengths by trip purpose and the daily Vehicle MilesTravelled (VMT) saved

• An annualization factor was estimated based on monthly ridership reports to calculate annual weekdayridership This factor was used to annualize the daily VMT calculated in the previous step

• Using EPA emission factors for Light Duty Vehicles or Trucks (LDV/LDT) the total savings in annualgreenhouse gas emissions and other pollutant emissions (NOx, CO, VOC and PM-10) were estimated

Keywords: Light Rail, survey, air quality, emissions

11-104: Prioritization of Future Freight Infrastructure Projects within the Anchorage Metropolitan Area

Topic Area: Coalitions for Freight Planning and Non-Freight Commercial Vehicle Issues

Teresa Brewer (Corresponding Author)

Transportation Planner, Freight Mobility Coordinator

University of Alaska Anchorage,School of Engineering, Science & Project Management

3211 Providence Drive, Anchorage, AK 99508, USA

To identify problem freight movement areas, and enhance efficiencies for both the freight industry and associatedfreight construction projects, the Municipality of Anchorage’s Freight Advisory Committee conducted a freight surveyand further analyzed what criteria and evaluation methods should be used to fund freight projects for the region Thiscommittee is comprised of members from the freight industry, freight organizations, the Port of Anchorage, TedStevens Anchorage International Airport, the Alaska Railroad, and local, state, and federal agencies This analysiscreated a database which was constructed based on a) Subjective criteria obtained via a survey of local freight drivers,and b) Objective criteria such as freight traffic volumes and freight accident data Then a second component usedmodeling to establish a relative weighting, for comparison at pre-determined intersections Both database constructionand its subsequent analysis resulted in a comprehensive prioritization list of freight infrastructure projects inAnchorage The resulting research provided multiple systematic prioritization techniques for future transportationinfrastructure projects In addition, the FAC in conjunction with the University of Alaska Anchorage is installing GPSunits in freight trucks to determine travel time, delays, and freight patterns within the area and adjacent boroughs.Keywords: Freight, Prioritization, Freight Survey

5

11-105: Driving Smart: Car2Go in Austin and Beyond

Topic Area: Planning for Use of Sustainable Modes: Transit, Biking and Walking

Katherine Kortum (Corresponding Author)

Graduate Research Assistant

University of Texas at Austin

11028 Jollyville Road #258, Austin, TX 78759, United States

Keywords: carshare, alternative transportation, Car2Go

11-106: Cell-based Data Collection

Topic Area: Transportation Data Collection and Management – Surveys, Counts and More

Kyle Ward (Corresponding Author)

Transportation Engineer

N.C Capital Area MPO

127 W Hargett St Suite 800, Raleigh, NC 27601, US

Kyle.Ward@campo-nc.us

Tel: (919) 996-4395

Abstract: The importance of reliable speed data for model validation cannot be understated, especially for MPOsdealing with air quality issues One of the biggest barriers to the collection of this data is often the time and costrequired to collect an adequate sample The N.C Capital Area MPO (CAMPO) has recently completed a pilotprogram to test the viability of using cell phones to collect speed and travel time data The program monitored 834centerline miles of roadway in the CAMPO and Durham-Chapel Hill-Carborro (DCHC) MPO 24 hours a day, 7 days aweek, during the entire month of March 2010 This data was collected at a significant cost savings over moretraditional methods, which translated into a more expansive coverage area Aggregation of this data, recorded at five-minute intervals, to the hour provided useful statistics like segment and route travel time data, average speeds byfacility type, and speed profile by time of day, including standard deviation for checking the variability in the data.Data review is currently underway to determine the full applicability of this data for both travel demand modeling andsupporting our Congestion Management Process

The purpose of this presentation is to report on the overall data collection process, project costs, findings, limitations,and possible applications For example, initially the data will be used to validate the next version of the TriangleRegional Model already under development, but in the future, key characteristics of the data can be further exploited.One of those characteristics is an accurate capture of free-flow speeds which may improve volume-delay functions.Finally, given the five-minute collection interval, a long-term goal could be to use an expanded program to accuratelymodel non-recurring congestion

The Congestion Management Process is another area where this data can shine If collected in the spring and fall ofeach year, we will be able to measure the impact of completed projects, as well as identify where new projects areneeded This will allow us to integrate our CMP with other prioritization and programming efforts directly

Keywords: cell, mobile, speed, data, pilot, CAMPO

7

11-107: Anchorage Bike Plan: On a Shoestring Budget

Topic Area: Planning for Use of Sustainable Modes: Transit, Biking and Walking

Lori Schanche

Non-motorized Transportation Coordinator

Municipality of Anchorage, Project Management and Engineering

4700 Elmore, Anchorage, Alaska 99507, USA

SchancheLE@muni.org

Tel: 907-343-8368

Fax: 907-343-8088

Jon Spring

Consultant, BCI and TransCad Model Consultant

Professional Business Services

807 G Street, Suite 200, Anchorage, AK 99501, USA

feebee@gci.net

Tel: 907-230-9287

Teresa Brewer (Corresponding Author)

Transportation Planner; TransCad modeler

Municipality of Anchorage, Anchorage Metropolitan AreaTransportation Solutions

4700 Elmore Rd, Anchorage, AK 99507, USA

In order to create a network of bicycle facilities for Anchorage, but still be budget conscious, the planners relied uponuse of the existing infrastructure where possible To determine which streets would be suitable to implement on-streetbicycle lanes, the Municipality of Anchorage asked people where they wanted to bike and then measured the existingroadways This was to utilize the Federal Highway Administration Bicycle Compatibility Index (BCI) to model andgrade the level of service of different routes The BCI measures the comfort of bicyclists riding on the roadway withtraffic and considers factors such as traffic speed and volume, adjacent land use and availability of width for bicyclelanes and paved shoulders

In locations where the BCI indicated that on-street bicycle lanes were not appropriate and where the numbers ofdriveway and intersection conflicts were minimized, the Plan recommends multi-use paths, separated from theroadway

The Anchorage Bicycle Plan has enjoyed tremendous support from bicyclists It will benefit the residents ofAnchorage by reducing vehicle/bicyclist accidents, easing traffic congestion, improving health and air quality, andaugmenting the system network

Keywords: Bicycle Plan, Non-motorized transportation, BCI index

11-108: Repurposing Turning Movement Counting Boards for PNR Counts

Topic Area: Transportation Data Collection and Management – Surveys, Counts and More

Andrew Rohne (Corresponding Author)

Transportation Modeling Manager

OKI Regional Council of Governments

720 E Pete Rose Way STE 420, Cincinnati, OH 45202, USA

Keywords: data collection, transit

9

11-110: HUB-CAP, A-HOW-TOOL TO MEET LEGAL CHALLENGES FOR LANE RENTAL DURING CONSTRUCTION

Topic Area: Negotiating Challenging Legal Issues in Planning

Edreece Azimi (Corresponding Author)

Abstract: Abstract - The Highway User Benefit-Cost Analysis Program (HUB-CAP) provides the Virginia Department

of Transportation (VDOT) with a standardized method to quantify road user benefits/costs to the traveling public based

on roadway geometric, traffic, and operating characteristics Based on this information, the Department can determinethe cost effectiveness of various alternatives including detours, temporary roadway or shoulder construction, off-peakhour, day work, night work, and the most appropriate project delivery method (i.e., Incentive/Disincentive, A+BBidding, etc.)

HUB-CAP provides valuable information to the designers and project managers when determining the mostappropriate construction staging and final design This application should be used early in the design process whilethere is still flexibility in the design The optimal design will mitigate or avoid disruptions before they can occur Thisapplication can also determine future highway user benefits-costs based on future improvement needs Byunderstanding the major factors influencing highway user benefits-costs, the analyst can take steps to minimize theeffect of any future rehabilitation activities on roadway users

This tool was developed after researching the methodologies and application used by several States’ Department ofTransportations and finally chose to utilize AASHTO’s 2003 Highway User Benefit Analysis method as a startingpoint AASHTO’s worksheets were modified to include before and after scenarios, look-up tables for various inputsnecessary to run the tool, and consistencies in the type of required data It consists of three main modules:

• Value of Time Savings / Benefits (both based on ADT and hourly volumes)

• Accident Costs / Benefits

• Operating and Ownership Costs / Benefits

The HUB-CAP application has already been implemented at VDOT by area construction engineers and constructionmanagers This has resulted in several projects being delivered on time with substantial saving to the users and VDOT

in construction duration, user costs and completion of the project on-time

Keywords: HUB-CAP

11-111: Optimising the Application of Urban HOV Lanes

Topic Area: Planning for Use of Sustainable Modes: Transit, Biking and Walking

Philip Stay (Corresponding Author)

Managing Director PSA Consulting

Member Engineers Australia; Chartered Engineer Aust; Registered Professional Engineer QLD

PO Box 15339 City East , Brisbane, Queensland 4002, Australia

However more and more road network operators are questioning the application of HOV lanes for predominantlyprivate vehicle use, particularly in light of the limited success of rideshare programs, high rates of infringements andthe ongoing public outcry of underutilised ‘empty’ traffic lanes

This paper / presentation will provide an Australian perspective on HOV lanes and draw on learnings from the recentevaluation and enhancement of a regional urban HOV network in South East Queensland It will examine the limitedsuccess of rideshare programs converting single occupant vehicles to carpools / vanpools compared to the growth inpatronage of high frequency bus services It will discuss the difficulties of maximising the efficiency of HOV laneswhen violations are high and the dangers of empty lane syndrome when car throughput rather than person throughputunderlies capacity assessments of urban arterial roads

The presentation will demystify many of the commonly held perceptions of HOV lanes by outlining the findings oflocal survey and research and will provide practical tips on enhancing HOV treatments to maximise the efficiency ofboth public transport and private vehicle transportation in an urban environment

Presenter Bio

Philip Stay is the Managing Director of PSA Consulting, a Brisbane (Australia) based specialist transport and land useplanning consultancy Prior to starting his own practice, Philip held senior and executive management positions withBrisbane City Council and State Government transport agencies for over 30 years

Since forming PSA Consulting in 2004, Philip and his staff have played an increasingly active role in transportplanning throughout Australia and have undertaken an impressive portfolio of regional, area, corridor and linktransportation studies and planning for major road and public transport projects

With qualifications in civil engineering, management and town planning and extensive experience as a transportplanner, Philip has been responsible for planning many of South East Queensland’s major transport projects includingthe recently opened A$1.8B upgrade of the Gateway Motorway and second Gateway Bridge

From 2008 - 2010, Philip was engaged by Queensland Department of Transport and Main Roads to develop the SEQHOV Network Plan, an extensive study that evaluated and determined the optimum rollout of HOV facilities acrossSouth East Queensland to 2031

Keywords: HOV network, transit lanes, bus lanes, bus transport, transport efficiency, empty lanes, violations, personthroughput, warrants

11

11-112: Evaluating and Communicating Model Results: Guidebook for Planners

[NCHRP 08-36 (Research for the AASHTO Standing Committee on Planning)]

Topic Area: Public Involvement for Successful Projects and Visioning for Long Range Transportation Planning

Daniel Goldfarb (Corresponding Author)

Senior Associate

Cambridge Systematics, Inc

4800 Hampden Lane, Suite 800, Bethesda, MD 20814, USA

Cambridge Systematics, Inc

2457 Care Drive Suite 101, Tallahassee, FL 32308, USA

Cambridge Systematics, Inc

4800 Hampden Lane, Suite 800, Bethesda, MD 20814, usa

jevans@camsys.com

Tel: 301-347-0100

Fax: 301-347-0101

Dalia Leven

Travel Demand Forecaster

Cambridge Systematics, Inc

4800 Hampden Lane, Suite 800, Bethesda, MD 20814, USA

dleven@camsys.com

Tel: 301-347-0100

Fax: 301-347-0101

Abstract: Often the division of responsibilities within MPOs relegates travel forecasting to technical experts while use

of model output for policy and plan development is in the hands of planners and policy makers This division of laborcan lead to unintended consequences in the decision-making arena As noted by the Committee for Determination ofthe State of the Practice in Metropolitan Area Travel Forecasting in the Transportation Research Board’s SpecialReport 288, Metropolitan Travel Forecasting – Current Practice and Future Direction, “There are many sources of errorand uncertainty in travel demand forecasting, but end users of most travel forecasts would not be aware of theselimitations” (page 85)

Planners are engaged in increasingly complex decision-making analyses relying on the output of ever-moresophisticated modeling tools, yet they often possess only a cursory understanding of travel forecasting models andtheir inherent assumptions, biases, and limitations This heightens the risk that the modeling process or its output will

be unintentionally misrepresented or misapplied, with unfortunate consequences for resulting decisions andinvestments

Transportation planners do not need to be able to develop a travel demand model or conduct a traffic forecast, but they

do need a solid understanding of key modeling fundamentals that go beyond recitation of the four-step process.Planners often act as the liaison between policy makers, the public, and modelers A stronger foundation in the

appropriate applications of forecasting models and the ability to effectively communicate this will strengthen thatplanning role.

This report, NCHRP 08-36/Task 89, identifies and describes assumptions, applications, and limitations thattransportation planners should understand about both conventional and advanced forecasting models and processes Itshould help planning practitioners to better understand the forecasting process, increase their ability to interpret andapply model output effectively, and better communicate this aspect of the transportation planning process to policymakers The report provides information to help transportation planners be able to (1) ask and answer criticalquestions about their agencies’ models and model development processes; (2) to understand how robust or sensitive theoutputs are, why that matters; and (3) incorporate that knowledge into planning and programming decision-makingprocesses

Keywords: travel demand, guidebook

13

11-113: Tour-Based Model for a Small Area

Topic Area: Travel Demand Modeling and Analysis - Advances in Practice

William Allen (Corresponding Author)

Principal

Transportation Planning Consultant

PO Box 390, Windsor, SC 29856-0390, USA

wgallen@isp.com

Tel: (803) 642-4489

Abstract: Models that represent travel as individual tours are replacing conventional aggregate models The transition

is too gradual for some, but the reasons include enormous budgets, schedules, data needs, and model run times.Research is still on-going the analytical approaches aren’t standardized The benefits seem largely theoretical andthere is little documentation of real-world improvement to the forecasts

The author believes that individual tour models can be simplified for smaller areas Retain the critical elements, butdrop items that are the most resource-intensive Simplifications such as omitting transit are often appropriate forsmaller cities Travel relationships can be derived from a larger city’s survey, then transferred to a smaller city

This is the approach taken in Glynn County, Georgia (Brunswick) This county of 77,000 wanted a new travel model,but had a limited schedule and budget and almost no data The model starts with a conventional generation step, butoutputs round-trip tours by individuals A doubly-constrained destination choice model is applied by income, withprobabilities from a conventional gravity formula

A key element is the intermediate stop model, estimated from a 2001 survey in Baltimore About 30% of tours havestops A logit model estimates the probability of 0/1/2/3 stops for each half-tour Key variables are HH size, income,production and attraction area type, and employment density Another destination choice model estimates the stoplocations from a locus of possible destinations The key variables are detour time, ln(employment), and stop zone areatype

Auto occupancy uses a logit model for 1/2/3/4+ occupancy modes Time of day uses fixed fractions by purpose, whichare converted to the probability of 4 time periods for each half-tour A trip accumulator step converts the tours plusstops into standard trip tables Assignment is performed conventionally The entire model runs in one hour on astandard computer using one Cube script

The conclusions from this work are that if the process is simplified enough, a reasonably robust tour-based model can

be made available to a smaller area In addition, the new methodology helped improve the assignment calibrationcompared to another model for this area

Keywords: tour-based model, small urban area, intermediate stop model, Cube

11-114: Sustainable Transportation Solutions in Austin Texas

Topic Area: Measuring and Planning for Livability, Sustainability & Environmental Justice

Kurt Schulte

Vice President

Kimley-Horn and Accosiates

2201 West Royal Lane, Irving , Texas 75063, USA

kurt.schulte@kimley-horn.com

Tel: 214-420-5619

Fax: 214-420-5580

Gordon Derr (Corresponding Author)

Assistant Director of Transportation

That's the mission and purpose of the Austin Strategic Mobility Plan (ASMP) Working with local and nationalexperts, the City of Austin is focusing on short- and long-term transportation needs and new and improved sustainablealternatives to driving alone The ASMP will also help support and inform the ongoing Imagine Austin comprehensiveplanning effort We're also building upon Austin's previous planning efforts and initiatives to provide the informationand tools we need for action Specifically, the ASMP is directly tied to the Six Principles of Livability currently beingdiscussed by HUD, EPA and FHWA

The ASMP will guide both near- and long-term coordinated investments that will further the City’s mobility goals inthe context of a shared community vision The following bulleted items provide a general outline of this innovativeprocess

• Gap analysis/Strategic Vision and GIS based automated project prioritization process, to identify near-termtransportation projects, from sidewalks to bike lanes to turn lanes to highway ramps, that will address currentchallenges A key element of this new process is to evaluate potential mobility investments based on how well theyfulfill eight key community objectives

• Corridor planning that will focus future investments so they can best connect people, routes and destinations inAustin and throughout Central Texas The ASMP team reviewed Austin’s key transportation corridors to identifystrategic needs for coordinated projects that integrate bus, rail, pedestrians, bicycles, and cars This will bring Austin’splanning and investment decisions in line with national best practices

o Urban Rail Program- As part of the Austin Strategic Mobility Plan, the City of Austin is exploring alternativesfor an urban rail system that would serve key destinations in Central Austin and provide needed connections within theCentral Texas transit and mobility network

Keywords: Sustainable Transportation Solutions in Austin Texas

15

11-115: Feedback on Feedback: CAMPO’s Findings from Testing Various Feedback Approaches

Topic Area: Travel Demand Modeling and Analysis - Advances in Practice

Kevin Lancaster

Senior Modeler

Capital Area Metropolitan Planning Organization

505 Barton Springs Rd, Ste 700, Austin, TX 78704-1245, USA

kevin.lancaster@campotexas.org

Tel: 512-974-2251

Fax: 512/974-6385

Jonathan Avner (Corresponding Author)

Senior Transportation Planner

Wilbur Smith Associates

9500 Arboretum Blvd, Ste 360, Austin, TX 78759, USA

javner@wilbursmith.com

Tel: 512-592-3842

Fax: 512-870-7701

Karen Lorenzini

Associate Research Engineer

Texas Transportation Institute

1106 Clayton Lane, Ste 300E, Austin, TX 78723, USA

to address the consistency issue Here we will demonstrate our findings with regard to feedback implementation anddiscuss the role feedback plays in project evaluation

Although feedback has been explored extensively in existing literature, there is still much debate on the bestapproach to take in implementing it Commonly-cited approaches for the feedback itself include Direct Feedback,Fictive Costs, Methods of Successive Averaging (MSA), and Constant Weight Methods This discussion also considerswhat model output is being manipulated and fed back, as well as various criteria for measuring feedback convergence

or stability of the model system

The Capital Area Metropolitan Planning Organization (CAMPO) of Austin, Texas, tested various feedbackapproaches for its base year 2005 model: Caliper Corporation’s recommended application of MSA (the model is run inTransCAD 5.0) based on feeding back link volumes versus three different percentage balances of the Constant WeightMethod applied to trip tables following the method advocated by David Boyce Also tested were measures ofconvergence and system stability, including total trip table change, percent root mean square error (RMSE) of the triptable, RMSE of the travel time skim table, maximum link flow change, and the GEH statistic How CAMPO ultimatelydecided on one feedback approach for model application will be shared, including balancing the issue of decreasingreturn per model iteration

A further question that the researchers have is in regard to the relevance of this work to project evaluation andhow feedback and choices made for feedback closure may or may not affect the results for major projects As part ofour analysis, we will test and present results of a sensitivity analysis of major projects in the model

Keywords: feedback, modeling

11-116: Testing the Transferability of Activity Based Model Parameters

Topic Area: Travel Demand Modeling and Analysis - Advances in Practice

Andrew Rohne (Corresponding Author)

Transportation Modeling Manager

OKI Regional Council of Governments

720 E Pete Rose Way STE 420, Cincinnati, OH 45202, USA

arohne@oki.org

Tel: 513-621-6300

Fax: 513-621-9325

Vince Bernardin

Chief of Transportation Modeling

Bernardin, Lochmuller, and Associates, Inc

6200 Vogel Rd, Evansville, IN 47715, USA

Four research test cases are to be compared:

* Columbus's model

* Re-calibration only of Columbus constants based on Cincinnati data

* Re-estimation of all parameters based on Cincinnati's household travel survey but using the same utilityspecification used in Columbus

* New utility specification and parameters based on Cincinnati data

The analysis will include chi-squared and likelihood ratio tests to compare each model and determine whether there is

a statistically significant difference between the borrowed and newly developed models as well as t-tests on thetransferability of individual parameters

Keywords: activity-based modeling, activity generation, auto ownership, model estimation, model calibration

17

11-117: Defining a Travel Demand Model Sharing Protocol

Topic Area: Travel Demand Modeling and Analysis - Advances in Practice

Karen Lorenzini

Associate Research Engineer

Texas Transportation Institute

1106 Clayton Lane, Suite 300 E, Austin, Texas 78723, USA

k-lorenzini@ttimail.tamu.edu

Tel: 512-467-0952

Fax: 512-467-8971

Daniel Yang

Program Manager of GIS-Modeling

Capital Area Metropolitan Planning Organization

P.O Box 1088 , Austin, Texas 78767, USA

daniel.yang@campotexas.org

Tel: 512-974-6423

Phillip Reeder (Corresponding Author)

Program Manager/Research Scientist

Texas Transportation Institute

1106 Clayton Lane, Suite 300 E, Austin, Texas 78723, USA

This paper will discuss the trade-offs involved in disseminating a regional travel model for application by externalentities It will also provide a brief overview of the procedures that a number of MPOs have implemented for sharingand disseminating their travel demand model with the objective of summarizing the benefits and disadvantages of thevarious MPO practices Additionally, it will offer some examples of what MPOs require for the use of their model.Finally, it will provide a summary of the model sharing protocol developed by CAMPO

The paper will provide useful information to agencies and urban areas that are considering developing their ownapproach for a model sharing protocol It is also an opportunity for agencies to consider the merits of a cost sharingprotocol whereby an MPO’s investment in model development and expenditure of staff time and resources can beacknowledged and accounted for in the dissemination of internally developed products More importantly, it will offer

an example of an implemented approach for model dissemination based on issues and trade-offs considered by oneMPO

Keywords: travel demand model

11-118: OPTIMIZATION OF SATURATED ARTERIAL NETWORK

Topic Area: Operational Planning, ITS, and Traffic Microsimulation

Sabbir Saiyed (Corresponding Author)

Manager

Regional Municipality of Peel

10 Peel Centre Drive, Brampton, Ontario L4C 4X1, Canada

sabbir.saiyed@peelregion.ca

Tel: 905-791-7800 ext 4253

Al Stewart

Vice Principal

Royal Military College of Canada

PO Box 17000, Kingston, Ontario K7K 7B4, Canada

stewart-j@rmc.ca

Tel: 613 541-6000 ext 6371

Abstract: The development of optimal signal timing plans for signalized intersections in an arterial network is acomplicated task This is due to several factors associated with the vehicular traffic and types of signal control Themajority of all network delays and increased fuel costs can be attributed to the delays being experienced at thesignalized intersections on the major arterials Currently, pre-timed and actuated control strategies are widely used atmany signalized intersections Adaptive signal control strategies, on the other hand, have not been extensively utilizeddue to the difficulty in determining their benefits This research paper investigates and compares the optimization andthe consequential benefits that can be achieved through the use of pre-timed, actuated and adaptive signal controlstrategies for saturated arterial network by carrying out a systematic simulation study

Traffic variables such as signal delays, level of service, volumes, capacities and turning movements are examined todetermine the primary measures of signalized intersections It is concluded that the optimization significantlyimproves the performance of pre-timed, actuated and adaptive signal controls, and further that adaptive signal controlperforms better than the pre-timed signal control and is comparable or better than the actuated signal control forsaturated arterial networks

Keywords: Adaptive signal control, actuation, optimization, simulation, saturated arterial network

19

11-119: Assessing the Marginal Cost of Freeway Congestion for Vehicle Fleets Using Passive GPS Data

Topic Area: Innovative Local Funding Sources for Transportation – Taxes and Tolls

Nick Wood (Corresponding Author)

Assistant Transportation Researcher

Texas Transportation Institute

1106 Clayton Lane, Suite 300E, Austin, Texas 78723, United States

nickwood@gatech.edu

Tel: 518-775-0292

Randall Guensler

Professor

Georgia Institute of Technology

790 Atlantic Drive, Atlanta, Georgia 30332, United States

randall.guensler@ce.gatech.edu

Tel: 404-894-0405

Abstract: This research examines the amount of time spent in congested freeway travel by a number of business fleetsoperating in Atlanta The researchers monitored actual time spent in congestion and using estimated labor costs foreach relevant industry calculated the business savings that could accrue if the congested travel could be shifted to anuncongested condition The results provide some insight into the potential benefits to businesses that could accrueshould variable toll pricing be implemented on their congested corridors, allowing the businesses to pay a toll andreduce operating costs The fleets in the scoping study represented commercial deliveries of goods and services,government agency vehicles, and transit systems Vehicles were monitored by using a passive GPS assembly thattransmitted speed and location data in real-time to an off-site location All limited-access expressway activity datacollected within the 13-county Atlanta metropolitan region were used in the analysis Over 217 hours of freewayactivity were monitored during 354 vehicle-days of travel Delay rates, expressed as a unit of lost minutes per miletraveled, were based upon the difference between observed speeds during congestion and an assumed toll facility free-flow speed of 45 mph The difference between the 50th and 95th percentile delay rates was then used as the measure

of travel unreliability Daily average values of extra time needed per fleet vehicle to ensure on-time arrivals yielded amedian buffer across all fleets of 1.65 hours of added time per week per vehicle Weekly marginal costs per fleetvehicle were estimated by factoring in the corresponding driver wages (or hourly operating costs for transit fleets).Equivalent toll rates were then calculated by multiplying the 95th percentile delay rate by fleet hourly labor costs.Hence, the equivalent toll per mile traveled was representative of the relationship between the marginal costs ofcongestion experienced and the hypothetical free-flow travel condition (under first-best rules of marginal cost pricing).The resulting median equivalent toll rates across all fleets by time of day period were $0.43 per mile for weekdaymorning, $0.13 per mile for weekday midday, $0.53 per mile for weekday afternoon, and $0.01 per mile for weekdaynight and weekend periods

Keywords: GIS, Atlanta, HOT, CMV-lane, Travel Time Reliability, Travel Time Delay, Municipal Waste Fleets,Express Bus Fleets, Concrete Trucks

11-120: Non-Traditional Public Engagement: E-Surveys and Virtual Public Meetings

at the NYSDOT

Topic Area: Public Involvement for Successful Projects and Visioning for Long Range Transportation Planning

Joel Kleinberg (Corresponding Author)

Transportation Analyst

New York State Department of Transportation

250 Veterans Memorial Highway, Room 4A4, Hauppauge, NY 11788, USA

To reach a broader audience as part of a corridor planning study, the NYSDOT produced an online survey andfollowed it with a second survey, billed a “virtual meeting.” The online survey engaged eight times the participantscompared to public meetings held a year prior Beyond increases in participation, the survey enabled involvement bythose typically absent from traditional public meetings For example, of the total, roughly one-third of the surveyswere completed by those living outside the study area Preliminary examination of the “virtual meeting” indicates asimilar response pattern

It is the experience of the NYSDOT that the internet allows many more voices to be heard in an equitable and highlycost-effective manner It is an important tool that may help planners to better identify areas of general publicagreement, and the use of online surveys should be highly encouraged in future public participation efforts

The survey provided a wealth of information specific to the study area that was utilized in identifying transportationchallenges, in developing and rating transportation improvement ideas, and in prioritizing these ideas Informationobtained by these online public involvement efforts challenged assumptions and made potentially significant impacts

to the study process

Keywords: Public Involvement; Engagement; Participation; Survey; Meetings; Internet; Online;

21

11-121: Impact of Crowding on Rail Ridership: Sydney Metro Experience and

Forecasting Approach

Topic Area: Travel Demand Modeling and Analysis - Advances in Practice

William Davidson (Corresponding Author)

Senior Vice President, Parsons Brinckerhoff

303 Secons Street, #700N, San Francisco, California 94107, USA

davidson@pbworld.com, Tel: 925-202-3395

Abstract: Sydney is a city of significance to the world as well as Australia The New South Wales (NSW) Governmenthas focused on the development of a metro network for Sydney as part of its integrated transport and land useframework to support the achievement of state and national goals

This paper describes one innovative aspect of the development and calibration of the Metro Network Transport ModelMNTM MNTM is a mode and route choice module which is applied downstream of the NSW Government’s StrategicTravel Model (STM) STM is a strategic forecasting tool that is used in the early stages of project development toprovide preliminary usage data for transport and infrastructure projects MNTM is a behavioural choice model whichwas refined and calibrated to meet the model platform objectives The overall approach was to disaggregate transittrips into access/main mode(s)/egress stages The model also includes functionality for feedback of rail station parkingcapacity and train capacity and crowding effects through inner and outer loops in the model application

Most travel models and subsequent user benefit calculations ignore the fact that transit vehicles have a limitedcapacity Contrary to traffic assignment procedures that are based on iterative equilibration of demand and supply,transit assignment procedures do not have an equilibration feedback and are based on an unrealistic assumption thatthe transit network can accommodate any ridership The approach developed and applied within MNTM deals withtransit vehicle capacity constraints and crowding in the vehicles

The first purpose of the developed method was to ensure feasibility of transit ridership forecast for each line andsegment with respect to the total capacity This means that cases where the transit volume exceeds total segmentcapacity should be penalized until the feasible solution is reached The most effective and behaviorally realistic way toimplement this is through extra weight functions applied at the boarding station (assuming that the riders will have towait for the next vehicle) A feasible solution might not exist especially if a restrictive transit assignment frameworkwith a fixed transit table is employed (i.e the riders of overcrowded lines can switch to some other lines) In order toensure a feasible solution a mode choice framework is also employed (i.e the riders of overcrowded lines can switch toalternative modes)

The second purpose of the developed method was to take into account crowding as a negative factor in the userperception of transit service quality From this standpoint, not only exceeding of the total vehicle capacity but alsoexceeding the seating capacity (or even approaching it) should be penalized since standing is generally considered bytransit passengers as a negative factor Standing, however, should not be penalized in the same way as exceeding thetotal capacity since it is still a feasible (and observed situation) It has to be calibrated for the base year to replicate theobserved pattern (i.e transit volumes including seating and standing) In terms of behavioral realism, the probability

of having a seat should be reflected in the segment in-vehicle time weight There is a strong indication, both fromexisting research and the Stated Preference survey undertaken in Sydney, that in-vehicle time for a standing passengershould be weighted at least by a factor of 2.0 compared to a seating passenger This factor should be incorporated both

in the transit assignment and mode choice models The associated transit crowding functions have been developed thatexpress average weight for the mix of standing and seating passengers for each transit volume and configuration oftotal/seating capacity of transit vehicles

The developed extra wait functions and in-vehicle time weight function have been incorporated in the integrated modechoice – assignment equilibrium procedure as part of MNTM This method can be useful for modelers and plannersdealing with urban transit systems (and specifically, mass transit projects) that have reached the capacity

Keywords: transit capacity and crowding model for Sydney

11-122: Predicting the Impacts of Housing and Jobs Site Decisions on Work Travel in Connecticut: A Model using Census Journey-To-Work Data

Topic Area: Integrated Transportation-Land Use Modeling and Planning for Smart Growth, Transit-Oriented-Design,and More

Amanda Kennedy (Corresponding Author)

Associate Planner

Regional Plan Association

2 Landmark Square Ste 108, Stamford, ct 06901, US

amanda@rpa.org

Tel: 4135638735

Abstract: This project identified those factors affecting work travel patterns throughout the State of Connecticut andcreated a user-friendly tool to model the VMT produced by different development patterns Demand is growing foruser-friendly, place-based tools to assess the transportation impacts of neighborhood and regional developmentpatterns Network models typically used in transportation are cumbersome, lack sensitivity to detailed land usechanges, and were not designed to answer the questions we currently ask of them

The tool predicts VMT tied to land use patterns across a large area with multiple employment centers, while usingnationally available data collected at a small geography that can describe the local built environment and localdemographics We used tract-level data from the 2000 Census Transportation Planning Package as inputs to the model.The tool will enable planners at the municipal level to estimate the vehicular travel that will result from theconstruction of additional housing or commercial facilities, taking into account their design and proximity to jobs,transit, and the existing labor force, and may be scaled up to the municipal, regional, and state levels as part of anassessment of plans of conservation and development

Two separate models together enable travel impacts to be estimated from both housing and commercial development.Travel to job sites is reduced by higher household density; share of jobs in education, health, and retail; proximity tobus; and the presence of more households than jobs in an area Worksites further from large employment centershaving higher wages and a larger share of professional office jobs are associated with higher than average commuterdriving Higher employment densities at work locations were also associated with higher than average commuter milestraveled, since Connecticut’s cities draw workers from a large commute shed and transit use is low

Our analysis of factors affecting work-related travel by home locations showed the same relationships found byprevious researchers: VMT was lower for communities with lower incomes, close to job centers, in areas with high job

to household ratios Higher household density and intersection density were both associated with lower average dailywork vehicle travel

Keywords: VMT, Carbon Emissions, CTPP, Census, Model, Regression, Connecticut, RPA

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11-123: Challenges and Findings Estimating Demand for Special-Event Transit, the

“Train-to-the-Game” Example

Topic Area: Travel Demand Modeling and Analysis - Advances in Practice

Greg Spitz (Corresponding Author)

Director

Resource Systems Group

55 Railroad Row, White River Junction, Vermont 05001, USA

Estimating demand for the TTTG service is different than for a typical transit service, as certain factors are moreimportant for special events trips This study found, for example, that party size is a significant factor in estimatingwhether special events goers will choose transit or not Not surprisingly, the study found that the higher the party sizethe less likely transit would be used to travel to an event While this also is true in regular demand studies, it was morepronounced for this study, as the vehicle occupancy for special events is much higher than for typical trips

The main events hosted at the New Meadowlands Stadium are NFL football games Tailgating is a major activity forfootball fans Based on its name, tailgating is also a mode-specific activity The study estimated that the likelihood oftailgating significantly curtails a traveler’s likelihood to use transit to travel to a football game While these demandvariables are not unexpected, they are different from a more traditional demand study and they have different modelapplication and forecasting challenges: what percentage of football fans currently tailgate? How many fans are seasonticket holders? Etc

Other challenges in the demand estimation included understanding from which transit market demand would come, asthe TTTG broadens the transit market considerably by improving service for CT, NJ, and NY residents across NJTRANSIT, Metro-North Railroad, and Long Island Rail Road The study also shows the differential impact of havingrail (current) versus bus access (previously) to the Sports Complex from NJ TRANSIT’s regional rail system usingcurrent ridership data

Keywords: demand forecasting, modeling, transit, special events

11-124: Adapting a Four-Step MPO Travel Model for Wildfire Evacuation Planning: A Practical Application from Colorado Springs

Topic Area: Others (Innovation Travel Model Application; Emergency Response/Evacuation Planning; InteragencyPartnership)

Maureen Paz de Araujo (Corresponding Author)

Senior Transportation Planner

Transportation Planning Director

Pikes Peak Area Council of Governments

15 S 7th Street, Colorado Springs, Colorado 80905, U.S.A

Midwest Regional Director

Wilbur Smith Associates

8164 Executive Drive, Suite A, Lansing, Michigan 48917, U.S.A

phershkowitz@wilbursmith.com

Tel: 517-622-2500

Bret Waters

Division Manager

City of Colorado Springs, Office of Emergency Management

375 Printers Parkway, Colorado Springs, Colorado 80910-3191, U.S.A

in Colorado Springs and how feedback from these groups improved evacuation response planning While theevacuation model was developed and refined for western mountainous cities like Colorado Springs that have

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residential areas in very dry foothill-type terrain, the approach has value for other areas of the west as well as flat areaswhere planned evacuation may take place.

Keywords: evacuation, wildfire, emergency services, transportation, traffic operations, safety, traffic model, four step,fire department, mobilization, contra-flow, signal planning, Rocky Mountains

11-125: An Innovative Approach to Mapping Vehicle Classification Data

Topic Area: Transportation Data Collection and Management – Surveys, Counts and More

Steve Farnsworth (Corresponding Author)

Assoc Research Scientist

Texas Transportation Institute

2929 Research Pkwy., College Station, TX 77843-3135, USA

of vehicle classification data was accumulated However, the data was in a raw format and, therefore, not easily used

by planners, modelers, and others interested in the data

As a result, the Texas Transportation Institute (TTI), at the request of TxDOT, undertook the task of developing ameans to disseminate the wealth of vehicle classification count data in a user-friendly format Using a Goggle mapsinterface, TTI has developed a means to provide traffic count data results that are easy to use for all interested parties.The presentation will provide an overview of the development of this useful tool, the process used to take raw countdata and convert it to HTML code, and the features of the mapping product Included in the features of the vehicleclassification mapping are summary statistics such as directional splits, commercial versus non-commercial totals, andaxle factors for every location that had a traffic count performed The mapping program also allows for sorting ofcount locations by the station name, roadway name, and functional classification of the roadway, and it provides access

to the raw data files that are provided in Excel format Lastly, the presentation will provide an overview of the varioususes of the program and alterations that can be made to the program to tailor it to the needs of the end-user

Keywords: traffic counts, vehicle classification counts, axle factors

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11-126: Congestion Management Process (CMP): Lessons Learned and Ongoing Challenges for Connecting Long-Range Plans and Projects

Topic Area: Integrated Transportation-Land Use Modeling and Planning for Smart Growth, Transit-Oriented-Design,and More

Zoe Neaderland (Corresponding Author)

Manager, Tr Safety & Cong Mgmt

Delaware Valley Regional Planning Commission

190 N Independence Mall W, Philadelphia, PA 19106, USA

ZNeaderland@dvrpc.org

Tel: 215-238-2839

Jesse Buerk

Transportation Planner, Tr Safety & Cong Mgmt

Delaware Valley Regional Planning Commission

190 N Independence Mall W, Philadelphia, PA 19106, USA

JBuerk@dvrpc.org

Tel: 215-238-2948

Abstract: The Delaware Valley Regional Planning Commission (DVRPC) has a nationally recognized CMP; forexample, it is one of six case studies in the FHWA’s CMP Guidebook The CMP is an integral part of how DVRPCadvances toward the Smart Growth goals of its Long-Range Plan This presentation would share transferable solutionsand highlight areas for further work, particularly in the following matters:

1 The DVRPC CMP has succeeded in more closely linking the Philadelphia region’s Long-Range Plan with itsTransportation Improvement Program (TIP) DVRPC is a regional planning commission as well as a MetropolitanPlanning Organization (MPO), so this involves addressing the Long-Range Plan goals to establish effectivemultimodal transportation, develop livable communities, preserve natural and historic resources, manage growth, andstrengthen the regional economy

2 It has been an ongoing effort to figure out how to assess the anticipated effects of strategies, as required in federalCMP regulations for nonattainment Transportation Management Areas DVRPC will share its progress with sketch-level software tools to perform analysis of sets of multimodal strategies This is a valuable area for professionaldiscussion

3 As a bi-state region where widespread operations data is just becoming available, DVRPC will share how an MPOcan realistically begin to use operations data for planning purposes

Keywords: Congestion Management Process (CMP), MPO, Long-Range Plan, Metropolitan Transportation Plan(MTP), multimodal transportation and land use planning

11-127: The Highway 82 Corridor: Planning for Alternatives to Sprawl in Rural Areas

Topic Area: Integrated Transportation-Land Use Modeling and Planning for Smart Growth, Transit-Oriented-Design,and More

John Poros (Corresponding Author)

Associate Professor

Mississippi State University

P.O Box AQ College of Architecture, Art, and Design, Mississippi State, Mississippi 39762, U.S.A

The Carl Small Town Center began a project in 2009 to map an alternate future for the Highway 82 corridor with atwenty-five year plan for how the corridor and the region could be developed A GIS based analysis of the 23 milecorridor was conducted to determine the existing natural, cultural and economic resources of the area From thisanalysis, a series of suitability maps were developed combining analysis data to determine scenarios to use land for thegreatest preservation of natural resources, agricultural use or commercial/residential development

Using the suitability analysis, a plan for development of the corridor has been conceived that limits development tosmall, mixed-use, transit oriented towns at strategic points along the corridor The towns are placed proximate toexisting interchanges and form a chain along the corridor These towns of approximately 5,000 residents would belinked together not only by the highway, but by bus, bicycle and pedestrian paths and, in some cases, light rail Thetowns would replace the existing unsustainable development pattern with one that would not only provide a sustainablecommunity for residents, but preserve farmland and natural areas along the corridor as well The lessons learned in thisproject can be applied to other rural transportation corridors where the continued low-density growth of micropoliseshas put pressure on farmland and natural habitat

Keywords: Rural transportation corridor Smart Growth Mississippi

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11-128: Some aspects of bush-based algorithms for the traffic assignment problem: turns handling, monotone volume/delay functions, and path analysis

Topic Area: Travel Demand Modeling and Analysis - Advances in Practice

Calin Dan Morosan (Corresponding Author)

Some published articles suggest the handling of turns by using the so called "node explosion" Practical examples arepresented where partial node explosion can lead to erroneous results A special type of graph duality can be exploitedfor the network representation in order to design bush-based algorithms for TAP which integrate turns efficiently.The implementations of bush-based algorithms described in the literature use second order derivatives information.This requires the volume-delay functions (VDF) to be continuously differentiable In practice there are numerousoccurrences of VDF’s that do not satisfy this property This constraint is not necessary and an efficient implementationcan be realized without using the derivatives of the VDF’s Certain bush based algorithms can perform poorly in thepresence of turns due to the lack of second order information if the turn penalties are constant and not flow dependent.Path analysis for bush-based algorithms poses performance problems on real life projects if proportionality andconsistency of paths is to be satisfied Possible solutions of these problems are discussed

A variant of Dial (2006) algorithm for TAP, which is handling turns using a dual graph approach, was implemented andapplied to transportation networks originating from practice Computational results on large scale networks arereported and compared with other algorithmic approaches Last, but not least, it is shown that an origin-baseddecomposition of the network flows is not necessary; the bush based algorithm implemented uses a destination-basedapproach

Keywords: equilibrium traffic assignment, bush-based algorithms

11-129: INTEGRATING UNCONVENTIONAL ARTERIAL INTERSECTION DESIGNS INTO TRANSPORTATION PLANNING PROJECTS

Topic Area: Operational Planning, ITS, and Traffic Microsimulation

Leta Huntsinger (Corresponding Author)

Technical Services Team Leader

Durham-Chapel Hill-Carrboro MPO

101 City Hall Plaza, 4th Floor, Durham, NC 27701, USA

leta.huntsinger@durhamnc.gov

Tel: 919-560-4366 ext 36423

Jessica Dimmick

Planner/Engineer

Renaissance Planning Group

455 Second Street SE, Suite 300, Charlottesville, Virginia 22902, USA

jdimmick@citiesthatwork.com

Tel: 434.296.2554 x304

Abstract: The benefits of unconventional arterial intersection designs (UAIDs) are well documented but theimplementation of such designs still lags behind traditional intersection improvements Previous research by theauthor in this area identified education as one of the strategies for overcoming barriers to more widespreadimplementation of UAIDs The purpose of this paper is to take a step forward in overcoming this particular barrier byproviding an educational overview of UAIDs, the benefits of such designs, and a strategy for incorporating them intoplanning projects The recommended strategy will cover initial screening, critical lane analysis, and modeling Thepaper will cover both system and corridor level analysis Implementation at the corridor level will be demonstratedthrough a case study using the NC 54 corridor study conducted for the Durham-Chapel Hill-Carrboro MetropolitanPlanning Organization (DCHC MPO) by Renaissance Planning Group The NC 54 study considered, evaluated, andrecommended unconventional designs

Keywords: Unconventional intersections, corridor planning, integrating planning and operations

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