Developing a high fidelity GIS based travel demand model framework for improved network wide traffic estimation
ii DEDICATION. iv ACKNOWLEDGMENT v TABLE OF CONTENTS vi LIST OF TABLES x LIST OF FIGURES xii LIST OF ABBREVIATIONS xiv 1.0 INTRODUCTION AND PREOBLEM DEFINITION 1 1.1 Overview 1 1.2 Problem Statement 4 1.3 Goals and Objectives 6 1.3.1 Primary Goal 6 1.3.2 Objectives 7 1.4 Research Hypothesis 8 1.5 Research Deliverables (Expected Outcomes) 9 1.6 Organization of the Dissertation 11 2.0 LITERATURE REVIEW 14 2.1 Critical Review of Literature 14 2.1.1 Review of Traditional Modeling Practices 15 2.1.2 Modeling Network wide Traffic Volumes with Improved Accuracy 18 2.1.3 Traffic Analysis Zone (TAZ) Design and Selection 21 2.1.4 HFTDM with Advanced Technology and "Intelligent" Data Sources 29 2.1.4.1 GIS in Relation to HFTDM 30 2.1.4.2 Remote Sensing (RS) Technologies in Relation to HFTDM 32 2.2 Conclusions and Recommendations 38 3.0 METHODOLOGY 42 3.1 Traditional Travel Demand Modeling Framework. 46 3.1.1 Trip Generation (Production/Attraction) 48 3.1.2 Trip Balancing 49 3.1.3 Trip Distribution 50 3.1.4 Conversion of Production/Attraction to Origin/Destination (P/A to O/D) 51 vii 3.1.5 Traffic Assignment 52 3.1.6 Model Validation and Calibration 54 3.2 Methodological Framework for Developing HFTDM 57 3.2.1 Utilizing Census based TAZ Structure at DA Level 59 3.2.2 Collecting and Tabulating the Modeling Database 61 3.2.3 Parcel based Digital Property Maps (DPMs) Database 62 3.2.4 Extracting Impervious Surface Area (ISA) from Rural DA TAZs 62 3.2.5 Developing an Automated Zone Design (AZD) Methodological Framework 63 3.2.6 Disaggregating Sociodemographic and Employment Data Using AIM 71 3.2.7 Developing and Running the HFTDM Framework 72 4.0 DEVELOPING A GIS BASED HFTDM FRAMEWORK FOR GFA 73 4.1 GFA General Sociodemographic and Employment (SD&E) Characteristics 75 4.1.1 Fredericton (CA320) General SD&E Characteristics 78 4.1.2 Oromocto (CSD1303012) General SD&E Characteristics..79 4.2 GFA Base Case Model Data Collection and Preparation. 80 4.2.1 Sociodemographic (SD) Data Collection and Preparation 83 4.2.2 Employment Data Collection and Preparation 84 4.2.2.1 Developing Employment Database for Fredericton (CA320) 86 4.2.2.2 Developing Employment Database for Oromocto (CSD1303012) 88 4.2.3 Sociodemographic Employment (SD&E) Database Compilation 91 4.3 Developing AZD Procedure for Fine Grained TAZ Structures Output 92 4.3.1 Acquiring Digital Property Maps (DPMs) Database 95 4.3.2 Extracting Impervious Surface Area (ISA) Layer 96 4.3.2.1 Remotely Sensed Image Preparation 97 4.3.2.2 Remotely Sensed Image Classification 99 4.3.3 Relating ISA Attribute to Human Utilization of Parcels 103 4.3.4 Running AZD Framework for Rural Area TAZ Design 112 4.3.5 Developing Final GFA TAZ Structures 116 4.4 Developing Areal Interpolation Method (AIM) for Data Transfer 121 4.5 Preparing Roadway Network and Network wide Traffic Count Data Files. 124 4.5.1 Preparing Roadway Network Model for GFA 124 4.5.2 Preparing Network wide Traffic Count Data File 126 4.5.2.1 Coding Base Case RN with Place and Street Names Attributes 128 4.5.2.2 Developing Annual Traffic Growth Rate (ATGR) for the Study Area 128 4.5.2.3 Coding PCSs TCSs from NBDTI Routine Counting Scheme 130 4.5.2.4 Coding STCSs from NBDTI Special Counting Scheme 130 4.5.2.5 NBDTI Designated Numbered Highways (DNHs) Counts. 131