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xvii PART 2: ISSUES IN ADOPTING INTEGRATED DYNAMIC MODELS SYSTEMS 125 CHAPTER 4 Introduction 127 Background 127 Purpose 128 CHAPTER 5 Case Examples 131 SHRP 2 C10A Jacksonville, Florida, and Burlington, Vermont 131 SHRP 2 C10B Sacramento, California 133 San Francisco County Transportation Authorityâs âDTA Anywayâ 135 Maricopa Association of Governments Inner Loop Traffic Model 137 CHAPTER 6 Implementation Issues 141 Institutional Awareness and Capacity 141 Costs and Schedule 144 Data 148 Methodology and Software 150 Application 152 CHAPTER 7 Conclusions 155 Migration Path 155 Model Implementation Issues 156 REFERENCES for Part 2 159 Online version of this guide: www.trb.org/Main/Blurbs/170963.aspx.
1INTRODUCTION In order to support informed decision making, transportation agencies have been increasingly developing and experimenting with activity-based travel demand models that describe how people plan and schedule their daily travel. Activity-based models more closely replicate actual traveler decisions and thus may provide better forecasts of future travel patterns. While there have been recent successes implement- ing practical activity-based models, these have been limited mostly to larger metro- politan planning organizations (MPOs) and a few state departments of transportation (DOTs). This guide has been developed to help directors, managers, and planners make informed decisions about forecasting model development and application. The guide is composed of two parts. Part 1 is a primer intended to provide a practical over- view of activity-based model development and application. Part 2 discusses issues in linking activity-based models to dynamic network assignment models. The fi rst part comprises three chapters. The fi rst chapter is for managers or directors who make decisions about what travel demand models an agency will use and begins with a brief introduction to the motivation and practice of developing and applying travel models. Chapter 1 also provides a pragmatic assessment of activity- based model development considerations, both technical and institutional, and exam- ines how activity-based models are integrated with other forecasting tools. The second chapter provides a technical road map for developing an activity- based model system for modeling or planning managers. Chapter 2 identifi es devel- opment strategies that agencies have used and discusses each aspect of the model development process, including ⢠designing the modeling system to address key policy considerations; ⢠specifying temporal, spatial, and typological resolutions; ⢠identifying activity-based model subcomponents and the relationship between the activity-based model and other forecasting tools;
2ACTIVITY-BASED TRAVEL DEMAND MODELS: A PRIMER ⢠developing data; ⢠implementing the models and model system linkages; and ⢠applying the model system. The third chapter presents and explains key activity-based model concepts. The intended audience for this section is modelers who have some familiarity with tradi- tional trip-based concepts. The demand-and-supply model framework is examined, discrete choice models are explained, and activity-based concepts are presented. The second part is a discussion of issues in adopting integrated dynamic model systems. The purpose of this element is to examine the benefits, barriers, and practical issues that MPOs, state DOTs, and other transportation agencies face in migrating from traditional to advanced travel demand forecasting models in which activity- based models are linked with regional-scale dynamic network assignment models. This information is included in the activity-based model primer because activity-based models are a core component of integrated dynamic model systems, and developing and applying dynamic model systems is an area of significant emerging research and practice.
