Getting Started

CHAPTER 1.0 INTRODUCTION

Note: Significant components of this chapter come from Stopher et al.’s (2008) NCHRP Report Chapter 1 and Tierney et al.’s FHWA Manual Introduction (Cambridge Systematics 1994). Other key contributors are Peter Endemann, Kara Kockelman and Charlene Wilder.

Travel is core to human activity, for personal and commercial reasons, of individuals and freight, over short and long distances, via motorized or non-motorized modes. As traveling populations and their communities evolve and adapt, transportation challenges have become more complex. Combating congestion, noise, emissions, loss of life, and other concerns while developing sustainable and reliable transportation systems and facilitating relatively low-cost travel is a tremendous task. Good data is fundamental to this task.

Appropriate solutions to transport and traffic problems require more holistic approaches than have often been pursued in the past, recognizing the complex interdependencies of economies and transport, population and mobility, spatial structure, landscape and environment. A solid sense of a population’s preferences and travel patterns is an indispensable ingredient in the planning, design, and management of transportation systems, at local, regional, national and international levels. Constrained budgets and great uncertainty in future conditions present challenges to the transportation profession, and travel data acquisition and analysis is one of the few low-cost ways of helping ensure more cost-effective solutions, robust to variations in preferences, prices, climate, technology, and other uncertain features of tomorrow’s terrain.

Several decades ago, the travel demand modeling field developed primarily to anticipate the demand for major changes in transportation infrastructure, such as the addition and expansion of freeways and rail systems. In the United States, the 1990 Clean Air Act Amendments (CAAA), 1991 Intermodal Surface Transportation Efficiency Act (ISTEA), 1998 1998's Transportation Equity Act for the 21st Century (TEA-21), and 2005 Safe, Accountable, Flexible, and Efficient Transportation Equity Act (SAFETEA-LU) have evolved to expect more detailed inputs for policy studies. The planning process must now provide a variety of specific values for air quality analysis, evaluation of non-highway investments and transport policies, and support of integrated regional and statewide transportation improvement efforts. New demands on travel demand models include anticipation of congestion pricing impacts, details of trip timing and peak spreading, greenhouse gas emissions, non-motorized travel choices, emergency response, and freight movement, among other behaviors. The Transportation Research Board's Special Report on Metropolitan Travel Forecasting (TRB 2007) concludes that there is a need to improve the state of metropolitan travel forecasting and that the data used in such models are lacking or questionable. In particular, there are insufficient data for model validation (especially relating to non-work travel) and a general lack of data on goods movement. A need for better travel data is a common issue, throughout the world.

The contents of this extensive Travel Survey Manual emphasize one of the most important aspects of transportation planning, demand modeling, and policy-making: the travel survey data. Such data are used to anticipate the future, calibrate and validate behavioral models, and inform a variety of policymaking efforts. Current transportation planning models rely to a great extent on the disaggregate behavioral travel data obtained from travel surveys for establishing trip generation, trip distribution, mode split, trip scheduling, vehicle ownership, tolling response, and other important relationships. Of course, the next generation of travel models will also rely extensively on very detailed household level analyses (Shunk 1994). In fact, given the level of detail required by the newer, activity-based and/or microscopic models of travel choices, the data demands will likely be greater - in terms of route choice and location reporting details. Fortunately, technologies such as GPS-enabled data loggers are helping moderate respondent burden while providing higher-quality data free of many errors of the past, as discussed later in this Manual.

1.1 Background

Personal travel surveys have been conducted for over 40 years, but during that time no attempt has been made to standardize the process or to institute consistent practices of acceptable quality or reliability. Two TRB conferences -“Household Travel Surveys: New Concepts and Research Needs,” in 1995 and “Information Needs to Support State and Local Transportation Decision Making into the 21st Century” in 1997 (TRB, 1996 and 1997) - and NCHRP Synthesis of Highway Practice 236: Methods for Household Travel Surveys (Stopher and Metcalf, 1996) emphasized the need for improved standardization in survey data collection. The contention is that standardization of the survey process can lead to efficiencies in the planning and execution of surveys, in the assessment of data quality, and in the comparison of data between one metropolitan area and another.