Measuring Travel Behavior and Transit Trip Generation Characteristics of Transit-Oriented Developments

Author:

Faghri Arsalan1,Venigalla Mohan2

Affiliation:

1. Virginia Department of Transportation, 4975 Alliance Drive, Fairfax, VA 22030.

2. Department of Civil, Environmental, and Infrastructure Engineering, George Mason University, 4400 University Drive, MS 6C1, Fairfax, VA 22030-4444.

Abstract

Transit-oriented developments (TODs) have been recognized as a promising proposition for policy makers and land developers in meeting the challenges of urban sprawl. The rapid pace with which TODs are being developed across the United States has left policy makers and transportation planners looking for methods aimed at modeling the travel characteristics of TODs. Current ITE trip-generation models are generally based on consolidated survey data from various land uses and are inadequate for serving the planning needs for travel demand parameters necessary to predict trip generation rates, develop trip distribution tables, identify mode choice characteristics, and determine trip assignment of TODs. The primary foci of this research were to understand the trip-making behavior of the TODs and develop a method for determining vehicular trip generation rates. A comparative assessment of TODs vis-à-vis non-TODs in relation to trip rates, transit usage, and primary travel mode was performed. A regression model relating TOD trip ends to gross floor area was developed and validated. Model behavior was consistent with the industry state of the practice; this factor would help transportation practitioners accurately forecast the trip generation rate for TODs. Validation of the regression model was performed by checks for normality, multicollinearity, and heteroscedasticity of the independent variable. The activity-based survey data used for this research were associated with the Washington, D.C., metropolitan area, which provided a wealth of transit-oriented corridors and diverse land use. Use of these data mitigated the loss of computational information frequently caused by aggregate data and therefore provided a more accurate quantitative forecast.

Publisher

SAGE Publications

Subject

Mechanical Engineering,Civil and Structural Engineering

Reference11 articles.

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