Multimodal Trip Generation Model to Assess Travel Impacts of Urban Developments in the District of Columbia

The research effort described in this paper aims to develop a state-of-the-practice methodology for estimating urban trip generation from mixed-use developments. The District Department of Transportation’s initiative focused on (a) developing and testing a data collection methodology, (b) collecting local data to complement the ITE’s national data in trip rate estimation, and (c) developing a model–tool that incorporates contextual factors identified as affecting overall trip rate as well as trip rate by mode. The final model accurately predicts total person trips and mode choice. The full set of models achieves better statistical performance in relation to average model error and goodness of fit than either ITE rates alone or other existing research. The model includes sensitivity to local environment and on-site components. The model advances site-level trip generation research in two major ways: first, it calculates total person trips independent of mode choice; second, it calculates mode choice with sensitivity to the amount of parking provided on site—a major finding in the connection between parking provision and travel behavior at a local-site level. The methodology allows agencies to improve their assessment of expected trips from proposed buildings and therefore the level of impact a planned building may have on the transportation system.

[1]  Reid Ewing,et al.  Trip and parking generation at transit-oriented developments: a case study of Redmond TOD, Seattle region , 2017 .

[2]  Peter Kauffmann,et al.  Estimating Parking Utilization in Multifamily Residential Buildings in Washington, D.C. , 2016 .

[3]  Brian S Bochner,et al.  Trip Generation Rates for Transportation Impact Analyses of Infill Developments , 2013 .

[4]  Ming Zhang,et al.  Predicting Transportation Outcomes for LEED Projects , 2013 .

[5]  R. Cervero,et al.  TRAVEL DEMAND AND THE 3DS: DENSITY, DIVERSITY, AND DESIGN , 1997 .

[6]  Jonathan Rogers,et al.  Methodology to Gather Multimodal Urban Trip Generation Data , 2015 .

[7]  Reid Ewing,et al.  Getting trip generation right: Eliminating the bias against mixed use development , 2013 .

[8]  Lawrence D. Frank,et al.  An assessment of urban form and pedestrian and transit improvements as an integrated GHG reduction strategy. , 2011 .

[9]  Nils Lid Hjort,et al.  Model Selection and Model Averaging , 2001 .

[10]  Reid Ewing,et al.  Traffic Generated by Mixed-Use Developments—13-Region Study Using Consistent Built Environment Measures , 2011 .

[11]  C. Kwak,et al.  Multinomial Logistic Regression , 2002, Nursing research.

[12]  Arefeh A. Nasri,et al.  How built environment affects travel behavior: A comparative analysis of the connections between land use and vehicle miles traveled in US cities , 2012 .

[13]  Robert Cervero,et al.  Are TODs Over-Parked? , 2009 .

[14]  Nils Lid Hjort,et al.  Model Selection and Model Averaging: Contents , 2008 .

[15]  Kelly J. Clifton,et al.  Contextual Influences on Trip Generation , 2012 .

[16]  Adam Millard-Ball Phantom trips: Overestimating the traffic impacts of new development , 2015 .

[17]  Benjamin R Sperry,et al.  Enhancing Internal Trip Capture Estimation for Mixed-Use Developments , 2011 .