Method to adjust Institute of Transportation Engineers vehicle trip-generation estimates in smart-growth areas

Schneider, Robert J.; Shafizadeh, Kevan; Handy, Susan

doi:10.5198/jtlu.2015.416

Cited by 34 publications

(42 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Generation Handbook (Institute of Transportation Engineers, 2014), but in recent years, a 45 growing number of data and methods have been made available through studies in academia and 46 practice, e.g., Ewing et al, 2011;Schneider et al, 2015). These data are 47 used for a variety of purposes including, but not limited to: transportation impact analysis, 48 transportation system development charges, impact or utility fees, re-zoning, scaling or scoping 49 projects, and estimating greenhouse gas emission impacts of personal driving vehicles.…”

Section: Model Form For Trip Generation Models 39mentioning

confidence: 99%

“…In the second case study, the semi-log PM peak model from the California SGTG study 279 (Schneider et al, 2015) is evaluated using the data published online . 280…”

Section: California Smart Growth Trip Generation (Sgtg) Study Semi-lomentioning

confidence: 99%

“…Based on existing literature and through numeric simulation andempirical case studies, this research demonstrates that log-transformed models introduce 343 Tables 471 Table 1 Table 3. Bias, precision, and accuracy of predicted trip generation rates ratio before and after correction for 504 (Schneider et al, 2015) 505 506…”

Section: Conclusion 340mentioning

confidence: 99%

See 2 more Smart Citations

Detransformation Bias in Nonlinear Trip Generation Models

Wang

Currans

2018

J. Urban Plann. Dev.

View full text Add to dashboard Cite

3In recent years, there have been substantial efforts from researchers and practitioners to improve 4 the site-level trip generation estimation methods to address some of the pitfalls of conventional 5 approaches for applications such as traffic impact analyses. These new trip generation models 6 often adopt sophisticated non-linear model forms to utilize new information and incorporate new 7 factors influencing trip generation. However, if sufficient caution is not taken in their 8 application, these new predictive models may introduce severe bias. This manuscript focuses on 9 a typical source of biases in the applications of such models arising from de-transformation of 10 predictions from models with a non-linearly transformed dependent variables in the prediction 11 process (for example, predicting from a semi-log model). While such biases are well-known and 12 corrections have been proposed in other disciplines, they have not been adopted in the site-level 13 trip generation models to our knowledge. The de-transformation bias is described and 14 demonstrated-focusing on log-transformed models-with numeric simulations and empirical 15 studies of trip generation models, before discuss their implications for trip generation 16 applications and research. 17

show abstract

Section: Model Form For Trip Generation Models 39mentioning

confidence: 99%

“…In the second case study, the semi-log PM peak model from the California SGTG study 279 (Schneider et al, 2015) is evaluated using the data published online . 280…”

Section: California Smart Growth Trip Generation (Sgtg) Study Semi-lomentioning

confidence: 99%

See 1 more Smart Citation

Detransformation Bias in Nonlinear Trip Generation Models

Wang

Currans

2018

J. Urban Plann. Dev.

View full text Add to dashboard Cite

show abstract

“…To date, there have been numerous efforts devoted to the collection of multimodal trip generation data, development of models that account for the built environment, and revisions to ITE's Trip Generation Handbook (2014) 1 to incorporate new recommendations for practice. Many studies have identified important features of the surrounding built environment that most impact trip rates and mode shares (Clifton, Currans, & Muhs, 2015;Schneider, Shafizadeh, & Handy, 2015), building on a long line of research on transportation and the built environment (Cevero & Kockelman, 1997;Ewing & Cevero, 2010;Handy S., 1992).…”

Section: Introductionmentioning

confidence: 99%

Improving Trip Generation Methods for Livable Communities

Clifton¹,

Larco²,

Currans³

et al. 2017

View full text Add to dashboard Cite

Type of Report and Period Covered Sponsoring Agency Code Supplementary Notes16. Abstract Recent efforts to improve trip generation data available for transportation impact analysis of new development include the collection of multimodal trip generation data, development of models that account for the built environment, and new recommendations for practice. Building on a long line of research on transportation and built environment, many studies have identified important features of the surrounding built environment that most impact trip rates and mode shares, building on a long line of research on transportation and the built environment. Despite these improvements in data and methods, less attention is placed on identifying the conditions of the site itself and the immediate surrounding environment that influence trip generation and mode choice. To fill this gap, this study builds upon previous work (Contextual Influences on Trip Generation (Project Number: OTREC 2011-407), 2012), re-examines the information collected in that study, and includes new site-level observations. The objective of this study is to examine establishment multimodal trip generation more closely from a finer-grained scale and identify site-level attributes of the built environment that help explain multimodal trip generation. From this, we have the additional objective of developing a framework for trip generation analysis that takes findings from this study into consideration. We placed emphasis on those sites in our previous study with a discord between the expected travel patterns, based upon the larger urban context and built environment of the site, and the observed. The research approach will make use of mixed methods. In addition to using archived data from the previous study, site visits provided direct observation of the overall performance of the site, including travel patterns on and around the site as well as specific site configuration, urban design details and traffic operations. The analysis of this combination of data provided a more complete picture of site-level trip generation and our findings highlight the influence of: people living nearby and using the site; the nature of the land use on the site; the development along arterial roadways; site permeability and access; and the local culture around walking and cycling. Finally, this report ends by reflecting on the numerous concerns identified from practice, the research findings from various recent studies, and the need for a sustainable process for evaluating the transportation impacts of new land development. We present a potential framework to advance the methods for how site plans fit into neighborhood and regional planning, using locally defined standards and goals. Here, we de-emphasize the site and its immediate environs as the primary (and only) scale of analysis and lessen the reliance on the problematic methodologies for estimating site-level travel demand. Rather, we argue that transportation impact analysis would benefit by first taking a district, neighbor...

show abstract

“…While the travel patterns and needs of low-income households have been documented in research, this information has yet to be incorporated into methods for reviewing the impacts of new housing development (Clifton et al, 2013;Schneider, Shafizadeh, Sperry, & Handy, 2013;Dock et al, 2015) and builds off of research focusing on housing and commercial land uses previously completed in California (Kimley-Horn and Associates, Inc., Economic & Planning Systems, & Gene Bregman & Associates, 2009;Schneider et al 2015). The industry standards for estimating transportation impacts are the data and methods presented in the Institute of Transportation Engineers' (ITE) Trip Generation Handbook (2014); but as yet, there are no standard methods or available data to differentiate the transportation impacts of affordable housing developments (as compared to market-rate housing) across urban, suburban, or rural contexts in the U.S.…”

Section: Introductionmentioning

confidence: 99%

Transportation impacts of affordable housing: Informing development review with travel behavior analysis

Howell

Currans

Gehrke

et al. 2018

JTLU

View full text Add to dashboard Cite

Abstract:Planning for affordable housing is challenged by development policies that assess transportation impacts based on methodologies that often do not distinguish between the travel patterns of residents of market-rate housing and those living in affordable units. Given the public goals of providing affordable housing in areas with good accessibility and transportation options, there is a need to reduce unnecessary costs imposed by the potential overestimation of automobile travel and its associated impacts. Thus, the primary objective of this paper is to examine and quantify the influences of urban characteristics, residential housing type, and income on metrics commonly used to assess the transportation impacts of new development, namely total home-based trips and home-based vehicle trips. Using the 2010-2012 California Household Travel Survey, we regressed these metrics on urban place type, regionally adjusted income, and housing type, controlling for household size, weekday travel, and home location. The results indicate significant reductions in vehicle trip making with lower incomes and increasing urbanization. These findings support more differentiation of affordable and market-rate housing in the development review process and emphasize the need for development standards to be more sensitive to the characteristics of future residents and location.

show abstract

Method to adjust Institute of Transportation Engineers vehicle trip-generation estimates in smart-growth areas

Cited by 34 publications

References 13 publications

Detransformation Bias in Nonlinear Trip Generation Models

Detransformation Bias in Nonlinear Trip Generation Models

Improving Trip Generation Methods for Livable Communities

Transportation impacts of affordable housing: Informing development review with travel behavior analysis

Contact Info

Product

Resources

About