Abstract
In recent years, there have been substantial efforts from researchers and practitioners to improve site-level trip generation estimation methods to address some of the pitfalls of conventional approaches for applications such as traffic impact analyses. These new trip generation models often adopt sophisticated nonlinear model forms to utilize new information and incorporate new factors influencing trip generation. However, if sufficient caution is not taken in their application, these new predictive models may introduce severe bias. This paper focuses on a typical source of biases in the applications of such models arising from detransformation of predictions from models with a nonlinearly transformed dependent variable in the prediction process (for example, predicting from a semilog model). While such biases are well known and corrections have been proposed in other disciplines, they have not been adopted in site-level trip generation models to the authors' knowledge. The detransformation bias is described and demonstrated-focusing on log-transformed models-with numeric simulations and empirical studies of trip generation models, before discussing their implications for trip generation applications and research.
| Original language | English (US) |
|---|---|
| Article number | 04018021 |
| Journal | Journal of Urban Planning and Development |
| Volume | 144 |
| Issue number | 3 |
| DOIs | |
| State | Published - Sep 1 2018 |
Fingerprint
Keywords
- Bias
- Development-level estimation
- Land-use development
- Predictive model
- Traffic impact analyses
- Transportation impact analyses
- Trip generation
ASJC Scopus subject areas
- Civil and Structural Engineering
- Geography, Planning and Development
- Development
- Urban Studies
Cite this
Detransformation bias in nonlinear trip generation models. / Wang, Liming; Currans, Kristina M.
In: Journal of Urban Planning and Development, Vol. 144, No. 3, 04018021, 01.09.2018.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Detransformation bias in nonlinear trip generation models
AU - Wang, Liming
AU - Currans, Kristina M.
PY - 2018/9/1
Y1 - 2018/9/1
N2 - In recent years, there have been substantial efforts from researchers and practitioners to improve site-level trip generation estimation methods to address some of the pitfalls of conventional approaches for applications such as traffic impact analyses. These new trip generation models often adopt sophisticated nonlinear model forms to utilize new information and incorporate new factors influencing trip generation. However, if sufficient caution is not taken in their application, these new predictive models may introduce severe bias. This paper focuses on a typical source of biases in the applications of such models arising from detransformation of predictions from models with a nonlinearly transformed dependent variable in the prediction process (for example, predicting from a semilog model). While such biases are well known and corrections have been proposed in other disciplines, they have not been adopted in site-level trip generation models to the authors' knowledge. The detransformation bias is described and demonstrated-focusing on log-transformed models-with numeric simulations and empirical studies of trip generation models, before discussing their implications for trip generation applications and research.
AB - In recent years, there have been substantial efforts from researchers and practitioners to improve site-level trip generation estimation methods to address some of the pitfalls of conventional approaches for applications such as traffic impact analyses. These new trip generation models often adopt sophisticated nonlinear model forms to utilize new information and incorporate new factors influencing trip generation. However, if sufficient caution is not taken in their application, these new predictive models may introduce severe bias. This paper focuses on a typical source of biases in the applications of such models arising from detransformation of predictions from models with a nonlinearly transformed dependent variable in the prediction process (for example, predicting from a semilog model). While such biases are well known and corrections have been proposed in other disciplines, they have not been adopted in site-level trip generation models to the authors' knowledge. The detransformation bias is described and demonstrated-focusing on log-transformed models-with numeric simulations and empirical studies of trip generation models, before discussing their implications for trip generation applications and research.
KW - Bias
KW - Development-level estimation
KW - Land-use development
KW - Predictive model
KW - Traffic impact analyses
KW - Transportation impact analyses
KW - Trip generation
UR - http://www.scopus.com/inward/record.url?scp=85046686214&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85046686214&partnerID=8YFLogxK
U2 - 10.1061/(ASCE)UP.1943-5444.0000455
DO - 10.1061/(ASCE)UP.1943-5444.0000455
M3 - Article
AN - SCOPUS:85046686214
VL - 144
JO - Journal of the Urban Planning and Development Division, ASCE
JF - Journal of the Urban Planning and Development Division, ASCE
SN - 0733-9488
IS - 3
M1 - 04018021
ER -