Using household travel surveys to adjust ITE trip generation rates

Abstract

Abstract:The Institute of Transportation Engineers (ITE) Trip Generation Handbook has become the predominant method for estimating vehicle trips generated by development. The handbook is often criticized for its inability to account for multimodal behavior in urban contexts, often overestimating vehicle traffic. The purpose of this research is to develop and test a ready-to-use method for adjusting the ITE handbook vehicle trip generation estimates for urban context. This method was created using household travel surveys from Oregon, Washington, and Maryland, as well as nationally available built environment data. Three adjustments were estimated for eight general land-use categories, including a "pooled" category considering all travel survey data. The performance of three adjustments were tested using 195 establishment-level vehicle trip generation datasets compiled from three independent sources. Using this data, the performances of four land-use categories were tested. The overall findings suggest that the simplest of the three adjustments developed provided similar results to the more complex adjustment methods. Moreover, adjustments applied using the "pooled" land-uses category also provided similar results to the more detailed segmentation of travel survey data. Both of these findings punctuate the need for a simple, urban adjustment for trip generation estimates.

Issues in Urban Trip Generation

“…Businesses located in offices that vary across the types of services they provide (e.g., More recently developed methods have produced generalized land use categories, relying upon small datasets spread across a wider range of urban contexts. The result has been a much aggregate categorization compared with ITE's taxonomy: residential (sometimes multifamily versus single-family), commercial or occasionally retail and service (sometimes restaurant), and recreation (Currans and Clifton 2015;Ewing et al 2011;Millard-Ball 2015). In these more recent improvements to methodologies, the authors have suggested or required the data used for their adjustments be segmented in much broader categories, avoiding too much specificity that would limit overall power and applicability while attempting to capture behavior as it varies from location to location (Clifton, Currans, and Muhs 2013;Millard-Ball 2015;Shoup 2003).…”

“…Decades later, these methods-essentially the same as when they were originally conceived-are used ubiquitously across the US and Canada. Only recently, with the guidelines in its third edition of the ITE's Trip Generation Handbook (Institute of Transportation Engineers 2014) have new data and approaches been adopted-despite substantial evidence that questions the accuracy of older data (Clifton, Currans, and Muhs 2012;Shafizadeh et al 2012;Weinberger et al 2015), automobile bias ; Millard-Ball 2015; Manville 2017), and lack of sensitivity to urban contexts (Currans and Clifton 2015;Ewing et al 2011;Schneider, Shafizadeh, and Handy 2015;Weinberger et al 2015). The corresponding ITE Trip Generation Manual, a supplement to the Handbook, continues to contain the nearly all the original suburban vehicle trip generation data collected since its creation (Institute of Transportation Engineers 2012).…”

Evaluation of Socio-Economic Criteria In Transportation Planning

“…Yolculuk yaratım ve çekimi, yolculuk dağıtımı, türel dağılım ve trafik atama olmak üzere dört aşamadan oluşur (McNally, 2007;Meyer and Miller, 1984). İlk aşama olan yolculuk yaratımı modeli kurulurken bir bölgeden diğer bölgeye olan araçla ya da yaya olarak yapılan tüm hareketler tanımlanmaktadır (Currans and Clifton, 2015). Bu çalışmada 4 aşamalı modelin yolculuk yaratım ve çekim modeli aşaması içi yalnızca yolculuk yaratımı modeli üzerinde durulmuştur.…”

Discovering Public Transit Riders’ Travel Pattern from GPS Data: A Case Study in Harbin

“…Furthermore, the sampling rate is usually very low, which 2 Journal of Sensors brings sampling errors [12]. Meanwhile, many human factors may affect the accuracy of the OD matrix, such as, willfully filtering some trips, forgetfulness, and other related factors [13].…”