Abstract
Smart growth developments are high density developments which have a mix of land uses like residential, retail, commercial for example, on the same location thereby providing good potential for interaction between them. Due to the proximity of these different land uses, there exists convenience for other modes of transport like walking, biking and the use of transit. The ITE Trip Generation manual, which has been the traditional source of trip generation data, is mostly based on suburban dispersed study sites where there is limited potential for using other modes of transport other than automobile. Therefore, relying on the conventional ITE Trip Generation manual for estimating trip generation rates for smart growth areas do not produce accurate results. Using suburban development trip rates for smart growth settings might overestimate the trip generation rates resulting in more transportation infrastructure than required. This project focuses on establishing the trip generation rates for smart growth settings in California focusing on the land use coffee shops and creates a mode share database of the different trips generated. The total vehicle trips from this study are compared with standard ITE trips to see if there are any differences in the rates. The methodology adopted in this study utilizes the same methodology adopted by Caltrans for the study on trip generation in urban infill areas and by UC Davis for the study on smart growth trip generation in California. The methodology uses a combination of intercept survey and in and out door counts to determine the trip generation rates of coffee shops. This methodology counts the trips generated by walking, biking and transit in addition to auto trips. It also helps in calculating the auto trip generation which uses shared parking and off-street parking which is common in smart growth sites. Six coffee shops in and around San Jose, California were chosen as study sites, and data were collected in May 2013. The responses from intercept surveys were combined with door count data to estimate the peak-hour trips along with the mode share during the peak hour. Results reveal that on an average, ITE overestimates peak-hour trips by 34% during the AM peak hour and by 18% during the PM peak hour at smart growth coffee shops. There is also a noticeable contribution of trips by other modes such as walk (24%), bike (2%) and transit (2%). As smart growth developments are becoming more and more popular, future studies with more sites focusing on a particular land use is recommended to gather more identified trip generation results of smart growth sites.