Unit of analysis in conventional trip generation modelling: an investigation

2004 ◽  
Vol 31 (2) ◽  
pp. 272-280 ◽  
Author(s):  
Daniel A Badoe ◽  
Chin-Cheng Chen
Keyword(s):  
Travel Demand ◽  
Empirical Test ◽  
Model Estimation ◽  
Trip Generation ◽  
Unit Of Analysis ◽  
Travel Survey ◽  
Household Travel Survey ◽  
Trip Production ◽  
Household Travel ◽  
Production Modelling

This paper examines the importance of the unit of analysis selected for trip generation modelling when the model estimation data are collected in a household travel survey. The paper reviews the literature on the arguments made for the use of the "individual" or the "household" as the unit of analysis in trip production modelling, and then through a statistical exposition it determines what should be the appropriate unit of analysis. An empirical test of the forecast performance of household- and person-trip generation models is conducted using data collected in a household-travel-behaviour survey in the Greater Toronto Area of Canada. The paper concludes that the household is theoretically the preferable analysis unit to use in trip production modelling when the model estimation data are collected in a household travel survey in which the household is the sampling unit. The empirical test indicates that household-trip generation models yield predictions of trips at the household and traffic zone level, respectively, that are marginally more accurate than those yielded by person-trip generation models.Key words: trip generation, travel demand forecasting, household trip generation, person trip generation, sampling unit, travel demand modeling, activity-based travel forecasting.

Impact of Underreporting on Mileage and Travel Time Estimates: Results from Global Positioning System-Enhanced Household Travel Survey

2003 ◽  
Vol 1854 (1) ◽  
pp. 189-198 ◽  
Author(s):  
Jean Wolf ◽  
Marcelo Oliveira ◽  
Miriam Thompson
Keyword(s):  
Travel Time ◽  
Global Positioning System ◽  
Travel Demand ◽  
Positioning System ◽  
Gps Technology ◽  
Travel Survey ◽  
Household Travel Survey ◽  
Time Estimates ◽  
Global Positioning ◽  
Household Travel

Trip underreporting has long been a problem in household travel surveys because of the self-reporting nature of traditional survey methods. Memory decay, failure to understand or to follow survey instructions, unwillingness to report full details of travel, and simple carelessness have all contributed to the incomplete collection of travel data in self-reporting surveys. Because household trip survey data are the primary input into trip generation models, it has a potentially serious impact on transportation model outputs, such as vehicle miles of travel (VMT) and travel time. Global Positioning System (GPS) technology has been used as a supplement in the collection of personal travel data. Previous studies confirmed the feasibility of applying GPS technology to improve both the accuracy and the completeness of travel data. An analysis of the impact of trip underreporting on modeled VMT and travel times is presented. This analysis compared VMT and travel time estimates with GPS-measured data. These VMT and travel time estimates were derived by the trip assignment module of each region's travel demand model by using the trips reported in computer-assisted telephone inter views. This analysis used a subset of data from the California Statewide Household Travel Survey GPS Study and was made possible through the cooperation of the metropolitan planning organizations of the three study areas (Alameda, Sacramento, and San Diego, California).

Does Investing in Rail Transit Benefit the Poor? A Comparative Study of Rail and Bus Travel by Low-Income Households in the California Household Travel Survey

2018 ◽  
Vol 2672 (6) ◽  
pp. 11-20
Author(s):  
Ryland Lu
Keyword(s):  
Low Income ◽  
Household Income ◽  
Bus Rapid Transit ◽  
Rail Transit ◽  
Rapid Transit ◽  
Travel Survey ◽  
Household Travel Survey ◽  
The Poor ◽  
Household Travel ◽  
Low Income Households

This paper addresses academic discourse that critiques urban rail transit projects for their regressive impacts on the poor and proposes bus funding as a more equitable investment for urban transit agencies. The author analyzed data from the 2012 California Household Travel Survey on transit trips in Los Angeles County. The author cross-tabulated data on the modal breakdown of transit trips by household income category and on the breakdown of household income associated with trips by bus and rail transit modes. The author also comparatively evaluated the speed of trips (as a ratio of miles per hour) taken by rail and by bus by low-income households in the county. The author found convincing evidence that, on average, trips low-income households made by rail transit covered a greater distance per hour than trips taken by bus transit, but that trips made on the county’s bus rapid transit services with dedicated rights-of-way had a higher mean speed than those taken by rail. Moreover, the mode and income cross-tabulations indicate that rail transit projects only partially serve low-income households’ travel needs. To the extent that equitable transit planning entails minimizing the disparities in access, both rail and bus rapid transit projects can advance social justice if they are targeted at corridors where they can serve travel demand by low-income, transit dependent households.

Development of Trip Production Rates for Synthesized Households

1998 ◽  
Vol 1625 (1) ◽  
pp. 79-85 ◽  
Author(s):  
Shari P. Scobee ◽  
Michael DuRoss ◽  
Edward C. Ratledge
Keyword(s):  
Household Member ◽  
University Of Delaware ◽  
Production Rates ◽  
Survey Nonresponse ◽  
Household Level ◽  
Travel Survey ◽  
Production Models ◽  
Trip Production ◽  
Household Travel ◽  
The University

Survey nonresponse bias is an important consideration in the development of survey designs for transportation studies. Researchers at the University of Delaware have developed a technique for reducing the survey nonresponse, as well as the cost of the travel survey. The method involves obtaining complete household and person characteristics for each household member; however, detailed travel data are gathered for only one randomly selected household member. Although the University of Delaware survey technique provides multiple benefits with respect to survey response rates and costs, it presents complications for travel model developers, particularly with respect to the development of trip production models. Because the trip production models are typically developed at the household level, the person-level trip rates from such a survey need to be expanded to represent a household’s trip rates. A method is presented for generating synthesized household trip production rates by using the 1995/96 Delaware Household Travel Survey, which gathered travel information for only one household member.

scholarly journals Trends in Taxi Use and the Advent of Ridehailing, 1995–2017: Evidence from the US National Household Travel Survey

Urban Science ◽  
2018 ◽  
Vol 2 (3) ◽  
pp. 79 ◽  
Author(s):  
Matthew Conway ◽  
Deborah Salon ◽  
David King
Keyword(s):  
Negative Relationship ◽  
The United States ◽  
Vehicle Ownership ◽  
Large Cities ◽  
Travel Survey ◽  
Household Travel Survey ◽  
Starting Point ◽  
Household Travel ◽  
National Household Travel Survey ◽  
Vehicle Market

The advent of ridehailing services such as Uber and Lyft has expanded for-hire vehicle travel. We use data from the 2017 National Household Travel Survey (NHTS) to investigate the extent of this expansion in the United States. We report changes in the for-hire vehicle market since ridehailing services became available and statistically estimate the determinants of ridehailing use. From 2009–2017, the for-hire vehicle market share doubled. While for-hire vehicles still only account for 0.5% of all trips, the percent of all Americans who use ridehailing in any given month is nearly 10%. Within the for-hire vehicle market, this trend of growth has not been uniformly distributed across demographic groups or geographies; it has been greater in mid-sized and large cities, and among younger individuals and wealthier households. This suggests that understanding the equity implications of ridehailing is an important avenue for research. Multivariate analysis provides evidence that both transit and nonmotorized transport use are correlated with ridehailing use, that ridehailing has a negative relationship with vehicle ownership, and that residents of denser areas have higher ridehailing use. Given the rapid growth of ridehailing, it has become important for cities to include for-hire vehicles in their planning going forward. These NHTS data provide a starting point, but more detailed and frequent data collection is needed to fully understand this many-faceted, rapidly-changing market.

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