Investigating the capacity of continuous household travel surveys in capturing the temporal rhythms of travel demand

2019 ◽  
Vol 47 (4) ◽  
pp. 1787-1808
Author(s):  
Wafic El-Assi ◽  
Catherine Morency ◽  
Eric J. Miller ◽  
Khandker Nurul Habib
Keyword(s):  
Travel Demand ◽  
Household Travel ◽  
Household Travel Surveys

Metaanalysis of Travel Survey Methods

1998 ◽  
Vol 1625 (1) ◽  
pp. 72-78 ◽  
Author(s):  
Madhuri S. Korimilli ◽  
Ram M. Pendyala ◽  
Elaine Murakami
Keyword(s):  
Travel Demand ◽  
Survey Methods ◽  
Response Rates ◽  
Design Parameters ◽  
Modeling Tools ◽  
Travel Survey ◽  
Household Travel ◽  
Household Travel Surveys ◽  
The Impact ◽  
Comparative Examination

Travel surveys often serve as the primary sources of information on travel demand characteristics. They provide critical data for transportation planning and decision making. In recent times, several factors motivate a comparative examination of travel survey methods. First, new travel demand modeling tools, such as those based on activity-based methods, are placing greater demands on travel behavior data gathered from household travel surveys. Second, response rates from household travel surveys have been showing a steady decline, possibly because of an increasingly survey-fatigued population. Third, declining resource availability at metropolitan planning agencies places emphasis on the need to maximize response rates to lower data collection costs per completed respondent. Ideally, a comparative examination of travel survey methods is best done through a carefully constructed experimental design that permits the isolation of the impact of various survey design parameters on response rates. However, the conduct of such a controlled experiment virtually is impractical. A metaanalysis of a sample of travel surveys conducted in the past 10 years is presented. A predictive model of response rates is developed by using linear regression techniques and the practical application of the model is demonstrated through several numerical examples.

Estimating Household Travel Energy Consumption in Conjunction with a Travel Demand Forecasting Model

2017 ◽  
Vol 2668 (1) ◽  
pp. 1-10 ◽  
Author(s):  
Venu M. Garikapati ◽  
Daehyun You ◽  
Wenwen Zhang ◽  
Ram M. Pendyala ◽  
Subhrajit Guhathakurta ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Urban Form ◽  
Travel Demand ◽  
Demand Forecasting ◽  
The United States ◽  
Metropolitan Region ◽  
Demand Model ◽  
Awareness Campaigns ◽  
Energy Footprint ◽  
Household Travel

This paper presents a methodology for the calculation of the consumption of household travel energy at the level of the traffic analysis zone (TAZ) in conjunction with information that is readily available from a standard four-step travel demand model system. This methodology embeds two algorithms. The first provides a means of allocating non-home-based trips to residential zones that are the source of such trips, whereas the second provides a mechanism for incorporating the effects of household vehicle fleet composition on fuel consumption. The methodology is applied to the greater Atlanta, Georgia, metropolitan region in the United States and is found to offer a robust mechanism for calculating the footprint of household travel energy at the level of the individual TAZ; this mechanism makes possible the study of variations in the energy footprint across space. The travel energy footprint is strongly correlated with the density of the built environment, although socioeconomic differences across TAZs also likely contribute to differences in travel energy footprints. The TAZ-level calculator of the footprint of household travel energy can be used to analyze alternative futures and relate differences in the energy footprint to differences in a number of contributing factors and thus enables the design of urban form, formulation of policy interventions, and implementation of awareness campaigns that may produce more-sustainable patterns of energy consumption.

Peak-Car Phenomenon Revisited for Urban Areas: Microdata Analysis of Household Travel Surveys from Five European Capital Cities

2019 ◽  
Vol 2673 (3) ◽  
pp. 686-699 ◽  
Author(s):  
Rico Wittwer ◽  
Regine Gerike ◽  
Stefan Hubrich
Keyword(s):  
Urban Areas ◽  
Transport Systems ◽  
Modal Shift ◽  
Car Use ◽  
Trip Purpose ◽  
Life Cycle Stages ◽  
Peak Car ◽  
Framework Conditions ◽  
Household Travel ◽  
Household Travel Surveys

This study investigates the peak-car phenomenon for the five European capital regions of Berlin, Copenhagen, London, Paris, and Vienna. Household travel survey (HTS) microdata was harmonized for the five regions and transferred to one consistent database; all time-series date back at least 20 years. Developments in car use were found to be surprisingly similar despite the substantial differences between the regions in terms of size, governance structures, built environments, transport systems, and societal framework conditions. Car use peaked earliest in Paris in the early 1990s; followed by Berlin, London, and Vienna in the late 1990s; and lastly in Copenhagen in the late 2000s. Working persons and mandatory trips were found to be the most relevant person group and trip purpose for the observed peak-car developments, both with declining overall trip numbers and a modal shift toward non-car modes. Young working persons had the most significant decline with substantial cohort effects. People seem to carry forward their behavior adopted in early life-cycle stages as they age. The person groups of seniors and women both damped the peak-car effect. Shopping trips were the second most relevant trip purpose for car use: car use for this purpose was high and stable over time. This study has elaborated potentials for reducing car use in relation to person groups and trip purposes. Findings from this retrospective analysis could be used for purposefully shaping future transport systems.

Comparison of Trip Determination Methods in Household Travel Surveys Enhanced by a Global Positioning System

2005 ◽  
Vol 1917 (1) ◽  
pp. 63-71 ◽  
Author(s):  
Timothy L. Forrest ◽  
David F. Pearson
Keyword(s):  
Travel Demand ◽  
Demand Forecasting ◽  
Data Retrieval ◽  
Geographical Information ◽  
Computer Assisted ◽  
Gps Data ◽  
Positioning Systems ◽  
Home Based ◽  
Global Positioning ◽  
Household Travel

Improvements in vehicular tracking with Global Positioning Systems (GPSs) have fostered new analysis methods in transportation planning. Emerging geographical information systems have helped in developing new techniques in the collection and analysis of data specifically for travel demand forecasting. In 2002, more than 150 households in Laredo, Texas, participated in a GPS-enhanced household travel survey. Trip diary data were collected by means of a computer-assisted telephone interview (CATI), and GPS trip data were collected from survey participants’ vehicles. For trip purpose, a comparison of the two data sets yielded significant results. It was found that the number of trips in the GPS data was much greater than the number reported in the CATI data. Despite that, almost all home-based work (HBW) trips found in the GPS data were also found in the CATI data. That result differs sharply from the other trip purposes: home-based nonwork (HBNW) and non-home-based (NHB); for these two trip purposes, less than half the trips found in the GPS data were found in the CATI data. That result indicates the potential for serious deficiencies in the CATI process for collecting certain types of trips in the region of study. In additional, household size and household income were found to be significant factors affecting the reporting accuracy in the CATI data. Despite that, the CATI method of household trip data retrieval is still considered to be an effective and valuable tool.

Proxy Respondents in Household Travel Surveys

2006 ◽  
pp. 201-212 ◽  
Author(s):  
Laurie Wargelin ◽  
Lidia Kostyniuk
Keyword(s):  
Household Travel ◽  
Household Travel Surveys

Household Travel, Household Characteristics, and Land Use: An Empirical Study from the 1994 Portland Activity-Based Travel Survey

1998 ◽  
Vol 1617 (1) ◽  
pp. 10-17 ◽  
Author(s):  
Xiaoduan Sun ◽  
Chester G. Wilmot ◽  
Tejonath Kasturi
Keyword(s):  
Land Use ◽  
Travel Demand ◽  
Land Development ◽  
Data Set ◽  
Travel Survey ◽  
Forecasting Models ◽  
Demand Models ◽  
Residence Location ◽  
Household Travel ◽  
Use Factors

How a household’s travel behavior is influenced by its socioeconomic and land use factors has been a subject of interest for the development of travel demand forecasting models. This study investigates the relative importance of these factors based on the number of household daily trips and vehicle miles traveled (VMT). The travel data used in the study come from the 1994 Portland Activity-Based Travel Survey. In addition to income, vehicle ownership, and household size, other significant factors in household travel have been identified, such as the presence of car phones, dwelling type, home ownership, and even the length of resident’s time in the current home. Most important, this study has qualitatively revealed that land use makes a big difference in household VMT, whereas its impact on the number of daily trips is rather limited. After controlling for the land use variables, such as density and land development balance, it appears that there is little difference in household income distribution among three different land use areas. The household life stage/lifestyle appears to be more relevant to the residence location. And the land use development of the residence location imposes the greatest impact on the household daily VMT. The results from this study provide some empirical evidence to the development of travel forecasting models. Especially by examining the relationship between land use and household travel, the results shed light on how to incorporate land use factors into comprehensive travel demand models that can be used by policy makers in evaluation of alternative land use policies. This study serves as a step toward more comprehensive studies on transportation and land use. The results presented represent a preliminary analysis of an extensive data set; considerable additional analysis is already in process.

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