Using GPS Technologies to Collect Multiday Travel Data

Global Positioning Systems (GPS) technologies have been used in conjunction with traditional one- or two-day travel diaries to audit respondent reporting patterns, but we used GPS-based monitoring to conduct the first assessment to our knowledge of travel reporting patterns using a seven-day travel log instrument, which could reduce response burden and provide multiple-day, policy-relevant information for evaluation studies. We found substantial agreement between participant-reported daily travel patterns and GPS-derived patterns among 116 adult residents of a largely low-income and non-white transportation corridor in urbanized Los Angeles in 2011-2013. For all modes, the average difference between daily GPS- and log-derived trip counts was only about 0.39 trips and the average difference between daily GPS- and log-derived walking duration was about -11.8 minutes. We found that the probability that a day would be associated with agreement or discrepancies between these measurement tools varied by travel mode and participant socio- demographic characteristics. Future research is needed to investigate the potential and limitations of this and other self-report instruments for a larger sample and a wider range of population groups and travel patterns.

2010

For the past decade, GPS devices have been used increasingly as a means to validate household travel surveys and more recently as a means to determine response to travel behaviour change policies. However, although several papers have put forward arguments that GPS is now ready to be used as a potential substitute for conventional travel surveys, there has been somewhat of a reluctance to proceed in this direction. This paper describes such an effort. In late 2008, a team of consultants was put under contract by the Ohio Department of Transportation to conduct a GPS survey of households in the Greater Cincinnati area of southwest Ohio, northwest Kentucky, and southeast Indiana. A pilot survey was conducted in March-April 2009, and this paper reports on the outcome of that pilot study. At the time of writing this paper, the main study is underway, with a goal of having at least 3,000 households use GPS devices for a three-day period within the 12 months from mid-August 2009 to mid-August 2010. In this paper, the procedures for recruitment of households, delivery and collection of GPS devices, and the rates of completion of the survey are described. A prompted recall survey, using a web-based survey is also described. The purpose of the web-based prompted recall survey is to collect sufficient data to allow improvement and addition to existing processing software, so that the GPS data will provide sufficient information to allow travel demand models to be estimated, as well as informing various policy issues. Preliminary results from the pilot survey indicate some issues with completion of the GPS task and also with the prompted recall survey. These issues have suggested the use of variable incentives in the main survey to improve overall response levels and significant changes to the prompted recall survey, which have been implemented. Statistics are also provided on some of the sociodemographic characteristics of the pilot sample, and this is compared to Census data for the Greater Cincinnati area. However, it was not expected or intended that the pilot sample would be a representative sample of the population. Preliminary analysis of the data collected by GPS indicates a substantially higher rate of trip-making than has been measured in the past, using conventional diary methods. While this latter result was expected, the magnitude of the increase is larger than expected.

2000

Generally travel behavior data are collected by self-reported questionnaire surveys. Problems with these methods include lack of reporting for short trips, poor data quality on travel start and end times, travel times, and destination locations. The burden on the respondent is also very large. The detailed and accurate travel data should be needed to evaluate the effects of TDM strategies and ITS technologies on travel behavior, but it would be difficult using traditional questionnaire surveys. This paper aims to develop travel behavior data collecting systems using global positioning system (GPS), personal handyphone system (PHS) and geographic information system (GIS), and to examine the effectiveness of the data collected by these systems and the applicability for travel behavior surveys. We conducted the surveys of collecting travel behavior data by GPS and PHS, with travel and activity diary over a week. We collected GPS data of vehicle trips in two local cities from 15 and 28 commuters respectively, and PHS data of person trips of 30 people in a local city. Examples of day-to-day variability analyses of travel distances, times, speeds and routes of trips, are shown using data collected by GPS. We examined the characteristics of PHS data and the differences of travel start and end times, comparing PHS data with diary data, for elderly and non-elderly people. Also comparison between datasets of the two age groups was done. Improvements of these new systems for practical use to travel surveys are summarized.

Transportation Research Part C: Emerging Technologies, 2012

Recent advances in global positioning systems (GPS) technology have resulted in a transition in household travel survey methods to test the use of GPS units to record travel details, followed by the application of an algorithm to both identify trips and impute trip purpose, typically supplemented with some level of respondent confirmation via prompted-recall surveys. As the research community evaluates this new approach to potentially replace the traditional surveyreported collection method, it is important to consider how well the GPS-recorded and algorithm-imputed details capture trip details and whether the traditional survey-reported collection method may be preferred with regards to some types of travel. This paper considers two measures of travel intensity (survey-reported and GPSrecorded) for two trip purposes (work and non-work) as dependent variables in a joint ordered response model. The empirical analysis uses a sample from the full-study of the 2009 Indianapolis regional household travel survey. Individuals in this sample provided diary details about their travel survey day as well as carried wearable GPS units for the same 24-hour period. The empirical results provide important insights regarding differences in measures of travel intensities related to the two different data collection modes (diary and GPS). The results suggest that more research is needed in the development of workplace identification algorithms, that GPS should continue to be used alongside rather than in lieu of the traditional diary approach, and that assignment of individuals to the GPS or diary survey approach should consider demographics and other characteristics.

EPJ Data Science, 2015

Transportation planning is strongly influenced by the assumption that every individual has for his daily mobility a constant daily budget of ≈1 hour. However, recent experimental results are proving this assumption as wrong. Here, we study the differences in daily travel-time expenditures among 24 Italian cities, extracted from a large set of GPS data on vehicles mobility. To understand these variations at the level of individual behaviour, we introduce a trip duration model that allows for a description of the distribution of travel-time expenditures in a given city using two parameters. The first parameter reflects the accessibility of desired destinations, whereas the second one can be associated to a travel-time budget and represents physiological limits due to stress and fatigue. Within the same city, we observe variations in the distributions according to home position, number of mobility days and a driver's average number of daily trips. These results can be interpreted by a stochastic time-consumption model, where the generalised cost of travel times is given by a logarithmic-like function, in agreement with the Weber-Fechner law. Our experimental results show a significant variability in the travel-time budgets in different cities and for different categories of drivers within the same city. This explicitly clashes with the idea of the existence of a constant travel-time budget and opens new perspectives for the modeling and governance of urban mobility.

Transportation Research Record, 2007

Traditional travel diary surveys collect one or two days of travel data from participant households. While cross-sectional travel diary surveys are useful in determining the overall average travel behavior of the regional population, they provide little insight into intra-household and intra-person trip variability. Longitudinal surveys are generally preferred for examining travel variability. The objective of this research effort is to study the intra-household travel variability observed in the Commute Atlanta Study. The Commute Atlanta Study is a GPSbased instrumented vehicle monitoring study that has collected vehicle trips from a fleet of approximately 500 vehicles in 260 representative households. The research effort uses travel data collected in the year 2004 for the Commute Atlanta Study. The average variability or deviation in the number of trips by a household in the Commute Atlanta Study was observed to be 3 trips/day. Demographic variables such as household size, household income, vehicles ownership, number of children, number of workers, and number of students have a significant effect on the day-to-day variability in the total number of household trips per day. The variability due to seasonal effects is controlled by separately analyzing travel data during specific months in the spring, summer, and fall. The analyses found that the demographic variables have a significant effect on day-to-day variability of the household number of trips when the variability associated with seasonal effects is excluded. The researchers noted that vehicles identified by participants as being used always or occasionally for business/commercial purposes undertake very different travel patterns than other vehicles, and that their presence in the sample will significantly bias analytical results in the analysis of longitudinal data. 'Commercial Use' vehicles are excluded from travel variability analysis and the argument is made that households with such vehicles present must be treated as an independent sample in future travel diary data collection and longitudinal studies.

University of California Transportation Center, 2009

HAL (Le Centre pour la Communication Scientifique Directe), 2017

GPS-based data collection methods have become particularly popular in travel behavior research, mainly because of the worldwide coverage and the accuracy of the GPS system. The main objective of this paper is compare the descriptions of mobility obtained by two methods survey reported and GPS recorded in the same days. This study shows that the GPS survey can be used successfully to complete the conventional transport surveys, but it is still too early to predict the complete substitution of conventional survey by the GPS mobility survey.

Procedia - Social and Behavioral Sciences, 2014

Data on household travel patterns represent key information to the development of travel demand models. The technology of Global Positioning Systems (GPS) may substitute or be used in association with traditional data collection approaches. However, it is important to know how the quality of this information influences the results for planning purposes, such as in travel demand analysis. The objective of this study is to evaluate the influence of different sources of travel information-GPS-recorded compared to self-reported-in travel demand models. Several structures of discrete choice models were tested to represent choice behavior: multinomial logit, mixed logit with random coefficients and nested logit, trying to include possible correlations between alternatives and heterogeneity of individuals. Subjects were recruited from a list of contacts of the Transport Laboratory at the Federal University of Rio Grande do Sul, Brazil. The results showed that GPS technology collects the travel patterns more precisely reducing the bias by collecting data from short trips not reported in traditional surveys. The models estimated with GPS data showed greater significance due to less measurement error. The cost of processing GPS information must be considered. An adequate modeling with self-reported data, by more complex models incorporating heterogeneity and correlation among alternatives, allowed an equivalent adjustment to those estimated with GPS data. The self-reported data is less precise due to respondents under / overestimation of travel times. More complex models allow capturing measurement errors inherent to self-reported travel surveys.

Global Positioning System (GPS) devices have advanced rapidly and their costs are decreasing. GPS is certainly a promising technology for surveying travel behaviour. It demonstrates great potential as a survey instrument for tracking individual mobility and travel behaviour, by enabling surveys to be conducted for longer periods and by providing more accurate data on the spatial and temporal framework of travel. Besides these improvements, the utilisation of new technologies may reduce respondent burden and survey cost, which should have significant impacts on data accuracy and quality. Moreover, the relatively low burden for the respondent allows substantially extended survey duration: at least one week with GPS, compared to one day with conventional questionnaires. The feasibility of large-scale GPS-based surveys depends (but not solely) on the acceptability of such technology to collect data. Based on the French National Travel Survey (FNTS) conducted in 2007-08 with a sub-sample...