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

Wolf, Jean; Oliveira, Marcelo; Thompson, M

doi:10.3141/1854-21

Cited by 145 publications

(94 citation statements)

References 3 publications

(5 reference statements)

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“…Wolf et al (2003a;2003b) found the main causes of trip "under-reporting" to be survey length, memory decay of respondents, failure to understand or to follow survey instructions, respondents considering the trips unimportant, unwillingness to report full details of travel, and carelessness. Zmud and Wolf (2003) compared survey diary-reported trips (or simply "survey trips" from hereon) with GPS-recorded trips, and identified key demographic factors that may contribute to misreporting of trips, again assuming GPS to be the correct source of data (in the rest of this section, for ease in wording, we will assume the GPS data to be the "truth", as has been the norm in earlier studies).…”

Section: Gps In Household Travel Surveysmentioning

confidence: 99%

An analysis of the factors influencing differences in survey-reported and GPS-recorded trips

Bricka

Sen

Paleti

et al. 2012

Transportation Research Part C: Emerging Technologies

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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.

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Section: Gps In Household Travel Surveysmentioning

confidence: 99%

An analysis of the factors influencing differences in survey-reported and GPS-recorded trips

Bricka

Sen

Paleti

et al. 2012

Transportation Research Part C: Emerging Technologies

View full text Add to dashboard Cite

show abstract

“…Various studies confirm that the use of GPS data loggers has resulted in greater data accuracy compared to conventional paper diaries and telephone surveys (Forrest and Pearson 2005;Ohmori et al 2005;Wolf et al 2003). Accuracy is further enhanced when GPS data is used on a geographic information system (GIS) application (Chung and Shalaby 2005;Schönfelder et al 2002;Tsui and Shalaby 2006;Wolf et al 2001).…”

Section: Gps Onlymentioning

confidence: 92%

Use of acceleration data for transportation mode prediction

Shafique

Hato

2014

Transportation

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Most smartphones today are equipped with an accelerometer, in addition to other sensors. Any data recorded by the accelerometer can be successfully utilised to determine the mode of transportation in use, which will provide an alternative to conventional household travel surveys and make it possible to implement customer-oriented advertising programmes. In this study, a comparison is made between changes in preprocessing, selection methods for generating training data, and classifiers, using the accelerometer data collected from three cities in Japan. The classifiers used were support vector machines (SVM), adaptive boosting (AdaBoost), decision tree and random forests. The results of this exercise suggest that using a 125-point moving average during preprocessing and selecting training data proportionally for all modes will maximise prediction accuracy. Moreover, random forests outperformed all other classifiers by yielding an overall prediction accuracy of 99.8 % for all three cities.

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“…One of the most common methods used to gather this type of information is via travel diaries, where users declare how and why they are traveling to their destinations during a certain time period (usually of one day). The classical approaches relied on users filling up paper diaries, having phone interviews, or declaring how they traveled via web forms, which have two main downsides: the participants are under-reporting their trips (Bricka & Bhat, 2006;Wolf, Oliveira, & Thompson, 2003), and the response rate is decreasing (Ogle et al, 2005;Zimowski, Tourangeau, Ghadialy, & Pedlow, 1997). As a solution to these problems, transportation scientists tried to automate parts of these diaries.…”

Section: Transportation Sciencementioning

confidence: 99%

Transportation mode detection – an in-depth review of applicability and reliability

2016

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The wide adoption of location-enabled devices, together with the acceptance of services that leverage (personal) data as payment, allows scientists to push through some of the previous barriers imposed by data insufficiency and privacy skepticism. The research problems whose study require hard-to-obtain data (e.g., transportation mode detection, service contextualization, etc.) have now become more accessible to scientists because of the availability of data collecting outlets. One such problem is the detection of a user's transportation mode. Different fields have approached the problem of transportation mode detection with different aims: Location Based Services is a field that focuses on understanding the transportation mode in real-time, Transportation Science is a field that focuses on measuring the daily travel patterns of individuals or groups of individuals, and Human Geography is a field that focuses on enriching a trajectory by adding domain-specific semantics. While different fields providing solutions to the same problem could be viewed as a positive outcome, it is difficult to compare these solutions because the reported performance indicators depend on the type of approach and its aim (e.g., the real-time availability of Location Based Services requires the performance to be computed on each classified location). The contributions of this paper are three fold. First, the paper reviews the critical aspects that are desired by each field of research when providing solutions to the transportation mode detection problem. Second, it proposes three dimensions that separate three branches of science based on their main interest. Finally, it identifies important gaps in research and future directions,i.e., proposing: widely accepted error measures meaningful for all disciplines, methods robust to new datasets and a benchmark dataset for performance validation.

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

Cited by 145 publications

References 3 publications

An analysis of the factors influencing differences in survey-reported and GPS-recorded trips

An analysis of the factors influencing differences in survey-reported and GPS-recorded trips

Use of acceleration data for transportation mode prediction

Transportation mode detection – an in-depth review of applicability and reliability

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