Diary Survey Quality Assessment Using GPS Traces

Raza, Ali; Knapen, Luk; Declercq, Katrien; Bellemans, Tom; Janssens, Davy; Wets, Geert

doi:10.1016/j.procs.2015.05.045

Cited by 5 publications

(3 citation statements)

References 7 publications

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“…Another limitation of GPS and smartphone measurements is related to battery drainage (resulting in empty batteries), which put some limits on their use as collectors of trip data. Due to the limitations of automatic detection, it has been suggested to combine GPS and other smartphone sensors with prompted call survey techniques to increase the quality of the trip-data collection (e.g., Raza et al, 2015).…”

Section: Literature Reviewmentioning

confidence: 99%

Automatic Trip Detection with the Dutch Mobile Mobility Panel: Towards Reliable Multiple-Week Trip Registration for Large Samples

Thomas¹,

Geurs²,

Koolwaaij³

et al. 2018

Journal of Urban Technology

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This paper examines the accuracy of trip and mode choice detection of the last wave of the Dutch Mobile Mobility Panel, a large-scale three-year, smartphone-based travel survey. Departure and arrival times, origins, destinations, modes, and travel purposes were recorded during a four week period in 2015, using the MoveSmarter app for a representative sample of 615 respondents, yielding over 60 thousand trips. During the monitoring period, respondents also participated in a web-based prompted recall survey and answered additional questions. This enables a comparison between automatic detected and reported trips. Most trips were detected with no clear biases in trip length or duration, and transport modes were classified correctly for over 80 percent of these trips. There is strong evidence that smartphone-based trip detection helps to reduce underreporting of trips, which is a common phenomenon in travel surveys. In the Dutch Mobile Mobility Panel, trip rates are substantially higher than trip-diary based travel surveys in the Netherlands, in particular for business and leisure trips which are often irregular. The rate of reporting also hardly decreased during the four-week period, which is a promising result for the use of smartphones in long duration travel surveys.

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Section: Literature Reviewmentioning

confidence: 99%

Automatic Trip Detection with the Dutch Mobile Mobility Panel: Towards Reliable Multiple-Week Trip Registration for Large Samples

Thomas¹,

Geurs²,

Koolwaaij³

et al. 2018

Journal of Urban Technology

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“…In this respect, traditional travel behavior studies make use of self-report activity-related travel diaries, typically requiring participants to provide information about activity purpose, travel mode, start and end times, location of trip origin and destination, and company of the trip. Over the past decade, recent advances in GPS technology and mobile positioning data have resulted in conventional paper self-report activity-related travel diaries being supplemented (and sometimes replaced) with GPS tracking devices as a collection method to record travel behavior ( 11–14 ). In the context of health promotion, accurate measurement of travel behavior by means of this combination of self-report and objective estimates allows, for example, a detailed assessment of the time spent using active modes of transport ( 15 ), or the classification of accelerometer-derived PA bouts into walking behavior ( 16 ).…”

mentioning

confidence: 99%

Data Quality of Travel Behavior Studies: Factors Influencing the Reporting Rate of Self-Reported and GPS-Recorded Trips in Persons with Disabilities

Neven

Schutter

Wets

et al. 2018

Transportation Research Record

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In persons with disabilities, the accuracy of self-report data collection methods in travel behaviour studies may be influenced by disease-related dysfunctions. The present study determines in detail whether disease-related physical, cognitive or psychosocial impairments contribute to the reporting rate of subjective self-report travel diaries and objective GPS tracking devices, besides sociodemographic and trip-related characteristics. The reporting rate of both data collection methods was analysed in 108 persons with Multiple Sclerosis with various disability severities during a seven days data collection period. The results demonstrated that there was only limited influence of diseaserelated dysfunctions on the reporting rate of both data collection methods, as well as only limited significant differences between subgroups with various disability severity. Overall, the data quality of the diary was higher than the quality of the GPS data: 66% of the trips were reported in both data collection methods, while overall more than one fifth of all trips were forgotten to be registered by GPS, and 11% were forgotten to be reported in the diary. Self-report travel diaries seemed to be more suitable for persons with a higher disability severity, as these persons more often forgot to take their GPS device with them when making a trip due to a number of organizational issues because of their mobility limitations. Neven, De Schutter, Wets, Feys and Janssens 1 INTRODUCTION Physical activity (PA) levels are often measured in order to identify current levels and changes in PA of specific population groups, and to evaluate the effectiveness of interventions designed to increase activity levels (1); which is an important part of health promotion research (2). The measurement method may have a significant impact on the observed PA levels, as self-report PA measures generally differ with direct, objective measures of PA (1). While self-report methods like questionnaires, interviews or diaries are often preferred as they are cheap to administer and offer additional information on the context of activities, objective measurement devices like pedometers or accelerometers provide more accurate measurement of PA and are not prone to certain biases in selfreport methods such as recall bias (3). Especially in research studies of persons with disabilities (PWD), the accuracy of self-report surveys for measuring PA can be influenced by cognitive dysfunctions like memory or emotional problems (4). Commonly used activity questionnaires and diaries rely on recall and honest reporting, and require individuals to have no or limited cognitive deficits to reduce potential bias in reporting results (5). According to research conducted by (6), (7) and (8), self-report subjective PA questionnaires show only limited reliability and validity regarding the frequency of different activity types (e.g. respondents tend to indicate their highest recent frequency of participation) and the duration and amount of PA. On the other hand, data of objective mon...

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“…Furthermore, while this report focuses on automated FGLD solutions, the use of self-reported travel diaries as a primary source of data bears discussion. Researchers have employed such methods extensively for decades, and their known limitations include participant burden, cost, accuracy, and the inability to record precise fluctuations in speed and trajectory during trips (Jan, Horowitz, and Peng 2000;Raza et al 2015;Vij and Shankari 2015). Houston, Luong, and Boarnet (2014) suggest self-reported diaries typically fail to record between 10% and 80% of trips, and a summary of eight transportation studies engaging 1,900 households or more (Bricka and Bhat 2006a) suggests that underreporting rates tend toward about 30% of trips on average.…”

Section: Introductionmentioning

confidence: 99%

An Overview of Technologies for Individual Trip History Collection: Mobility Decision Science Pillar SMART Mobility Consortium

Kwasnik

Carmichael

Isley

2019

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Diary Survey Quality Assessment Using GPS Traces

Cited by 5 publications

References 7 publications

Automatic Trip Detection with the Dutch Mobile Mobility Panel: Towards Reliable Multiple-Week Trip Registration for Large Samples

Automatic Trip Detection with the Dutch Mobile Mobility Panel: Towards Reliable Multiple-Week Trip Registration for Large Samples

Data Quality of Travel Behavior Studies: Factors Influencing the Reporting Rate of Self-Reported and GPS-Recorded Trips in Persons with Disabilities

An Overview of Technologies for Individual Trip History Collection: Mobility Decision Science Pillar SMART Mobility Consortium

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