Trip Purpose Identification from GPS Tracks

Montini, Lara; Rieser-Schüssler, Nadine; Horni, Andreas; Axhausen, Kay W.

doi:10.3141/2405-03

Cited by 67 publications

(46 citation statements)

References 20 publications

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“…This approach is different from that used by national survey tools, which rely on subjective self-reported trip purposes (e.g., the 2001 and 2009 NHTS) (Agrawal and Schimek, 2007; Yang and Diez-Roux, 2012). Previous studies proposed to classify trip purposes by using land use types at destination, which were identified by GPS data (Wolf et al, 2001) or machine learning approaches based on participants’ survey, accelerometer, and GPS data (Montini et al, 2014). However, these studies considered all trips, did not focus on walking, and required contextual land use data or intensive computational processes.…”

Section: Discussionmentioning

confidence: 99%

Differences in behavior, time, location, and built environment between objectively measured utilitarian and recreational walking

Kang

Moudon

Hurvitz

et al. 2017

Transportation Research Part D: Transport and Environment

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Objectives Utilitarian and recreational walking both contribute to physical activity. Yet walking for these two purposes may be different behaviors. We sought to provide operational definitions of utilitarian and recreational walking and to objectively measure their behavioral, spatial, and temporal differences in order to inform transportation and public health policies and interventions. Methods Data were collected 2008–2009 from 651 Seattle-King County residents, wearing an accelerometer and a GPS unit, and filling-in a travel diary for 7 days. Walking activity bouts were classified as utilitarian or recreational based on whether walking had a destination or not. Differences between the two walking purposes were analyzed, adjusting for the nested structure of walking activity within participants. Results Of the 4,905 observed walking bouts, 87.4% were utilitarian and 12.6% recreational walking. Utilitarian walking bouts were 45% shorter in duration (−12.1 min) and 9% faster in speed (+0.3km/h) than recreational walking bouts. Recreational walking occurred more frequently in the home neighborhood and was not associated with recreational land uses. Utilitarian walking occurred in areas having higher residential, employment, and street density, lower residential property value, higher area percentage of mixed-use neighborhood destinations, lower percentage of parks/trails, and lower average topographic slope than recreational walking. Conclusion Utilitarian and recreational walking are substantially different in terms of frequency, speed, duration, location, and related built environment. Policies that promote walking should adopt type-specific strategies. The high occurrence of recreational walking near home highlights the importance of the home neighborhood for this activity.

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Section: Discussionmentioning

confidence: 99%

Differences in behavior, time, location, and built environment between objectively measured utilitarian and recreational walking

Kang

Moudon

Hurvitz

et al. 2017

Transportation Research Part D: Transport and Environment

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“…activity start times, activity durations, land use information) (Gong et al 2014;Wolf, Guensler, and Bachman 2001;Lu, Zhu, and Zhang 2012;S. Lee and Hickman 2014;Shen and Stopher 2013;Montini et al 2014;Lu and Zhang 2015;Simas-Oliveira et al 2014;Lu, Zhu, and Zhang 2013;Feng and Timmermans 2015;Nurul Habib and Miller 2009). In the field of ITS (Intelligent Transportation Systems), transportation data mining methods that aim to integrate different data sources are generally denoted as data fusion (DF) techniques.…”

Section: Big Transport Data Mining Methodsmentioning

confidence: 99%

Developing an optimised activity type annotation method based on classification accuracy and entropy indices

Ectors

Reumers

Lee

et al. 2017

Transportmetrica A: Transport Science

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Developing an optimised activity type annotation method based on classification accuracy and entropy indices The generation of substantial amounts of travel and mobility related data has spawned the emergence of the era of big data. However, this data generally lacks activity-travel information such as trip purpose. This deficiency led to the development of trip purpose inference (activity type imputation / annotation) techniques, of which the performance depends on the available input data and the (number of) activity type classes to infer. Aggregating activity types strongly increases the inference accuracy and is usually left to the discretion of the researcher. As this is open for interpretation, it undermines the reported inference accuracy. This study developed an optimised classification methodology by identifying classes of activity types with an optimal balance between improving model accuracy, and preserving activity information from the original data set. A sensitivity analysis was performed. Additionally, several machine learning algorithms are experimented with. The proposed method may be applied to any study area.

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“…2 State of the art GPS tracking makes it possible to collect long-term travel diaries while placing a very low burden on respondents (Stopher et al (2008)). Initial studies using GPS loggers for travel diary collection were promising, although they required a substantial effort to distribute the devices and to obtain additional information from respondents necessary to interpret the GPS records (Bohte and Maat (2009); Oliveira et al (2011); Schuessler and Axhausen (2009); Montini et al (2014)). Today, the focus has shifted away from GPS loggers towards smartphone applications (Cottrill et al (2013)), due to their easier administration and the development of automatic methods for detecting transport mode based on GPS data.…”

Section: Introductionmentioning

confidence: 99%

Developing a passive GPS tracking system to study long-term travel behavior

Marra

Becker

Corman

2019

Transportation Research Part C: Emerging Technologies

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This paper describes development and testing of a passive GPS tracking smartphone application and corresponding data analysis methodology designed to increase the quality of travel behavior information collected in long-term travel surveys. The new approach is intended to replace the pencil-and-paper travel diaries and prompted recall methods that require more user involvement due to requirements for manual data entry and/or high battery usage. Reducing the burden placed on users enables researchers to collect data over longer periods, thus improving the quality of travel behavior research. To reduce battery use the smartphone-based application collects GPS data less frequently than other methods. Therefore, new algorithms were developed to identify trips and activities, transport mode, and even the specific vehicle used by the traveler. An important finding was the significant advantage of using users past data to improve mode detection results. The system was tested successfully in Zürich and Basel (Switzerland).

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Trip Purpose Identification from GPS Tracks

Cited by 67 publications

References 20 publications

Differences in behavior, time, location, and built environment between objectively measured utilitarian and recreational walking

Differences in behavior, time, location, and built environment between objectively measured utilitarian and recreational walking

Developing an optimised activity type annotation method based on classification accuracy and entropy indices

Developing a passive GPS tracking system to study long-term travel behavior

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