Insights on data quality from a large-scale application of smartphone-based travel survey technology in the Phoenix metropolitan area, Arizona, USA

Hong, Shuyao; Zhao, Fang; Livshits, Vladimir; Gershenfeld, Shari; Santos, Jerffson Lucas; Ben‐Akiva, Moshe

doi:10.1016/j.tra.2021.10.002

Cited by 16 publications

(8 citation statements)

References 25 publications

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“…A total of 94% of devices reported more than one day of activity, 83% reported more than three days, while 69% of devices reported more than seven days of mobility behavior. These findings are in line with Hong [58], reporting that many participants collected three or more days of data and some participants verified up to eight days, despite participants only being required to collect and verify two days of data. They conclude that many users are willing to continue running the app and could be incentivized to verify additional days.…”

Section: Number Of Days With Trip Datasupporting

confidence: 86%

“…FMS also captured travel and activity that were underreported in the traditional surveys, such as multiple stops in a tour and work-based sub-tours. A similar comparison between a traditional survey and a GPS-based survey in Phoenix, Arizona [58], reports on the known issues of underreporting of trips and travel time rounding in traditional surveys. The most significant magnitude of differences between trips rates in the traditional and FMS-based surveys-up to almost 85%-was for non-home-based trips.…”

Section: Introductionmentioning

confidence: 99%

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An Evaluation of Smartphone Tracking for Travel Behavior Studies

Gillis

Lopez

Gautama

2023

IJGI

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The use of smartphone tracking is seen as the way forward in data collection for travel behavior studies. It overcomes some of the weaknesses of the classical approach (which uses paper trip diaries) in terms of accuracy and user annoyance. This article evaluates if these benefits hold in the practical application of smartphone tracking and compares the findings of a travel behavior survey using smartphone tracking to the findings of a previous paper survey. We compare three phases of the travel behavior study. In the recruitment phase, we expect smartphone tracking to make people more willing to participate in surveys, given the innovative nature and reduced burden to participants. However, we found the recruitment of participants equally challenging as for classical methods. In the data collection phase, however, we observe that participants entering the smartphone tracking survey are much more likely to complete the data collection period than when using paper trip diaries. Because of the limited burden, the risk of drop-out from the survey is significantly lower, making the actual data collection more efficient, even for longer survey periods. Finally, in the data analysis phase, the travel behavior indicators derived from smartphone tracking data result in higher average trip rates, shorter average trip lengths and a higher share of active modes (bike, walking) than the results from the paper survey. Although this is explained by more complete and more consistent trip registration, this finding is problematic for comparability between surveys based on different methods, both for longitudinal monitoring (comparability over consequent surveys) and for benchmarking (comparability over geographical areas). Therefore, it is crucial to clearly report the applied data collection methods when describing or comparing travel indicators. In surveys, a combined approach of both written trip diaries and smartphone tracking is advised, where each method can complement the shortcomings of the other.

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Section: Number Of Days With Trip Datasupporting

confidence: 86%

Section: Introductionmentioning

confidence: 99%

An Evaluation of Smartphone Tracking for Travel Behavior Studies

Gillis

Lopez

Gautama

2023

IJGI

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“…From the perspective of a single trip, data preprocessing includes an examination of the validity of the segment itself and the filtering of GPS point logs within this trip. It is worth mentioning that travel survey platforms such as Daynamica and FMS (Future Mobility Survey) integrate threshold-based dwell point segmentation (39)(40)(41) and ML-based travel mode identification (42)(43)(44), providing a template for this study and further applications of the model.…”

Section: Literature Reviewmentioning

confidence: 99%

Machine-Learning Approaches to Identify Travel Modes Using Smartphone-Assisted Survey and Map Application Programming Interface

Sun

Dong

Waygood

et al. 2022

Transportation Research Record: Journal of the Transportation R

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Travel mode choice prediction is essential for transportation planning and travel demand prediction. One of the conventional travel survey methods is collecting data over landline telephones, which lacks efficiency because of financial and time resource needs. In this regard, smartphone-assisted travel surveys can be applied to overcome the mentioned deficiencies. Smartphone-assisted travel surveys allow respondents to record GPS data, travel purpose, and travel mode via an application, simplifying the survey process. With various sensors equipped, the precision of data is ensured. Based on the survey results, varied approaches have been seen to travel mode identification. For this study, a travel survey was conducted in Hangzhou, China, supported by the smartphone application TraceRecord integrated with online mapping services. Several steps were undertaken to recognize different kinds of travel modes. First, preprocessing was adopted to screen out defective logs. With the employment of A-Map Application Programming Interface (API), trajectory segmentation was substantially boosted. Then, separately, features related to velocity, acceleration, and heading were extracted from the survey data. To achieve better accuracy and efficacy, two classification algorithms—support vector machine (SVM) and gradient boosting decision tree (GBDT)—were applied to model the travel mode identification problem. Compared with the SVM, GBDT produced a higher prediction accuracy of 90.16%. Further analysis was implemented based on the results of the GBDT model, and velocity-related features contributed the most to the identification problem. The study explores the possibility of applying travel mode recognition in real-world conditions and discusses further mining of the survey data.

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“…MMM har gennem en årraekke udviklet og forbedret en smartphone applikation til indsamling af transportvane data, og denne er blevet benyttet i en raekke projekter rundt om i verden, eksempelvis Singapore og Tel Aviv (se Nahmias-Biran et al, 2018) og Arizona (se Hong et al, 2021). I projektet benyttes dataindsamlingsmetoderne fra denne applikation i en customiseret version, hvor Transportvaneundersøgelsens spørgeskema og logikker laegges oven på den eksisterende app, og tilpasses således at det kan forsøges at indsamle TU data på denne nye platform.…”

Section: Udbud Og Udvikling Af Smartphone-applikationunclassified

Smartphone-baseret dataindsamling i Transportvaneundersøgelsen - Resultater og erfaringer fra pilotprojekt

Anderson¹

2022

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Det ønskes fortsat at undersøge mulighederne for fremadrettet at tilbyde smartphone-baseret dataindsamling i forbindelse med den nationale Transportvaneundersøgelse (TU). Center for Transport Analytics (CTA) på DTU har gennem en årrække samlet information og erfaring samt testet forskellige smartphone-baserede løsninger, og flere af disse projekter er tidligere præsenteret ved Aalborg Trafikdage.I slutningen af 2021 blev der efter en udbudsproces skrevet kontrakt med amerikanske Mobile Market Monitor om udvikling af en smartphone-applikation baseret på deres eksisterende, velfungerende app (se bl.a. Nahmias-Biran et al. 2018), som bliver customiseret til at ligne Transportvaneundersøgelsen på spørgeskemasiden. Der planlægges en large scale test af app’en i forsommeren 2022, og resultaterne fra denne undersøgelse, samt erfaringer og udfordringer med udbud, udvikling, GDPR mm. vil blive præsenteret på konferencen.Der har været en lang række udfordringer i forbindelse med udbuddet og tildelingen og delingen af disse erfaringer kan med fordel bruges til inspiration for andre, der ønsker at arbejde med smartphone-baseret dataindsamling af transportdata.

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Insights on data quality from a large-scale application of smartphone-based travel survey technology in the Phoenix metropolitan area, Arizona, USA

Cited by 16 publications

References 25 publications

An Evaluation of Smartphone Tracking for Travel Behavior Studies

An Evaluation of Smartphone Tracking for Travel Behavior Studies

Machine-Learning Approaches to Identify Travel Modes Using Smartphone-Assisted Survey and Map Application Programming Interface

Smartphone-baseret dataindsamling i Transportvaneundersøgelsen - Resultater og erfaringer fra pilotprojekt

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