Emerging trends in household travel survey programs

Lawson, Catherine T.; Krans, Eric; Rentz, Elizabeth; Lynch, Joann

doi:10.1016/j.ssaho.2023.100466

Cited by 2 publications

(3 citation statements)

References 31 publications

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“…Stronger methods, for example, by personal contact via telephone or a face-to-face or motivating communication strategy, remain necessary to increase the response rate. This experience confirms the finding in [34,41], that the challenge of increasing participation in household surveys is one of the emerging trends in household travel surveys.…”

Section: Recruitment Of Participantssupporting

confidence: 87%

“…This reduces the burden on the user, but the correctness of the detection algorithm is determining for the quality of the data, with the risk of over-or underreporting trips in case of missed starts/stops. A review of emerging trends in household travel surveys [34] points to the exploration of more passive data enhancement as a way forward to supplement household surveying efforts. However, a comparison of 17 smartphones apps for travel diary collection [35] indicated that purely passive apps are not ready for rollout and that active contribution from respondents is still highly recommended.…”

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: Recruitment Of Participantssupporting

confidence: 87%

Section: Introductionmentioning

confidence: 99%

An Evaluation of Smartphone Tracking for Travel Behavior Studies

Gillis

Lopez

Gautama

2023

IJGI

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“…Due to a variety of challenging factors over the past two decades, household travel surveys have experienced increasingly declining participation rates and reduced funding. To overcome these challenges, transportation researchers have been exploring alternative methods for acquiring travel behavioral data using technologies such as Global Positioning Systems (GPS) data collected via smartphones (Lawson et al, 2023). Many smartphone travel survey apps were deployed as pilot studies.…”

Section: Travel Data Sources In Transit Systemsmentioning

confidence: 99%

Constructing Origin-Destination Matrix using Wi-Fi and AFC Data

Silva,

Elsaid,

Shalaby

2023

Preprint

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Transportation systems planning and management rely heavily on Origin-Destination (OD) demand matrices. However, the traditional approach of creating these matrices using household travel survey data is not only time-consuming but also expensive, making it challenging to apply them to detailed and time-sensitive analyses. Automated fare collection (AFC) systems can provide a solution to these challenges. However, many transit fare systems are entry-only with no requirement to tap at the exit station, which makes it challenging to determine the location of alighting stations and analyze spatial demand patterns. This study proposes a framework that uses Wi-Fi traces and passenger counts at AFC entry/exit gates to construct OD matrices for entry-only Urban Rail Transit (URT) systems. The City of Toronto's subway system was used as a case study, and the framework was compared to 2016 Transportation Tomorrow Survey (TTS), which is the primary source of OD-matrix estimation in the city. The generated OD-matrices were found to be very close to the OD-matrix from the household travel survey, with a cosine similarity close to one for most subway regions. Our estimated OD-matrices offer several advantages over traditional methods. They have low matrix sparsity and fast computational time and convergence, and they exhibit strong capability of recognizing demand patterns at the station-level.

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Emerging trends in household travel survey programs

Cited by 2 publications

References 31 publications

An Evaluation of Smartphone Tracking for Travel Behavior Studies

An Evaluation of Smartphone Tracking for Travel Behavior Studies

Constructing Origin-Destination Matrix using Wi-Fi and AFC Data

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