Eliminating Recall & Proxy Bias in Household Travel Surveys: Sample Correction Methodology under the Core-Satellite Fusion Paradigm Article Swipe
Melvyn Li
,
Kaili Wang
,
Khandker Nurul Habib
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-4832044/v1
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-4832044/v1
Travel diary is one of the fundamental methods for collecting data critical for transportation planning, demand modelling, and analyses. Self-reported household travel surveys are known for recall and proxy biases. Both biases lead to underreporting of travel demand in the dataset. On the other hand, travel diaries collected through GPS-based methods are exempted from recall and proxy biases. Thus, this study will investigate the recall and proxy bias in self-reported travel surveys and propose correction procedures. The investigation and correction will be conducted under the core-satellite paradigm of urban passenger travel surveys under the core-satellite survey design paradigm. The study uses the Transportation Tomorrow survey (TTS) in the Greater Toronto and Hamilton area (GTHA) as the core dataset. The Google Timeline Travel Survey (GTTS) will be used as the satellite survey. The direct comparison between the core and satellite surveys shows that respondents who participated in both TTS and GTTS demonstrated bias-free diary reporting in the self-reported core survey. However, significant recall and proxy bias are identified for individuals who directly participate in the TTS but refuse to participate in GTTS and the proxy respondents in the TTS. Then, this study proposes a correction procedure based on respondents’ willingness-to-participate estimation for satellite surveys, dataset filtration, and re-weighting techniques. The effectiveness of the correction procedure is empirically demonstrated in this study.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.21203/rs.3.rs-4832044/v1
- https://www.researchsquare.com/article/rs-4832044/latest.pdf
- OA Status
- gold
- Cited By
- 1
- References
- 31
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4402007127
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4402007127Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.21203/rs.3.rs-4832044/v1Digital Object Identifier
- Title
-
Eliminating Recall & Proxy Bias in Household Travel Surveys: Sample Correction Methodology under the Core-Satellite Fusion ParadigmWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-08-29Full publication date if available
- Authors
-
Melvyn Li, Kaili Wang, Khandker Nurul HabibList of authors in order
- Landing page
-
https://doi.org/10.21203/rs.3.rs-4832044/v1Publisher landing page
- PDF URL
-
https://www.researchsquare.com/article/rs-4832044/latest.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.researchsquare.com/article/rs-4832044/latest.pdfDirect OA link when available
- Concepts
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Proxy (statistics), Satellite, Recall, Core (optical fiber), Sample (material), Fusion, Computer science, Psychology, Telecommunications, Chemistry, Engineering, Cognitive psychology, Machine learning, Chromatography, Linguistics, Philosophy, Aerospace engineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1Per-year citation counts (last 5 years)
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31Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.Timeline | 121 |
| abstract_inverted_index.Tomorrow | 104 |
| abstract_inverted_index.critical | 12 |
| abstract_inverted_index.dataset. | 41, 118 |
| abstract_inverted_index.directly | 171 |
| abstract_inverted_index.exempted | 53 |
| abstract_inverted_index.paradigm | 87 |
| abstract_inverted_index.proposes | 192 |
| abstract_inverted_index.surveys, | 203 |
| abstract_inverted_index.GPS-based | 50 |
| abstract_inverted_index.analyses. | 19 |
| abstract_inverted_index.bias-free | 152 |
| abstract_inverted_index.collected | 48 |
| abstract_inverted_index.conducted | 83 |
| abstract_inverted_index.household | 21 |
| abstract_inverted_index.paradigm. | 98 |
| abstract_inverted_index.passenger | 90 |
| abstract_inverted_index.planning, | 15 |
| abstract_inverted_index.procedure | 195, 214 |
| abstract_inverted_index.reporting | 154 |
| abstract_inverted_index.satellite | 130, 139, 202 |
| abstract_inverted_index.collecting | 10 |
| abstract_inverted_index.comparison | 134 |
| abstract_inverted_index.correction | 75, 80, 194, 213 |
| abstract_inverted_index.estimation | 200 |
| abstract_inverted_index.identified | 167 |
| abstract_inverted_index.modelling, | 17 |
| abstract_inverted_index.empirically | 216 |
| abstract_inverted_index.filtration, | 205 |
| abstract_inverted_index.fundamental | 7 |
| abstract_inverted_index.individuals | 169 |
| abstract_inverted_index.investigate | 63 |
| abstract_inverted_index.participate | 172, 179 |
| abstract_inverted_index.procedures. | 76 |
| abstract_inverted_index.respondents | 143, 185 |
| abstract_inverted_index.significant | 161 |
| abstract_inverted_index.techniques. | 208 |
| abstract_inverted_index.demonstrated | 151, 217 |
| abstract_inverted_index.participated | 145 |
| abstract_inverted_index.re-weighting | 207 |
| abstract_inverted_index.Self-reported | 20 |
| abstract_inverted_index.effectiveness | 210 |
| abstract_inverted_index.investigation | 78 |
| abstract_inverted_index.self-reported | 70, 157 |
| abstract_inverted_index.Transportation | 103 |
| abstract_inverted_index.core-satellite | 86, 95 |
| abstract_inverted_index.respondents’ | 198 |
| abstract_inverted_index.transportation | 14 |
| abstract_inverted_index.underreporting | 35 |
| abstract_inverted_index.<title>Abstract</title> | 0 |
| abstract_inverted_index.willingness-to-participate | 199 |
| cited_by_percentile_year.max | 95 |
| cited_by_percentile_year.min | 91 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/11 |
| sustainable_development_goals[0].score | 0.7799999713897705 |
| sustainable_development_goals[0].display_name | Sustainable cities and communities |
| citation_normalized_percentile.value | 0.79032258 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |