Estimating Traffic Volume for Local Streets with Imbalanced Data
Annual average daily traffic (AADT) is an important measurement used in traffic engineering. Local streets are major components of a road network. However, automatic traffic recorders (ATRs) used to collect AADT are often limited to arterial roads, and such information is, therefore, often unavailable for local streets. Estimating AADT on local streets becomes a necessity as local street traffic continues to grow and the capacity of arterial roads becomes insufficient. A challenge is that an under-represented sample of local street AADT may result in biased estimation. A synthetic minority oversampling technique (SMOTE) is applied to oversample local streets to correct the imbalanced sampling among different road types. A generalized linear mixed model (GLMM) is employed to estimate AADT incorporating various independent variables, including factors of roadway design, socio-demographics, and land use. The model is examined with an AADT dataset from Seattle, WA. Results show that: (1) SMOTE helps to correct imbalanced sampling proportions and improve model performance significantly; (2) the number of lanes and the number of crosswalks are both positively associated with AADT; (3) road segments located in areas with a higher population density or more mixed land use have a higher AADT; (4) distance to the nearest arterial road is negatively correlated with AADT; and (5) AADT creates spatial spillover effects on neighboring road segments. The combination of SMOTE and GLMM improves the estimation accuracy on AADT, which contributes to better data for transportation planning and traffic monitoring, and to cost saving on data collection.
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/issn/03611981
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Supplemental Notes:
- The Standing Committee on Urban Transportation Data and Information Systems (ABJ30 ) peer-reviewed this paper (19-01257). © National Academy of Sciences: Transportation Research Board 2019.
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Authors:
- Chen, Peng
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0000-0002-3201-7339
- Hu, Songhua
- Shen, Qing
- Lin, Hangfei
- Xie, Chi
- Publication Date: 2019-3
Language
- English
Media Info
- Media Type: Digital/other
- Features: Figures; References; Tables;
- Pagination: pp 598-610
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Serial:
- Transportation Research Record: Journal of the Transportation Research Board
- Volume: 2673
- Issue Number: 3
- Publisher: Sage Publications, Incorporated
- ISSN: 0361-1981
- EISSN: 2169-4052
- Serial URL: http://journals.sagepub.com/home/trr
Subject/Index Terms
- TRT Terms: Annual average daily traffic; Data collection; Data files; Statistical sampling; Streets; Traffic estimation; Traffic volume
- Uncontrolled Terms: Generalized linear mixed model; Local streets
- Geographic Terms: Seattle (Washington)
- Subject Areas: Data and Information Technology; Highways; Planning and Forecasting;
Filing Info
- Accession Number: 01698691
- Record Type: Publication
- Report/Paper Numbers: 19-01257
- Files: TRIS, TRB, ATRI
- Created Date: Mar 8 2019 4:38PM