Data-Driven Analytics on Traffic Volume Calibration and Estimation for Town-Maintained Highways: A Case Study from Connecticut
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 150, Issue 10
Abstract
Annual average daily traffic (AADT) is one of the most inevitable elements for both transportation planning and traffic safety analysis. For state Departments of Transportations (DOTs), collecting AADT data is a critical and demanding task, normally accomplished through a combination of permanent and temporary traffic count stations, which has been proved to be extremely labor-intensive and time-consuming. Consequently, due to limited resources, it is typically performed for the state-maintained highways rather than the low-volume roadways maintained by town jurisdictions. Therefore, it is necessary to develop innovative and cost-effective approaches to generate AADT for low-volume roadways while still maintaining accuracy, including data driven analytical methodologies. To this end, this study conducted a comprehensive literature review of methodologies and relevant data used for AADT generation and estimation. Based on the data availability, data elements used for modeling AADT in Connecticut (CT) were collected. Specifically, AADT data for town-maintained highways from 2016 to 2020 was collected from the Streetlight platform and calibrated to fit the local condition in CT. Furthermore, machine learning algorithms were developed to predict future AADT beyond 2020. The model validation results indicate that the AADT estimated by this study is robust and reliable in terms of prediction accuracy, and it can serve as a valid asset in transportation planning and traffic safety analysis to practitioners and transportation agencies.
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Data Availability Statement
Some or all data, models, or code generated or used during the study are proprietary or confidential in nature and may only be provided with restrictions.
Acknowledgments
This study is jointly funded by the Federal Highway Administration (FHWA) and Connecticut Department of Transportation (CTDOT), through the project “CTDOT 170-3565–Further Advancing the Transportation Safety Analysis Capabilities of the Connecticut Department of Transportation. The authors gratefully acknowledge the funding, support and suggestions provided by the FHWA and CTDOT.
References
Apronti, D., K. Ksaibati, K. Gerow, and J. J. Hepner. 2016. “Estimating traffic volume on Wyoming low volume roads using linear and logistic regression methods.” J. Traffic Transp. Eng. 3 (6): 493–506. https://doi.org/10.1016/j.jtte.2016.02.004.
Apronti, D. T., and K. Ksaibati. 2018. “Four-step travel demand model implementation for estimating traffic volumes on rural low-volume roads in Wyoming.” Transp. Plann. Technol. 41 (5): 557–571. https://doi.org/10.1080/03081060.2018.1469288.
Bowling, S. T., and L. Aultman-Hall. 2003. “Development of a random sampling procedure for local road traffic count locations.” J. Transp. Stat. 6 (1): 59–69.
Castro-Neto, M., Y. Jeong, M. K. Jeong, and L. D. Han. 2009. “AADT prediction using support vector regression with data-dependent parameters.” Expert Syst. Appl. 36 (2): 2979–2986. https://doi.org/10.1016/j.eswa.2008.01.073.
Das, S., and I. Tsapakis. 2020. “Interpretable machine learning approach in estimating traffic volume on low-volume roadways.” Int. J. Transp. Sci. Technol. 9 (1): 76–88. https://doi.org/10.1016/j.ijtst.2019.09.004.
Dixon, M. 2004. The effects of errors in annual average daily traffic forecasting: Study of highways in Rural Idaho. Moscow, ID: Univ. of Idaho.
Doustmohammadi, M., M. Anderson, and E. Doustmohammadi. 2017. “Using log transformations to improve AADT forecasting models in small and medium sized communities.” Int. J. Traffic Transp. Eng. 6 (2): 23–27. https://doi.org/10.5923.j.ijtte.20170602.01.html.
Duddu, V. R., and S. S. Pulugurtha. 2013. “Principle of demographic gravitation to estimate annual average daily traffic: Comparison of statistical and neural network models.” J. Transp. Eng. 139 (6): 585–595. https://doi.org/10.1061/(ASCE)TE.1943-5436.0000537.
Eom, J. K., M. S. Park, T. Y. Heo, and L. F. Huntsinger. 2006. “Improving the prediction of annual average daily traffic for nonfreeway facilities by applying a spatial statistical method.” Transp. Res. Rec. 1968 (1): 20–29. https://doi.org/10.1177/0361198106196800103.
FHWA (Federal Highway Administration). 2013. Traffic monitoring guide. Washington, DC: FHWA.
INRIX. 2023. “Unlock the power of big data to make an impact our data, insights and tools can help you shape the future of mobility.” Accessed March 6, 2024. https://inrix.com/.
Islam, S. 2016. Estimation of annual average daily traffic (AADT) and missing hourly volume using artificial intelligence. Clemson, SC: Clemson Univ.
ITE (Institute of Transportation Engineers). 2021. Trip generation manual. 11th ed. Washington, DC: Institute of Transportation Engineers.
Khan, S. M., S. Islam, M. D. Khan, K. Dey, M. Chowdhury, and N. Huynh. 2017. “Development of statewide AADT estimation model from short-term counts: A comparative study for South Carolina.” Preprint, submitted November 30, 2017. https://arxiv.org/abs/1712.01257.
Lowry, M. 2014. “Spatial interpolation of traffic counts based on origin–destination centrality.” J. Transp. Geogr. 36 (Apr): 98–105. https://doi.org/10.1016/j.jtrangeo.2014.03.007.
Lu, J., T. Pan, and P. Liu. 2017. Assignment of estimated average annual daily traffic volumes on all roads in Florida. Tallahassee, FL: Florida DOT.
Mohamad, D., K. C. Sinha, T. Kuczek, and C. F. Scholer. 1998. “Annual average daily traffic prediction model for county roads.” Transp. Res. Rec. 1617 (1): 69–77. https://doi.org/10.3141/1617-10.
OSM (OpenStreetMap). 2023. “OpenStreetMap.” Accessed June 8, 2024. https://www.openstreetmap.org/#map=5/38.007/-95.844.
Pan, T. 2008. “Assignment of estimated average annual daily traffic on all roads in Florida.” Master’s thesis, Dept. of Civil Engineering, Univ. of South Florida.
Raja, P., M. Doustmohammadi, and M. D. Anderson. 2018. Estimation of average daily traffic on low-volume roads in Alabama. Rosemead, CA: Scientific & Academic Publishing.
Roll, J. 2019. “Evaluating streetlight estimates of annual average daily traffic in Oregon.” Accessed June 1, 2019. https://www.oregon.gov/odot/Programs/ResearchDocuments/StreetlightEvaluation.pdf.
Selby, B., and K. M. Kockelman. 2013. “Spatial prediction of traffic levels in unmeasured locations: Applications of universal kriging and geographically weighted regression.” J. Transp. Geogr. 29 (May): 24–32. https://doi.org/10.1016/j.jtrangeo.2012.12.009.
Shamo, B., E. Asa, and J. Membah. 2012. “Linear spatial interpolation and analysis of annual average daily traffic data.” J. Comput. Civ. Eng. 29 (1): 04014022. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000281.
Souleyrette, R. R., B. K. Howell, E. Green, and W. Staats. 2016. Estimation of average daily traffic on local roads in Kentucky. Lexington, KY: Univ. of Kentucky Transportation Center.
Staats, W. N. 2016. “Estimation of annual average daily traffic on local roads in Kentucky.” Master’s thesis, Dept. of Civil Engineering, Univ. of Kentucky.
Streetlight. 2022. “AADT 2022: Methodology and validation.” Accessed June 8, 2024. https://learn.streetlightdata.com/aadt-2022-traffic-counts-white-paper.
Sun, X., and S. Das. 2015. Developing a method for estimating AADT on all Louisiana roads. Baton Rouge, LA: Louisiana Transportation Research Center.
Tsapakis, I., W. A. Holik, A. Vandervalk, S. M. Turner, and J. Habermann. 2017. Innovative procedures in traffic volume estimation. Washington, DC: Federal Highway Administration. Office of Safety.
Turner, S., and P. Koeneman. 2017. Using mobile device samples to estimate traffic volumes. Saint Paul, MN: Minnesota DOT.
Wang, K., S. Zhao, and E. Jackson. 2019. “Functional forms of the negative binomial models in safety performance functions for rural two-lane intersections.” Accid. Anal. Prev. 124 (Mar): 193–201. https://doi.org/10.1016/j.aap.2019.01.015.
Wang, T., A. Gan, and P. Alluri. 2013. “Estimating annual average daily traffic for local roads for highway safety analysis.” Transp. Res. Rec. 2398 (1): 60–66. https://doi.org/10.3141/2398-07.
Wang, X. 2012. “Spatial analysis of travel behavior and response to traveler information.” Ph.D. dissertation, Dept. of Civil and Environmental Engineering, Old Dominion Univ.
Wang, X., and K. M. Kockelman. 2009. “Forecasting network data: Spatial interpolation of traffic counts from Texas data.” Transp. Res. Rec. 2105 (1): 100–108. https://doi.org/10.3141/2105-13.
Wu, J., and H. Xu. 2019. “Annual average daily traffic prediction model for minor roads at intersections.” J. Transp. Eng. Part A: Syst. 145 (10): 04019041. https://doi.org/10.1061/JTEPBS.0000262.
Xia, Q., F. Zhao, Z. Chen, L. D. Shen, and D. Ospina. 1999. “Estimation of annual average daily traffic for nonstate roads in a Florida county.” Transp. Res. Rec. 1660 (1): 32–40. https://doi.org/10.3141/1660-05.
Zhao, F., and S. Chung. 2001. Estimation of annual average daily traffic in a Florida county using GIS and regression. Miami: Florida International Univ.
Zhao, F., and N. Park. 2004. “Using geographically weighted regression models to estimate annual average daily traffic.” Transp. Res. Rec. 1879 (1): 99–107. https://doi.org/10.3141/1879-12.
Zhong, M., and B. L. Hanson. 2009. “GIS-based travel demand modeling for estimating traffic on low-class roads.” Transp. Plann. Technol. 32 (5): 423–439. https://doi.org/10.1080/03081060903257053.
Information & Authors
Information
Published In
Journal of Transportation Engineering, Part A: Systems
Volume 150 • Issue 10 • October 2024
Copyright
© 2024 American Society of Civil Engineers.
History
Received: Nov 3, 2023
Accepted: May 23, 2024
Published online: Aug 13, 2024
Published in print: Oct 1, 2024
Discussion open until: Jan 13, 2025
ASCE Technical Topics:
- Business management
- Calibration
- Data collection
- Engineering fundamentals
- Highway and road management
- Highway transportation
- Highways and roads
- Infrastructure
- Measurement (by type)
- Methodology (by type)
- Practice and Profession
- Public administration
- Public health and safety
- Research methods (by type)
- Safety
- Traffic analysis
- Traffic engineering
- Traffic management
- Traffic safety
- Traffic volume
- Transportation engineering
- Transportation management
- Transportation studies
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