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7 Conclusions and Suggested Research
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135
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Suggested Citation:
"Acronyms." National Academies of Sciences, Engineering, and Medicine. 2024. Methods for Assigning Short-Duration Traffic Volume Counts to Adjustment Factor Groups to Estimate AADT. Washington, DC: The National Academies Press.
doi: 10.17226/27926.
ACRONYMS
| 6R | Rural minor collectors |
| 7R | Rural local roads |
| 7U | Urban local roads |
| AADP | Annual average daily probe |
| AADPC | Annual average daily probe count |
| AADT | Annual average daily traffic |
| AADWPC | Annual average day-of-week probe count |
| AASHTO | American Association of State Highway and Transportation Officials |
| ACS | American Community Survey |
| ACV | Average coefficient of variation |
| ADPC | Average daily probe count |
| ADT | Average daily traffic |
| AIC | Akaike information criterion |
| ANN | Artificial neural network |
| ANOVA | Analysis of variance |
| AP | Average precision |
| BC | Betweenness-centrality |
| BIC | Bayesian information criterion |
| CCS | Continuous count station |
| CV | Coefficient of variation |
| CRD | Coordinate |
| DA | Discriminant analysis |
| DF | Daily factor |
| DOT | Department of transportation |
| DOW | Day of week |
| DT | Decision tree |
| FC | Functional classification |
| FHWA | Federal Highway Administration |
| GB | Gradient boosting |
| GC | Geographical classification |
| GIS | Geographic information system |
| GPS | Global positioning system |
| GWR | Geographical weighted regression |
| HF | Hourly factor |
| IAE | Index of assignment effectiveness |
| KNN | K-nearest neighbors |
| LBS | Location-based service |
| LOO | Leave one out |
| MAD | Mean absolute difference |
| MADPC | Monthly average daily probe count |
| MADWPC | Monthly average day-of-week probe count |
| MAE | Mean absolute error |
| MAPE | Mean absolute percent error |
| MdAE | Median absolute error |
Page
136
Share
Suggested Citation:
"Acronyms." National Academies of Sciences, Engineering, and Medicine. 2024. Methods for Assigning Short-Duration Traffic Volume Counts to Adjustment Factor Groups to Estimate AADT. Washington, DC: The National Academies Press.
doi: 10.17226/27926.
| MdAPE | Median absolute percent error |
| MDWF | Monthly day-of-week factor |
| MF | Monthly factor |
| ML | Machine learning |
| MSD | Mean signed difference |
| MSE | Mean squared error |
| NCHRP | National Cooperative Highway Research Program |
| NFAS | Non–Federal aid system |
| NRMSE | Normalized root mean squared error |
| NYSDOT | New York State Department of Transportation |
| OD | Origin-destination |
| OEM | Original equipment manufacturer |
| OLS | Ordinary least square |
| OSM | OpenStreetMap |
| PE | Percent error |
| RF | Random forest |
| RMSE | Root mean squared error |
| RU | Rural/urban |
| SCAD | Smoothly clipped absolute deviation penalty |
| SDAPE | Standard deviation of absolute percent error |
| SDC | Short-duration count |
| StL | StreetLight Data Inc. |
| SVM | Support vector machine |
| SVR | Support vector regression |
| TBDWGF | Time-block day-of-week group factor |
| TBDWGSF | Time-block day-of-week group seasonal factor |
| TBMDWF | Time-block monthly day-of-week factor |
| TMC | Turning movement count |
| TMG | Traffic Monitoring Guide |
| TRB | Transportation Research Board |
| vpd | Vehicles per day |
| WACV | Weighted average coefficient of variation |
| WAP | Weighted average precision |
| wCoV | Weighted coefficient of variation |
| WCSS | Within-cluster sum of squares |
| XGB | Extreme gradient boosting |
Page
135
Share
Suggested Citation:
"Acronyms." National Academies of Sciences, Engineering, and Medicine. 2024. Methods for Assigning Short-Duration Traffic Volume Counts to Adjustment Factor Groups to Estimate AADT. Washington, DC: The National Academies Press.
doi: 10.17226/27926.
Page
136
Share
Suggested Citation:
"Acronyms." National Academies of Sciences, Engineering, and Medicine. 2024. Methods for Assigning Short-Duration Traffic Volume Counts to Adjustment Factor Groups to Estimate AADT. Washington, DC: The National Academies Press.
doi: 10.17226/27926.
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