Estimation of annual average daily traffic (AADT) data for low-volume roads: a systematic literature review and meta-analysis [version 1; peer review: 2 approved]
Background: The annual average daily traffic (AADT) data from road segments are critical for roadway projects, especially with the decision-making processes about operations, travel demand, safety-performance evaluation, and maintenance. Regular updates help to determine traffic patterns for decisio...
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| Format: | Article |
| Language: | English |
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Emerald Publishing
2022-03-01
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| Series: | Emerald Open Research |
| Subjects: | |
| Online Access: | https://emeraldopenresearch.com/articles/4-13/v1 |
| _version_ | 1797770894147321856 |
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| author | Bright Awuku Edmund Baffoe-Twum Eric Asa |
| author_facet | Bright Awuku Edmund Baffoe-Twum Eric Asa |
| author_sort | Bright Awuku |
| collection | DOAJ |
| description | Background: The annual average daily traffic (AADT) data from road segments are critical for roadway projects, especially with the decision-making processes about operations, travel demand, safety-performance evaluation, and maintenance. Regular updates help to determine traffic patterns for decision-making. Unfortunately, the luxury of having permanent recorders on all road segments, especially low-volume roads, is virtually impossible. Consequently, insufficient AADT information is acquired for planning and new developments. A growing number of statistical, mathematical, and machine-learning algorithms have helped estimate AADT data values accurately, to some extent, at both sampled and unsampled locations on low-volume roadways. In some cases, roads with no representative AADT data are resolved with information from roadways with similar traffic patterns. Methods: This study adopted an integrative approach with a combined systematic literature review (SLR) and meta-analysis (MA) to identify and to evaluate the performance, the sources of error, and possible advantages and disadvantages of the techniques utilized most for estimating AADT data. As a result, an SLR of various peer-reviewed articles and reports was completed to answer four research questions. Results: The study showed that the most frequent techniques utilized to estimate AADT data on low-volume roadways were regression, artificial neural-network techniques, travel-demand models, the traditional factor approach, and spatial interpolation techniques. These AADT data-estimating methods’ performance was subjected to meta-analysis. Three studies were completed: R squared, root means square error, and mean absolute percentage error. The meta-analysis results indicated a mixed summary effect: 1. all studies were equal; 2. all studies were not comparable. However, the integrated qualitative and quantitative approach indicated that spatial-interpolation (Kriging) methods outperformed the others. Conclusions: Spatial-interpolation methods may be selected over others to generate accurate AADT data by practitioners at all levels for decision making. Besides, the resulting cross-validation statistics give statistics like the other methods' performance measures. |
| first_indexed | 2024-03-12T21:29:38Z |
| format | Article |
| id | doaj.art-8039d65583954db2a2937d8d427888ff |
| institution | Directory Open Access Journal |
| issn | 2631-3952 |
| language | English |
| last_indexed | 2024-03-12T21:29:38Z |
| publishDate | 2022-03-01 |
| publisher | Emerald Publishing |
| record_format | Article |
| series | Emerald Open Research |
| spelling | doaj.art-8039d65583954db2a2937d8d427888ff2023-07-28T00:00:00ZengEmerald PublishingEmerald Open Research2631-39522022-03-01415612Estimation of annual average daily traffic (AADT) data for low-volume roads: a systematic literature review and meta-analysis [version 1; peer review: 2 approved]Bright Awuku0Edmund Baffoe-Twum1https://orcid.org/0000-0002-9665-9525Eric Asa2Department of Construction Management and Engineering, North Dakota State University, Fargo, North Dakota, 58108, United StatesDepartment of Construction Management, West Virginia University Institute of Technology, Beckley, West Virginia, 25801, United StatesDepartment of Construction Management and Engineering, North Dakota State University, Fargo, North Dakota, 58108, United StatesBackground: The annual average daily traffic (AADT) data from road segments are critical for roadway projects, especially with the decision-making processes about operations, travel demand, safety-performance evaluation, and maintenance. Regular updates help to determine traffic patterns for decision-making. Unfortunately, the luxury of having permanent recorders on all road segments, especially low-volume roads, is virtually impossible. Consequently, insufficient AADT information is acquired for planning and new developments. A growing number of statistical, mathematical, and machine-learning algorithms have helped estimate AADT data values accurately, to some extent, at both sampled and unsampled locations on low-volume roadways. In some cases, roads with no representative AADT data are resolved with information from roadways with similar traffic patterns. Methods: This study adopted an integrative approach with a combined systematic literature review (SLR) and meta-analysis (MA) to identify and to evaluate the performance, the sources of error, and possible advantages and disadvantages of the techniques utilized most for estimating AADT data. As a result, an SLR of various peer-reviewed articles and reports was completed to answer four research questions. Results: The study showed that the most frequent techniques utilized to estimate AADT data on low-volume roadways were regression, artificial neural-network techniques, travel-demand models, the traditional factor approach, and spatial interpolation techniques. These AADT data-estimating methods’ performance was subjected to meta-analysis. Three studies were completed: R squared, root means square error, and mean absolute percentage error. The meta-analysis results indicated a mixed summary effect: 1. all studies were equal; 2. all studies were not comparable. However, the integrated qualitative and quantitative approach indicated that spatial-interpolation (Kriging) methods outperformed the others. Conclusions: Spatial-interpolation methods may be selected over others to generate accurate AADT data by practitioners at all levels for decision making. Besides, the resulting cross-validation statistics give statistics like the other methods' performance measures.https://emeraldopenresearch.com/articles/4-13/v1AADT Low-Volume Roads Rural Roadways Estimating Techniques Meta-Analysiseng |
| spellingShingle | Bright Awuku Edmund Baffoe-Twum Eric Asa Estimation of annual average daily traffic (AADT) data for low-volume roads: a systematic literature review and meta-analysis [version 1; peer review: 2 approved] Emerald Open Research AADT Low-Volume Roads Rural Roadways Estimating Techniques Meta-Analysis eng |
| title | Estimation of annual average daily traffic (AADT) data for low-volume roads: a systematic literature review and meta-analysis [version 1; peer review: 2 approved] |
| title_full | Estimation of annual average daily traffic (AADT) data for low-volume roads: a systematic literature review and meta-analysis [version 1; peer review: 2 approved] |
| title_fullStr | Estimation of annual average daily traffic (AADT) data for low-volume roads: a systematic literature review and meta-analysis [version 1; peer review: 2 approved] |
| title_full_unstemmed | Estimation of annual average daily traffic (AADT) data for low-volume roads: a systematic literature review and meta-analysis [version 1; peer review: 2 approved] |
| title_short | Estimation of annual average daily traffic (AADT) data for low-volume roads: a systematic literature review and meta-analysis [version 1; peer review: 2 approved] |
| title_sort | estimation of annual average daily traffic aadt data for low volume roads a systematic literature review and meta analysis version 1 peer review 2 approved |
| topic | AADT Low-Volume Roads Rural Roadways Estimating Techniques Meta-Analysis eng |
| url | https://emeraldopenresearch.com/articles/4-13/v1 |
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