The changing accuracy of traffic forecasts
Abstract Researchers have improved travel demand forecasting methods in recent decades but invested relatively little to understand their accuracy. A major barrier has been the lack of necessary data. We compiled the largest known database of traffic forecast accuracy, composed of forecast traffic,...
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Gespeichert in:
| Autor*in: |
Hoque, Jawad Mahmud [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2021 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature 2021 |
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| Übergeordnetes Werk: |
Enthalten in: Transportation - Springer US, 1972, 49(2021), 2 vom: 26. Feb., Seite 445-466 |
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| Übergeordnetes Werk: |
volume:49 ; year:2021 ; number:2 ; day:26 ; month:02 ; pages:445-466 |
| Links: |
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| DOI / URN: |
10.1007/s11116-021-10182-8 |
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| Katalog-ID: |
OLC2078398403 |
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| 520 | |a Abstract Researchers have improved travel demand forecasting methods in recent decades but invested relatively little to understand their accuracy. A major barrier has been the lack of necessary data. We compiled the largest known database of traffic forecast accuracy, composed of forecast traffic, post-opening counts and project attributes for 1291 road projects in the United States and Europe. We compared measured versus forecast traffic and identified the factors associated with accuracy. We found measured traffic is on average 6% lower than forecast volumes, with a mean absolute deviation of 17% from the forecast. Higher volume roads, higher functional classes, shorter time spans, and the use of travel models all improved accuracy. Unemployment rates also affected accuracy—traffic would be 1% greater than forecast on average, rather than 6% lower, if we adjust for higher unemployment during the post-recession years (2008 to 2014). Forecast accuracy was not consistent over time: more recent forecasts were more accurate, and the mean deviation changed direction. Traffic on projects that opened from the 1980s through early 2000s was higher on average than forecast, while traffic on more recent projects was lower on average than forecast. This research provides insight into the degree of confidence that planners and policy makers can expect from traffic forecasts and suggests that we should view forecasts as a range of possible outcomes rather than a single expected outcome. | ||
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10.1007/s11116-021-10182-8 doi (DE-627)OLC2078398403 (DE-He213)s11116-021-10182-8-p DE-627 ger DE-627 rakwb eng 380 VZ Hoque, Jawad Mahmud verfasserin (orcid)0000-0002-8803-3899 aut The changing accuracy of traffic forecasts 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature 2021 Abstract Researchers have improved travel demand forecasting methods in recent decades but invested relatively little to understand their accuracy. A major barrier has been the lack of necessary data. We compiled the largest known database of traffic forecast accuracy, composed of forecast traffic, post-opening counts and project attributes for 1291 road projects in the United States and Europe. We compared measured versus forecast traffic and identified the factors associated with accuracy. We found measured traffic is on average 6% lower than forecast volumes, with a mean absolute deviation of 17% from the forecast. Higher volume roads, higher functional classes, shorter time spans, and the use of travel models all improved accuracy. Unemployment rates also affected accuracy—traffic would be 1% greater than forecast on average, rather than 6% lower, if we adjust for higher unemployment during the post-recession years (2008 to 2014). Forecast accuracy was not consistent over time: more recent forecasts were more accurate, and the mean deviation changed direction. Traffic on projects that opened from the 1980s through early 2000s was higher on average than forecast, while traffic on more recent projects was lower on average than forecast. This research provides insight into the degree of confidence that planners and policy makers can expect from traffic forecasts and suggests that we should view forecasts as a range of possible outcomes rather than a single expected outcome. Forecast accuracy Traffic forecasting Travel demand modeling Induced demand Erhardt, Gregory D. (orcid)0000-0001-8133-3381 aut Schmitt, David (orcid)0000-0003-3942-6917 aut Chen, Mei (orcid)0000-0002-7737-2895 aut Chaudhary, Ankita aut Wachs, Martin (orcid)0000-0002-6739-1654 aut Souleyrette, Reginald R. (orcid)0000-0002-3240-7943 aut Enthalten in Transportation Springer US, 1972 49(2021), 2 vom: 26. Feb., Seite 445-466 (DE-627)129299804 (DE-600)121857-8 (DE-576)014492407 0049-4488 nnns volume:49 year:2021 number:2 day:26 month:02 pages:445-466 https://doi.org/10.1007/s11116-021-10182-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-GEO SSG-OLC-WIW AR 49 2021 2 26 02 445-466 |
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10.1007/s11116-021-10182-8 doi (DE-627)OLC2078398403 (DE-He213)s11116-021-10182-8-p DE-627 ger DE-627 rakwb eng 380 VZ Hoque, Jawad Mahmud verfasserin (orcid)0000-0002-8803-3899 aut The changing accuracy of traffic forecasts 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature 2021 Abstract Researchers have improved travel demand forecasting methods in recent decades but invested relatively little to understand their accuracy. A major barrier has been the lack of necessary data. We compiled the largest known database of traffic forecast accuracy, composed of forecast traffic, post-opening counts and project attributes for 1291 road projects in the United States and Europe. We compared measured versus forecast traffic and identified the factors associated with accuracy. We found measured traffic is on average 6% lower than forecast volumes, with a mean absolute deviation of 17% from the forecast. Higher volume roads, higher functional classes, shorter time spans, and the use of travel models all improved accuracy. Unemployment rates also affected accuracy—traffic would be 1% greater than forecast on average, rather than 6% lower, if we adjust for higher unemployment during the post-recession years (2008 to 2014). Forecast accuracy was not consistent over time: more recent forecasts were more accurate, and the mean deviation changed direction. Traffic on projects that opened from the 1980s through early 2000s was higher on average than forecast, while traffic on more recent projects was lower on average than forecast. This research provides insight into the degree of confidence that planners and policy makers can expect from traffic forecasts and suggests that we should view forecasts as a range of possible outcomes rather than a single expected outcome. Forecast accuracy Traffic forecasting Travel demand modeling Induced demand Erhardt, Gregory D. (orcid)0000-0001-8133-3381 aut Schmitt, David (orcid)0000-0003-3942-6917 aut Chen, Mei (orcid)0000-0002-7737-2895 aut Chaudhary, Ankita aut Wachs, Martin (orcid)0000-0002-6739-1654 aut Souleyrette, Reginald R. (orcid)0000-0002-3240-7943 aut Enthalten in Transportation Springer US, 1972 49(2021), 2 vom: 26. Feb., Seite 445-466 (DE-627)129299804 (DE-600)121857-8 (DE-576)014492407 0049-4488 nnns volume:49 year:2021 number:2 day:26 month:02 pages:445-466 https://doi.org/10.1007/s11116-021-10182-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-GEO SSG-OLC-WIW AR 49 2021 2 26 02 445-466 |
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10.1007/s11116-021-10182-8 doi (DE-627)OLC2078398403 (DE-He213)s11116-021-10182-8-p DE-627 ger DE-627 rakwb eng 380 VZ Hoque, Jawad Mahmud verfasserin (orcid)0000-0002-8803-3899 aut The changing accuracy of traffic forecasts 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature 2021 Abstract Researchers have improved travel demand forecasting methods in recent decades but invested relatively little to understand their accuracy. A major barrier has been the lack of necessary data. We compiled the largest known database of traffic forecast accuracy, composed of forecast traffic, post-opening counts and project attributes for 1291 road projects in the United States and Europe. We compared measured versus forecast traffic and identified the factors associated with accuracy. We found measured traffic is on average 6% lower than forecast volumes, with a mean absolute deviation of 17% from the forecast. Higher volume roads, higher functional classes, shorter time spans, and the use of travel models all improved accuracy. Unemployment rates also affected accuracy—traffic would be 1% greater than forecast on average, rather than 6% lower, if we adjust for higher unemployment during the post-recession years (2008 to 2014). Forecast accuracy was not consistent over time: more recent forecasts were more accurate, and the mean deviation changed direction. Traffic on projects that opened from the 1980s through early 2000s was higher on average than forecast, while traffic on more recent projects was lower on average than forecast. This research provides insight into the degree of confidence that planners and policy makers can expect from traffic forecasts and suggests that we should view forecasts as a range of possible outcomes rather than a single expected outcome. Forecast accuracy Traffic forecasting Travel demand modeling Induced demand Erhardt, Gregory D. (orcid)0000-0001-8133-3381 aut Schmitt, David (orcid)0000-0003-3942-6917 aut Chen, Mei (orcid)0000-0002-7737-2895 aut Chaudhary, Ankita aut Wachs, Martin (orcid)0000-0002-6739-1654 aut Souleyrette, Reginald R. (orcid)0000-0002-3240-7943 aut Enthalten in Transportation Springer US, 1972 49(2021), 2 vom: 26. Feb., Seite 445-466 (DE-627)129299804 (DE-600)121857-8 (DE-576)014492407 0049-4488 nnns volume:49 year:2021 number:2 day:26 month:02 pages:445-466 https://doi.org/10.1007/s11116-021-10182-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-GEO SSG-OLC-WIW AR 49 2021 2 26 02 445-466 |
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The changing accuracy of traffic forecasts |
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The changing accuracy of traffic forecasts |
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the changing accuracy of traffic forecasts |
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The changing accuracy of traffic forecasts |
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Abstract Researchers have improved travel demand forecasting methods in recent decades but invested relatively little to understand their accuracy. A major barrier has been the lack of necessary data. We compiled the largest known database of traffic forecast accuracy, composed of forecast traffic, post-opening counts and project attributes for 1291 road projects in the United States and Europe. We compared measured versus forecast traffic and identified the factors associated with accuracy. We found measured traffic is on average 6% lower than forecast volumes, with a mean absolute deviation of 17% from the forecast. Higher volume roads, higher functional classes, shorter time spans, and the use of travel models all improved accuracy. Unemployment rates also affected accuracy—traffic would be 1% greater than forecast on average, rather than 6% lower, if we adjust for higher unemployment during the post-recession years (2008 to 2014). Forecast accuracy was not consistent over time: more recent forecasts were more accurate, and the mean deviation changed direction. Traffic on projects that opened from the 1980s through early 2000s was higher on average than forecast, while traffic on more recent projects was lower on average than forecast. This research provides insight into the degree of confidence that planners and policy makers can expect from traffic forecasts and suggests that we should view forecasts as a range of possible outcomes rather than a single expected outcome. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature 2021 |
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Abstract Researchers have improved travel demand forecasting methods in recent decades but invested relatively little to understand their accuracy. A major barrier has been the lack of necessary data. We compiled the largest known database of traffic forecast accuracy, composed of forecast traffic, post-opening counts and project attributes for 1291 road projects in the United States and Europe. We compared measured versus forecast traffic and identified the factors associated with accuracy. We found measured traffic is on average 6% lower than forecast volumes, with a mean absolute deviation of 17% from the forecast. Higher volume roads, higher functional classes, shorter time spans, and the use of travel models all improved accuracy. Unemployment rates also affected accuracy—traffic would be 1% greater than forecast on average, rather than 6% lower, if we adjust for higher unemployment during the post-recession years (2008 to 2014). Forecast accuracy was not consistent over time: more recent forecasts were more accurate, and the mean deviation changed direction. Traffic on projects that opened from the 1980s through early 2000s was higher on average than forecast, while traffic on more recent projects was lower on average than forecast. This research provides insight into the degree of confidence that planners and policy makers can expect from traffic forecasts and suggests that we should view forecasts as a range of possible outcomes rather than a single expected outcome. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature 2021 |
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Abstract Researchers have improved travel demand forecasting methods in recent decades but invested relatively little to understand their accuracy. A major barrier has been the lack of necessary data. We compiled the largest known database of traffic forecast accuracy, composed of forecast traffic, post-opening counts and project attributes for 1291 road projects in the United States and Europe. We compared measured versus forecast traffic and identified the factors associated with accuracy. We found measured traffic is on average 6% lower than forecast volumes, with a mean absolute deviation of 17% from the forecast. Higher volume roads, higher functional classes, shorter time spans, and the use of travel models all improved accuracy. Unemployment rates also affected accuracy—traffic would be 1% greater than forecast on average, rather than 6% lower, if we adjust for higher unemployment during the post-recession years (2008 to 2014). Forecast accuracy was not consistent over time: more recent forecasts were more accurate, and the mean deviation changed direction. Traffic on projects that opened from the 1980s through early 2000s was higher on average than forecast, while traffic on more recent projects was lower on average than forecast. This research provides insight into the degree of confidence that planners and policy makers can expect from traffic forecasts and suggests that we should view forecasts as a range of possible outcomes rather than a single expected outcome. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature 2021 |
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