Computer Simulation Modeling of Driver Behavior at Roundabouts
Abstract The Connecticut Department of Transportation (CT DOT) has constructed four roundabouts in the State of Connecticut within the past ten years. VISSIM traffic simulation software was utilized to analyze these roundabouts during their design phase. The objectives of this project are to: identi...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Clara Fang, F. [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2017 |
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| Schlagwörter: |
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| Anmerkung: |
© Springer Science+Business Media New York 2017 |
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| Übergeordnetes Werk: |
Enthalten in: International journal of intelligent transportation systems research - Berlin : Springer, 2010, 16(2017), 1 vom: 10. Feb., Seite 66-77 |
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| Übergeordnetes Werk: |
volume:16 ; year:2017 ; number:1 ; day:10 ; month:02 ; pages:66-77 |
| Links: |
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| DOI / URN: |
10.1007/s13177-017-0138-2 |
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| Katalog-ID: |
SPR030748623 |
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| 520 | |a Abstract The Connecticut Department of Transportation (CT DOT) has constructed four roundabouts in the State of Connecticut within the past ten years. VISSIM traffic simulation software was utilized to analyze these roundabouts during their design phase. The objectives of this project are to: identify VISSIM input variables most critical to accurate modeling; and, provide other recommendations for roundabout traffic operations modeling. Cameras were set up at these roundabouts to capture vehicle circulating activity and entry behavior for two weekdays. The data extracted from the videos include: vehicle circulating speed; critical gap estimated by Maximum Likelihood Method; peak hour volume; follow-up headway; travel time; and, vehicle queue length. A VISSIM simulation of existing roundabouts was built to compare both queue length and travel time predicted from simulation with measured in the field. The research investigated a variety of simulation parameters as calibration factors for describing driver behaviors at roundabouts. Among them, critical gap is the most effective calibration variable in roundabout simulation. The results will improve the design of future roundabouts in Connecticut and provide decision makers with insights on the relationship between various choices and future performance. | ||
| 650 | 4 | |a Roundabout analysis |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a VISSIM modeling |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Driver critical gap |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Simulation |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Calibration |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Castaneda, Hernan |4 aut | |
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10.1007/s13177-017-0138-2 doi (DE-627)SPR030748623 (SPR)s13177-017-0138-2-e DE-627 ger DE-627 rakwb eng Clara Fang, F. verfasserin aut Computer Simulation Modeling of Driver Behavior at Roundabouts 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer Science+Business Media New York 2017 Abstract The Connecticut Department of Transportation (CT DOT) has constructed four roundabouts in the State of Connecticut within the past ten years. VISSIM traffic simulation software was utilized to analyze these roundabouts during their design phase. The objectives of this project are to: identify VISSIM input variables most critical to accurate modeling; and, provide other recommendations for roundabout traffic operations modeling. Cameras were set up at these roundabouts to capture vehicle circulating activity and entry behavior for two weekdays. The data extracted from the videos include: vehicle circulating speed; critical gap estimated by Maximum Likelihood Method; peak hour volume; follow-up headway; travel time; and, vehicle queue length. A VISSIM simulation of existing roundabouts was built to compare both queue length and travel time predicted from simulation with measured in the field. The research investigated a variety of simulation parameters as calibration factors for describing driver behaviors at roundabouts. Among them, critical gap is the most effective calibration variable in roundabout simulation. The results will improve the design of future roundabouts in Connecticut and provide decision makers with insights on the relationship between various choices and future performance. Roundabout analysis (dpeaa)DE-He213 VISSIM modeling (dpeaa)DE-He213 Driver critical gap (dpeaa)DE-He213 Simulation (dpeaa)DE-He213 Calibration (dpeaa)DE-He213 Castaneda, Hernan aut Enthalten in International journal of intelligent transportation systems research Berlin : Springer, 2010 16(2017), 1 vom: 10. Feb., Seite 66-77 (DE-627)620772212 (DE-600)2542664-3 1868-8659 nnns volume:16 year:2017 number:1 day:10 month:02 pages:66-77 https://dx.doi.org/10.1007/s13177-017-0138-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 16 2017 1 10 02 66-77 |
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10.1007/s13177-017-0138-2 doi (DE-627)SPR030748623 (SPR)s13177-017-0138-2-e DE-627 ger DE-627 rakwb eng Clara Fang, F. verfasserin aut Computer Simulation Modeling of Driver Behavior at Roundabouts 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer Science+Business Media New York 2017 Abstract The Connecticut Department of Transportation (CT DOT) has constructed four roundabouts in the State of Connecticut within the past ten years. VISSIM traffic simulation software was utilized to analyze these roundabouts during their design phase. The objectives of this project are to: identify VISSIM input variables most critical to accurate modeling; and, provide other recommendations for roundabout traffic operations modeling. Cameras were set up at these roundabouts to capture vehicle circulating activity and entry behavior for two weekdays. The data extracted from the videos include: vehicle circulating speed; critical gap estimated by Maximum Likelihood Method; peak hour volume; follow-up headway; travel time; and, vehicle queue length. A VISSIM simulation of existing roundabouts was built to compare both queue length and travel time predicted from simulation with measured in the field. The research investigated a variety of simulation parameters as calibration factors for describing driver behaviors at roundabouts. Among them, critical gap is the most effective calibration variable in roundabout simulation. The results will improve the design of future roundabouts in Connecticut and provide decision makers with insights on the relationship between various choices and future performance. Roundabout analysis (dpeaa)DE-He213 VISSIM modeling (dpeaa)DE-He213 Driver critical gap (dpeaa)DE-He213 Simulation (dpeaa)DE-He213 Calibration (dpeaa)DE-He213 Castaneda, Hernan aut Enthalten in International journal of intelligent transportation systems research Berlin : Springer, 2010 16(2017), 1 vom: 10. Feb., Seite 66-77 (DE-627)620772212 (DE-600)2542664-3 1868-8659 nnns volume:16 year:2017 number:1 day:10 month:02 pages:66-77 https://dx.doi.org/10.1007/s13177-017-0138-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 16 2017 1 10 02 66-77 |
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10.1007/s13177-017-0138-2 doi (DE-627)SPR030748623 (SPR)s13177-017-0138-2-e DE-627 ger DE-627 rakwb eng Clara Fang, F. verfasserin aut Computer Simulation Modeling of Driver Behavior at Roundabouts 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer Science+Business Media New York 2017 Abstract The Connecticut Department of Transportation (CT DOT) has constructed four roundabouts in the State of Connecticut within the past ten years. VISSIM traffic simulation software was utilized to analyze these roundabouts during their design phase. The objectives of this project are to: identify VISSIM input variables most critical to accurate modeling; and, provide other recommendations for roundabout traffic operations modeling. Cameras were set up at these roundabouts to capture vehicle circulating activity and entry behavior for two weekdays. The data extracted from the videos include: vehicle circulating speed; critical gap estimated by Maximum Likelihood Method; peak hour volume; follow-up headway; travel time; and, vehicle queue length. A VISSIM simulation of existing roundabouts was built to compare both queue length and travel time predicted from simulation with measured in the field. The research investigated a variety of simulation parameters as calibration factors for describing driver behaviors at roundabouts. Among them, critical gap is the most effective calibration variable in roundabout simulation. The results will improve the design of future roundabouts in Connecticut and provide decision makers with insights on the relationship between various choices and future performance. Roundabout analysis (dpeaa)DE-He213 VISSIM modeling (dpeaa)DE-He213 Driver critical gap (dpeaa)DE-He213 Simulation (dpeaa)DE-He213 Calibration (dpeaa)DE-He213 Castaneda, Hernan aut Enthalten in International journal of intelligent transportation systems research Berlin : Springer, 2010 16(2017), 1 vom: 10. Feb., Seite 66-77 (DE-627)620772212 (DE-600)2542664-3 1868-8659 nnns volume:16 year:2017 number:1 day:10 month:02 pages:66-77 https://dx.doi.org/10.1007/s13177-017-0138-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 16 2017 1 10 02 66-77 |
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10.1007/s13177-017-0138-2 doi (DE-627)SPR030748623 (SPR)s13177-017-0138-2-e DE-627 ger DE-627 rakwb eng Clara Fang, F. verfasserin aut Computer Simulation Modeling of Driver Behavior at Roundabouts 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer Science+Business Media New York 2017 Abstract The Connecticut Department of Transportation (CT DOT) has constructed four roundabouts in the State of Connecticut within the past ten years. VISSIM traffic simulation software was utilized to analyze these roundabouts during their design phase. The objectives of this project are to: identify VISSIM input variables most critical to accurate modeling; and, provide other recommendations for roundabout traffic operations modeling. Cameras were set up at these roundabouts to capture vehicle circulating activity and entry behavior for two weekdays. The data extracted from the videos include: vehicle circulating speed; critical gap estimated by Maximum Likelihood Method; peak hour volume; follow-up headway; travel time; and, vehicle queue length. A VISSIM simulation of existing roundabouts was built to compare both queue length and travel time predicted from simulation with measured in the field. The research investigated a variety of simulation parameters as calibration factors for describing driver behaviors at roundabouts. Among them, critical gap is the most effective calibration variable in roundabout simulation. The results will improve the design of future roundabouts in Connecticut and provide decision makers with insights on the relationship between various choices and future performance. Roundabout analysis (dpeaa)DE-He213 VISSIM modeling (dpeaa)DE-He213 Driver critical gap (dpeaa)DE-He213 Simulation (dpeaa)DE-He213 Calibration (dpeaa)DE-He213 Castaneda, Hernan aut Enthalten in International journal of intelligent transportation systems research Berlin : Springer, 2010 16(2017), 1 vom: 10. Feb., Seite 66-77 (DE-627)620772212 (DE-600)2542664-3 1868-8659 nnns volume:16 year:2017 number:1 day:10 month:02 pages:66-77 https://dx.doi.org/10.1007/s13177-017-0138-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 16 2017 1 10 02 66-77 |
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10.1007/s13177-017-0138-2 doi (DE-627)SPR030748623 (SPR)s13177-017-0138-2-e DE-627 ger DE-627 rakwb eng Clara Fang, F. verfasserin aut Computer Simulation Modeling of Driver Behavior at Roundabouts 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer Science+Business Media New York 2017 Abstract The Connecticut Department of Transportation (CT DOT) has constructed four roundabouts in the State of Connecticut within the past ten years. VISSIM traffic simulation software was utilized to analyze these roundabouts during their design phase. The objectives of this project are to: identify VISSIM input variables most critical to accurate modeling; and, provide other recommendations for roundabout traffic operations modeling. Cameras were set up at these roundabouts to capture vehicle circulating activity and entry behavior for two weekdays. The data extracted from the videos include: vehicle circulating speed; critical gap estimated by Maximum Likelihood Method; peak hour volume; follow-up headway; travel time; and, vehicle queue length. A VISSIM simulation of existing roundabouts was built to compare both queue length and travel time predicted from simulation with measured in the field. The research investigated a variety of simulation parameters as calibration factors for describing driver behaviors at roundabouts. Among them, critical gap is the most effective calibration variable in roundabout simulation. The results will improve the design of future roundabouts in Connecticut and provide decision makers with insights on the relationship between various choices and future performance. Roundabout analysis (dpeaa)DE-He213 VISSIM modeling (dpeaa)DE-He213 Driver critical gap (dpeaa)DE-He213 Simulation (dpeaa)DE-He213 Calibration (dpeaa)DE-He213 Castaneda, Hernan aut Enthalten in International journal of intelligent transportation systems research Berlin : Springer, 2010 16(2017), 1 vom: 10. Feb., Seite 66-77 (DE-627)620772212 (DE-600)2542664-3 1868-8659 nnns volume:16 year:2017 number:1 day:10 month:02 pages:66-77 https://dx.doi.org/10.1007/s13177-017-0138-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 16 2017 1 10 02 66-77 |
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Clara Fang, F. |
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Clara Fang, F. misc Roundabout analysis misc VISSIM modeling misc Driver critical gap misc Simulation misc Calibration Computer Simulation Modeling of Driver Behavior at Roundabouts |
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Computer Simulation Modeling of Driver Behavior at Roundabouts Roundabout analysis (dpeaa)DE-He213 VISSIM modeling (dpeaa)DE-He213 Driver critical gap (dpeaa)DE-He213 Simulation (dpeaa)DE-He213 Calibration (dpeaa)DE-He213 |
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misc Roundabout analysis misc VISSIM modeling misc Driver critical gap misc Simulation misc Calibration |
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Computer Simulation Modeling of Driver Behavior at Roundabouts |
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computer simulation modeling of driver behavior at roundabouts |
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Computer Simulation Modeling of Driver Behavior at Roundabouts |
| abstract |
Abstract The Connecticut Department of Transportation (CT DOT) has constructed four roundabouts in the State of Connecticut within the past ten years. VISSIM traffic simulation software was utilized to analyze these roundabouts during their design phase. The objectives of this project are to: identify VISSIM input variables most critical to accurate modeling; and, provide other recommendations for roundabout traffic operations modeling. Cameras were set up at these roundabouts to capture vehicle circulating activity and entry behavior for two weekdays. The data extracted from the videos include: vehicle circulating speed; critical gap estimated by Maximum Likelihood Method; peak hour volume; follow-up headway; travel time; and, vehicle queue length. A VISSIM simulation of existing roundabouts was built to compare both queue length and travel time predicted from simulation with measured in the field. The research investigated a variety of simulation parameters as calibration factors for describing driver behaviors at roundabouts. Among them, critical gap is the most effective calibration variable in roundabout simulation. The results will improve the design of future roundabouts in Connecticut and provide decision makers with insights on the relationship between various choices and future performance. © Springer Science+Business Media New York 2017 |
| abstractGer |
Abstract The Connecticut Department of Transportation (CT DOT) has constructed four roundabouts in the State of Connecticut within the past ten years. VISSIM traffic simulation software was utilized to analyze these roundabouts during their design phase. The objectives of this project are to: identify VISSIM input variables most critical to accurate modeling; and, provide other recommendations for roundabout traffic operations modeling. Cameras were set up at these roundabouts to capture vehicle circulating activity and entry behavior for two weekdays. The data extracted from the videos include: vehicle circulating speed; critical gap estimated by Maximum Likelihood Method; peak hour volume; follow-up headway; travel time; and, vehicle queue length. A VISSIM simulation of existing roundabouts was built to compare both queue length and travel time predicted from simulation with measured in the field. The research investigated a variety of simulation parameters as calibration factors for describing driver behaviors at roundabouts. Among them, critical gap is the most effective calibration variable in roundabout simulation. The results will improve the design of future roundabouts in Connecticut and provide decision makers with insights on the relationship between various choices and future performance. © Springer Science+Business Media New York 2017 |
| abstract_unstemmed |
Abstract The Connecticut Department of Transportation (CT DOT) has constructed four roundabouts in the State of Connecticut within the past ten years. VISSIM traffic simulation software was utilized to analyze these roundabouts during their design phase. The objectives of this project are to: identify VISSIM input variables most critical to accurate modeling; and, provide other recommendations for roundabout traffic operations modeling. Cameras were set up at these roundabouts to capture vehicle circulating activity and entry behavior for two weekdays. The data extracted from the videos include: vehicle circulating speed; critical gap estimated by Maximum Likelihood Method; peak hour volume; follow-up headway; travel time; and, vehicle queue length. A VISSIM simulation of existing roundabouts was built to compare both queue length and travel time predicted from simulation with measured in the field. The research investigated a variety of simulation parameters as calibration factors for describing driver behaviors at roundabouts. Among them, critical gap is the most effective calibration variable in roundabout simulation. The results will improve the design of future roundabouts in Connecticut and provide decision makers with insights on the relationship between various choices and future performance. © Springer Science+Business Media New York 2017 |
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Computer Simulation Modeling of Driver Behavior at Roundabouts |
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https://dx.doi.org/10.1007/s13177-017-0138-2 |
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| author2 |
Castaneda, Hernan |
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| doi_str |
10.1007/s13177-017-0138-2 |
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2024-07-03T19:53:06.914Z |
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