Developing a grouped random parameter beta model to analyze drivers’ speeding behavior on urban and suburban arterials with probe speed data

Speeding is one of the major contributing factors to traffic fatalities. Various speed management strategies have been proposed to encourage drivers to select more appropriate speeds. This study aims to explore the different effects of the speed management strategies on the speeding proportions at u...
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Gespeichert in:

Autor*in:	Cai, Qing [verfasserIn] Abdel-Aty, Mohamed [verfasserIn] Mahmoud, Nada [verfasserIn] Ugan, Jorge [verfasserIn] Al-Omari, Ma'en M.A. [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	Grouped random parameter beta model Speeding proportion Speed management strategies Probe speed data Fractional split model

Übergeordnetes Werk:	Enthalten in: Accident analysis & prevention - Amsterdam [u.a.] : Elsevier, 1969, 161
Übergeordnetes Werk:	volume:161

DOI / URN:	10.1016/j.aap.2021.106386

Katalog-ID:	ELV006674275

Internformat


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024	7		\|a 10.1016/j.aap.2021.106386 \|2 doi
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245	1	0	\|a Developing a grouped random parameter beta model to analyze drivers’ speeding behavior on urban and suburban arterials with probe speed data
264		1	\|c 2021
336			\|a nicht spezifiziert \|b zzz \|2 rdacontent
337			\|a Computermedien \|b c \|2 rdamedia
338			\|a Online-Ressource \|b cr \|2 rdacarrier
520			\|a Speeding is one of the major contributing factors to traffic fatalities. Various speed management strategies have been proposed to encourage drivers to select more appropriate speeds. This study aims to explore the different effects of the speed management strategies on the speeding proportions at urban and suburban arterials. Probe speed data was used to calculate the speeding proportions. To overcome the variability of probe speed data caused by the signalized intersections, a new method was suggested to calculate the speeding proportion, and a fractional split model was estimated to adjust the probe speed data. A Beta regression model was developed to analyze the speeding proportion. A grouped random parameter modeling structure was adopted to realize the different effects of speed management strategies and other road attributes on speeding proportions by different road types. Besides, a fixed beta model was developed for the comparison. The results suggested the grouped random parameter model could provide better performance over the counterpart and could realize the different effects of road features and other contributing factors on the speeding of different roads. It is expected that the findings could help inform more appropriate road design in order to reduce speed limit violations on urban and suburban arterials.
650		4	\|a Grouped random parameter beta model
650		4	\|a Speeding proportion
650		4	\|a Speed management strategies
650		4	\|a Probe speed data
650		4	\|a Fractional split model
700	1		\|a Abdel-Aty, Mohamed \|e verfasserin \|4 aut
700	1		\|a Mahmoud, Nada \|e verfasserin \|4 aut
700	1		\|a Ugan, Jorge \|e verfasserin \|4 aut
700	1		\|a Al-Omari, Ma'en M.A. \|e verfasserin \|4 aut
773	0	8	\|i Enthalten in \|t Accident analysis & prevention \|d Amsterdam [u.a.] : Elsevier, 1969 \|g 161 \|h Online-Ressource \|w (DE-627)306591707 \|w (DE-600)1498752-1 \|w (DE-576)081926820 \|x 1879-2057 \|7 nnns
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912			\|a GBV_ILN_63
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912			\|a GBV_ILN_70
912			\|a GBV_ILN_73
912			\|a GBV_ILN_74
912			\|a GBV_ILN_90
912			\|a GBV_ILN_95
912			\|a GBV_ILN_100
912			\|a GBV_ILN_105
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912			\|a GBV_ILN_602
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912			\|a GBV_ILN_2027
912			\|a GBV_ILN_2034
912			\|a GBV_ILN_2037
912			\|a GBV_ILN_2038
912			\|a GBV_ILN_2044
912			\|a GBV_ILN_2048
912			\|a GBV_ILN_2049
912			\|a GBV_ILN_2050
912			\|a GBV_ILN_2056
912			\|a GBV_ILN_2059
912			\|a GBV_ILN_2061
912			\|a GBV_ILN_2064
912			\|a GBV_ILN_2065
912			\|a GBV_ILN_2068
912			\|a GBV_ILN_2111
912			\|a GBV_ILN_2112
912			\|a GBV_ILN_2113
912			\|a GBV_ILN_2118
912			\|a GBV_ILN_2122
912			\|a GBV_ILN_2129
912			\|a GBV_ILN_2143
912			\|a GBV_ILN_2147
912			\|a GBV_ILN_2148
912			\|a GBV_ILN_2152
912			\|a GBV_ILN_2153
912			\|a GBV_ILN_2190
912			\|a GBV_ILN_2336
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912			\|a GBV_ILN_2522
912			\|a GBV_ILN_4035
912			\|a GBV_ILN_4037
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912			\|a GBV_ILN_4126
912			\|a GBV_ILN_4242
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912			\|a GBV_ILN_4326
912			\|a GBV_ILN_4333
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912			\|a GBV_ILN_4393
936	b	k	\|a 55.84 \|j Straßenverkehr
936	b	k	\|a 55.24 \|j Fahrzeugführung \|j Fahrtechnik
936	b	k	\|a 44.80 \|j Unfallmedizin \|j Notfallmedizin
951			\|a AR
952			\|d 161

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matchkey_str	article:18792057:2021----::eeoiggopdadmaaeebtmdlonlzdiespeigeairnraadu
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bklnumber	55.84 55.24 44.80
publishDate	2021
allfields	10.1016/j.aap.2021.106386 doi (DE-627)ELV006674275 (ELSEVIER)S0001-4575(21)00417-6 DE-627 ger DE-627 rda eng 380 DE-600 55.84 bkl 55.24 bkl 44.80 bkl Cai, Qing verfasserin aut Developing a grouped random parameter beta model to analyze drivers’ speeding behavior on urban and suburban arterials with probe speed data 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Speeding is one of the major contributing factors to traffic fatalities. Various speed management strategies have been proposed to encourage drivers to select more appropriate speeds. This study aims to explore the different effects of the speed management strategies on the speeding proportions at urban and suburban arterials. Probe speed data was used to calculate the speeding proportions. To overcome the variability of probe speed data caused by the signalized intersections, a new method was suggested to calculate the speeding proportion, and a fractional split model was estimated to adjust the probe speed data. A Beta regression model was developed to analyze the speeding proportion. A grouped random parameter modeling structure was adopted to realize the different effects of speed management strategies and other road attributes on speeding proportions by different road types. Besides, a fixed beta model was developed for the comparison. The results suggested the grouped random parameter model could provide better performance over the counterpart and could realize the different effects of road features and other contributing factors on the speeding of different roads. It is expected that the findings could help inform more appropriate road design in order to reduce speed limit violations on urban and suburban arterials. Grouped random parameter beta model Speeding proportion Speed management strategies Probe speed data Fractional split model Abdel-Aty, Mohamed verfasserin aut Mahmoud, Nada verfasserin aut Ugan, Jorge verfasserin aut Al-Omari, Ma'en M.A. verfasserin aut Enthalten in Accident analysis & prevention Amsterdam [u.a.] : Elsevier, 1969 161 Online-Ressource (DE-627)306591707 (DE-600)1498752-1 (DE-576)081926820 1879-2057 nnns volume:161 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 55.84 Straßenverkehr 55.24 Fahrzeugführung Fahrtechnik 44.80 Unfallmedizin Notfallmedizin AR 161
spelling	10.1016/j.aap.2021.106386 doi (DE-627)ELV006674275 (ELSEVIER)S0001-4575(21)00417-6 DE-627 ger DE-627 rda eng 380 DE-600 55.84 bkl 55.24 bkl 44.80 bkl Cai, Qing verfasserin aut Developing a grouped random parameter beta model to analyze drivers’ speeding behavior on urban and suburban arterials with probe speed data 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Speeding is one of the major contributing factors to traffic fatalities. Various speed management strategies have been proposed to encourage drivers to select more appropriate speeds. This study aims to explore the different effects of the speed management strategies on the speeding proportions at urban and suburban arterials. Probe speed data was used to calculate the speeding proportions. To overcome the variability of probe speed data caused by the signalized intersections, a new method was suggested to calculate the speeding proportion, and a fractional split model was estimated to adjust the probe speed data. A Beta regression model was developed to analyze the speeding proportion. A grouped random parameter modeling structure was adopted to realize the different effects of speed management strategies and other road attributes on speeding proportions by different road types. Besides, a fixed beta model was developed for the comparison. The results suggested the grouped random parameter model could provide better performance over the counterpart and could realize the different effects of road features and other contributing factors on the speeding of different roads. It is expected that the findings could help inform more appropriate road design in order to reduce speed limit violations on urban and suburban arterials. Grouped random parameter beta model Speeding proportion Speed management strategies Probe speed data Fractional split model Abdel-Aty, Mohamed verfasserin aut Mahmoud, Nada verfasserin aut Ugan, Jorge verfasserin aut Al-Omari, Ma'en M.A. verfasserin aut Enthalten in Accident analysis & prevention Amsterdam [u.a.] : Elsevier, 1969 161 Online-Ressource (DE-627)306591707 (DE-600)1498752-1 (DE-576)081926820 1879-2057 nnns volume:161 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 55.84 Straßenverkehr 55.24 Fahrzeugführung Fahrtechnik 44.80 Unfallmedizin Notfallmedizin AR 161
allfields_unstemmed	10.1016/j.aap.2021.106386 doi (DE-627)ELV006674275 (ELSEVIER)S0001-4575(21)00417-6 DE-627 ger DE-627 rda eng 380 DE-600 55.84 bkl 55.24 bkl 44.80 bkl Cai, Qing verfasserin aut Developing a grouped random parameter beta model to analyze drivers’ speeding behavior on urban and suburban arterials with probe speed data 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Speeding is one of the major contributing factors to traffic fatalities. Various speed management strategies have been proposed to encourage drivers to select more appropriate speeds. This study aims to explore the different effects of the speed management strategies on the speeding proportions at urban and suburban arterials. Probe speed data was used to calculate the speeding proportions. To overcome the variability of probe speed data caused by the signalized intersections, a new method was suggested to calculate the speeding proportion, and a fractional split model was estimated to adjust the probe speed data. A Beta regression model was developed to analyze the speeding proportion. A grouped random parameter modeling structure was adopted to realize the different effects of speed management strategies and other road attributes on speeding proportions by different road types. Besides, a fixed beta model was developed for the comparison. The results suggested the grouped random parameter model could provide better performance over the counterpart and could realize the different effects of road features and other contributing factors on the speeding of different roads. It is expected that the findings could help inform more appropriate road design in order to reduce speed limit violations on urban and suburban arterials. Grouped random parameter beta model Speeding proportion Speed management strategies Probe speed data Fractional split model Abdel-Aty, Mohamed verfasserin aut Mahmoud, Nada verfasserin aut Ugan, Jorge verfasserin aut Al-Omari, Ma'en M.A. verfasserin aut Enthalten in Accident analysis & prevention Amsterdam [u.a.] : Elsevier, 1969 161 Online-Ressource (DE-627)306591707 (DE-600)1498752-1 (DE-576)081926820 1879-2057 nnns volume:161 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 55.84 Straßenverkehr 55.24 Fahrzeugführung Fahrtechnik 44.80 Unfallmedizin Notfallmedizin AR 161
allfieldsGer	10.1016/j.aap.2021.106386 doi (DE-627)ELV006674275 (ELSEVIER)S0001-4575(21)00417-6 DE-627 ger DE-627 rda eng 380 DE-600 55.84 bkl 55.24 bkl 44.80 bkl Cai, Qing verfasserin aut Developing a grouped random parameter beta model to analyze drivers’ speeding behavior on urban and suburban arterials with probe speed data 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Speeding is one of the major contributing factors to traffic fatalities. Various speed management strategies have been proposed to encourage drivers to select more appropriate speeds. This study aims to explore the different effects of the speed management strategies on the speeding proportions at urban and suburban arterials. Probe speed data was used to calculate the speeding proportions. To overcome the variability of probe speed data caused by the signalized intersections, a new method was suggested to calculate the speeding proportion, and a fractional split model was estimated to adjust the probe speed data. A Beta regression model was developed to analyze the speeding proportion. A grouped random parameter modeling structure was adopted to realize the different effects of speed management strategies and other road attributes on speeding proportions by different road types. Besides, a fixed beta model was developed for the comparison. The results suggested the grouped random parameter model could provide better performance over the counterpart and could realize the different effects of road features and other contributing factors on the speeding of different roads. It is expected that the findings could help inform more appropriate road design in order to reduce speed limit violations on urban and suburban arterials. Grouped random parameter beta model Speeding proportion Speed management strategies Probe speed data Fractional split model Abdel-Aty, Mohamed verfasserin aut Mahmoud, Nada verfasserin aut Ugan, Jorge verfasserin aut Al-Omari, Ma'en M.A. verfasserin aut Enthalten in Accident analysis & prevention Amsterdam [u.a.] : Elsevier, 1969 161 Online-Ressource (DE-627)306591707 (DE-600)1498752-1 (DE-576)081926820 1879-2057 nnns volume:161 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 55.84 Straßenverkehr 55.24 Fahrzeugführung Fahrtechnik 44.80 Unfallmedizin Notfallmedizin AR 161
allfieldsSound	10.1016/j.aap.2021.106386 doi (DE-627)ELV006674275 (ELSEVIER)S0001-4575(21)00417-6 DE-627 ger DE-627 rda eng 380 DE-600 55.84 bkl 55.24 bkl 44.80 bkl Cai, Qing verfasserin aut Developing a grouped random parameter beta model to analyze drivers’ speeding behavior on urban and suburban arterials with probe speed data 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Speeding is one of the major contributing factors to traffic fatalities. Various speed management strategies have been proposed to encourage drivers to select more appropriate speeds. This study aims to explore the different effects of the speed management strategies on the speeding proportions at urban and suburban arterials. Probe speed data was used to calculate the speeding proportions. To overcome the variability of probe speed data caused by the signalized intersections, a new method was suggested to calculate the speeding proportion, and a fractional split model was estimated to adjust the probe speed data. A Beta regression model was developed to analyze the speeding proportion. A grouped random parameter modeling structure was adopted to realize the different effects of speed management strategies and other road attributes on speeding proportions by different road types. Besides, a fixed beta model was developed for the comparison. The results suggested the grouped random parameter model could provide better performance over the counterpart and could realize the different effects of road features and other contributing factors on the speeding of different roads. It is expected that the findings could help inform more appropriate road design in order to reduce speed limit violations on urban and suburban arterials. Grouped random parameter beta model Speeding proportion Speed management strategies Probe speed data Fractional split model Abdel-Aty, Mohamed verfasserin aut Mahmoud, Nada verfasserin aut Ugan, Jorge verfasserin aut Al-Omari, Ma'en M.A. verfasserin aut Enthalten in Accident analysis & prevention Amsterdam [u.a.] : Elsevier, 1969 161 Online-Ressource (DE-627)306591707 (DE-600)1498752-1 (DE-576)081926820 1879-2057 nnns volume:161 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 55.84 Straßenverkehr 55.24 Fahrzeugführung Fahrtechnik 44.80 Unfallmedizin Notfallmedizin AR 161
language	English
source	Enthalten in Accident analysis & prevention 161 volume:161
sourceStr	Enthalten in Accident analysis & prevention 161 volume:161
format_phy_str_mv	Article
bklname	Straßenverkehr Fahrzeugführung Fahrtechnik Unfallmedizin Notfallmedizin
institution	findex.gbv.de
topic_facet	Grouped random parameter beta model Speeding proportion Speed management strategies Probe speed data Fractional split model
dewey-raw	380
isfreeaccess_bool	false
container_title	Accident analysis & prevention
authorswithroles_txt_mv	Cai, Qing @@aut@@ Abdel-Aty, Mohamed @@aut@@ Mahmoud, Nada @@aut@@ Ugan, Jorge @@aut@@ Al-Omari, Ma'en M.A. @@aut@@
publishDateDaySort_date	2021-01-01T00:00:00Z
hierarchy_top_id	306591707
dewey-sort	3380
id	ELV006674275
language_de	englisch
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author	Cai, Qing
spellingShingle	Cai, Qing ddc 380 bkl 55.84 bkl 55.24 bkl 44.80 misc Grouped random parameter beta model misc Speeding proportion misc Speed management strategies misc Probe speed data misc Fractional split model Developing a grouped random parameter beta model to analyze drivers’ speeding behavior on urban and suburban arterials with probe speed data
authorStr	Cai, Qing
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topic_title	380 DE-600 55.84 bkl 55.24 bkl 44.80 bkl Developing a grouped random parameter beta model to analyze drivers’ speeding behavior on urban and suburban arterials with probe speed data Grouped random parameter beta model Speeding proportion Speed management strategies Probe speed data Fractional split model
topic	ddc 380 bkl 55.84 bkl 55.24 bkl 44.80 misc Grouped random parameter beta model misc Speeding proportion misc Speed management strategies misc Probe speed data misc Fractional split model
topic_unstemmed	ddc 380 bkl 55.84 bkl 55.24 bkl 44.80 misc Grouped random parameter beta model misc Speeding proportion misc Speed management strategies misc Probe speed data misc Fractional split model
topic_browse	ddc 380 bkl 55.84 bkl 55.24 bkl 44.80 misc Grouped random parameter beta model misc Speeding proportion misc Speed management strategies misc Probe speed data misc Fractional split model
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title	Developing a grouped random parameter beta model to analyze drivers’ speeding behavior on urban and suburban arterials with probe speed data
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title_full	Developing a grouped random parameter beta model to analyze drivers’ speeding behavior on urban and suburban arterials with probe speed data
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author_browse	Cai, Qing Abdel-Aty, Mohamed Mahmoud, Nada Ugan, Jorge Al-Omari, Ma'en M.A.
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title_sort	developing a grouped random parameter beta model to analyze drivers’ speeding behavior on urban and suburban arterials with probe speed data
title_auth	Developing a grouped random parameter beta model to analyze drivers’ speeding behavior on urban and suburban arterials with probe speed data
abstract	Speeding is one of the major contributing factors to traffic fatalities. Various speed management strategies have been proposed to encourage drivers to select more appropriate speeds. This study aims to explore the different effects of the speed management strategies on the speeding proportions at urban and suburban arterials. Probe speed data was used to calculate the speeding proportions. To overcome the variability of probe speed data caused by the signalized intersections, a new method was suggested to calculate the speeding proportion, and a fractional split model was estimated to adjust the probe speed data. A Beta regression model was developed to analyze the speeding proportion. A grouped random parameter modeling structure was adopted to realize the different effects of speed management strategies and other road attributes on speeding proportions by different road types. Besides, a fixed beta model was developed for the comparison. The results suggested the grouped random parameter model could provide better performance over the counterpart and could realize the different effects of road features and other contributing factors on the speeding of different roads. It is expected that the findings could help inform more appropriate road design in order to reduce speed limit violations on urban and suburban arterials.
abstractGer	Speeding is one of the major contributing factors to traffic fatalities. Various speed management strategies have been proposed to encourage drivers to select more appropriate speeds. This study aims to explore the different effects of the speed management strategies on the speeding proportions at urban and suburban arterials. Probe speed data was used to calculate the speeding proportions. To overcome the variability of probe speed data caused by the signalized intersections, a new method was suggested to calculate the speeding proportion, and a fractional split model was estimated to adjust the probe speed data. A Beta regression model was developed to analyze the speeding proportion. A grouped random parameter modeling structure was adopted to realize the different effects of speed management strategies and other road attributes on speeding proportions by different road types. Besides, a fixed beta model was developed for the comparison. The results suggested the grouped random parameter model could provide better performance over the counterpart and could realize the different effects of road features and other contributing factors on the speeding of different roads. It is expected that the findings could help inform more appropriate road design in order to reduce speed limit violations on urban and suburban arterials.
abstract_unstemmed	Speeding is one of the major contributing factors to traffic fatalities. Various speed management strategies have been proposed to encourage drivers to select more appropriate speeds. This study aims to explore the different effects of the speed management strategies on the speeding proportions at urban and suburban arterials. Probe speed data was used to calculate the speeding proportions. To overcome the variability of probe speed data caused by the signalized intersections, a new method was suggested to calculate the speeding proportion, and a fractional split model was estimated to adjust the probe speed data. A Beta regression model was developed to analyze the speeding proportion. A grouped random parameter modeling structure was adopted to realize the different effects of speed management strategies and other road attributes on speeding proportions by different road types. Besides, a fixed beta model was developed for the comparison. The results suggested the grouped random parameter model could provide better performance over the counterpart and could realize the different effects of road features and other contributing factors on the speeding of different roads. It is expected that the findings could help inform more appropriate road design in order to reduce speed limit violations on urban and suburban arterials.
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title_short	Developing a grouped random parameter beta model to analyze drivers’ speeding behavior on urban and suburban arterials with probe speed data
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author2	Abdel-Aty, Mohamed Mahmoud, Nada Ugan, Jorge Al-Omari, Ma'en M.A.
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doi_str	10.1016/j.aap.2021.106386
up_date	2024-07-06T22:10:49.073Z
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Developing a grouped random parameter beta model to analyze drivers’ speeding behavior on urban and suburban arterials with probe speed data

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