Quantification and management of urban traffic emissions based on individual vehicle data
Urban traffic pollution poses a serious threat to the environment and human health, especially in urban centres with high population density. Traditional traffic pollution quantification and management methods can be improved based on fine-grained individual vehicle data provided by intelligent tran...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Yu, Zhi [verfasserIn] Li, Weichi [verfasserIn] Liu, Yonghong [verfasserIn] Zeng, Xuelan [verfasserIn] Zhao, Yongming [verfasserIn] Chen, Kaiying [verfasserIn] Zou, Bin [verfasserIn] He, Jiajun [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2021 |
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| Schlagwörter: |
Licence plate recognition data |
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| Übergeordnetes Werk: |
Enthalten in: Journal of cleaner production - Amsterdam [u.a.] : Elsevier Science, 1993, 328 |
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| Übergeordnetes Werk: |
volume:328 |
| DOI / URN: |
10.1016/j.jclepro.2021.129386 |
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| Katalog-ID: |
ELV007003854 |
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| 245 | 1 | 0 | |a Quantification and management of urban traffic emissions based on individual vehicle data |
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| 520 | |a Urban traffic pollution poses a serious threat to the environment and human health, especially in urban centres with high population density. Traditional traffic pollution quantification and management methods can be improved based on fine-grained individual vehicle data provided by intelligent transportation systems. Traditional traffic emission quantification and management are often based on simulated or relatively coarse-grained measured data. Such data lack a comprehensive reflection of the actual conditions of all vehicles travelling on roads, which leads to deviations in emission quantification; thus, they cannot support the delicate control policy of traffic pollution. This paper presents a high-resolution individual vehicle emission quantification method based on real-time, real-world individual vehicle data, with a combination of automatic licence plate recognition data and vehicle registration data currently used for traffic management. In this study, we quantified the emissions of each vehicle driving in the urban centre of the case city and analysed regional traffic emission characteristics. We found that there was an apparent uneven distribution of vehicle emissions; that is, the emissions from a small number of high-emission vehicles accounted for a large proportion of the regional traffic emissions. Different pollutants and vehicle types had different emission distribution characteristics. Furthermore, we explored emission reduction policies based on the management of high-emission vehicles identified by individual vehicle data and conduct fine-scale analysis of the link-level hourly emission reduction effects. In addition, a comparison between traditional methods and the method used in this paper for emission quantification was performed. This paper provides a basis for the accurate analysis of regional traffic emission characteristics, individual-based emission reduction policy formulation, and refined policy effect analysis, which has great significance for the control of traffic pollution. | ||
| 650 | 4 | |a Individual vehicle emissions | |
| 650 | 4 | |a Emission quantification | |
| 650 | 4 | |a Licence plate recognition data | |
| 650 | 4 | |a Spatiotemporal emission characteristics | |
| 650 | 4 | |a Traffic emission reduction policy | |
| 700 | 1 | |a Li, Weichi |e verfasserin |4 aut | |
| 700 | 1 | |a Liu, Yonghong |e verfasserin |4 aut | |
| 700 | 1 | |a Zeng, Xuelan |e verfasserin |4 aut | |
| 700 | 1 | |a Zhao, Yongming |e verfasserin |4 aut | |
| 700 | 1 | |a Chen, Kaiying |e verfasserin |4 aut | |
| 700 | 1 | |a Zou, Bin |e verfasserin |4 aut | |
| 700 | 1 | |a He, Jiajun |e verfasserin |0 (orcid)0000-0003-3986-8707 |4 aut | |
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10.1016/j.jclepro.2021.129386 doi (DE-627)ELV007003854 (ELSEVIER)S0959-6526(21)03570-8 DE-627 ger DE-627 rda eng 690 330 DE-600 43.35 bkl 85.35 bkl Yu, Zhi verfasserin aut Quantification and management of urban traffic emissions based on individual vehicle data 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Urban traffic pollution poses a serious threat to the environment and human health, especially in urban centres with high population density. Traditional traffic pollution quantification and management methods can be improved based on fine-grained individual vehicle data provided by intelligent transportation systems. Traditional traffic emission quantification and management are often based on simulated or relatively coarse-grained measured data. Such data lack a comprehensive reflection of the actual conditions of all vehicles travelling on roads, which leads to deviations in emission quantification; thus, they cannot support the delicate control policy of traffic pollution. This paper presents a high-resolution individual vehicle emission quantification method based on real-time, real-world individual vehicle data, with a combination of automatic licence plate recognition data and vehicle registration data currently used for traffic management. In this study, we quantified the emissions of each vehicle driving in the urban centre of the case city and analysed regional traffic emission characteristics. We found that there was an apparent uneven distribution of vehicle emissions; that is, the emissions from a small number of high-emission vehicles accounted for a large proportion of the regional traffic emissions. Different pollutants and vehicle types had different emission distribution characteristics. Furthermore, we explored emission reduction policies based on the management of high-emission vehicles identified by individual vehicle data and conduct fine-scale analysis of the link-level hourly emission reduction effects. In addition, a comparison between traditional methods and the method used in this paper for emission quantification was performed. This paper provides a basis for the accurate analysis of regional traffic emission characteristics, individual-based emission reduction policy formulation, and refined policy effect analysis, which has great significance for the control of traffic pollution. Individual vehicle emissions Emission quantification Licence plate recognition data Spatiotemporal emission characteristics Traffic emission reduction policy Li, Weichi verfasserin aut Liu, Yonghong verfasserin aut Zeng, Xuelan verfasserin aut Zhao, Yongming verfasserin aut Chen, Kaiying verfasserin aut Zou, Bin verfasserin aut He, Jiajun verfasserin (orcid)0000-0003-3986-8707 aut Enthalten in Journal of cleaner production Amsterdam [u.a.] : Elsevier Science, 1993 328 Online-Ressource (DE-627)324655878 (DE-600)2029338-0 (DE-576)252613988 0959-6526 nnns volume:328 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OPC-GGO 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_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 43.35 Umweltrichtlinien Umweltnormen 85.35 Fertigung AR 328 |
| spelling |
10.1016/j.jclepro.2021.129386 doi (DE-627)ELV007003854 (ELSEVIER)S0959-6526(21)03570-8 DE-627 ger DE-627 rda eng 690 330 DE-600 43.35 bkl 85.35 bkl Yu, Zhi verfasserin aut Quantification and management of urban traffic emissions based on individual vehicle data 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Urban traffic pollution poses a serious threat to the environment and human health, especially in urban centres with high population density. Traditional traffic pollution quantification and management methods can be improved based on fine-grained individual vehicle data provided by intelligent transportation systems. Traditional traffic emission quantification and management are often based on simulated or relatively coarse-grained measured data. Such data lack a comprehensive reflection of the actual conditions of all vehicles travelling on roads, which leads to deviations in emission quantification; thus, they cannot support the delicate control policy of traffic pollution. This paper presents a high-resolution individual vehicle emission quantification method based on real-time, real-world individual vehicle data, with a combination of automatic licence plate recognition data and vehicle registration data currently used for traffic management. In this study, we quantified the emissions of each vehicle driving in the urban centre of the case city and analysed regional traffic emission characteristics. We found that there was an apparent uneven distribution of vehicle emissions; that is, the emissions from a small number of high-emission vehicles accounted for a large proportion of the regional traffic emissions. Different pollutants and vehicle types had different emission distribution characteristics. Furthermore, we explored emission reduction policies based on the management of high-emission vehicles identified by individual vehicle data and conduct fine-scale analysis of the link-level hourly emission reduction effects. In addition, a comparison between traditional methods and the method used in this paper for emission quantification was performed. This paper provides a basis for the accurate analysis of regional traffic emission characteristics, individual-based emission reduction policy formulation, and refined policy effect analysis, which has great significance for the control of traffic pollution. Individual vehicle emissions Emission quantification Licence plate recognition data Spatiotemporal emission characteristics Traffic emission reduction policy Li, Weichi verfasserin aut Liu, Yonghong verfasserin aut Zeng, Xuelan verfasserin aut Zhao, Yongming verfasserin aut Chen, Kaiying verfasserin aut Zou, Bin verfasserin aut He, Jiajun verfasserin (orcid)0000-0003-3986-8707 aut Enthalten in Journal of cleaner production Amsterdam [u.a.] : Elsevier Science, 1993 328 Online-Ressource (DE-627)324655878 (DE-600)2029338-0 (DE-576)252613988 0959-6526 nnns volume:328 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OPC-GGO 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_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 43.35 Umweltrichtlinien Umweltnormen 85.35 Fertigung AR 328 |
| allfields_unstemmed |
10.1016/j.jclepro.2021.129386 doi (DE-627)ELV007003854 (ELSEVIER)S0959-6526(21)03570-8 DE-627 ger DE-627 rda eng 690 330 DE-600 43.35 bkl 85.35 bkl Yu, Zhi verfasserin aut Quantification and management of urban traffic emissions based on individual vehicle data 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Urban traffic pollution poses a serious threat to the environment and human health, especially in urban centres with high population density. Traditional traffic pollution quantification and management methods can be improved based on fine-grained individual vehicle data provided by intelligent transportation systems. Traditional traffic emission quantification and management are often based on simulated or relatively coarse-grained measured data. Such data lack a comprehensive reflection of the actual conditions of all vehicles travelling on roads, which leads to deviations in emission quantification; thus, they cannot support the delicate control policy of traffic pollution. This paper presents a high-resolution individual vehicle emission quantification method based on real-time, real-world individual vehicle data, with a combination of automatic licence plate recognition data and vehicle registration data currently used for traffic management. In this study, we quantified the emissions of each vehicle driving in the urban centre of the case city and analysed regional traffic emission characteristics. We found that there was an apparent uneven distribution of vehicle emissions; that is, the emissions from a small number of high-emission vehicles accounted for a large proportion of the regional traffic emissions. Different pollutants and vehicle types had different emission distribution characteristics. Furthermore, we explored emission reduction policies based on the management of high-emission vehicles identified by individual vehicle data and conduct fine-scale analysis of the link-level hourly emission reduction effects. In addition, a comparison between traditional methods and the method used in this paper for emission quantification was performed. This paper provides a basis for the accurate analysis of regional traffic emission characteristics, individual-based emission reduction policy formulation, and refined policy effect analysis, which has great significance for the control of traffic pollution. Individual vehicle emissions Emission quantification Licence plate recognition data Spatiotemporal emission characteristics Traffic emission reduction policy Li, Weichi verfasserin aut Liu, Yonghong verfasserin aut Zeng, Xuelan verfasserin aut Zhao, Yongming verfasserin aut Chen, Kaiying verfasserin aut Zou, Bin verfasserin aut He, Jiajun verfasserin (orcid)0000-0003-3986-8707 aut Enthalten in Journal of cleaner production Amsterdam [u.a.] : Elsevier Science, 1993 328 Online-Ressource (DE-627)324655878 (DE-600)2029338-0 (DE-576)252613988 0959-6526 nnns volume:328 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OPC-GGO 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_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 43.35 Umweltrichtlinien Umweltnormen 85.35 Fertigung AR 328 |
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10.1016/j.jclepro.2021.129386 doi (DE-627)ELV007003854 (ELSEVIER)S0959-6526(21)03570-8 DE-627 ger DE-627 rda eng 690 330 DE-600 43.35 bkl 85.35 bkl Yu, Zhi verfasserin aut Quantification and management of urban traffic emissions based on individual vehicle data 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Urban traffic pollution poses a serious threat to the environment and human health, especially in urban centres with high population density. Traditional traffic pollution quantification and management methods can be improved based on fine-grained individual vehicle data provided by intelligent transportation systems. Traditional traffic emission quantification and management are often based on simulated or relatively coarse-grained measured data. Such data lack a comprehensive reflection of the actual conditions of all vehicles travelling on roads, which leads to deviations in emission quantification; thus, they cannot support the delicate control policy of traffic pollution. This paper presents a high-resolution individual vehicle emission quantification method based on real-time, real-world individual vehicle data, with a combination of automatic licence plate recognition data and vehicle registration data currently used for traffic management. In this study, we quantified the emissions of each vehicle driving in the urban centre of the case city and analysed regional traffic emission characteristics. We found that there was an apparent uneven distribution of vehicle emissions; that is, the emissions from a small number of high-emission vehicles accounted for a large proportion of the regional traffic emissions. Different pollutants and vehicle types had different emission distribution characteristics. Furthermore, we explored emission reduction policies based on the management of high-emission vehicles identified by individual vehicle data and conduct fine-scale analysis of the link-level hourly emission reduction effects. In addition, a comparison between traditional methods and the method used in this paper for emission quantification was performed. This paper provides a basis for the accurate analysis of regional traffic emission characteristics, individual-based emission reduction policy formulation, and refined policy effect analysis, which has great significance for the control of traffic pollution. Individual vehicle emissions Emission quantification Licence plate recognition data Spatiotemporal emission characteristics Traffic emission reduction policy Li, Weichi verfasserin aut Liu, Yonghong verfasserin aut Zeng, Xuelan verfasserin aut Zhao, Yongming verfasserin aut Chen, Kaiying verfasserin aut Zou, Bin verfasserin aut He, Jiajun verfasserin (orcid)0000-0003-3986-8707 aut Enthalten in Journal of cleaner production Amsterdam [u.a.] : Elsevier Science, 1993 328 Online-Ressource (DE-627)324655878 (DE-600)2029338-0 (DE-576)252613988 0959-6526 nnns volume:328 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OPC-GGO 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_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 43.35 Umweltrichtlinien Umweltnormen 85.35 Fertigung AR 328 |
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10.1016/j.jclepro.2021.129386 doi (DE-627)ELV007003854 (ELSEVIER)S0959-6526(21)03570-8 DE-627 ger DE-627 rda eng 690 330 DE-600 43.35 bkl 85.35 bkl Yu, Zhi verfasserin aut Quantification and management of urban traffic emissions based on individual vehicle data 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Urban traffic pollution poses a serious threat to the environment and human health, especially in urban centres with high population density. Traditional traffic pollution quantification and management methods can be improved based on fine-grained individual vehicle data provided by intelligent transportation systems. Traditional traffic emission quantification and management are often based on simulated or relatively coarse-grained measured data. Such data lack a comprehensive reflection of the actual conditions of all vehicles travelling on roads, which leads to deviations in emission quantification; thus, they cannot support the delicate control policy of traffic pollution. This paper presents a high-resolution individual vehicle emission quantification method based on real-time, real-world individual vehicle data, with a combination of automatic licence plate recognition data and vehicle registration data currently used for traffic management. In this study, we quantified the emissions of each vehicle driving in the urban centre of the case city and analysed regional traffic emission characteristics. We found that there was an apparent uneven distribution of vehicle emissions; that is, the emissions from a small number of high-emission vehicles accounted for a large proportion of the regional traffic emissions. Different pollutants and vehicle types had different emission distribution characteristics. Furthermore, we explored emission reduction policies based on the management of high-emission vehicles identified by individual vehicle data and conduct fine-scale analysis of the link-level hourly emission reduction effects. In addition, a comparison between traditional methods and the method used in this paper for emission quantification was performed. This paper provides a basis for the accurate analysis of regional traffic emission characteristics, individual-based emission reduction policy formulation, and refined policy effect analysis, which has great significance for the control of traffic pollution. Individual vehicle emissions Emission quantification Licence plate recognition data Spatiotemporal emission characteristics Traffic emission reduction policy Li, Weichi verfasserin aut Liu, Yonghong verfasserin aut Zeng, Xuelan verfasserin aut Zhao, Yongming verfasserin aut Chen, Kaiying verfasserin aut Zou, Bin verfasserin aut He, Jiajun verfasserin (orcid)0000-0003-3986-8707 aut Enthalten in Journal of cleaner production Amsterdam [u.a.] : Elsevier Science, 1993 328 Online-Ressource (DE-627)324655878 (DE-600)2029338-0 (DE-576)252613988 0959-6526 nnns volume:328 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OPC-GGO 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_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 43.35 Umweltrichtlinien Umweltnormen 85.35 Fertigung AR 328 |
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690 330 DE-600 43.35 bkl 85.35 bkl Quantification and management of urban traffic emissions based on individual vehicle data Individual vehicle emissions Emission quantification Licence plate recognition data Spatiotemporal emission characteristics Traffic emission reduction policy |
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quantification and management of urban traffic emissions based on individual vehicle data |
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Quantification and management of urban traffic emissions based on individual vehicle data |
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Urban traffic pollution poses a serious threat to the environment and human health, especially in urban centres with high population density. Traditional traffic pollution quantification and management methods can be improved based on fine-grained individual vehicle data provided by intelligent transportation systems. Traditional traffic emission quantification and management are often based on simulated or relatively coarse-grained measured data. Such data lack a comprehensive reflection of the actual conditions of all vehicles travelling on roads, which leads to deviations in emission quantification; thus, they cannot support the delicate control policy of traffic pollution. This paper presents a high-resolution individual vehicle emission quantification method based on real-time, real-world individual vehicle data, with a combination of automatic licence plate recognition data and vehicle registration data currently used for traffic management. In this study, we quantified the emissions of each vehicle driving in the urban centre of the case city and analysed regional traffic emission characteristics. We found that there was an apparent uneven distribution of vehicle emissions; that is, the emissions from a small number of high-emission vehicles accounted for a large proportion of the regional traffic emissions. Different pollutants and vehicle types had different emission distribution characteristics. Furthermore, we explored emission reduction policies based on the management of high-emission vehicles identified by individual vehicle data and conduct fine-scale analysis of the link-level hourly emission reduction effects. In addition, a comparison between traditional methods and the method used in this paper for emission quantification was performed. This paper provides a basis for the accurate analysis of regional traffic emission characteristics, individual-based emission reduction policy formulation, and refined policy effect analysis, which has great significance for the control of traffic pollution. |
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Urban traffic pollution poses a serious threat to the environment and human health, especially in urban centres with high population density. Traditional traffic pollution quantification and management methods can be improved based on fine-grained individual vehicle data provided by intelligent transportation systems. Traditional traffic emission quantification and management are often based on simulated or relatively coarse-grained measured data. Such data lack a comprehensive reflection of the actual conditions of all vehicles travelling on roads, which leads to deviations in emission quantification; thus, they cannot support the delicate control policy of traffic pollution. This paper presents a high-resolution individual vehicle emission quantification method based on real-time, real-world individual vehicle data, with a combination of automatic licence plate recognition data and vehicle registration data currently used for traffic management. In this study, we quantified the emissions of each vehicle driving in the urban centre of the case city and analysed regional traffic emission characteristics. We found that there was an apparent uneven distribution of vehicle emissions; that is, the emissions from a small number of high-emission vehicles accounted for a large proportion of the regional traffic emissions. Different pollutants and vehicle types had different emission distribution characteristics. Furthermore, we explored emission reduction policies based on the management of high-emission vehicles identified by individual vehicle data and conduct fine-scale analysis of the link-level hourly emission reduction effects. In addition, a comparison between traditional methods and the method used in this paper for emission quantification was performed. This paper provides a basis for the accurate analysis of regional traffic emission characteristics, individual-based emission reduction policy formulation, and refined policy effect analysis, which has great significance for the control of traffic pollution. |
| abstract_unstemmed |
Urban traffic pollution poses a serious threat to the environment and human health, especially in urban centres with high population density. Traditional traffic pollution quantification and management methods can be improved based on fine-grained individual vehicle data provided by intelligent transportation systems. Traditional traffic emission quantification and management are often based on simulated or relatively coarse-grained measured data. Such data lack a comprehensive reflection of the actual conditions of all vehicles travelling on roads, which leads to deviations in emission quantification; thus, they cannot support the delicate control policy of traffic pollution. This paper presents a high-resolution individual vehicle emission quantification method based on real-time, real-world individual vehicle data, with a combination of automatic licence plate recognition data and vehicle registration data currently used for traffic management. In this study, we quantified the emissions of each vehicle driving in the urban centre of the case city and analysed regional traffic emission characteristics. We found that there was an apparent uneven distribution of vehicle emissions; that is, the emissions from a small number of high-emission vehicles accounted for a large proportion of the regional traffic emissions. Different pollutants and vehicle types had different emission distribution characteristics. Furthermore, we explored emission reduction policies based on the management of high-emission vehicles identified by individual vehicle data and conduct fine-scale analysis of the link-level hourly emission reduction effects. In addition, a comparison between traditional methods and the method used in this paper for emission quantification was performed. This paper provides a basis for the accurate analysis of regional traffic emission characteristics, individual-based emission reduction policy formulation, and refined policy effect analysis, which has great significance for the control of traffic pollution. |
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| score |
7.4004793 |