Transportation resilience under Covid-19 Uncertainty: A traffic severity analysis

Transportation systems are critical lifelines and vulnerable to various disruptions, including unforeseen social events such as public health crises, and have far-reaching social impacts such as economic instability. This paper aims to determine the key factors influencing the severity of traffic ac...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Peng, Qiao [verfasserIn] Bakkar, Yassine [verfasserIn] Wu, Liangpeng [verfasserIn] Liu, Weilong [verfasserIn] Kou, Ruibing [verfasserIn] Liu, Kailong [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Transportation resilience Traffic severity Covid-19 uncertainty Explainable machine learning

Übergeordnetes Werk:	Enthalten in: Transportation research / A - Amsterdam [u.a.] : Elsevier Science, 1979, 179
Übergeordnetes Werk:	volume:179

DOI / URN:	10.1016/j.tra.2023.103947

Katalog-ID:	ELV066392195

Internformat


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245	1	0	\|a Transportation resilience under Covid-19 Uncertainty: A traffic severity analysis
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520			\|a Transportation systems are critical lifelines and vulnerable to various disruptions, including unforeseen social events such as public health crises, and have far-reaching social impacts such as economic instability. This paper aims to determine the key factors influencing the severity of traffic accidents in four different stages during the pre- and the post Covid-19 pandemic in Illinois, USA. For this purpose, a Random Forest-based model is developed, which is combined with techniques of explainable machine learning. The results reveal that during the pandemic, human perceptual factors, notably increased air pressure, humidity and temperature, play an important role in accident severity. This suggests that alleviating driver anxiety, caused by these factors, may be more effective in curbing crash severity than conventional road condition improvements. Further analysis shows that the pandemic leads notable shifts in residents' daily travel time and accident-prone spatial segments, indicating the need for increased regulatory measures. Our findings provide new insights for policy makers seeking to improve transportation resilience during disruptive events.
650		4	\|a Transportation resilience
650		4	\|a Traffic severity
650		4	\|a Covid-19 uncertainty
650		4	\|a Explainable machine learning
700	1		\|a Bakkar, Yassine \|e verfasserin \|0 (orcid)0000-0002-3392-7174 \|4 aut
700	1		\|a Wu, Liangpeng \|e verfasserin \|4 aut
700	1		\|a Liu, Weilong \|e verfasserin \|4 aut
700	1		\|a Kou, Ruibing \|e verfasserin \|4 aut
700	1		\|a Liu, Kailong \|e verfasserin \|4 aut
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matchkey_str	pengqiaobakkaryassinewuliangpengliuweilo:2023----:rnpraineiinenecvd9netitar
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publishDate	2023
allfields	10.1016/j.tra.2023.103947 doi (DE-627)ELV066392195 (ELSEVIER)S0965-8564(23)00367-1 DE-627 ger DE-627 rda eng 380 VZ 55.80 bkl 74.75 bkl Peng, Qiao verfasserin (orcid)0000-0002-3837-3932 aut Transportation resilience under Covid-19 Uncertainty: A traffic severity analysis 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Transportation systems are critical lifelines and vulnerable to various disruptions, including unforeseen social events such as public health crises, and have far-reaching social impacts such as economic instability. This paper aims to determine the key factors influencing the severity of traffic accidents in four different stages during the pre- and the post Covid-19 pandemic in Illinois, USA. For this purpose, a Random Forest-based model is developed, which is combined with techniques of explainable machine learning. The results reveal that during the pandemic, human perceptual factors, notably increased air pressure, humidity and temperature, play an important role in accident severity. This suggests that alleviating driver anxiety, caused by these factors, may be more effective in curbing crash severity than conventional road condition improvements. Further analysis shows that the pandemic leads notable shifts in residents' daily travel time and accident-prone spatial segments, indicating the need for increased regulatory measures. Our findings provide new insights for policy makers seeking to improve transportation resilience during disruptive events. Transportation resilience Traffic severity Covid-19 uncertainty Explainable machine learning Bakkar, Yassine verfasserin (orcid)0000-0002-3392-7174 aut Wu, Liangpeng verfasserin aut Liu, Weilong verfasserin aut Kou, Ruibing verfasserin aut Liu, Kailong verfasserin aut Enthalten in Transportation research / A Amsterdam [u.a.] : Elsevier Science, 1979 179 Online-Ressource (DE-627)320532046 (DE-600)2015887-7 (DE-576)099210908 nnns volume:179 GBV_USEFLAG_U GBV_ELV SYSFLAG_U 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_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_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 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_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 55.80 Verkehrswesen Transportwesen: Allgemeines VZ 74.75 Verkehrsplanung Verkehrspolitik VZ AR 179
spelling	10.1016/j.tra.2023.103947 doi (DE-627)ELV066392195 (ELSEVIER)S0965-8564(23)00367-1 DE-627 ger DE-627 rda eng 380 VZ 55.80 bkl 74.75 bkl Peng, Qiao verfasserin (orcid)0000-0002-3837-3932 aut Transportation resilience under Covid-19 Uncertainty: A traffic severity analysis 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Transportation systems are critical lifelines and vulnerable to various disruptions, including unforeseen social events such as public health crises, and have far-reaching social impacts such as economic instability. This paper aims to determine the key factors influencing the severity of traffic accidents in four different stages during the pre- and the post Covid-19 pandemic in Illinois, USA. For this purpose, a Random Forest-based model is developed, which is combined with techniques of explainable machine learning. The results reveal that during the pandemic, human perceptual factors, notably increased air pressure, humidity and temperature, play an important role in accident severity. This suggests that alleviating driver anxiety, caused by these factors, may be more effective in curbing crash severity than conventional road condition improvements. Further analysis shows that the pandemic leads notable shifts in residents' daily travel time and accident-prone spatial segments, indicating the need for increased regulatory measures. Our findings provide new insights for policy makers seeking to improve transportation resilience during disruptive events. Transportation resilience Traffic severity Covid-19 uncertainty Explainable machine learning Bakkar, Yassine verfasserin (orcid)0000-0002-3392-7174 aut Wu, Liangpeng verfasserin aut Liu, Weilong verfasserin aut Kou, Ruibing verfasserin aut Liu, Kailong verfasserin aut Enthalten in Transportation research / A Amsterdam [u.a.] : Elsevier Science, 1979 179 Online-Ressource (DE-627)320532046 (DE-600)2015887-7 (DE-576)099210908 nnns volume:179 GBV_USEFLAG_U GBV_ELV SYSFLAG_U 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_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_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 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_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 55.80 Verkehrswesen Transportwesen: Allgemeines VZ 74.75 Verkehrsplanung Verkehrspolitik VZ AR 179
allfields_unstemmed	10.1016/j.tra.2023.103947 doi (DE-627)ELV066392195 (ELSEVIER)S0965-8564(23)00367-1 DE-627 ger DE-627 rda eng 380 VZ 55.80 bkl 74.75 bkl Peng, Qiao verfasserin (orcid)0000-0002-3837-3932 aut Transportation resilience under Covid-19 Uncertainty: A traffic severity analysis 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Transportation systems are critical lifelines and vulnerable to various disruptions, including unforeseen social events such as public health crises, and have far-reaching social impacts such as economic instability. This paper aims to determine the key factors influencing the severity of traffic accidents in four different stages during the pre- and the post Covid-19 pandemic in Illinois, USA. For this purpose, a Random Forest-based model is developed, which is combined with techniques of explainable machine learning. The results reveal that during the pandemic, human perceptual factors, notably increased air pressure, humidity and temperature, play an important role in accident severity. This suggests that alleviating driver anxiety, caused by these factors, may be more effective in curbing crash severity than conventional road condition improvements. Further analysis shows that the pandemic leads notable shifts in residents' daily travel time and accident-prone spatial segments, indicating the need for increased regulatory measures. Our findings provide new insights for policy makers seeking to improve transportation resilience during disruptive events. Transportation resilience Traffic severity Covid-19 uncertainty Explainable machine learning Bakkar, Yassine verfasserin (orcid)0000-0002-3392-7174 aut Wu, Liangpeng verfasserin aut Liu, Weilong verfasserin aut Kou, Ruibing verfasserin aut Liu, Kailong verfasserin aut Enthalten in Transportation research / A Amsterdam [u.a.] : Elsevier Science, 1979 179 Online-Ressource (DE-627)320532046 (DE-600)2015887-7 (DE-576)099210908 nnns volume:179 GBV_USEFLAG_U GBV_ELV SYSFLAG_U 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_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_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 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_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 55.80 Verkehrswesen Transportwesen: Allgemeines VZ 74.75 Verkehrsplanung Verkehrspolitik VZ AR 179
allfieldsGer	10.1016/j.tra.2023.103947 doi (DE-627)ELV066392195 (ELSEVIER)S0965-8564(23)00367-1 DE-627 ger DE-627 rda eng 380 VZ 55.80 bkl 74.75 bkl Peng, Qiao verfasserin (orcid)0000-0002-3837-3932 aut Transportation resilience under Covid-19 Uncertainty: A traffic severity analysis 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Transportation systems are critical lifelines and vulnerable to various disruptions, including unforeseen social events such as public health crises, and have far-reaching social impacts such as economic instability. This paper aims to determine the key factors influencing the severity of traffic accidents in four different stages during the pre- and the post Covid-19 pandemic in Illinois, USA. For this purpose, a Random Forest-based model is developed, which is combined with techniques of explainable machine learning. The results reveal that during the pandemic, human perceptual factors, notably increased air pressure, humidity and temperature, play an important role in accident severity. This suggests that alleviating driver anxiety, caused by these factors, may be more effective in curbing crash severity than conventional road condition improvements. Further analysis shows that the pandemic leads notable shifts in residents' daily travel time and accident-prone spatial segments, indicating the need for increased regulatory measures. Our findings provide new insights for policy makers seeking to improve transportation resilience during disruptive events. Transportation resilience Traffic severity Covid-19 uncertainty Explainable machine learning Bakkar, Yassine verfasserin (orcid)0000-0002-3392-7174 aut Wu, Liangpeng verfasserin aut Liu, Weilong verfasserin aut Kou, Ruibing verfasserin aut Liu, Kailong verfasserin aut Enthalten in Transportation research / A Amsterdam [u.a.] : Elsevier Science, 1979 179 Online-Ressource (DE-627)320532046 (DE-600)2015887-7 (DE-576)099210908 nnns volume:179 GBV_USEFLAG_U GBV_ELV SYSFLAG_U 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_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_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 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_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 55.80 Verkehrswesen Transportwesen: Allgemeines VZ 74.75 Verkehrsplanung Verkehrspolitik VZ AR 179
allfieldsSound	10.1016/j.tra.2023.103947 doi (DE-627)ELV066392195 (ELSEVIER)S0965-8564(23)00367-1 DE-627 ger DE-627 rda eng 380 VZ 55.80 bkl 74.75 bkl Peng, Qiao verfasserin (orcid)0000-0002-3837-3932 aut Transportation resilience under Covid-19 Uncertainty: A traffic severity analysis 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Transportation systems are critical lifelines and vulnerable to various disruptions, including unforeseen social events such as public health crises, and have far-reaching social impacts such as economic instability. This paper aims to determine the key factors influencing the severity of traffic accidents in four different stages during the pre- and the post Covid-19 pandemic in Illinois, USA. For this purpose, a Random Forest-based model is developed, which is combined with techniques of explainable machine learning. The results reveal that during the pandemic, human perceptual factors, notably increased air pressure, humidity and temperature, play an important role in accident severity. This suggests that alleviating driver anxiety, caused by these factors, may be more effective in curbing crash severity than conventional road condition improvements. Further analysis shows that the pandemic leads notable shifts in residents' daily travel time and accident-prone spatial segments, indicating the need for increased regulatory measures. Our findings provide new insights for policy makers seeking to improve transportation resilience during disruptive events. Transportation resilience Traffic severity Covid-19 uncertainty Explainable machine learning Bakkar, Yassine verfasserin (orcid)0000-0002-3392-7174 aut Wu, Liangpeng verfasserin aut Liu, Weilong verfasserin aut Kou, Ruibing verfasserin aut Liu, Kailong verfasserin aut Enthalten in Transportation research / A Amsterdam [u.a.] : Elsevier Science, 1979 179 Online-Ressource (DE-627)320532046 (DE-600)2015887-7 (DE-576)099210908 nnns volume:179 GBV_USEFLAG_U GBV_ELV SYSFLAG_U 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_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_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 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_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 55.80 Verkehrswesen Transportwesen: Allgemeines VZ 74.75 Verkehrsplanung Verkehrspolitik VZ AR 179
language	English
source	Enthalten in Transportation research / A 179 volume:179
sourceStr	Enthalten in Transportation research / A 179 volume:179
format_phy_str_mv	Article
bklname	Verkehrswesen Transportwesen: Allgemeines Verkehrsplanung Verkehrspolitik
institution	findex.gbv.de
topic_facet	Transportation resilience Traffic severity Covid-19 uncertainty Explainable machine learning
dewey-raw	380
isfreeaccess_bool	false
container_title	Transportation research / A
authorswithroles_txt_mv	Peng, Qiao @@aut@@ Bakkar, Yassine @@aut@@ Wu, Liangpeng @@aut@@ Liu, Weilong @@aut@@ Kou, Ruibing @@aut@@ Liu, Kailong @@aut@@
publishDateDaySort_date	2023-01-01T00:00:00Z
hierarchy_top_id	320532046
dewey-sort	3380
id	ELV066392195
language_de	englisch
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author	Peng, Qiao
spellingShingle	Peng, Qiao ddc 380 bkl 55.80 bkl 74.75 misc Transportation resilience misc Traffic severity misc Covid-19 uncertainty misc Explainable machine learning Transportation resilience under Covid-19 Uncertainty: A traffic severity analysis
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topic	ddc 380 bkl 55.80 bkl 74.75 misc Transportation resilience misc Traffic severity misc Covid-19 uncertainty misc Explainable machine learning
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title	Transportation resilience under Covid-19 Uncertainty: A traffic severity analysis
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title_sort	transportation resilience under covid-19 uncertainty: a traffic severity analysis
title_auth	Transportation resilience under Covid-19 Uncertainty: A traffic severity analysis
abstract	Transportation systems are critical lifelines and vulnerable to various disruptions, including unforeseen social events such as public health crises, and have far-reaching social impacts such as economic instability. This paper aims to determine the key factors influencing the severity of traffic accidents in four different stages during the pre- and the post Covid-19 pandemic in Illinois, USA. For this purpose, a Random Forest-based model is developed, which is combined with techniques of explainable machine learning. The results reveal that during the pandemic, human perceptual factors, notably increased air pressure, humidity and temperature, play an important role in accident severity. This suggests that alleviating driver anxiety, caused by these factors, may be more effective in curbing crash severity than conventional road condition improvements. Further analysis shows that the pandemic leads notable shifts in residents' daily travel time and accident-prone spatial segments, indicating the need for increased regulatory measures. Our findings provide new insights for policy makers seeking to improve transportation resilience during disruptive events.
abstractGer	Transportation systems are critical lifelines and vulnerable to various disruptions, including unforeseen social events such as public health crises, and have far-reaching social impacts such as economic instability. This paper aims to determine the key factors influencing the severity of traffic accidents in four different stages during the pre- and the post Covid-19 pandemic in Illinois, USA. For this purpose, a Random Forest-based model is developed, which is combined with techniques of explainable machine learning. The results reveal that during the pandemic, human perceptual factors, notably increased air pressure, humidity and temperature, play an important role in accident severity. This suggests that alleviating driver anxiety, caused by these factors, may be more effective in curbing crash severity than conventional road condition improvements. Further analysis shows that the pandemic leads notable shifts in residents' daily travel time and accident-prone spatial segments, indicating the need for increased regulatory measures. Our findings provide new insights for policy makers seeking to improve transportation resilience during disruptive events.
abstract_unstemmed	Transportation systems are critical lifelines and vulnerable to various disruptions, including unforeseen social events such as public health crises, and have far-reaching social impacts such as economic instability. This paper aims to determine the key factors influencing the severity of traffic accidents in four different stages during the pre- and the post Covid-19 pandemic in Illinois, USA. For this purpose, a Random Forest-based model is developed, which is combined with techniques of explainable machine learning. The results reveal that during the pandemic, human perceptual factors, notably increased air pressure, humidity and temperature, play an important role in accident severity. This suggests that alleviating driver anxiety, caused by these factors, may be more effective in curbing crash severity than conventional road condition improvements. Further analysis shows that the pandemic leads notable shifts in residents' daily travel time and accident-prone spatial segments, indicating the need for increased regulatory measures. Our findings provide new insights for policy makers seeking to improve transportation resilience during disruptive events.
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title_short	Transportation resilience under Covid-19 Uncertainty: A traffic severity analysis
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author2	Bakkar, Yassine Wu, Liangpeng Liu, Weilong Kou, Ruibing Liu, Kailong
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up_date	2024-07-06T17:35:44.872Z
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