Urban rail transit network topology evolutionary stage has influence on rail ridership: Insights from linear mixed-effects models with heterogeneity in variances
Determining the impact of the evolutionary stage (ES) of urban rail transit (URT) network topology on daily ridership (DR) can be beneficial for policymakers when analyzing recent stagnated ridership in some cities. This paper used city-level data from 26 Chinese cities from 2013 to 2019 to investig...
Ausführliche Beschreibung
Autor*in: |
Xin, Mengwei [verfasserIn] Feng, Shumin [verfasserIn] |
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Format: |
E-Artikel |
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Sprache: |
Englisch |
Erschienen: |
2023 |
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Schlagwörter: |
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Übergeordnetes Werk: |
Enthalten in: Transportation research / A - Amsterdam [u.a.] : Elsevier Science, 1979, 180 |
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Übergeordnetes Werk: |
volume:180 |
DOI / URN: |
10.1016/j.tra.2023.103951 |
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Katalog-ID: |
ELV066852021 |
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520 | |a Determining the impact of the evolutionary stage (ES) of urban rail transit (URT) network topology on daily ridership (DR) can be beneficial for policymakers when analyzing recent stagnated ridership in some cities. This paper used city-level data from 26 Chinese cities from 2013 to 2019 to investigate this. Eleven variables comprising socioeconomic attributes (population density and the average wage of employees), the availability of other transit modes (the number of taxis, the number of private cars, the bus network length, and the street network length), service-related attributes (the number of trains in operation, the average headway during peak period, the opened days, and rail fare), and a network topological characteristic (the average path length) were introduced as covariates. In addition, city class was introduced as a grouping variable to capture the unobserved characteristics of the investigated cities. Two linear mixed-effects models (M1 and M2) were developed, with an interaction term between city class and ES being introduced in M2. The variances were allowed to vary to account for the heterogeneity of the predictor, which improved the overall fit of the models. The estimation results reveal a significant positive within-subject effect of ES on DR in both models, which varied according to city class. This implies that for each city, improving ES increased DR and the scale of the increase in DR is associated with the city’s class. However, there is no evidence of a relationship between ES and DR across different cities. In addition, the heterogeneity in the variance indicates that underestimating the variances of the effects could lead to inaccurate conclusions. The results of this study can help transit agencies in mastering ridership and assessing the designs of URT network topology. | ||
650 | 4 | |a Urban rail transit | |
650 | 4 | |a Evolutionary stage | |
650 | 4 | |a Daily ridership | |
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650 | 4 | |a Heterogeneity in variances | |
700 | 1 | |a Feng, Shumin |e verfasserin |4 aut | |
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allfields |
10.1016/j.tra.2023.103951 doi (DE-627)ELV066852021 (ELSEVIER)S0965-8564(23)00371-3 DE-627 ger DE-627 rda eng 380 VZ 55.80 bkl 74.75 bkl Xin, Mengwei verfasserin (orcid)0000-0002-0467-2133 aut Urban rail transit network topology evolutionary stage has influence on rail ridership: Insights from linear mixed-effects models with heterogeneity in variances 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Determining the impact of the evolutionary stage (ES) of urban rail transit (URT) network topology on daily ridership (DR) can be beneficial for policymakers when analyzing recent stagnated ridership in some cities. This paper used city-level data from 26 Chinese cities from 2013 to 2019 to investigate this. Eleven variables comprising socioeconomic attributes (population density and the average wage of employees), the availability of other transit modes (the number of taxis, the number of private cars, the bus network length, and the street network length), service-related attributes (the number of trains in operation, the average headway during peak period, the opened days, and rail fare), and a network topological characteristic (the average path length) were introduced as covariates. In addition, city class was introduced as a grouping variable to capture the unobserved characteristics of the investigated cities. Two linear mixed-effects models (M1 and M2) were developed, with an interaction term between city class and ES being introduced in M2. The variances were allowed to vary to account for the heterogeneity of the predictor, which improved the overall fit of the models. The estimation results reveal a significant positive within-subject effect of ES on DR in both models, which varied according to city class. This implies that for each city, improving ES increased DR and the scale of the increase in DR is associated with the city’s class. However, there is no evidence of a relationship between ES and DR across different cities. In addition, the heterogeneity in the variance indicates that underestimating the variances of the effects could lead to inaccurate conclusions. The results of this study can help transit agencies in mastering ridership and assessing the designs of URT network topology. Urban rail transit Evolutionary stage Daily ridership Linear mixed-effects model Heterogeneity in variances Feng, Shumin verfasserin aut Enthalten in Transportation research / A Amsterdam [u.a.] : Elsevier Science, 1979 180 Online-Ressource (DE-627)320532046 (DE-600)2015887-7 (DE-576)099210908 nnns volume:180 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 180 |
spelling |
10.1016/j.tra.2023.103951 doi (DE-627)ELV066852021 (ELSEVIER)S0965-8564(23)00371-3 DE-627 ger DE-627 rda eng 380 VZ 55.80 bkl 74.75 bkl Xin, Mengwei verfasserin (orcid)0000-0002-0467-2133 aut Urban rail transit network topology evolutionary stage has influence on rail ridership: Insights from linear mixed-effects models with heterogeneity in variances 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Determining the impact of the evolutionary stage (ES) of urban rail transit (URT) network topology on daily ridership (DR) can be beneficial for policymakers when analyzing recent stagnated ridership in some cities. This paper used city-level data from 26 Chinese cities from 2013 to 2019 to investigate this. Eleven variables comprising socioeconomic attributes (population density and the average wage of employees), the availability of other transit modes (the number of taxis, the number of private cars, the bus network length, and the street network length), service-related attributes (the number of trains in operation, the average headway during peak period, the opened days, and rail fare), and a network topological characteristic (the average path length) were introduced as covariates. In addition, city class was introduced as a grouping variable to capture the unobserved characteristics of the investigated cities. Two linear mixed-effects models (M1 and M2) were developed, with an interaction term between city class and ES being introduced in M2. The variances were allowed to vary to account for the heterogeneity of the predictor, which improved the overall fit of the models. The estimation results reveal a significant positive within-subject effect of ES on DR in both models, which varied according to city class. This implies that for each city, improving ES increased DR and the scale of the increase in DR is associated with the city’s class. However, there is no evidence of a relationship between ES and DR across different cities. In addition, the heterogeneity in the variance indicates that underestimating the variances of the effects could lead to inaccurate conclusions. The results of this study can help transit agencies in mastering ridership and assessing the designs of URT network topology. Urban rail transit Evolutionary stage Daily ridership Linear mixed-effects model Heterogeneity in variances Feng, Shumin verfasserin aut Enthalten in Transportation research / A Amsterdam [u.a.] : Elsevier Science, 1979 180 Online-Ressource (DE-627)320532046 (DE-600)2015887-7 (DE-576)099210908 nnns volume:180 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 180 |
allfields_unstemmed |
10.1016/j.tra.2023.103951 doi (DE-627)ELV066852021 (ELSEVIER)S0965-8564(23)00371-3 DE-627 ger DE-627 rda eng 380 VZ 55.80 bkl 74.75 bkl Xin, Mengwei verfasserin (orcid)0000-0002-0467-2133 aut Urban rail transit network topology evolutionary stage has influence on rail ridership: Insights from linear mixed-effects models with heterogeneity in variances 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Determining the impact of the evolutionary stage (ES) of urban rail transit (URT) network topology on daily ridership (DR) can be beneficial for policymakers when analyzing recent stagnated ridership in some cities. This paper used city-level data from 26 Chinese cities from 2013 to 2019 to investigate this. Eleven variables comprising socioeconomic attributes (population density and the average wage of employees), the availability of other transit modes (the number of taxis, the number of private cars, the bus network length, and the street network length), service-related attributes (the number of trains in operation, the average headway during peak period, the opened days, and rail fare), and a network topological characteristic (the average path length) were introduced as covariates. In addition, city class was introduced as a grouping variable to capture the unobserved characteristics of the investigated cities. Two linear mixed-effects models (M1 and M2) were developed, with an interaction term between city class and ES being introduced in M2. The variances were allowed to vary to account for the heterogeneity of the predictor, which improved the overall fit of the models. The estimation results reveal a significant positive within-subject effect of ES on DR in both models, which varied according to city class. This implies that for each city, improving ES increased DR and the scale of the increase in DR is associated with the city’s class. However, there is no evidence of a relationship between ES and DR across different cities. In addition, the heterogeneity in the variance indicates that underestimating the variances of the effects could lead to inaccurate conclusions. The results of this study can help transit agencies in mastering ridership and assessing the designs of URT network topology. Urban rail transit Evolutionary stage Daily ridership Linear mixed-effects model Heterogeneity in variances Feng, Shumin verfasserin aut Enthalten in Transportation research / A Amsterdam [u.a.] : Elsevier Science, 1979 180 Online-Ressource (DE-627)320532046 (DE-600)2015887-7 (DE-576)099210908 nnns volume:180 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 180 |
allfieldsGer |
10.1016/j.tra.2023.103951 doi (DE-627)ELV066852021 (ELSEVIER)S0965-8564(23)00371-3 DE-627 ger DE-627 rda eng 380 VZ 55.80 bkl 74.75 bkl Xin, Mengwei verfasserin (orcid)0000-0002-0467-2133 aut Urban rail transit network topology evolutionary stage has influence on rail ridership: Insights from linear mixed-effects models with heterogeneity in variances 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Determining the impact of the evolutionary stage (ES) of urban rail transit (URT) network topology on daily ridership (DR) can be beneficial for policymakers when analyzing recent stagnated ridership in some cities. This paper used city-level data from 26 Chinese cities from 2013 to 2019 to investigate this. Eleven variables comprising socioeconomic attributes (population density and the average wage of employees), the availability of other transit modes (the number of taxis, the number of private cars, the bus network length, and the street network length), service-related attributes (the number of trains in operation, the average headway during peak period, the opened days, and rail fare), and a network topological characteristic (the average path length) were introduced as covariates. In addition, city class was introduced as a grouping variable to capture the unobserved characteristics of the investigated cities. Two linear mixed-effects models (M1 and M2) were developed, with an interaction term between city class and ES being introduced in M2. The variances were allowed to vary to account for the heterogeneity of the predictor, which improved the overall fit of the models. The estimation results reveal a significant positive within-subject effect of ES on DR in both models, which varied according to city class. This implies that for each city, improving ES increased DR and the scale of the increase in DR is associated with the city’s class. However, there is no evidence of a relationship between ES and DR across different cities. In addition, the heterogeneity in the variance indicates that underestimating the variances of the effects could lead to inaccurate conclusions. The results of this study can help transit agencies in mastering ridership and assessing the designs of URT network topology. Urban rail transit Evolutionary stage Daily ridership Linear mixed-effects model Heterogeneity in variances Feng, Shumin verfasserin aut Enthalten in Transportation research / A Amsterdam [u.a.] : Elsevier Science, 1979 180 Online-Ressource (DE-627)320532046 (DE-600)2015887-7 (DE-576)099210908 nnns volume:180 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 180 |
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10.1016/j.tra.2023.103951 doi (DE-627)ELV066852021 (ELSEVIER)S0965-8564(23)00371-3 DE-627 ger DE-627 rda eng 380 VZ 55.80 bkl 74.75 bkl Xin, Mengwei verfasserin (orcid)0000-0002-0467-2133 aut Urban rail transit network topology evolutionary stage has influence on rail ridership: Insights from linear mixed-effects models with heterogeneity in variances 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Determining the impact of the evolutionary stage (ES) of urban rail transit (URT) network topology on daily ridership (DR) can be beneficial for policymakers when analyzing recent stagnated ridership in some cities. This paper used city-level data from 26 Chinese cities from 2013 to 2019 to investigate this. Eleven variables comprising socioeconomic attributes (population density and the average wage of employees), the availability of other transit modes (the number of taxis, the number of private cars, the bus network length, and the street network length), service-related attributes (the number of trains in operation, the average headway during peak period, the opened days, and rail fare), and a network topological characteristic (the average path length) were introduced as covariates. In addition, city class was introduced as a grouping variable to capture the unobserved characteristics of the investigated cities. Two linear mixed-effects models (M1 and M2) were developed, with an interaction term between city class and ES being introduced in M2. The variances were allowed to vary to account for the heterogeneity of the predictor, which improved the overall fit of the models. The estimation results reveal a significant positive within-subject effect of ES on DR in both models, which varied according to city class. This implies that for each city, improving ES increased DR and the scale of the increase in DR is associated with the city’s class. However, there is no evidence of a relationship between ES and DR across different cities. In addition, the heterogeneity in the variance indicates that underestimating the variances of the effects could lead to inaccurate conclusions. The results of this study can help transit agencies in mastering ridership and assessing the designs of URT network topology. Urban rail transit Evolutionary stage Daily ridership Linear mixed-effects model Heterogeneity in variances Feng, Shumin verfasserin aut Enthalten in Transportation research / A Amsterdam [u.a.] : Elsevier Science, 1979 180 Online-Ressource (DE-627)320532046 (DE-600)2015887-7 (DE-576)099210908 nnns volume:180 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 180 |
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380 VZ 55.80 bkl 74.75 bkl Urban rail transit network topology evolutionary stage has influence on rail ridership: Insights from linear mixed-effects models with heterogeneity in variances Urban rail transit Evolutionary stage Daily ridership Linear mixed-effects model Heterogeneity in variances |
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Urban rail transit network topology evolutionary stage has influence on rail ridership: Insights from linear mixed-effects models with heterogeneity in variances |
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Urban rail transit network topology evolutionary stage has influence on rail ridership: Insights from linear mixed-effects models with heterogeneity in variances |
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urban rail transit network topology evolutionary stage has influence on rail ridership: insights from linear mixed-effects models with heterogeneity in variances |
title_auth |
Urban rail transit network topology evolutionary stage has influence on rail ridership: Insights from linear mixed-effects models with heterogeneity in variances |
abstract |
Determining the impact of the evolutionary stage (ES) of urban rail transit (URT) network topology on daily ridership (DR) can be beneficial for policymakers when analyzing recent stagnated ridership in some cities. This paper used city-level data from 26 Chinese cities from 2013 to 2019 to investigate this. Eleven variables comprising socioeconomic attributes (population density and the average wage of employees), the availability of other transit modes (the number of taxis, the number of private cars, the bus network length, and the street network length), service-related attributes (the number of trains in operation, the average headway during peak period, the opened days, and rail fare), and a network topological characteristic (the average path length) were introduced as covariates. In addition, city class was introduced as a grouping variable to capture the unobserved characteristics of the investigated cities. Two linear mixed-effects models (M1 and M2) were developed, with an interaction term between city class and ES being introduced in M2. The variances were allowed to vary to account for the heterogeneity of the predictor, which improved the overall fit of the models. The estimation results reveal a significant positive within-subject effect of ES on DR in both models, which varied according to city class. This implies that for each city, improving ES increased DR and the scale of the increase in DR is associated with the city’s class. However, there is no evidence of a relationship between ES and DR across different cities. In addition, the heterogeneity in the variance indicates that underestimating the variances of the effects could lead to inaccurate conclusions. The results of this study can help transit agencies in mastering ridership and assessing the designs of URT network topology. |
abstractGer |
Determining the impact of the evolutionary stage (ES) of urban rail transit (URT) network topology on daily ridership (DR) can be beneficial for policymakers when analyzing recent stagnated ridership in some cities. This paper used city-level data from 26 Chinese cities from 2013 to 2019 to investigate this. Eleven variables comprising socioeconomic attributes (population density and the average wage of employees), the availability of other transit modes (the number of taxis, the number of private cars, the bus network length, and the street network length), service-related attributes (the number of trains in operation, the average headway during peak period, the opened days, and rail fare), and a network topological characteristic (the average path length) were introduced as covariates. In addition, city class was introduced as a grouping variable to capture the unobserved characteristics of the investigated cities. Two linear mixed-effects models (M1 and M2) were developed, with an interaction term between city class and ES being introduced in M2. The variances were allowed to vary to account for the heterogeneity of the predictor, which improved the overall fit of the models. The estimation results reveal a significant positive within-subject effect of ES on DR in both models, which varied according to city class. This implies that for each city, improving ES increased DR and the scale of the increase in DR is associated with the city’s class. However, there is no evidence of a relationship between ES and DR across different cities. In addition, the heterogeneity in the variance indicates that underestimating the variances of the effects could lead to inaccurate conclusions. The results of this study can help transit agencies in mastering ridership and assessing the designs of URT network topology. |
abstract_unstemmed |
Determining the impact of the evolutionary stage (ES) of urban rail transit (URT) network topology on daily ridership (DR) can be beneficial for policymakers when analyzing recent stagnated ridership in some cities. This paper used city-level data from 26 Chinese cities from 2013 to 2019 to investigate this. Eleven variables comprising socioeconomic attributes (population density and the average wage of employees), the availability of other transit modes (the number of taxis, the number of private cars, the bus network length, and the street network length), service-related attributes (the number of trains in operation, the average headway during peak period, the opened days, and rail fare), and a network topological characteristic (the average path length) were introduced as covariates. In addition, city class was introduced as a grouping variable to capture the unobserved characteristics of the investigated cities. Two linear mixed-effects models (M1 and M2) were developed, with an interaction term between city class and ES being introduced in M2. The variances were allowed to vary to account for the heterogeneity of the predictor, which improved the overall fit of the models. The estimation results reveal a significant positive within-subject effect of ES on DR in both models, which varied according to city class. This implies that for each city, improving ES increased DR and the scale of the increase in DR is associated with the city’s class. However, there is no evidence of a relationship between ES and DR across different cities. In addition, the heterogeneity in the variance indicates that underestimating the variances of the effects could lead to inaccurate conclusions. The results of this study can help transit agencies in mastering ridership and assessing the designs of URT network topology. |
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