Analyzing User Behavior in Selection of Ride-Hailing Services for Urban Travel in Developing Countries

Abstract Recent developments in urban transportation services are rapidly transforming the way people make their trips. Around the world, the most controversial and rapidly growing mobility services in recent years are ride-hailing services (RHS) offered by transportation network companies (TNCs) su...
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Autor*in:	Raj, Priyanshu [verfasserIn] Bhaduri, Eeshan Moeckel, Rolf Goswami, Arkopal Kishore

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	Ride-hailing services Mode choice Multinomial logistic regression Ordered logistic regression Public transport

Anmerkung:	© The Author(s), under exclusive licence to Springer Nature Switzerland AG 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Übergeordnetes Werk:	Enthalten in: Transportation in developing economies - Cham : Springer International Publishing AG, 2015, 9(2022), 1 vom: 03. Okt.
Übergeordnetes Werk:	volume:9 ; year:2022 ; number:1 ; day:03 ; month:10

Links:	Volltext

DOI / URN:	10.1007/s40890-022-00172-5

Katalog-ID:	SPR048279153

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520			\|a Abstract Recent developments in urban transportation services are rapidly transforming the way people make their trips. Around the world, the most controversial and rapidly growing mobility services in recent years are ride-hailing services (RHS) offered by transportation network companies (TNCs) such as Uber and Ola. This research estimates the demand for RHS vis-à-vis other modes and further expands to estimate usage propensity of RHS in the capital city of India, New Delhi. A discrete choice modeling framework is developed based on a household travel surveys (N = 426) conducted in 2019. Two models were developed, a multinomial logit (MNL) model, to estimate the factors that lead to the adoption of RHS, and an ordered logit (OL) model, to estimate the frequency of usage of RHS. The results reveal a comprehensive set of socio-demographic and behavioral factors which leads to greater adoption of RHS. The variables such as household income, vehicle ownership, and use of smartphone are found to be important predictors (with a 95% significance level) of service adoption of RHS. The model results also suggest that RHS are likely to be used infrequently, and when it is being used, they are more likely to be used by the younger population and during the weekends. Overall, this research brings valuable and novel insights into the adoption and usage of RHS in India.
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912			\|a GBV_ILN_170
912			\|a GBV_ILN_171
912			\|a GBV_ILN_187
912			\|a GBV_ILN_213
912			\|a GBV_ILN_224
912			\|a GBV_ILN_230
912			\|a GBV_ILN_250
912			\|a GBV_ILN_281
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912			\|a GBV_ILN_293
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912			\|a GBV_ILN_2027
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allfields	10.1007/s40890-022-00172-5 doi (DE-627)SPR048279153 (SPR)s40890-022-00172-5-e DE-627 ger DE-627 rakwb eng Raj, Priyanshu verfasserin aut Analyzing User Behavior in Selection of Ride-Hailing Services for Urban Travel in Developing Countries 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Recent developments in urban transportation services are rapidly transforming the way people make their trips. Around the world, the most controversial and rapidly growing mobility services in recent years are ride-hailing services (RHS) offered by transportation network companies (TNCs) such as Uber and Ola. This research estimates the demand for RHS vis-à-vis other modes and further expands to estimate usage propensity of RHS in the capital city of India, New Delhi. A discrete choice modeling framework is developed based on a household travel surveys (N = 426) conducted in 2019. Two models were developed, a multinomial logit (MNL) model, to estimate the factors that lead to the adoption of RHS, and an ordered logit (OL) model, to estimate the frequency of usage of RHS. The results reveal a comprehensive set of socio-demographic and behavioral factors which leads to greater adoption of RHS. The variables such as household income, vehicle ownership, and use of smartphone are found to be important predictors (with a 95% significance level) of service adoption of RHS. The model results also suggest that RHS are likely to be used infrequently, and when it is being used, they are more likely to be used by the younger population and during the weekends. Overall, this research brings valuable and novel insights into the adoption and usage of RHS in India. Ride-hailing services (dpeaa)DE-He213 Mode choice (dpeaa)DE-He213 Multinomial logistic regression (dpeaa)DE-He213 Ordered logistic regression (dpeaa)DE-He213 Public transport (dpeaa)DE-He213 Bhaduri, Eeshan (orcid)0000-0002-7020-0986 aut Moeckel, Rolf (orcid)0000-0002-6874-0393 aut Goswami, Arkopal Kishore (orcid)0000-0003-1369-215X aut Enthalten in Transportation in developing economies Cham : Springer International Publishing AG, 2015 9(2022), 1 vom: 03. Okt. (DE-627)828841098 (DE-600)2825393-0 2199-9295 nnns volume:9 year:2022 number:1 day:03 month:10 https://dx.doi.org/10.1007/s40890-022-00172-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 9 2022 1 03 10
spelling	10.1007/s40890-022-00172-5 doi (DE-627)SPR048279153 (SPR)s40890-022-00172-5-e DE-627 ger DE-627 rakwb eng Raj, Priyanshu verfasserin aut Analyzing User Behavior in Selection of Ride-Hailing Services for Urban Travel in Developing Countries 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Recent developments in urban transportation services are rapidly transforming the way people make their trips. Around the world, the most controversial and rapidly growing mobility services in recent years are ride-hailing services (RHS) offered by transportation network companies (TNCs) such as Uber and Ola. This research estimates the demand for RHS vis-à-vis other modes and further expands to estimate usage propensity of RHS in the capital city of India, New Delhi. A discrete choice modeling framework is developed based on a household travel surveys (N = 426) conducted in 2019. Two models were developed, a multinomial logit (MNL) model, to estimate the factors that lead to the adoption of RHS, and an ordered logit (OL) model, to estimate the frequency of usage of RHS. The results reveal a comprehensive set of socio-demographic and behavioral factors which leads to greater adoption of RHS. The variables such as household income, vehicle ownership, and use of smartphone are found to be important predictors (with a 95% significance level) of service adoption of RHS. The model results also suggest that RHS are likely to be used infrequently, and when it is being used, they are more likely to be used by the younger population and during the weekends. Overall, this research brings valuable and novel insights into the adoption and usage of RHS in India. Ride-hailing services (dpeaa)DE-He213 Mode choice (dpeaa)DE-He213 Multinomial logistic regression (dpeaa)DE-He213 Ordered logistic regression (dpeaa)DE-He213 Public transport (dpeaa)DE-He213 Bhaduri, Eeshan (orcid)0000-0002-7020-0986 aut Moeckel, Rolf (orcid)0000-0002-6874-0393 aut Goswami, Arkopal Kishore (orcid)0000-0003-1369-215X aut Enthalten in Transportation in developing economies Cham : Springer International Publishing AG, 2015 9(2022), 1 vom: 03. Okt. (DE-627)828841098 (DE-600)2825393-0 2199-9295 nnns volume:9 year:2022 number:1 day:03 month:10 https://dx.doi.org/10.1007/s40890-022-00172-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 9 2022 1 03 10
allfields_unstemmed	10.1007/s40890-022-00172-5 doi (DE-627)SPR048279153 (SPR)s40890-022-00172-5-e DE-627 ger DE-627 rakwb eng Raj, Priyanshu verfasserin aut Analyzing User Behavior in Selection of Ride-Hailing Services for Urban Travel in Developing Countries 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Recent developments in urban transportation services are rapidly transforming the way people make their trips. Around the world, the most controversial and rapidly growing mobility services in recent years are ride-hailing services (RHS) offered by transportation network companies (TNCs) such as Uber and Ola. This research estimates the demand for RHS vis-à-vis other modes and further expands to estimate usage propensity of RHS in the capital city of India, New Delhi. A discrete choice modeling framework is developed based on a household travel surveys (N = 426) conducted in 2019. Two models were developed, a multinomial logit (MNL) model, to estimate the factors that lead to the adoption of RHS, and an ordered logit (OL) model, to estimate the frequency of usage of RHS. The results reveal a comprehensive set of socio-demographic and behavioral factors which leads to greater adoption of RHS. The variables such as household income, vehicle ownership, and use of smartphone are found to be important predictors (with a 95% significance level) of service adoption of RHS. The model results also suggest that RHS are likely to be used infrequently, and when it is being used, they are more likely to be used by the younger population and during the weekends. Overall, this research brings valuable and novel insights into the adoption and usage of RHS in India. Ride-hailing services (dpeaa)DE-He213 Mode choice (dpeaa)DE-He213 Multinomial logistic regression (dpeaa)DE-He213 Ordered logistic regression (dpeaa)DE-He213 Public transport (dpeaa)DE-He213 Bhaduri, Eeshan (orcid)0000-0002-7020-0986 aut Moeckel, Rolf (orcid)0000-0002-6874-0393 aut Goswami, Arkopal Kishore (orcid)0000-0003-1369-215X aut Enthalten in Transportation in developing economies Cham : Springer International Publishing AG, 2015 9(2022), 1 vom: 03. Okt. (DE-627)828841098 (DE-600)2825393-0 2199-9295 nnns volume:9 year:2022 number:1 day:03 month:10 https://dx.doi.org/10.1007/s40890-022-00172-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 9 2022 1 03 10
allfieldsGer	10.1007/s40890-022-00172-5 doi (DE-627)SPR048279153 (SPR)s40890-022-00172-5-e DE-627 ger DE-627 rakwb eng Raj, Priyanshu verfasserin aut Analyzing User Behavior in Selection of Ride-Hailing Services for Urban Travel in Developing Countries 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Recent developments in urban transportation services are rapidly transforming the way people make their trips. Around the world, the most controversial and rapidly growing mobility services in recent years are ride-hailing services (RHS) offered by transportation network companies (TNCs) such as Uber and Ola. This research estimates the demand for RHS vis-à-vis other modes and further expands to estimate usage propensity of RHS in the capital city of India, New Delhi. A discrete choice modeling framework is developed based on a household travel surveys (N = 426) conducted in 2019. Two models were developed, a multinomial logit (MNL) model, to estimate the factors that lead to the adoption of RHS, and an ordered logit (OL) model, to estimate the frequency of usage of RHS. The results reveal a comprehensive set of socio-demographic and behavioral factors which leads to greater adoption of RHS. The variables such as household income, vehicle ownership, and use of smartphone are found to be important predictors (with a 95% significance level) of service adoption of RHS. The model results also suggest that RHS are likely to be used infrequently, and when it is being used, they are more likely to be used by the younger population and during the weekends. Overall, this research brings valuable and novel insights into the adoption and usage of RHS in India. Ride-hailing services (dpeaa)DE-He213 Mode choice (dpeaa)DE-He213 Multinomial logistic regression (dpeaa)DE-He213 Ordered logistic regression (dpeaa)DE-He213 Public transport (dpeaa)DE-He213 Bhaduri, Eeshan (orcid)0000-0002-7020-0986 aut Moeckel, Rolf (orcid)0000-0002-6874-0393 aut Goswami, Arkopal Kishore (orcid)0000-0003-1369-215X aut Enthalten in Transportation in developing economies Cham : Springer International Publishing AG, 2015 9(2022), 1 vom: 03. Okt. (DE-627)828841098 (DE-600)2825393-0 2199-9295 nnns volume:9 year:2022 number:1 day:03 month:10 https://dx.doi.org/10.1007/s40890-022-00172-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 9 2022 1 03 10
allfieldsSound	10.1007/s40890-022-00172-5 doi (DE-627)SPR048279153 (SPR)s40890-022-00172-5-e DE-627 ger DE-627 rakwb eng Raj, Priyanshu verfasserin aut Analyzing User Behavior in Selection of Ride-Hailing Services for Urban Travel in Developing Countries 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Recent developments in urban transportation services are rapidly transforming the way people make their trips. Around the world, the most controversial and rapidly growing mobility services in recent years are ride-hailing services (RHS) offered by transportation network companies (TNCs) such as Uber and Ola. This research estimates the demand for RHS vis-à-vis other modes and further expands to estimate usage propensity of RHS in the capital city of India, New Delhi. A discrete choice modeling framework is developed based on a household travel surveys (N = 426) conducted in 2019. Two models were developed, a multinomial logit (MNL) model, to estimate the factors that lead to the adoption of RHS, and an ordered logit (OL) model, to estimate the frequency of usage of RHS. The results reveal a comprehensive set of socio-demographic and behavioral factors which leads to greater adoption of RHS. The variables such as household income, vehicle ownership, and use of smartphone are found to be important predictors (with a 95% significance level) of service adoption of RHS. The model results also suggest that RHS are likely to be used infrequently, and when it is being used, they are more likely to be used by the younger population and during the weekends. Overall, this research brings valuable and novel insights into the adoption and usage of RHS in India. Ride-hailing services (dpeaa)DE-He213 Mode choice (dpeaa)DE-He213 Multinomial logistic regression (dpeaa)DE-He213 Ordered logistic regression (dpeaa)DE-He213 Public transport (dpeaa)DE-He213 Bhaduri, Eeshan (orcid)0000-0002-7020-0986 aut Moeckel, Rolf (orcid)0000-0002-6874-0393 aut Goswami, Arkopal Kishore (orcid)0000-0003-1369-215X aut Enthalten in Transportation in developing economies Cham : Springer International Publishing AG, 2015 9(2022), 1 vom: 03. Okt. (DE-627)828841098 (DE-600)2825393-0 2199-9295 nnns volume:9 year:2022 number:1 day:03 month:10 https://dx.doi.org/10.1007/s40890-022-00172-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 9 2022 1 03 10
language	English
source	Enthalten in Transportation in developing economies 9(2022), 1 vom: 03. Okt. volume:9 year:2022 number:1 day:03 month:10
sourceStr	Enthalten in Transportation in developing economies 9(2022), 1 vom: 03. Okt. volume:9 year:2022 number:1 day:03 month:10
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Ride-hailing services Mode choice Multinomial logistic regression Ordered logistic regression Public transport
isfreeaccess_bool	false
container_title	Transportation in developing economies
authorswithroles_txt_mv	Raj, Priyanshu @@aut@@ Bhaduri, Eeshan @@aut@@ Moeckel, Rolf @@aut@@ Goswami, Arkopal Kishore @@aut@@
publishDateDaySort_date	2022-10-03T00:00:00Z
hierarchy_top_id	828841098
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language_de	englisch
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author	Raj, Priyanshu
spellingShingle	Raj, Priyanshu misc Ride-hailing services misc Mode choice misc Multinomial logistic regression misc Ordered logistic regression misc Public transport Analyzing User Behavior in Selection of Ride-Hailing Services for Urban Travel in Developing Countries
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topic_title	Analyzing User Behavior in Selection of Ride-Hailing Services for Urban Travel in Developing Countries Ride-hailing services (dpeaa)DE-He213 Mode choice (dpeaa)DE-He213 Multinomial logistic regression (dpeaa)DE-He213 Ordered logistic regression (dpeaa)DE-He213 Public transport (dpeaa)DE-He213
topic	misc Ride-hailing services misc Mode choice misc Multinomial logistic regression misc Ordered logistic regression misc Public transport
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title	Analyzing User Behavior in Selection of Ride-Hailing Services for Urban Travel in Developing Countries
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title_full	Analyzing User Behavior in Selection of Ride-Hailing Services for Urban Travel in Developing Countries
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author_browse	Raj, Priyanshu Bhaduri, Eeshan Moeckel, Rolf Goswami, Arkopal Kishore
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title_sort	analyzing user behavior in selection of ride-hailing services for urban travel in developing countries
title_auth	Analyzing User Behavior in Selection of Ride-Hailing Services for Urban Travel in Developing Countries
abstract	Abstract Recent developments in urban transportation services are rapidly transforming the way people make their trips. Around the world, the most controversial and rapidly growing mobility services in recent years are ride-hailing services (RHS) offered by transportation network companies (TNCs) such as Uber and Ola. This research estimates the demand for RHS vis-à-vis other modes and further expands to estimate usage propensity of RHS in the capital city of India, New Delhi. A discrete choice modeling framework is developed based on a household travel surveys (N = 426) conducted in 2019. Two models were developed, a multinomial logit (MNL) model, to estimate the factors that lead to the adoption of RHS, and an ordered logit (OL) model, to estimate the frequency of usage of RHS. The results reveal a comprehensive set of socio-demographic and behavioral factors which leads to greater adoption of RHS. The variables such as household income, vehicle ownership, and use of smartphone are found to be important predictors (with a 95% significance level) of service adoption of RHS. The model results also suggest that RHS are likely to be used infrequently, and when it is being used, they are more likely to be used by the younger population and during the weekends. Overall, this research brings valuable and novel insights into the adoption and usage of RHS in India. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
abstractGer	Abstract Recent developments in urban transportation services are rapidly transforming the way people make their trips. Around the world, the most controversial and rapidly growing mobility services in recent years are ride-hailing services (RHS) offered by transportation network companies (TNCs) such as Uber and Ola. This research estimates the demand for RHS vis-à-vis other modes and further expands to estimate usage propensity of RHS in the capital city of India, New Delhi. A discrete choice modeling framework is developed based on a household travel surveys (N = 426) conducted in 2019. Two models were developed, a multinomial logit (MNL) model, to estimate the factors that lead to the adoption of RHS, and an ordered logit (OL) model, to estimate the frequency of usage of RHS. The results reveal a comprehensive set of socio-demographic and behavioral factors which leads to greater adoption of RHS. The variables such as household income, vehicle ownership, and use of smartphone are found to be important predictors (with a 95% significance level) of service adoption of RHS. The model results also suggest that RHS are likely to be used infrequently, and when it is being used, they are more likely to be used by the younger population and during the weekends. Overall, this research brings valuable and novel insights into the adoption and usage of RHS in India. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
abstract_unstemmed	Abstract Recent developments in urban transportation services are rapidly transforming the way people make their trips. Around the world, the most controversial and rapidly growing mobility services in recent years are ride-hailing services (RHS) offered by transportation network companies (TNCs) such as Uber and Ola. This research estimates the demand for RHS vis-à-vis other modes and further expands to estimate usage propensity of RHS in the capital city of India, New Delhi. A discrete choice modeling framework is developed based on a household travel surveys (N = 426) conducted in 2019. Two models were developed, a multinomial logit (MNL) model, to estimate the factors that lead to the adoption of RHS, and an ordered logit (OL) model, to estimate the frequency of usage of RHS. The results reveal a comprehensive set of socio-demographic and behavioral factors which leads to greater adoption of RHS. The variables such as household income, vehicle ownership, and use of smartphone are found to be important predictors (with a 95% significance level) of service adoption of RHS. The model results also suggest that RHS are likely to be used infrequently, and when it is being used, they are more likely to be used by the younger population and during the weekends. Overall, this research brings valuable and novel insights into the adoption and usage of RHS in India. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
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title_short	Analyzing User Behavior in Selection of Ride-Hailing Services for Urban Travel in Developing Countries
url	https://dx.doi.org/10.1007/s40890-022-00172-5
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author2	Bhaduri, Eeshan Moeckel, Rolf Goswami, Arkopal Kishore
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doi_str	10.1007/s40890-022-00172-5
up_date	2024-07-03T18:11:22.471Z
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Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Recent developments in urban transportation services are rapidly transforming the way people make their trips. Around the world, the most controversial and rapidly growing mobility services in recent years are ride-hailing services (RHS) offered by transportation network companies (TNCs) such as Uber and Ola. This research estimates the demand for RHS vis-à-vis other modes and further expands to estimate usage propensity of RHS in the capital city of India, New Delhi. A discrete choice modeling framework is developed based on a household travel surveys (N = 426) conducted in 2019. 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