A technology selection and design model of a semi-rapid transit line

Abstract We present a new optimization model for technology selection and design of a semi-rapid transit line. With respect to previous studies, we improve the synthetic representation of the temporal and spatial variability of demand, and of several operational and design aspects. We apply the mode...
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Gespeichert in:

Autor*in:	Moccia, Luigi [verfasserIn] Allen, Duncan W. [verfasserIn] Bruun, Eric C. [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2018

Schlagwörter:	Semi-rapid transit Bus rapid transit Light rail transit Transit line optimization

Übergeordnetes Werk:	Enthalten in: Public transport - Berlin : Springer, 2009, 10(2018), 3 vom: 17. Sept., Seite 455-497
Übergeordnetes Werk:	volume:10 ; year:2018 ; number:3 ; day:17 ; month:09 ; pages:455-497

Links:	Volltext

DOI / URN:	10.1007/s12469-018-0187-1

Katalog-ID:	SPR025711490

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520			\|a Abstract We present a new optimization model for technology selection and design of a semi-rapid transit line. With respect to previous studies, we improve the synthetic representation of the temporal and spatial variability of demand, and of several operational and design aspects. We apply the model to two scenarios offering comparable performance by commercially available technologies in terms of service, rather than assuming that service quality is strongly associated with technology. The model is validated by comparing some computed performance indices with best practices. We show that planning for a faster technology can be more important than the choice between bus and rail per se, except at very low demand density, and that differences of total cost, sum of passengers’ time value and operator’s cost, between the technologies are smaller than commonly held across a wide range of higher demands. At high demand density multiple-unit rail offers the most cost-effective way to achieve high capacities under many conditions. A scenario variation analysis shows the relevance of differences between value of time components, the bias of averaging vehicle load ratios when assessing the crowding disutility, the usefulness of a demand index abstracting from some specific parameter choices, and the high impact of the project discount rate.
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allfields	10.1007/s12469-018-0187-1 doi (DE-627)SPR025711490 (SPR)s12469-018-0187-1-e DE-627 ger DE-627 rakwb eng 380 ASE Moccia, Luigi verfasserin aut A technology selection and design model of a semi-rapid transit line 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract We present a new optimization model for technology selection and design of a semi-rapid transit line. With respect to previous studies, we improve the synthetic representation of the temporal and spatial variability of demand, and of several operational and design aspects. We apply the model to two scenarios offering comparable performance by commercially available technologies in terms of service, rather than assuming that service quality is strongly associated with technology. The model is validated by comparing some computed performance indices with best practices. We show that planning for a faster technology can be more important than the choice between bus and rail per se, except at very low demand density, and that differences of total cost, sum of passengers’ time value and operator’s cost, between the technologies are smaller than commonly held across a wide range of higher demands. At high demand density multiple-unit rail offers the most cost-effective way to achieve high capacities under many conditions. A scenario variation analysis shows the relevance of differences between value of time components, the bias of averaging vehicle load ratios when assessing the crowding disutility, the usefulness of a demand index abstracting from some specific parameter choices, and the high impact of the project discount rate. Semi-rapid transit (dpeaa)DE-He213 Bus rapid transit (dpeaa)DE-He213 Light rail transit (dpeaa)DE-He213 Transit line optimization (dpeaa)DE-He213 Allen, Duncan W. verfasserin aut Bruun, Eric C. verfasserin aut Enthalten in Public transport Berlin : Springer, 2009 10(2018), 3 vom: 17. Sept., Seite 455-497 (DE-627)595712576 (DE-600)2487250-7 1613-7159 nnns volume:10 year:2018 number:3 day:17 month:09 pages:455-497 https://dx.doi.org/10.1007/s12469-018-0187-1 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_26 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_2018 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_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 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_2116 GBV_ILN_2118 GBV_ILN_2119 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 10 2018 3 17 09 455-497
spelling	10.1007/s12469-018-0187-1 doi (DE-627)SPR025711490 (SPR)s12469-018-0187-1-e DE-627 ger DE-627 rakwb eng 380 ASE Moccia, Luigi verfasserin aut A technology selection and design model of a semi-rapid transit line 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract We present a new optimization model for technology selection and design of a semi-rapid transit line. With respect to previous studies, we improve the synthetic representation of the temporal and spatial variability of demand, and of several operational and design aspects. We apply the model to two scenarios offering comparable performance by commercially available technologies in terms of service, rather than assuming that service quality is strongly associated with technology. The model is validated by comparing some computed performance indices with best practices. We show that planning for a faster technology can be more important than the choice between bus and rail per se, except at very low demand density, and that differences of total cost, sum of passengers’ time value and operator’s cost, between the technologies are smaller than commonly held across a wide range of higher demands. At high demand density multiple-unit rail offers the most cost-effective way to achieve high capacities under many conditions. A scenario variation analysis shows the relevance of differences between value of time components, the bias of averaging vehicle load ratios when assessing the crowding disutility, the usefulness of a demand index abstracting from some specific parameter choices, and the high impact of the project discount rate. Semi-rapid transit (dpeaa)DE-He213 Bus rapid transit (dpeaa)DE-He213 Light rail transit (dpeaa)DE-He213 Transit line optimization (dpeaa)DE-He213 Allen, Duncan W. verfasserin aut Bruun, Eric C. verfasserin aut Enthalten in Public transport Berlin : Springer, 2009 10(2018), 3 vom: 17. Sept., Seite 455-497 (DE-627)595712576 (DE-600)2487250-7 1613-7159 nnns volume:10 year:2018 number:3 day:17 month:09 pages:455-497 https://dx.doi.org/10.1007/s12469-018-0187-1 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_26 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_2018 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_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 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_2116 GBV_ILN_2118 GBV_ILN_2119 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 10 2018 3 17 09 455-497
allfields_unstemmed	10.1007/s12469-018-0187-1 doi (DE-627)SPR025711490 (SPR)s12469-018-0187-1-e DE-627 ger DE-627 rakwb eng 380 ASE Moccia, Luigi verfasserin aut A technology selection and design model of a semi-rapid transit line 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract We present a new optimization model for technology selection and design of a semi-rapid transit line. With respect to previous studies, we improve the synthetic representation of the temporal and spatial variability of demand, and of several operational and design aspects. We apply the model to two scenarios offering comparable performance by commercially available technologies in terms of service, rather than assuming that service quality is strongly associated with technology. The model is validated by comparing some computed performance indices with best practices. We show that planning for a faster technology can be more important than the choice between bus and rail per se, except at very low demand density, and that differences of total cost, sum of passengers’ time value and operator’s cost, between the technologies are smaller than commonly held across a wide range of higher demands. At high demand density multiple-unit rail offers the most cost-effective way to achieve high capacities under many conditions. A scenario variation analysis shows the relevance of differences between value of time components, the bias of averaging vehicle load ratios when assessing the crowding disutility, the usefulness of a demand index abstracting from some specific parameter choices, and the high impact of the project discount rate. Semi-rapid transit (dpeaa)DE-He213 Bus rapid transit (dpeaa)DE-He213 Light rail transit (dpeaa)DE-He213 Transit line optimization (dpeaa)DE-He213 Allen, Duncan W. verfasserin aut Bruun, Eric C. verfasserin aut Enthalten in Public transport Berlin : Springer, 2009 10(2018), 3 vom: 17. Sept., Seite 455-497 (DE-627)595712576 (DE-600)2487250-7 1613-7159 nnns volume:10 year:2018 number:3 day:17 month:09 pages:455-497 https://dx.doi.org/10.1007/s12469-018-0187-1 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_26 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_2018 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_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 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_2116 GBV_ILN_2118 GBV_ILN_2119 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 10 2018 3 17 09 455-497
allfieldsGer	10.1007/s12469-018-0187-1 doi (DE-627)SPR025711490 (SPR)s12469-018-0187-1-e DE-627 ger DE-627 rakwb eng 380 ASE Moccia, Luigi verfasserin aut A technology selection and design model of a semi-rapid transit line 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract We present a new optimization model for technology selection and design of a semi-rapid transit line. With respect to previous studies, we improve the synthetic representation of the temporal and spatial variability of demand, and of several operational and design aspects. We apply the model to two scenarios offering comparable performance by commercially available technologies in terms of service, rather than assuming that service quality is strongly associated with technology. The model is validated by comparing some computed performance indices with best practices. We show that planning for a faster technology can be more important than the choice between bus and rail per se, except at very low demand density, and that differences of total cost, sum of passengers’ time value and operator’s cost, between the technologies are smaller than commonly held across a wide range of higher demands. At high demand density multiple-unit rail offers the most cost-effective way to achieve high capacities under many conditions. A scenario variation analysis shows the relevance of differences between value of time components, the bias of averaging vehicle load ratios when assessing the crowding disutility, the usefulness of a demand index abstracting from some specific parameter choices, and the high impact of the project discount rate. Semi-rapid transit (dpeaa)DE-He213 Bus rapid transit (dpeaa)DE-He213 Light rail transit (dpeaa)DE-He213 Transit line optimization (dpeaa)DE-He213 Allen, Duncan W. verfasserin aut Bruun, Eric C. verfasserin aut Enthalten in Public transport Berlin : Springer, 2009 10(2018), 3 vom: 17. Sept., Seite 455-497 (DE-627)595712576 (DE-600)2487250-7 1613-7159 nnns volume:10 year:2018 number:3 day:17 month:09 pages:455-497 https://dx.doi.org/10.1007/s12469-018-0187-1 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_26 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_2018 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_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 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_2116 GBV_ILN_2118 GBV_ILN_2119 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 10 2018 3 17 09 455-497
allfieldsSound	10.1007/s12469-018-0187-1 doi (DE-627)SPR025711490 (SPR)s12469-018-0187-1-e DE-627 ger DE-627 rakwb eng 380 ASE Moccia, Luigi verfasserin aut A technology selection and design model of a semi-rapid transit line 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract We present a new optimization model for technology selection and design of a semi-rapid transit line. With respect to previous studies, we improve the synthetic representation of the temporal and spatial variability of demand, and of several operational and design aspects. We apply the model to two scenarios offering comparable performance by commercially available technologies in terms of service, rather than assuming that service quality is strongly associated with technology. The model is validated by comparing some computed performance indices with best practices. We show that planning for a faster technology can be more important than the choice between bus and rail per se, except at very low demand density, and that differences of total cost, sum of passengers’ time value and operator’s cost, between the technologies are smaller than commonly held across a wide range of higher demands. At high demand density multiple-unit rail offers the most cost-effective way to achieve high capacities under many conditions. A scenario variation analysis shows the relevance of differences between value of time components, the bias of averaging vehicle load ratios when assessing the crowding disutility, the usefulness of a demand index abstracting from some specific parameter choices, and the high impact of the project discount rate. Semi-rapid transit (dpeaa)DE-He213 Bus rapid transit (dpeaa)DE-He213 Light rail transit (dpeaa)DE-He213 Transit line optimization (dpeaa)DE-He213 Allen, Duncan W. verfasserin aut Bruun, Eric C. verfasserin aut Enthalten in Public transport Berlin : Springer, 2009 10(2018), 3 vom: 17. Sept., Seite 455-497 (DE-627)595712576 (DE-600)2487250-7 1613-7159 nnns volume:10 year:2018 number:3 day:17 month:09 pages:455-497 https://dx.doi.org/10.1007/s12469-018-0187-1 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_26 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_2018 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_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 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_2116 GBV_ILN_2118 GBV_ILN_2119 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 10 2018 3 17 09 455-497
language	English
source	Enthalten in Public transport 10(2018), 3 vom: 17. Sept., Seite 455-497 volume:10 year:2018 number:3 day:17 month:09 pages:455-497
sourceStr	Enthalten in Public transport 10(2018), 3 vom: 17. Sept., Seite 455-497 volume:10 year:2018 number:3 day:17 month:09 pages:455-497
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Semi-rapid transit Bus rapid transit Light rail transit Transit line optimization
dewey-raw	380
isfreeaccess_bool	false
container_title	Public transport
authorswithroles_txt_mv	Moccia, Luigi @@aut@@ Allen, Duncan W. @@aut@@ Bruun, Eric C. @@aut@@
publishDateDaySort_date	2018-09-17T00:00:00Z
hierarchy_top_id	595712576
dewey-sort	3380
id	SPR025711490
language_de	englisch
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author	Moccia, Luigi
spellingShingle	Moccia, Luigi ddc 380 misc Semi-rapid transit misc Bus rapid transit misc Light rail transit misc Transit line optimization A technology selection and design model of a semi-rapid transit line
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topic_title	380 ASE A technology selection and design model of a semi-rapid transit line Semi-rapid transit (dpeaa)DE-He213 Bus rapid transit (dpeaa)DE-He213 Light rail transit (dpeaa)DE-He213 Transit line optimization (dpeaa)DE-He213
topic	ddc 380 misc Semi-rapid transit misc Bus rapid transit misc Light rail transit misc Transit line optimization
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title	A technology selection and design model of a semi-rapid transit line
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title_full	A technology selection and design model of a semi-rapid transit line
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title_sort	technology selection and design model of a semi-rapid transit line
title_auth	A technology selection and design model of a semi-rapid transit line
abstract	Abstract We present a new optimization model for technology selection and design of a semi-rapid transit line. With respect to previous studies, we improve the synthetic representation of the temporal and spatial variability of demand, and of several operational and design aspects. We apply the model to two scenarios offering comparable performance by commercially available technologies in terms of service, rather than assuming that service quality is strongly associated with technology. The model is validated by comparing some computed performance indices with best practices. We show that planning for a faster technology can be more important than the choice between bus and rail per se, except at very low demand density, and that differences of total cost, sum of passengers’ time value and operator’s cost, between the technologies are smaller than commonly held across a wide range of higher demands. At high demand density multiple-unit rail offers the most cost-effective way to achieve high capacities under many conditions. A scenario variation analysis shows the relevance of differences between value of time components, the bias of averaging vehicle load ratios when assessing the crowding disutility, the usefulness of a demand index abstracting from some specific parameter choices, and the high impact of the project discount rate.
abstractGer	Abstract We present a new optimization model for technology selection and design of a semi-rapid transit line. With respect to previous studies, we improve the synthetic representation of the temporal and spatial variability of demand, and of several operational and design aspects. We apply the model to two scenarios offering comparable performance by commercially available technologies in terms of service, rather than assuming that service quality is strongly associated with technology. The model is validated by comparing some computed performance indices with best practices. We show that planning for a faster technology can be more important than the choice between bus and rail per se, except at very low demand density, and that differences of total cost, sum of passengers’ time value and operator’s cost, between the technologies are smaller than commonly held across a wide range of higher demands. At high demand density multiple-unit rail offers the most cost-effective way to achieve high capacities under many conditions. A scenario variation analysis shows the relevance of differences between value of time components, the bias of averaging vehicle load ratios when assessing the crowding disutility, the usefulness of a demand index abstracting from some specific parameter choices, and the high impact of the project discount rate.
abstract_unstemmed	Abstract We present a new optimization model for technology selection and design of a semi-rapid transit line. With respect to previous studies, we improve the synthetic representation of the temporal and spatial variability of demand, and of several operational and design aspects. We apply the model to two scenarios offering comparable performance by commercially available technologies in terms of service, rather than assuming that service quality is strongly associated with technology. The model is validated by comparing some computed performance indices with best practices. We show that planning for a faster technology can be more important than the choice between bus and rail per se, except at very low demand density, and that differences of total cost, sum of passengers’ time value and operator’s cost, between the technologies are smaller than commonly held across a wide range of higher demands. At high demand density multiple-unit rail offers the most cost-effective way to achieve high capacities under many conditions. A scenario variation analysis shows the relevance of differences between value of time components, the bias of averaging vehicle load ratios when assessing the crowding disutility, the usefulness of a demand index abstracting from some specific parameter choices, and the high impact of the project discount rate.
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title_short	A technology selection and design model of a semi-rapid transit line
url	https://dx.doi.org/10.1007/s12469-018-0187-1
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author2	Allen, Duncan W. Bruun, Eric C.
author2Str	Allen, Duncan W. Bruun, Eric C.
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doi_str	10.1007/s12469-018-0187-1
up_date	2024-07-03T17:09:56.573Z
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A technology selection and design model of a semi-rapid transit line

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