RETRACTED ARTICLE: Application of optimized genetic algorithm based on big data in bus dynamic scheduling

Abstract To realize the intelligent vehicle scheduling of public transportation, starting from the statistics data of GPS positioning and video surveillance, the optimization model of bus departure frequency was established. The BP neural network algorithm was used to predict the arrival time of the...
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Autor*in:	Wang, Xiaoqiang [verfasserIn] Qing-dao-er-ji, Ren

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2018

Schlagwörter:	Bus scheduling Artificial neural network Genetic algorithm

Anmerkung:	© Springer Science+Business Media, LLC, part of Springer Nature 2018. Springer Nature or its licensor (e.g. a society or other partner) 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: Cluster computing - Dordrecht [u.a.] : Springer Science + Business Media B.V, 1998, 22(2018), Suppl 6 vom: 26. März, Seite 15439-15446
Übergeordnetes Werk:	volume:22 ; year:2018 ; number:Suppl 6 ; day:26 ; month:03 ; pages:15439-15446

Links:	Volltext

DOI / URN:	10.1007/s10586-018-2625-x

Katalog-ID:	SPR011526130

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520			\|a Abstract To realize the intelligent vehicle scheduling of public transportation, starting from the statistics data of GPS positioning and video surveillance, the optimization model of bus departure frequency was established. The BP neural network algorithm was used to predict the arrival time of the bus. The passenger flow of bus stops was forecasted, and according to the number of passengers on and off the bus collected by video, the number of passengers on different routes and stations at different time periods was predicted, and the prediction method was exponential smoothing. The bus departure frequency was arranged rationally, and through the establishment of objective function, the waiting time was reduced, the bus utilization rate was improved and the profitability of the bus company was increased. In the case of a variety of constraints, the final objective function was obtained by weighting, and the improved genetic algorithm was applied to obtain the optimal solution. The results showed that the bus frequency target was the minimum average waiting time of passengers and the bus average per trip passenger volume the maximum. To sum up, it is required to meet the standard deviation of the maximum section of every shift reach the minimum, the target with the maximum is transformed into the solution to the minimum, and the three comprehensively calculate the optimal calculation by the weighted sum.
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allfields	10.1007/s10586-018-2625-x doi (DE-627)SPR011526130 (SPR)s10586-018-2625-x-e DE-627 ger DE-627 rakwb eng Wang, Xiaoqiang verfasserin aut RETRACTED ARTICLE: Application of optimized genetic algorithm based on big data in bus dynamic scheduling 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2018. Springer Nature or its licensor (e.g. a society or other partner) 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 To realize the intelligent vehicle scheduling of public transportation, starting from the statistics data of GPS positioning and video surveillance, the optimization model of bus departure frequency was established. The BP neural network algorithm was used to predict the arrival time of the bus. The passenger flow of bus stops was forecasted, and according to the number of passengers on and off the bus collected by video, the number of passengers on different routes and stations at different time periods was predicted, and the prediction method was exponential smoothing. The bus departure frequency was arranged rationally, and through the establishment of objective function, the waiting time was reduced, the bus utilization rate was improved and the profitability of the bus company was increased. In the case of a variety of constraints, the final objective function was obtained by weighting, and the improved genetic algorithm was applied to obtain the optimal solution. The results showed that the bus frequency target was the minimum average waiting time of passengers and the bus average per trip passenger volume the maximum. To sum up, it is required to meet the standard deviation of the maximum section of every shift reach the minimum, the target with the maximum is transformed into the solution to the minimum, and the three comprehensively calculate the optimal calculation by the weighted sum. Bus scheduling (dpeaa)DE-He213 Artificial neural network (dpeaa)DE-He213 Genetic algorithm (dpeaa)DE-He213 Qing-dao-er-ji, Ren aut Enthalten in Cluster computing Dordrecht [u.a.] : Springer Science + Business Media B.V, 1998 22(2018), Suppl 6 vom: 26. März, Seite 15439-15446 (DE-627)320505332 (DE-600)2012757-1 1573-7543 nnns volume:22 year:2018 number:Suppl 6 day:26 month:03 pages:15439-15446 https://dx.doi.org/10.1007/s10586-018-2625-x 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_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 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_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_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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 22 2018 Suppl 6 26 03 15439-15446
spelling	10.1007/s10586-018-2625-x doi (DE-627)SPR011526130 (SPR)s10586-018-2625-x-e DE-627 ger DE-627 rakwb eng Wang, Xiaoqiang verfasserin aut RETRACTED ARTICLE: Application of optimized genetic algorithm based on big data in bus dynamic scheduling 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2018. Springer Nature or its licensor (e.g. a society or other partner) 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 To realize the intelligent vehicle scheduling of public transportation, starting from the statistics data of GPS positioning and video surveillance, the optimization model of bus departure frequency was established. The BP neural network algorithm was used to predict the arrival time of the bus. The passenger flow of bus stops was forecasted, and according to the number of passengers on and off the bus collected by video, the number of passengers on different routes and stations at different time periods was predicted, and the prediction method was exponential smoothing. The bus departure frequency was arranged rationally, and through the establishment of objective function, the waiting time was reduced, the bus utilization rate was improved and the profitability of the bus company was increased. In the case of a variety of constraints, the final objective function was obtained by weighting, and the improved genetic algorithm was applied to obtain the optimal solution. The results showed that the bus frequency target was the minimum average waiting time of passengers and the bus average per trip passenger volume the maximum. To sum up, it is required to meet the standard deviation of the maximum section of every shift reach the minimum, the target with the maximum is transformed into the solution to the minimum, and the three comprehensively calculate the optimal calculation by the weighted sum. Bus scheduling (dpeaa)DE-He213 Artificial neural network (dpeaa)DE-He213 Genetic algorithm (dpeaa)DE-He213 Qing-dao-er-ji, Ren aut Enthalten in Cluster computing Dordrecht [u.a.] : Springer Science + Business Media B.V, 1998 22(2018), Suppl 6 vom: 26. März, Seite 15439-15446 (DE-627)320505332 (DE-600)2012757-1 1573-7543 nnns volume:22 year:2018 number:Suppl 6 day:26 month:03 pages:15439-15446 https://dx.doi.org/10.1007/s10586-018-2625-x 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_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 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_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_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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 22 2018 Suppl 6 26 03 15439-15446
allfields_unstemmed	10.1007/s10586-018-2625-x doi (DE-627)SPR011526130 (SPR)s10586-018-2625-x-e DE-627 ger DE-627 rakwb eng Wang, Xiaoqiang verfasserin aut RETRACTED ARTICLE: Application of optimized genetic algorithm based on big data in bus dynamic scheduling 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2018. Springer Nature or its licensor (e.g. a society or other partner) 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 To realize the intelligent vehicle scheduling of public transportation, starting from the statistics data of GPS positioning and video surveillance, the optimization model of bus departure frequency was established. The BP neural network algorithm was used to predict the arrival time of the bus. The passenger flow of bus stops was forecasted, and according to the number of passengers on and off the bus collected by video, the number of passengers on different routes and stations at different time periods was predicted, and the prediction method was exponential smoothing. The bus departure frequency was arranged rationally, and through the establishment of objective function, the waiting time was reduced, the bus utilization rate was improved and the profitability of the bus company was increased. In the case of a variety of constraints, the final objective function was obtained by weighting, and the improved genetic algorithm was applied to obtain the optimal solution. The results showed that the bus frequency target was the minimum average waiting time of passengers and the bus average per trip passenger volume the maximum. To sum up, it is required to meet the standard deviation of the maximum section of every shift reach the minimum, the target with the maximum is transformed into the solution to the minimum, and the three comprehensively calculate the optimal calculation by the weighted sum. Bus scheduling (dpeaa)DE-He213 Artificial neural network (dpeaa)DE-He213 Genetic algorithm (dpeaa)DE-He213 Qing-dao-er-ji, Ren aut Enthalten in Cluster computing Dordrecht [u.a.] : Springer Science + Business Media B.V, 1998 22(2018), Suppl 6 vom: 26. März, Seite 15439-15446 (DE-627)320505332 (DE-600)2012757-1 1573-7543 nnns volume:22 year:2018 number:Suppl 6 day:26 month:03 pages:15439-15446 https://dx.doi.org/10.1007/s10586-018-2625-x 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_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 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_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_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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 22 2018 Suppl 6 26 03 15439-15446
allfieldsGer	10.1007/s10586-018-2625-x doi (DE-627)SPR011526130 (SPR)s10586-018-2625-x-e DE-627 ger DE-627 rakwb eng Wang, Xiaoqiang verfasserin aut RETRACTED ARTICLE: Application of optimized genetic algorithm based on big data in bus dynamic scheduling 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2018. Springer Nature or its licensor (e.g. a society or other partner) 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 To realize the intelligent vehicle scheduling of public transportation, starting from the statistics data of GPS positioning and video surveillance, the optimization model of bus departure frequency was established. The BP neural network algorithm was used to predict the arrival time of the bus. The passenger flow of bus stops was forecasted, and according to the number of passengers on and off the bus collected by video, the number of passengers on different routes and stations at different time periods was predicted, and the prediction method was exponential smoothing. The bus departure frequency was arranged rationally, and through the establishment of objective function, the waiting time was reduced, the bus utilization rate was improved and the profitability of the bus company was increased. In the case of a variety of constraints, the final objective function was obtained by weighting, and the improved genetic algorithm was applied to obtain the optimal solution. The results showed that the bus frequency target was the minimum average waiting time of passengers and the bus average per trip passenger volume the maximum. To sum up, it is required to meet the standard deviation of the maximum section of every shift reach the minimum, the target with the maximum is transformed into the solution to the minimum, and the three comprehensively calculate the optimal calculation by the weighted sum. Bus scheduling (dpeaa)DE-He213 Artificial neural network (dpeaa)DE-He213 Genetic algorithm (dpeaa)DE-He213 Qing-dao-er-ji, Ren aut Enthalten in Cluster computing Dordrecht [u.a.] : Springer Science + Business Media B.V, 1998 22(2018), Suppl 6 vom: 26. März, Seite 15439-15446 (DE-627)320505332 (DE-600)2012757-1 1573-7543 nnns volume:22 year:2018 number:Suppl 6 day:26 month:03 pages:15439-15446 https://dx.doi.org/10.1007/s10586-018-2625-x 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_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 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_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_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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 22 2018 Suppl 6 26 03 15439-15446
allfieldsSound	10.1007/s10586-018-2625-x doi (DE-627)SPR011526130 (SPR)s10586-018-2625-x-e DE-627 ger DE-627 rakwb eng Wang, Xiaoqiang verfasserin aut RETRACTED ARTICLE: Application of optimized genetic algorithm based on big data in bus dynamic scheduling 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2018. Springer Nature or its licensor (e.g. a society or other partner) 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 To realize the intelligent vehicle scheduling of public transportation, starting from the statistics data of GPS positioning and video surveillance, the optimization model of bus departure frequency was established. The BP neural network algorithm was used to predict the arrival time of the bus. The passenger flow of bus stops was forecasted, and according to the number of passengers on and off the bus collected by video, the number of passengers on different routes and stations at different time periods was predicted, and the prediction method was exponential smoothing. The bus departure frequency was arranged rationally, and through the establishment of objective function, the waiting time was reduced, the bus utilization rate was improved and the profitability of the bus company was increased. In the case of a variety of constraints, the final objective function was obtained by weighting, and the improved genetic algorithm was applied to obtain the optimal solution. The results showed that the bus frequency target was the minimum average waiting time of passengers and the bus average per trip passenger volume the maximum. To sum up, it is required to meet the standard deviation of the maximum section of every shift reach the minimum, the target with the maximum is transformed into the solution to the minimum, and the three comprehensively calculate the optimal calculation by the weighted sum. Bus scheduling (dpeaa)DE-He213 Artificial neural network (dpeaa)DE-He213 Genetic algorithm (dpeaa)DE-He213 Qing-dao-er-ji, Ren aut Enthalten in Cluster computing Dordrecht [u.a.] : Springer Science + Business Media B.V, 1998 22(2018), Suppl 6 vom: 26. März, Seite 15439-15446 (DE-627)320505332 (DE-600)2012757-1 1573-7543 nnns volume:22 year:2018 number:Suppl 6 day:26 month:03 pages:15439-15446 https://dx.doi.org/10.1007/s10586-018-2625-x 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_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 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_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_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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 22 2018 Suppl 6 26 03 15439-15446
language	English
source	Enthalten in Cluster computing 22(2018), Suppl 6 vom: 26. März, Seite 15439-15446 volume:22 year:2018 number:Suppl 6 day:26 month:03 pages:15439-15446
sourceStr	Enthalten in Cluster computing 22(2018), Suppl 6 vom: 26. März, Seite 15439-15446 volume:22 year:2018 number:Suppl 6 day:26 month:03 pages:15439-15446
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Bus scheduling Artificial neural network Genetic algorithm
isfreeaccess_bool	false
container_title	Cluster computing
authorswithroles_txt_mv	Wang, Xiaoqiang @@aut@@ Qing-dao-er-ji, Ren @@aut@@
publishDateDaySort_date	2018-03-26T00:00:00Z
hierarchy_top_id	320505332
id	SPR011526130
language_de	englisch
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Springer Nature or its licensor (e.g. a society or other partner) 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 To realize the intelligent vehicle scheduling of public transportation, starting from the statistics data of GPS positioning and video surveillance, the optimization model of bus departure frequency was established. The BP neural network algorithm was used to predict the arrival time of the bus. The passenger flow of bus stops was forecasted, and according to the number of passengers on and off the bus collected by video, the number of passengers on different routes and stations at different time periods was predicted, and the prediction method was exponential smoothing. 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author	Wang, Xiaoqiang
spellingShingle	Wang, Xiaoqiang misc Bus scheduling misc Artificial neural network misc Genetic algorithm RETRACTED ARTICLE: Application of optimized genetic algorithm based on big data in bus dynamic scheduling
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topic_title	RETRACTED ARTICLE: Application of optimized genetic algorithm based on big data in bus dynamic scheduling Bus scheduling (dpeaa)DE-He213 Artificial neural network (dpeaa)DE-He213 Genetic algorithm (dpeaa)DE-He213
topic	misc Bus scheduling misc Artificial neural network misc Genetic algorithm
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title	RETRACTED ARTICLE: Application of optimized genetic algorithm based on big data in bus dynamic scheduling
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title_full	RETRACTED ARTICLE: Application of optimized genetic algorithm based on big data in bus dynamic scheduling
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author_browse	Wang, Xiaoqiang Qing-dao-er-ji, Ren
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author-letter	Wang, Xiaoqiang
doi_str_mv	10.1007/s10586-018-2625-x
title_sort	retracted article: application of optimized genetic algorithm based on big data in bus dynamic scheduling
title_auth	RETRACTED ARTICLE: Application of optimized genetic algorithm based on big data in bus dynamic scheduling
abstract	Abstract To realize the intelligent vehicle scheduling of public transportation, starting from the statistics data of GPS positioning and video surveillance, the optimization model of bus departure frequency was established. The BP neural network algorithm was used to predict the arrival time of the bus. The passenger flow of bus stops was forecasted, and according to the number of passengers on and off the bus collected by video, the number of passengers on different routes and stations at different time periods was predicted, and the prediction method was exponential smoothing. The bus departure frequency was arranged rationally, and through the establishment of objective function, the waiting time was reduced, the bus utilization rate was improved and the profitability of the bus company was increased. In the case of a variety of constraints, the final objective function was obtained by weighting, and the improved genetic algorithm was applied to obtain the optimal solution. The results showed that the bus frequency target was the minimum average waiting time of passengers and the bus average per trip passenger volume the maximum. To sum up, it is required to meet the standard deviation of the maximum section of every shift reach the minimum, the target with the maximum is transformed into the solution to the minimum, and the three comprehensively calculate the optimal calculation by the weighted sum. © Springer Science+Business Media, LLC, part of Springer Nature 2018. Springer Nature or its licensor (e.g. a society or other partner) 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 To realize the intelligent vehicle scheduling of public transportation, starting from the statistics data of GPS positioning and video surveillance, the optimization model of bus departure frequency was established. The BP neural network algorithm was used to predict the arrival time of the bus. The passenger flow of bus stops was forecasted, and according to the number of passengers on and off the bus collected by video, the number of passengers on different routes and stations at different time periods was predicted, and the prediction method was exponential smoothing. The bus departure frequency was arranged rationally, and through the establishment of objective function, the waiting time was reduced, the bus utilization rate was improved and the profitability of the bus company was increased. In the case of a variety of constraints, the final objective function was obtained by weighting, and the improved genetic algorithm was applied to obtain the optimal solution. The results showed that the bus frequency target was the minimum average waiting time of passengers and the bus average per trip passenger volume the maximum. To sum up, it is required to meet the standard deviation of the maximum section of every shift reach the minimum, the target with the maximum is transformed into the solution to the minimum, and the three comprehensively calculate the optimal calculation by the weighted sum. © Springer Science+Business Media, LLC, part of Springer Nature 2018. Springer Nature or its licensor (e.g. a society or other partner) 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 To realize the intelligent vehicle scheduling of public transportation, starting from the statistics data of GPS positioning and video surveillance, the optimization model of bus departure frequency was established. The BP neural network algorithm was used to predict the arrival time of the bus. The passenger flow of bus stops was forecasted, and according to the number of passengers on and off the bus collected by video, the number of passengers on different routes and stations at different time periods was predicted, and the prediction method was exponential smoothing. The bus departure frequency was arranged rationally, and through the establishment of objective function, the waiting time was reduced, the bus utilization rate was improved and the profitability of the bus company was increased. In the case of a variety of constraints, the final objective function was obtained by weighting, and the improved genetic algorithm was applied to obtain the optimal solution. The results showed that the bus frequency target was the minimum average waiting time of passengers and the bus average per trip passenger volume the maximum. To sum up, it is required to meet the standard deviation of the maximum section of every shift reach the minimum, the target with the maximum is transformed into the solution to the minimum, and the three comprehensively calculate the optimal calculation by the weighted sum. © Springer Science+Business Media, LLC, part of Springer Nature 2018. Springer Nature or its licensor (e.g. a society or other partner) 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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container_issue	Suppl 6
title_short	RETRACTED ARTICLE: Application of optimized genetic algorithm based on big data in bus dynamic scheduling
url	https://dx.doi.org/10.1007/s10586-018-2625-x
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author2	Qing-dao-er-ji, Ren
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Springer Nature or its licensor (e.g. a society or other partner) 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 To realize the intelligent vehicle scheduling of public transportation, starting from the statistics data of GPS positioning and video surveillance, the optimization model of bus departure frequency was established. The BP neural network algorithm was used to predict the arrival time of the bus. The passenger flow of bus stops was forecasted, and according to the number of passengers on and off the bus collected by video, the number of passengers on different routes and stations at different time periods was predicted, and the prediction method was exponential smoothing. The bus departure frequency was arranged rationally, and through the establishment of objective function, the waiting time was reduced, the bus utilization rate was improved and the profitability of the bus company was increased. In the case of a variety of constraints, the final objective function was obtained by weighting, and the improved genetic algorithm was applied to obtain the optimal solution. The results showed that the bus frequency target was the minimum average waiting time of passengers and the bus average per trip passenger volume the maximum. To sum up, it is required to meet the standard deviation of the maximum section of every shift reach the minimum, the target with the maximum is transformed into the solution to the minimum, and the three comprehensively calculate the optimal calculation by the weighted sum.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Bus scheduling</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Artificial neural network</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Genetic algorithm</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Qing-dao-er-ji, Ren</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Cluster computing</subfield><subfield code="d">Dordrecht [u.a.] : Springer Science + Business Media B.V, 1998</subfield><subfield code="g">22(2018), Suppl 6 vom: 26. März, Seite 15439-15446</subfield><subfield code="w">(DE-627)320505332</subfield><subfield code="w">(DE-600)2012757-1</subfield><subfield code="x">1573-7543</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:22</subfield><subfield code="g">year:2018</subfield><subfield code="g">number:Suppl 6</subfield><subfield code="g">day:26</subfield><subfield code="g">month:03</subfield><subfield code="g">pages:15439-15446</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://dx.doi.org/10.1007/s10586-018-2625-x</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield 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