A competitive analysis approach for route choice with uncertain travel times and blocked nodes
Abstract This paper investigates the route choice problem on a network with uncertain travel times along the arcs and possible blocked nodes. The route choice decision is made with the simultaneous consideration of these two kinds of nondeterministic factors. In order to deal with these nondetermini...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Wu, Liyao [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2018 |
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| Schlagwörter: |
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| Anmerkung: |
© Springer-Verlag GmbH Germany, part of Springer Nature 2018 |
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| Übergeordnetes Werk: |
Enthalten in: Journal of ambient intelligence and humanized computing - Berlin : Springer, 2010, 10(2018), 1 vom: 04. Apr., Seite 345-355 |
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| Übergeordnetes Werk: |
volume:10 ; year:2018 ; number:1 ; day:04 ; month:04 ; pages:345-355 |
| Links: |
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| DOI / URN: |
10.1007/s12652-018-0780-9 |
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| Katalog-ID: |
SPR026595443 |
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| 245 | 1 | 2 | |a A competitive analysis approach for route choice with uncertain travel times and blocked nodes |
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| 520 | |a Abstract This paper investigates the route choice problem on a network with uncertain travel times along the arcs and possible blocked nodes. The route choice decision is made with the simultaneous consideration of these two kinds of nondeterministic factors. In order to deal with these nondeterministic factors, online algorithms are introduced to develop adaptive route choice strategies responding to the successively observed blocked nodes; and accordingly, an uncertain competitive analysis framework, taking expected competitive ratio as the metric, is proposed to evaluate the performances of the online algorithms associated with uncertain travel times. It is proved that the competitive analysis for this route choice problem can be handled in the framework of traditional online shortest path problem via its equivalent counterpart on the corresponding deterministic network. Furthermore, two typical online strategies, i.e., reset strategy and greedy strategy, for route choice decision-making and the resulted performance difference are analyzed in detail following from the proposed framework. | ||
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| 650 | 4 | |a Uncertain competitive analysis |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Expected competitive ratio |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Ma, Weimin |4 aut | |
| 700 | 1 | |a Yang, Yan |4 aut | |
| 700 | 1 | |a Wang, Ke |4 aut | |
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10.1007/s12652-018-0780-9 doi (DE-627)SPR026595443 (SPR)s12652-018-0780-9-e DE-627 ger DE-627 rakwb eng Wu, Liyao verfasserin aut A competitive analysis approach for route choice with uncertain travel times and blocked nodes 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag GmbH Germany, part of Springer Nature 2018 Abstract This paper investigates the route choice problem on a network with uncertain travel times along the arcs and possible blocked nodes. The route choice decision is made with the simultaneous consideration of these two kinds of nondeterministic factors. In order to deal with these nondeterministic factors, online algorithms are introduced to develop adaptive route choice strategies responding to the successively observed blocked nodes; and accordingly, an uncertain competitive analysis framework, taking expected competitive ratio as the metric, is proposed to evaluate the performances of the online algorithms associated with uncertain travel times. It is proved that the competitive analysis for this route choice problem can be handled in the framework of traditional online shortest path problem via its equivalent counterpart on the corresponding deterministic network. Furthermore, two typical online strategies, i.e., reset strategy and greedy strategy, for route choice decision-making and the resulted performance difference are analyzed in detail following from the proposed framework. Route choice (dpeaa)DE-He213 Online routing (dpeaa)DE-He213 Shortest path problem (dpeaa)DE-He213 Uncertain competitive analysis (dpeaa)DE-He213 Expected competitive ratio (dpeaa)DE-He213 Ma, Weimin aut Yang, Yan aut Wang, Ke aut Enthalten in Journal of ambient intelligence and humanized computing Berlin : Springer, 2010 10(2018), 1 vom: 04. Apr., Seite 345-355 (DE-627)620775734 (DE-600)2543187-0 1868-5145 nnns volume:10 year:2018 number:1 day:04 month:04 pages:345-355 https://dx.doi.org/10.1007/s12652-018-0780-9 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_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 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_4277 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 10 2018 1 04 04 345-355 |
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10.1007/s12652-018-0780-9 doi (DE-627)SPR026595443 (SPR)s12652-018-0780-9-e DE-627 ger DE-627 rakwb eng Wu, Liyao verfasserin aut A competitive analysis approach for route choice with uncertain travel times and blocked nodes 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag GmbH Germany, part of Springer Nature 2018 Abstract This paper investigates the route choice problem on a network with uncertain travel times along the arcs and possible blocked nodes. The route choice decision is made with the simultaneous consideration of these two kinds of nondeterministic factors. In order to deal with these nondeterministic factors, online algorithms are introduced to develop adaptive route choice strategies responding to the successively observed blocked nodes; and accordingly, an uncertain competitive analysis framework, taking expected competitive ratio as the metric, is proposed to evaluate the performances of the online algorithms associated with uncertain travel times. It is proved that the competitive analysis for this route choice problem can be handled in the framework of traditional online shortest path problem via its equivalent counterpart on the corresponding deterministic network. Furthermore, two typical online strategies, i.e., reset strategy and greedy strategy, for route choice decision-making and the resulted performance difference are analyzed in detail following from the proposed framework. Route choice (dpeaa)DE-He213 Online routing (dpeaa)DE-He213 Shortest path problem (dpeaa)DE-He213 Uncertain competitive analysis (dpeaa)DE-He213 Expected competitive ratio (dpeaa)DE-He213 Ma, Weimin aut Yang, Yan aut Wang, Ke aut Enthalten in Journal of ambient intelligence and humanized computing Berlin : Springer, 2010 10(2018), 1 vom: 04. Apr., Seite 345-355 (DE-627)620775734 (DE-600)2543187-0 1868-5145 nnns volume:10 year:2018 number:1 day:04 month:04 pages:345-355 https://dx.doi.org/10.1007/s12652-018-0780-9 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_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 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_4277 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 10 2018 1 04 04 345-355 |
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10.1007/s12652-018-0780-9 doi (DE-627)SPR026595443 (SPR)s12652-018-0780-9-e DE-627 ger DE-627 rakwb eng Wu, Liyao verfasserin aut A competitive analysis approach for route choice with uncertain travel times and blocked nodes 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag GmbH Germany, part of Springer Nature 2018 Abstract This paper investigates the route choice problem on a network with uncertain travel times along the arcs and possible blocked nodes. The route choice decision is made with the simultaneous consideration of these two kinds of nondeterministic factors. In order to deal with these nondeterministic factors, online algorithms are introduced to develop adaptive route choice strategies responding to the successively observed blocked nodes; and accordingly, an uncertain competitive analysis framework, taking expected competitive ratio as the metric, is proposed to evaluate the performances of the online algorithms associated with uncertain travel times. It is proved that the competitive analysis for this route choice problem can be handled in the framework of traditional online shortest path problem via its equivalent counterpart on the corresponding deterministic network. Furthermore, two typical online strategies, i.e., reset strategy and greedy strategy, for route choice decision-making and the resulted performance difference are analyzed in detail following from the proposed framework. Route choice (dpeaa)DE-He213 Online routing (dpeaa)DE-He213 Shortest path problem (dpeaa)DE-He213 Uncertain competitive analysis (dpeaa)DE-He213 Expected competitive ratio (dpeaa)DE-He213 Ma, Weimin aut Yang, Yan aut Wang, Ke aut Enthalten in Journal of ambient intelligence and humanized computing Berlin : Springer, 2010 10(2018), 1 vom: 04. Apr., Seite 345-355 (DE-627)620775734 (DE-600)2543187-0 1868-5145 nnns volume:10 year:2018 number:1 day:04 month:04 pages:345-355 https://dx.doi.org/10.1007/s12652-018-0780-9 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_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 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_4277 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 10 2018 1 04 04 345-355 |
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10.1007/s12652-018-0780-9 doi (DE-627)SPR026595443 (SPR)s12652-018-0780-9-e DE-627 ger DE-627 rakwb eng Wu, Liyao verfasserin aut A competitive analysis approach for route choice with uncertain travel times and blocked nodes 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag GmbH Germany, part of Springer Nature 2018 Abstract This paper investigates the route choice problem on a network with uncertain travel times along the arcs and possible blocked nodes. The route choice decision is made with the simultaneous consideration of these two kinds of nondeterministic factors. In order to deal with these nondeterministic factors, online algorithms are introduced to develop adaptive route choice strategies responding to the successively observed blocked nodes; and accordingly, an uncertain competitive analysis framework, taking expected competitive ratio as the metric, is proposed to evaluate the performances of the online algorithms associated with uncertain travel times. It is proved that the competitive analysis for this route choice problem can be handled in the framework of traditional online shortest path problem via its equivalent counterpart on the corresponding deterministic network. Furthermore, two typical online strategies, i.e., reset strategy and greedy strategy, for route choice decision-making and the resulted performance difference are analyzed in detail following from the proposed framework. Route choice (dpeaa)DE-He213 Online routing (dpeaa)DE-He213 Shortest path problem (dpeaa)DE-He213 Uncertain competitive analysis (dpeaa)DE-He213 Expected competitive ratio (dpeaa)DE-He213 Ma, Weimin aut Yang, Yan aut Wang, Ke aut Enthalten in Journal of ambient intelligence and humanized computing Berlin : Springer, 2010 10(2018), 1 vom: 04. Apr., Seite 345-355 (DE-627)620775734 (DE-600)2543187-0 1868-5145 nnns volume:10 year:2018 number:1 day:04 month:04 pages:345-355 https://dx.doi.org/10.1007/s12652-018-0780-9 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_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 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_4277 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 10 2018 1 04 04 345-355 |
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10.1007/s12652-018-0780-9 doi (DE-627)SPR026595443 (SPR)s12652-018-0780-9-e DE-627 ger DE-627 rakwb eng Wu, Liyao verfasserin aut A competitive analysis approach for route choice with uncertain travel times and blocked nodes 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag GmbH Germany, part of Springer Nature 2018 Abstract This paper investigates the route choice problem on a network with uncertain travel times along the arcs and possible blocked nodes. The route choice decision is made with the simultaneous consideration of these two kinds of nondeterministic factors. In order to deal with these nondeterministic factors, online algorithms are introduced to develop adaptive route choice strategies responding to the successively observed blocked nodes; and accordingly, an uncertain competitive analysis framework, taking expected competitive ratio as the metric, is proposed to evaluate the performances of the online algorithms associated with uncertain travel times. It is proved that the competitive analysis for this route choice problem can be handled in the framework of traditional online shortest path problem via its equivalent counterpart on the corresponding deterministic network. Furthermore, two typical online strategies, i.e., reset strategy and greedy strategy, for route choice decision-making and the resulted performance difference are analyzed in detail following from the proposed framework. Route choice (dpeaa)DE-He213 Online routing (dpeaa)DE-He213 Shortest path problem (dpeaa)DE-He213 Uncertain competitive analysis (dpeaa)DE-He213 Expected competitive ratio (dpeaa)DE-He213 Ma, Weimin aut Yang, Yan aut Wang, Ke aut Enthalten in Journal of ambient intelligence and humanized computing Berlin : Springer, 2010 10(2018), 1 vom: 04. Apr., Seite 345-355 (DE-627)620775734 (DE-600)2543187-0 1868-5145 nnns volume:10 year:2018 number:1 day:04 month:04 pages:345-355 https://dx.doi.org/10.1007/s12652-018-0780-9 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_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 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_4277 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 10 2018 1 04 04 345-355 |
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Wu, Liyao |
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A competitive analysis approach for route choice with uncertain travel times and blocked nodes Route choice (dpeaa)DE-He213 Online routing (dpeaa)DE-He213 Shortest path problem (dpeaa)DE-He213 Uncertain competitive analysis (dpeaa)DE-He213 Expected competitive ratio (dpeaa)DE-He213 |
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competitive analysis approach for route choice with uncertain travel times and blocked nodes |
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A competitive analysis approach for route choice with uncertain travel times and blocked nodes |
| abstract |
Abstract This paper investigates the route choice problem on a network with uncertain travel times along the arcs and possible blocked nodes. The route choice decision is made with the simultaneous consideration of these two kinds of nondeterministic factors. In order to deal with these nondeterministic factors, online algorithms are introduced to develop adaptive route choice strategies responding to the successively observed blocked nodes; and accordingly, an uncertain competitive analysis framework, taking expected competitive ratio as the metric, is proposed to evaluate the performances of the online algorithms associated with uncertain travel times. It is proved that the competitive analysis for this route choice problem can be handled in the framework of traditional online shortest path problem via its equivalent counterpart on the corresponding deterministic network. Furthermore, two typical online strategies, i.e., reset strategy and greedy strategy, for route choice decision-making and the resulted performance difference are analyzed in detail following from the proposed framework. © Springer-Verlag GmbH Germany, part of Springer Nature 2018 |
| abstractGer |
Abstract This paper investigates the route choice problem on a network with uncertain travel times along the arcs and possible blocked nodes. The route choice decision is made with the simultaneous consideration of these two kinds of nondeterministic factors. In order to deal with these nondeterministic factors, online algorithms are introduced to develop adaptive route choice strategies responding to the successively observed blocked nodes; and accordingly, an uncertain competitive analysis framework, taking expected competitive ratio as the metric, is proposed to evaluate the performances of the online algorithms associated with uncertain travel times. It is proved that the competitive analysis for this route choice problem can be handled in the framework of traditional online shortest path problem via its equivalent counterpart on the corresponding deterministic network. Furthermore, two typical online strategies, i.e., reset strategy and greedy strategy, for route choice decision-making and the resulted performance difference are analyzed in detail following from the proposed framework. © Springer-Verlag GmbH Germany, part of Springer Nature 2018 |
| abstract_unstemmed |
Abstract This paper investigates the route choice problem on a network with uncertain travel times along the arcs and possible blocked nodes. The route choice decision is made with the simultaneous consideration of these two kinds of nondeterministic factors. In order to deal with these nondeterministic factors, online algorithms are introduced to develop adaptive route choice strategies responding to the successively observed blocked nodes; and accordingly, an uncertain competitive analysis framework, taking expected competitive ratio as the metric, is proposed to evaluate the performances of the online algorithms associated with uncertain travel times. It is proved that the competitive analysis for this route choice problem can be handled in the framework of traditional online shortest path problem via its equivalent counterpart on the corresponding deterministic network. Furthermore, two typical online strategies, i.e., reset strategy and greedy strategy, for route choice decision-making and the resulted performance difference are analyzed in detail following from the proposed framework. © Springer-Verlag GmbH Germany, part of Springer Nature 2018 |
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A competitive analysis approach for route choice with uncertain travel times and blocked nodes |
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https://dx.doi.org/10.1007/s12652-018-0780-9 |
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Ma, Weimin Yang, Yan Wang, Ke |
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Ma, Weimin Yang, Yan Wang, Ke |
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| doi_str |
10.1007/s12652-018-0780-9 |
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2024-07-03T21:45:08.009Z |
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