In Free Float: Developing Business Analytics Support for Carsharing Providers
As a rapidly expanding market, carsharing presents a possible remedy for traffic congestion in urban centers. Especially free-floating carsharing, which allows customers to leave their car anywhere within the operator's business area, provides users with flexibility, and complements public tran...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Wagner, Sebastian [verfasserIn] |
|---|
| Format: |
Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2015 |
|---|
| Schlagwörter: |
|---|
| Übergeordnetes Werk: |
Enthalten in: Omega - Oxford [u.a.] : Elsevier, 1973, 59(2015), Seite 4 |
|---|---|
| Übergeordnetes Werk: |
volume:59 ; year:2015 ; pages:4 |
| Links: |
|---|
| DOI / URN: |
10.1016/j.omega.2015.02.011 |
|---|
| Katalog-ID: |
OLC1972924397 |
|---|
| LEADER | 01000caa a2200265 4500 | ||
|---|---|---|---|
| 001 | OLC1972924397 | ||
| 003 | DE-627 | ||
| 005 | 20230714183821.0 | ||
| 007 | tu | ||
| 008 | 160427s2015 xx ||||| 00| ||eng c | ||
| 024 | 7 | |a 10.1016/j.omega.2015.02.011 |2 doi | |
| 028 | 5 | 2 | |a PQ20160430 |
| 035 | |a (DE-627)OLC1972924397 | ||
| 035 | |a (DE-599)GBVOLC1972924397 | ||
| 035 | |a (PRQ)c1312-710f3517bdd9f310d54793825567ff5ce5fcd1ffffce331de02f5f01c7feaf290 | ||
| 035 | |a (KEY)0082059820150000059000000004infreefloatdevelopingbusinessanalyticssupportforca | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 082 | 0 | 4 | |a 650 |q DNB |
| 100 | 1 | |a Wagner, Sebastian |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a In Free Float: Developing Business Analytics Support for Carsharing Providers |
| 264 | 1 | |c 2015 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ohne Hilfsmittel zu benutzen |b n |2 rdamedia | ||
| 338 | |a Band |b nc |2 rdacarrier | ||
| 520 | |a As a rapidly expanding market, carsharing presents a possible remedy for traffic congestion in urban centers. Especially free-floating carsharing, which allows customers to leave their car anywhere within the operator's business area, provides users with flexibility, and complements public transportation. We present a novel method that provides strategic and operational decision support to companies maneuvering this competitive and constantly changing market environment. Using an extensive set of customer data in a zero-inflated regression model, we explain spatial variation in carsharing activity through the proximity of particular points of interests, such as movie theaters and airports. As an application case, as well as a validation of the model, we use the resulting indicators to predict the number of rentals before an expansion of the business area and compare it to the actual demand post-expansion. We find that our approach correctly identifies areas with a high carsharing activity and can be easily adapted to other cities. | ||
| 650 | 4 | |a Traffic congestion | |
| 650 | 4 | |a Management | |
| 650 | 4 | |a Car sharing | |
| 650 | 4 | |a Planning | |
| 650 | 4 | |a Regression analysis | |
| 650 | 4 | |a Studies | |
| 650 | 4 | |a Ride sharing services | |
| 650 | 4 | |a Data analysis | |
| 650 | 4 | |a Decision support systems | |
| 700 | 1 | |a Brandt, Tobias |4 oth | |
| 700 | 1 | |a Neumann, Dirk |4 oth | |
| 773 | 0 | 8 | |i Enthalten in |t Omega |d Oxford [u.a.] : Elsevier, 1973 |g 59(2015), Seite 4 |w (DE-627)129306827 |w (DE-600)124502-8 |w (DE-576)014503905 |x 0305-0483 |7 nnns |
| 773 | 1 | 8 | |g volume:59 |g year:2015 |g pages:4 |
| 856 | 4 | 1 | |u http://dx.doi.org/10.1016/j.omega.2015.02.011 |3 Volltext |
| 856 | 4 | 2 | |u http://search.proquest.com/docview/1761751027 |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a SYSFLAG_A | ||
| 912 | |a GBV_OLC | ||
| 912 | |a SSG-OLC-WIW | ||
| 912 | |a GBV_ILN_11 | ||
| 912 | |a GBV_ILN_21 | ||
| 912 | |a GBV_ILN_26 | ||
| 912 | |a GBV_ILN_2009 | ||
| 912 | |a GBV_ILN_4126 | ||
| 912 | |a GBV_ILN_4266 | ||
| 951 | |a AR | ||
| 952 | |d 59 |j 2015 |h 4 | ||
| author_variant |
s w sw |
|---|---|
| matchkey_str |
article:03050483:2015----::nreladvlpnbsnsaayisuprfr |
| hierarchy_sort_str |
2015 |
| publishDate |
2015 |
| allfields |
10.1016/j.omega.2015.02.011 doi PQ20160430 (DE-627)OLC1972924397 (DE-599)GBVOLC1972924397 (PRQ)c1312-710f3517bdd9f310d54793825567ff5ce5fcd1ffffce331de02f5f01c7feaf290 (KEY)0082059820150000059000000004infreefloatdevelopingbusinessanalyticssupportforca DE-627 ger DE-627 rakwb eng 650 DNB Wagner, Sebastian verfasserin aut In Free Float: Developing Business Analytics Support for Carsharing Providers 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier As a rapidly expanding market, carsharing presents a possible remedy for traffic congestion in urban centers. Especially free-floating carsharing, which allows customers to leave their car anywhere within the operator's business area, provides users with flexibility, and complements public transportation. We present a novel method that provides strategic and operational decision support to companies maneuvering this competitive and constantly changing market environment. Using an extensive set of customer data in a zero-inflated regression model, we explain spatial variation in carsharing activity through the proximity of particular points of interests, such as movie theaters and airports. As an application case, as well as a validation of the model, we use the resulting indicators to predict the number of rentals before an expansion of the business area and compare it to the actual demand post-expansion. We find that our approach correctly identifies areas with a high carsharing activity and can be easily adapted to other cities. Traffic congestion Management Car sharing Planning Regression analysis Studies Ride sharing services Data analysis Decision support systems Brandt, Tobias oth Neumann, Dirk oth Enthalten in Omega Oxford [u.a.] : Elsevier, 1973 59(2015), Seite 4 (DE-627)129306827 (DE-600)124502-8 (DE-576)014503905 0305-0483 nnns volume:59 year:2015 pages:4 http://dx.doi.org/10.1016/j.omega.2015.02.011 Volltext http://search.proquest.com/docview/1761751027 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-WIW GBV_ILN_11 GBV_ILN_21 GBV_ILN_26 GBV_ILN_2009 GBV_ILN_4126 GBV_ILN_4266 AR 59 2015 4 |
| spelling |
10.1016/j.omega.2015.02.011 doi PQ20160430 (DE-627)OLC1972924397 (DE-599)GBVOLC1972924397 (PRQ)c1312-710f3517bdd9f310d54793825567ff5ce5fcd1ffffce331de02f5f01c7feaf290 (KEY)0082059820150000059000000004infreefloatdevelopingbusinessanalyticssupportforca DE-627 ger DE-627 rakwb eng 650 DNB Wagner, Sebastian verfasserin aut In Free Float: Developing Business Analytics Support for Carsharing Providers 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier As a rapidly expanding market, carsharing presents a possible remedy for traffic congestion in urban centers. Especially free-floating carsharing, which allows customers to leave their car anywhere within the operator's business area, provides users with flexibility, and complements public transportation. We present a novel method that provides strategic and operational decision support to companies maneuvering this competitive and constantly changing market environment. Using an extensive set of customer data in a zero-inflated regression model, we explain spatial variation in carsharing activity through the proximity of particular points of interests, such as movie theaters and airports. As an application case, as well as a validation of the model, we use the resulting indicators to predict the number of rentals before an expansion of the business area and compare it to the actual demand post-expansion. We find that our approach correctly identifies areas with a high carsharing activity and can be easily adapted to other cities. Traffic congestion Management Car sharing Planning Regression analysis Studies Ride sharing services Data analysis Decision support systems Brandt, Tobias oth Neumann, Dirk oth Enthalten in Omega Oxford [u.a.] : Elsevier, 1973 59(2015), Seite 4 (DE-627)129306827 (DE-600)124502-8 (DE-576)014503905 0305-0483 nnns volume:59 year:2015 pages:4 http://dx.doi.org/10.1016/j.omega.2015.02.011 Volltext http://search.proquest.com/docview/1761751027 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-WIW GBV_ILN_11 GBV_ILN_21 GBV_ILN_26 GBV_ILN_2009 GBV_ILN_4126 GBV_ILN_4266 AR 59 2015 4 |
| allfields_unstemmed |
10.1016/j.omega.2015.02.011 doi PQ20160430 (DE-627)OLC1972924397 (DE-599)GBVOLC1972924397 (PRQ)c1312-710f3517bdd9f310d54793825567ff5ce5fcd1ffffce331de02f5f01c7feaf290 (KEY)0082059820150000059000000004infreefloatdevelopingbusinessanalyticssupportforca DE-627 ger DE-627 rakwb eng 650 DNB Wagner, Sebastian verfasserin aut In Free Float: Developing Business Analytics Support for Carsharing Providers 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier As a rapidly expanding market, carsharing presents a possible remedy for traffic congestion in urban centers. Especially free-floating carsharing, which allows customers to leave their car anywhere within the operator's business area, provides users with flexibility, and complements public transportation. We present a novel method that provides strategic and operational decision support to companies maneuvering this competitive and constantly changing market environment. Using an extensive set of customer data in a zero-inflated regression model, we explain spatial variation in carsharing activity through the proximity of particular points of interests, such as movie theaters and airports. As an application case, as well as a validation of the model, we use the resulting indicators to predict the number of rentals before an expansion of the business area and compare it to the actual demand post-expansion. We find that our approach correctly identifies areas with a high carsharing activity and can be easily adapted to other cities. Traffic congestion Management Car sharing Planning Regression analysis Studies Ride sharing services Data analysis Decision support systems Brandt, Tobias oth Neumann, Dirk oth Enthalten in Omega Oxford [u.a.] : Elsevier, 1973 59(2015), Seite 4 (DE-627)129306827 (DE-600)124502-8 (DE-576)014503905 0305-0483 nnns volume:59 year:2015 pages:4 http://dx.doi.org/10.1016/j.omega.2015.02.011 Volltext http://search.proquest.com/docview/1761751027 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-WIW GBV_ILN_11 GBV_ILN_21 GBV_ILN_26 GBV_ILN_2009 GBV_ILN_4126 GBV_ILN_4266 AR 59 2015 4 |
| allfieldsGer |
10.1016/j.omega.2015.02.011 doi PQ20160430 (DE-627)OLC1972924397 (DE-599)GBVOLC1972924397 (PRQ)c1312-710f3517bdd9f310d54793825567ff5ce5fcd1ffffce331de02f5f01c7feaf290 (KEY)0082059820150000059000000004infreefloatdevelopingbusinessanalyticssupportforca DE-627 ger DE-627 rakwb eng 650 DNB Wagner, Sebastian verfasserin aut In Free Float: Developing Business Analytics Support for Carsharing Providers 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier As a rapidly expanding market, carsharing presents a possible remedy for traffic congestion in urban centers. Especially free-floating carsharing, which allows customers to leave their car anywhere within the operator's business area, provides users with flexibility, and complements public transportation. We present a novel method that provides strategic and operational decision support to companies maneuvering this competitive and constantly changing market environment. Using an extensive set of customer data in a zero-inflated regression model, we explain spatial variation in carsharing activity through the proximity of particular points of interests, such as movie theaters and airports. As an application case, as well as a validation of the model, we use the resulting indicators to predict the number of rentals before an expansion of the business area and compare it to the actual demand post-expansion. We find that our approach correctly identifies areas with a high carsharing activity and can be easily adapted to other cities. Traffic congestion Management Car sharing Planning Regression analysis Studies Ride sharing services Data analysis Decision support systems Brandt, Tobias oth Neumann, Dirk oth Enthalten in Omega Oxford [u.a.] : Elsevier, 1973 59(2015), Seite 4 (DE-627)129306827 (DE-600)124502-8 (DE-576)014503905 0305-0483 nnns volume:59 year:2015 pages:4 http://dx.doi.org/10.1016/j.omega.2015.02.011 Volltext http://search.proquest.com/docview/1761751027 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-WIW GBV_ILN_11 GBV_ILN_21 GBV_ILN_26 GBV_ILN_2009 GBV_ILN_4126 GBV_ILN_4266 AR 59 2015 4 |
| allfieldsSound |
10.1016/j.omega.2015.02.011 doi PQ20160430 (DE-627)OLC1972924397 (DE-599)GBVOLC1972924397 (PRQ)c1312-710f3517bdd9f310d54793825567ff5ce5fcd1ffffce331de02f5f01c7feaf290 (KEY)0082059820150000059000000004infreefloatdevelopingbusinessanalyticssupportforca DE-627 ger DE-627 rakwb eng 650 DNB Wagner, Sebastian verfasserin aut In Free Float: Developing Business Analytics Support for Carsharing Providers 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier As a rapidly expanding market, carsharing presents a possible remedy for traffic congestion in urban centers. Especially free-floating carsharing, which allows customers to leave their car anywhere within the operator's business area, provides users with flexibility, and complements public transportation. We present a novel method that provides strategic and operational decision support to companies maneuvering this competitive and constantly changing market environment. Using an extensive set of customer data in a zero-inflated regression model, we explain spatial variation in carsharing activity through the proximity of particular points of interests, such as movie theaters and airports. As an application case, as well as a validation of the model, we use the resulting indicators to predict the number of rentals before an expansion of the business area and compare it to the actual demand post-expansion. We find that our approach correctly identifies areas with a high carsharing activity and can be easily adapted to other cities. Traffic congestion Management Car sharing Planning Regression analysis Studies Ride sharing services Data analysis Decision support systems Brandt, Tobias oth Neumann, Dirk oth Enthalten in Omega Oxford [u.a.] : Elsevier, 1973 59(2015), Seite 4 (DE-627)129306827 (DE-600)124502-8 (DE-576)014503905 0305-0483 nnns volume:59 year:2015 pages:4 http://dx.doi.org/10.1016/j.omega.2015.02.011 Volltext http://search.proquest.com/docview/1761751027 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-WIW GBV_ILN_11 GBV_ILN_21 GBV_ILN_26 GBV_ILN_2009 GBV_ILN_4126 GBV_ILN_4266 AR 59 2015 4 |
| language |
English |
| source |
Enthalten in Omega 59(2015), Seite 4 volume:59 year:2015 pages:4 |
| sourceStr |
Enthalten in Omega 59(2015), Seite 4 volume:59 year:2015 pages:4 |
| format_phy_str_mv |
Article |
| institution |
findex.gbv.de |
| topic_facet |
Traffic congestion Management Car sharing Planning Regression analysis Studies Ride sharing services Data analysis Decision support systems |
| dewey-raw |
650 |
| isfreeaccess_bool |
false |
| container_title |
Omega |
| authorswithroles_txt_mv |
Wagner, Sebastian @@aut@@ Brandt, Tobias @@oth@@ Neumann, Dirk @@oth@@ |
| publishDateDaySort_date |
2015-01-01T00:00:00Z |
| hierarchy_top_id |
129306827 |
| dewey-sort |
3650 |
| id |
OLC1972924397 |
| language_de |
englisch |
| fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a2200265 4500</leader><controlfield tag="001">OLC1972924397</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230714183821.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">160427s2015 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.omega.2015.02.011</subfield><subfield code="2">doi</subfield></datafield><datafield tag="028" ind1="5" ind2="2"><subfield code="a">PQ20160430</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC1972924397</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)GBVOLC1972924397</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(PRQ)c1312-710f3517bdd9f310d54793825567ff5ce5fcd1ffffce331de02f5f01c7feaf290</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(KEY)0082059820150000059000000004infreefloatdevelopingbusinessanalyticssupportforca</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">650</subfield><subfield code="q">DNB</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Wagner, Sebastian</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">In Free Float: Developing Business Analytics Support for Carsharing Providers</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2015</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">As a rapidly expanding market, carsharing presents a possible remedy for traffic congestion in urban centers. Especially free-floating carsharing, which allows customers to leave their car anywhere within the operator's business area, provides users with flexibility, and complements public transportation. We present a novel method that provides strategic and operational decision support to companies maneuvering this competitive and constantly changing market environment. Using an extensive set of customer data in a zero-inflated regression model, we explain spatial variation in carsharing activity through the proximity of particular points of interests, such as movie theaters and airports. As an application case, as well as a validation of the model, we use the resulting indicators to predict the number of rentals before an expansion of the business area and compare it to the actual demand post-expansion. We find that our approach correctly identifies areas with a high carsharing activity and can be easily adapted to other cities.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Traffic congestion</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Management</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Car sharing</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Planning</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Regression analysis</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Studies</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Ride sharing services</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Data analysis</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Decision support systems</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Brandt, Tobias</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Neumann, Dirk</subfield><subfield code="4">oth</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Omega</subfield><subfield code="d">Oxford [u.a.] : Elsevier, 1973</subfield><subfield code="g">59(2015), Seite 4</subfield><subfield code="w">(DE-627)129306827</subfield><subfield code="w">(DE-600)124502-8</subfield><subfield code="w">(DE-576)014503905</subfield><subfield code="x">0305-0483</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:59</subfield><subfield code="g">year:2015</subfield><subfield code="g">pages:4</subfield></datafield><datafield tag="856" ind1="4" ind2="1"><subfield code="u">http://dx.doi.org/10.1016/j.omega.2015.02.011</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">http://search.proquest.com/docview/1761751027</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 code="a">GBV_OLC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-WIW</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_11</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_21</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_26</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2009</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4266</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">59</subfield><subfield code="j">2015</subfield><subfield code="h">4</subfield></datafield></record></collection>
|
| author |
Wagner, Sebastian |
| spellingShingle |
Wagner, Sebastian ddc 650 misc Traffic congestion misc Management misc Car sharing misc Planning misc Regression analysis misc Studies misc Ride sharing services misc Data analysis misc Decision support systems In Free Float: Developing Business Analytics Support for Carsharing Providers |
| authorStr |
Wagner, Sebastian |
| ppnlink_with_tag_str_mv |
@@773@@(DE-627)129306827 |
| format |
Article |
| dewey-ones |
650 - Management & auxiliary services |
| delete_txt_mv |
keep |
| author_role |
aut |
| collection |
OLC |
| remote_str |
false |
| illustrated |
Not Illustrated |
| issn |
0305-0483 |
| topic_title |
650 DNB In Free Float: Developing Business Analytics Support for Carsharing Providers Traffic congestion Management Car sharing Planning Regression analysis Studies Ride sharing services Data analysis Decision support systems |
| topic |
ddc 650 misc Traffic congestion misc Management misc Car sharing misc Planning misc Regression analysis misc Studies misc Ride sharing services misc Data analysis misc Decision support systems |
| topic_unstemmed |
ddc 650 misc Traffic congestion misc Management misc Car sharing misc Planning misc Regression analysis misc Studies misc Ride sharing services misc Data analysis misc Decision support systems |
| topic_browse |
ddc 650 misc Traffic congestion misc Management misc Car sharing misc Planning misc Regression analysis misc Studies misc Ride sharing services misc Data analysis misc Decision support systems |
| format_facet |
Aufsätze Gedruckte Aufsätze |
| format_main_str_mv |
Text Zeitschrift/Artikel |
| carriertype_str_mv |
nc |
| author2_variant |
t b tb d n dn |
| hierarchy_parent_title |
Omega |
| hierarchy_parent_id |
129306827 |
| dewey-tens |
650 - Management & public relations |
| hierarchy_top_title |
Omega |
| isfreeaccess_txt |
false |
| familylinks_str_mv |
(DE-627)129306827 (DE-600)124502-8 (DE-576)014503905 |
| title |
In Free Float: Developing Business Analytics Support for Carsharing Providers |
| ctrlnum |
(DE-627)OLC1972924397 (DE-599)GBVOLC1972924397 (PRQ)c1312-710f3517bdd9f310d54793825567ff5ce5fcd1ffffce331de02f5f01c7feaf290 (KEY)0082059820150000059000000004infreefloatdevelopingbusinessanalyticssupportforca |
| title_full |
In Free Float: Developing Business Analytics Support for Carsharing Providers |
| author_sort |
Wagner, Sebastian |
| journal |
Omega |
| journalStr |
Omega |
| lang_code |
eng |
| isOA_bool |
false |
| dewey-hundreds |
600 - Technology |
| recordtype |
marc |
| publishDateSort |
2015 |
| contenttype_str_mv |
txt |
| container_start_page |
4 |
| author_browse |
Wagner, Sebastian |
| container_volume |
59 |
| class |
650 DNB |
| format_se |
Aufsätze |
| author-letter |
Wagner, Sebastian |
| doi_str_mv |
10.1016/j.omega.2015.02.011 |
| dewey-full |
650 |
| title_sort |
in free float: developing business analytics support for carsharing providers |
| title_auth |
In Free Float: Developing Business Analytics Support for Carsharing Providers |
| abstract |
As a rapidly expanding market, carsharing presents a possible remedy for traffic congestion in urban centers. Especially free-floating carsharing, which allows customers to leave their car anywhere within the operator's business area, provides users with flexibility, and complements public transportation. We present a novel method that provides strategic and operational decision support to companies maneuvering this competitive and constantly changing market environment. Using an extensive set of customer data in a zero-inflated regression model, we explain spatial variation in carsharing activity through the proximity of particular points of interests, such as movie theaters and airports. As an application case, as well as a validation of the model, we use the resulting indicators to predict the number of rentals before an expansion of the business area and compare it to the actual demand post-expansion. We find that our approach correctly identifies areas with a high carsharing activity and can be easily adapted to other cities. |
| abstractGer |
As a rapidly expanding market, carsharing presents a possible remedy for traffic congestion in urban centers. Especially free-floating carsharing, which allows customers to leave their car anywhere within the operator's business area, provides users with flexibility, and complements public transportation. We present a novel method that provides strategic and operational decision support to companies maneuvering this competitive and constantly changing market environment. Using an extensive set of customer data in a zero-inflated regression model, we explain spatial variation in carsharing activity through the proximity of particular points of interests, such as movie theaters and airports. As an application case, as well as a validation of the model, we use the resulting indicators to predict the number of rentals before an expansion of the business area and compare it to the actual demand post-expansion. We find that our approach correctly identifies areas with a high carsharing activity and can be easily adapted to other cities. |
| abstract_unstemmed |
As a rapidly expanding market, carsharing presents a possible remedy for traffic congestion in urban centers. Especially free-floating carsharing, which allows customers to leave their car anywhere within the operator's business area, provides users with flexibility, and complements public transportation. We present a novel method that provides strategic and operational decision support to companies maneuvering this competitive and constantly changing market environment. Using an extensive set of customer data in a zero-inflated regression model, we explain spatial variation in carsharing activity through the proximity of particular points of interests, such as movie theaters and airports. As an application case, as well as a validation of the model, we use the resulting indicators to predict the number of rentals before an expansion of the business area and compare it to the actual demand post-expansion. We find that our approach correctly identifies areas with a high carsharing activity and can be easily adapted to other cities. |
| collection_details |
GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-WIW GBV_ILN_11 GBV_ILN_21 GBV_ILN_26 GBV_ILN_2009 GBV_ILN_4126 GBV_ILN_4266 |
| title_short |
In Free Float: Developing Business Analytics Support for Carsharing Providers |
| url |
http://dx.doi.org/10.1016/j.omega.2015.02.011 http://search.proquest.com/docview/1761751027 |
| remote_bool |
false |
| author2 |
Brandt, Tobias Neumann, Dirk |
| author2Str |
Brandt, Tobias Neumann, Dirk |
| ppnlink |
129306827 |
| mediatype_str_mv |
n |
| isOA_txt |
false |
| hochschulschrift_bool |
false |
| author2_role |
oth oth |
| doi_str |
10.1016/j.omega.2015.02.011 |
| up_date |
2024-07-04T01:04:23.714Z |
| _version_ |
1803608458490019840 |
| fullrecord_marcxml |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a2200265 4500</leader><controlfield tag="001">OLC1972924397</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230714183821.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">160427s2015 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.omega.2015.02.011</subfield><subfield code="2">doi</subfield></datafield><datafield tag="028" ind1="5" ind2="2"><subfield code="a">PQ20160430</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC1972924397</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)GBVOLC1972924397</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(PRQ)c1312-710f3517bdd9f310d54793825567ff5ce5fcd1ffffce331de02f5f01c7feaf290</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(KEY)0082059820150000059000000004infreefloatdevelopingbusinessanalyticssupportforca</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">650</subfield><subfield code="q">DNB</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Wagner, Sebastian</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">In Free Float: Developing Business Analytics Support for Carsharing Providers</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2015</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">As a rapidly expanding market, carsharing presents a possible remedy for traffic congestion in urban centers. Especially free-floating carsharing, which allows customers to leave their car anywhere within the operator's business area, provides users with flexibility, and complements public transportation. We present a novel method that provides strategic and operational decision support to companies maneuvering this competitive and constantly changing market environment. Using an extensive set of customer data in a zero-inflated regression model, we explain spatial variation in carsharing activity through the proximity of particular points of interests, such as movie theaters and airports. As an application case, as well as a validation of the model, we use the resulting indicators to predict the number of rentals before an expansion of the business area and compare it to the actual demand post-expansion. We find that our approach correctly identifies areas with a high carsharing activity and can be easily adapted to other cities.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Traffic congestion</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Management</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Car sharing</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Planning</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Regression analysis</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Studies</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Ride sharing services</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Data analysis</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Decision support systems</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Brandt, Tobias</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Neumann, Dirk</subfield><subfield code="4">oth</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Omega</subfield><subfield code="d">Oxford [u.a.] : Elsevier, 1973</subfield><subfield code="g">59(2015), Seite 4</subfield><subfield code="w">(DE-627)129306827</subfield><subfield code="w">(DE-600)124502-8</subfield><subfield code="w">(DE-576)014503905</subfield><subfield code="x">0305-0483</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:59</subfield><subfield code="g">year:2015</subfield><subfield code="g">pages:4</subfield></datafield><datafield tag="856" ind1="4" ind2="1"><subfield code="u">http://dx.doi.org/10.1016/j.omega.2015.02.011</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">http://search.proquest.com/docview/1761751027</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 code="a">GBV_OLC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-WIW</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_11</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_21</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_26</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2009</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4266</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">59</subfield><subfield code="j">2015</subfield><subfield code="h">4</subfield></datafield></record></collection>
|
| score |
7.401025 |