File Download

There are no files associated with this item.

  Links for fulltext
     (May Require Subscription)
Supplementary

Conference Paper: Planning for electric taxi charging system from the perspective of transport energy supply chain: A data-driven approach in Beijing

TitlePlanning for electric taxi charging system from the perspective of transport energy supply chain: A data-driven approach in Beijing
Authors
Jia, YinghaoChen, HuimiaoLi, JiaoyangHe, FangLi, MengHu, ZechunShen, Zuo Jun Max
KeywordsCharging station planning
Data-driven approach
Transport energy supply chain
Electric taxis
Issue Date2017
Citation
2017 IEEE Transportation Electrification Conference and Expo, Asia-Pacific, ITEC Asia-Pacific 2017, 2017, p. 501-506 How to Cite?
DOI: http://dx.doi.org/10.1109/ITEC-AP.2017.8080844
Abstract© 2017 IEEE. Administration in big cities is strongly promoting electric taxis (ETs) by providing purchasing subsidies, accessorial public facilities and many other encouraging policies. However, how to allocate the limited resources to optimize the benefits brought by ETs remains a headache for most researchers. Applying data mining technology, this research gathers real-time vehicle trajectory data of 39,053 urban conventional taxis (CTs) and 408 suburban ETs in Beijing for 4 weeks to extract the model of customers' travel demand and ET driving patterns. Based on the transport energy supply chain derived from Global Positioning System (GPS) data, we develop a data-driven method to design ET charging infrastructure in the near future.
Persistent Identifierhttp://hdl.handle.net/10722/296161
ISI Accession Number ID
WOS:000426996500089

 

DC FieldValueLanguage
dc.contributor.authorJia, Yinghao-
dc.contributor.authorChen, Huimiao-
dc.contributor.authorLi, Jiaoyang-
dc.contributor.authorHe, Fang-
dc.contributor.authorLi, Meng-
dc.contributor.authorHu, Zechun-
dc.contributor.authorShen, Zuo Jun Max-
dc.date.accessioned2021-02-11T04:52:58Z-
dc.date.available2021-02-11T04:52:58Z-
dc.date.issued2017-
dc.identifier.citation2017 IEEE Transportation Electrification Conference and Expo, Asia-Pacific, ITEC Asia-Pacific 2017, 2017, p. 501-506-
dc.identifier.urihttp://hdl.handle.net/10722/296161-
dc.description.abstract© 2017 IEEE. Administration in big cities is strongly promoting electric taxis (ETs) by providing purchasing subsidies, accessorial public facilities and many other encouraging policies. However, how to allocate the limited resources to optimize the benefits brought by ETs remains a headache for most researchers. Applying data mining technology, this research gathers real-time vehicle trajectory data of 39,053 urban conventional taxis (CTs) and 408 suburban ETs in Beijing for 4 weeks to extract the model of customers' travel demand and ET driving patterns. Based on the transport energy supply chain derived from Global Positioning System (GPS) data, we develop a data-driven method to design ET charging infrastructure in the near future.-
dc.languageeng-
dc.relation.ispartof2017 IEEE Transportation Electrification Conference and Expo, Asia-Pacific, ITEC Asia-Pacific 2017-
dc.subjectCharging station planning-
dc.subjectData-driven approach-
dc.subjectTransport energy supply chain-
dc.subjectElectric taxis-
dc.titlePlanning for electric taxi charging system from the perspective of transport energy supply chain: A data-driven approach in Beijing-
dc.typeConference_Paper-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1109/ITEC-AP.2017.8080844-
dc.identifier.scopuseid_2-s2.0-85040101761-
dc.identifier.spage501-
dc.identifier.epage506-
dc.identifier.isiWOS:000426996500089-

Export via OAI-PMH Interface in XML Formats

OR

Export to Other Non-XML Formats