@article{
 title = {Charging infrastructure planning for promoting battery electric vehicles: An activity-based approach using multiday travel data},
 type = {article},
 year = {2014},
 identifiers = {[object Object]},
 keywords = {Battery electric vehicle,Charging infrastructure,GPS-based travel survey,Genetic algorithm,Range anxiety},
 pages = {44-55},
 volume = {38},
 websites = {http://www.sciencedirect.com/science/article/pii/S0968090X13002283},
 month = {1},
 id = {ae48f52b-9200-3daf-acb5-05ca614d1937},
 created = {2015-01-13T16:35:41.000Z},
 accessed = {2015-01-06},
 file_attached = {false},
 profile_id = {9696f304-db07-31d9-a8cd-d208939f9889},
 group_id = {062cfb24-9ce4-3a92-a41f-541a783d7b95},
 last_modified = {2017-03-09T15:29:23.010Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {Dong2014},
 private_publication = {false},
 abstract = {This paper studies electric vehicle charger location problems and analyzes the impact of public charging infrastructure deployment on increasing electric miles traveled, thus promoting battery electric vehicle (BEV) market penetration. An activity-based assessment method is proposed to evaluate BEV feasibility for the heterogeneous traveling population in the real world driving context. Genetic algorithm is applied to find (sub)optimal locations for siting public charging stations. A case study using the GPS-based travel survey data collected in the greater Seattle metropolitan area shows that electric miles and trips could be significantly increased by installing public chargers at popular destinations, with a reasonable infrastructure investment.},
 bibtype = {article},
 author = {Dong, Jing and Liu, Changzheng and Lin, Zhenhong},
 journal = {Transportation Research Part C: Emerging Technologies}
}

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