Optimizing Electric Transit Network and Fleet Transition: A Deep Reinforcement Learning Approach Article Swipe
F. F. Jing
,
Di Huang
,
Yiliu He
,
Yujian Ye
,
Hao Wang
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.2139/ssrn.5401011
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.2139/ssrn.5401011
Related Topics
Concepts
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.2139/ssrn.5401011
- OA Status
- green
- References
- 45
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4413414966
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4413414966Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.2139/ssrn.5401011Digital Object Identifier
- Title
-
Optimizing Electric Transit Network and Fleet Transition: A Deep Reinforcement Learning ApproachWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-01-01Full publication date if available
- Authors
-
F. F. Jing, Di Huang, Yiliu He, Yujian Ye, Hao WangList of authors in order
- Landing page
-
https://doi.org/10.2139/ssrn.5401011Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.2139/ssrn.5401011Direct OA link when available
- Concepts
-
Reinforcement learning, Transit (satellite), Transition (genetics), Computer science, Reinforcement, Transport engineering, Engineering, Public transport, Artificial intelligence, Chemistry, Structural engineering, Biochemistry, GeneTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- References (count)
-
45Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4413414966 |
|---|---|
| doi | https://doi.org/10.2139/ssrn.5401011 |
| ids.doi | https://doi.org/10.2139/ssrn.5401011 |
| ids.openalex | https://openalex.org/W4413414966 |
| fwci | 0.0 |
| type | article |
| title | Optimizing Electric Transit Network and Fleet Transition: A Deep Reinforcement Learning Approach |
| biblio.issue | |
| biblio.volume | |
| biblio.last_page | |
| biblio.first_page | |
| topics[0].id | https://openalex.org/T11942 |
| topics[0].field.id | https://openalex.org/fields/22 |
| topics[0].field.display_name | Engineering |
| topics[0].score | 0.9998999834060669 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/2203 |
| topics[0].subfield.display_name | Automotive Engineering |
| topics[0].display_name | Transportation and Mobility Innovations |
| topics[1].id | https://openalex.org/T10698 |
| topics[1].field.id | https://openalex.org/fields/33 |
| topics[1].field.display_name | Social Sciences |
| topics[1].score | 0.9998000264167786 |
| topics[1].domain.id | https://openalex.org/domains/2 |
| topics[1].domain.display_name | Social Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/3313 |
| topics[1].subfield.display_name | Transportation |
| topics[1].display_name | Transportation Planning and Optimization |
| topics[2].id | https://openalex.org/T10768 |
| topics[2].field.id | https://openalex.org/fields/22 |
| topics[2].field.display_name | Engineering |
| topics[2].score | 0.9995999932289124 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/2208 |
| topics[2].subfield.display_name | Electrical and Electronic Engineering |
| topics[2].display_name | Electric Vehicles and Infrastructure |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C97541855 |
| concepts[0].level | 2 |
| concepts[0].score | 0.7933119535446167 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q830687 |
| concepts[0].display_name | Reinforcement learning |
| concepts[1].id | https://openalex.org/C2778022998 |
| concepts[1].level | 3 |
| concepts[1].score | 0.735087513923645 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q651136 |
| concepts[1].display_name | Transit (satellite) |
| concepts[2].id | https://openalex.org/C194232998 |
| concepts[2].level | 3 |
| concepts[2].score | 0.5593767762184143 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q1606712 |
| concepts[2].display_name | Transition (genetics) |
| concepts[3].id | https://openalex.org/C41008148 |
| concepts[3].level | 0 |
| concepts[3].score | 0.4410719871520996 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[3].display_name | Computer science |
| concepts[4].id | https://openalex.org/C67203356 |
| concepts[4].level | 2 |
| concepts[4].score | 0.4247778058052063 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q1321905 |
| concepts[4].display_name | Reinforcement |
| concepts[5].id | https://openalex.org/C22212356 |
| concepts[5].level | 1 |
| concepts[5].score | 0.3304448127746582 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q775325 |
| concepts[5].display_name | Transport engineering |
| concepts[6].id | https://openalex.org/C127413603 |
| concepts[6].level | 0 |
| concepts[6].score | 0.294121116399765 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q11023 |
| concepts[6].display_name | Engineering |
| concepts[7].id | https://openalex.org/C539828613 |
| concepts[7].level | 2 |
| concepts[7].score | 0.29104697704315186 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q178512 |
| concepts[7].display_name | Public transport |
| concepts[8].id | https://openalex.org/C154945302 |
| concepts[8].level | 1 |
| concepts[8].score | 0.2584417164325714 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[8].display_name | Artificial intelligence |
| concepts[9].id | https://openalex.org/C185592680 |
| concepts[9].level | 0 |
| concepts[9].score | 0.09763818979263306 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q2329 |
| concepts[9].display_name | Chemistry |
| concepts[10].id | https://openalex.org/C66938386 |
| concepts[10].level | 1 |
| concepts[10].score | 0.08038690686225891 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q633538 |
| concepts[10].display_name | Structural engineering |
| concepts[11].id | https://openalex.org/C55493867 |
| concepts[11].level | 1 |
| concepts[11].score | 0.0 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q7094 |
| concepts[11].display_name | Biochemistry |
| concepts[12].id | https://openalex.org/C104317684 |
| concepts[12].level | 2 |
| concepts[12].score | 0.0 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q7187 |
| concepts[12].display_name | Gene |
| keywords[0].id | https://openalex.org/keywords/reinforcement-learning |
| keywords[0].score | 0.7933119535446167 |
| keywords[0].display_name | Reinforcement learning |
| keywords[1].id | https://openalex.org/keywords/transit |
| keywords[1].score | 0.735087513923645 |
| keywords[1].display_name | Transit (satellite) |
| keywords[2].id | https://openalex.org/keywords/transition |
| keywords[2].score | 0.5593767762184143 |
| keywords[2].display_name | Transition (genetics) |
| keywords[3].id | https://openalex.org/keywords/computer-science |
| keywords[3].score | 0.4410719871520996 |
| keywords[3].display_name | Computer science |
| keywords[4].id | https://openalex.org/keywords/reinforcement |
| keywords[4].score | 0.4247778058052063 |
| keywords[4].display_name | Reinforcement |
| keywords[5].id | https://openalex.org/keywords/transport-engineering |
| keywords[5].score | 0.3304448127746582 |
| keywords[5].display_name | Transport engineering |
| keywords[6].id | https://openalex.org/keywords/engineering |
| keywords[6].score | 0.294121116399765 |
| keywords[6].display_name | Engineering |
| keywords[7].id | https://openalex.org/keywords/public-transport |
| keywords[7].score | 0.29104697704315186 |
| keywords[7].display_name | Public transport |
| keywords[8].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[8].score | 0.2584417164325714 |
| keywords[8].display_name | Artificial intelligence |
| keywords[9].id | https://openalex.org/keywords/chemistry |
| keywords[9].score | 0.09763818979263306 |
| keywords[9].display_name | Chemistry |
| keywords[10].id | https://openalex.org/keywords/structural-engineering |
| keywords[10].score | 0.08038690686225891 |
| keywords[10].display_name | Structural engineering |
| language | en |
| locations[0].id | doi:10.2139/ssrn.5401011 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S4210172589 |
| locations[0].source.issn | 1556-5068 |
| locations[0].source.type | repository |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | 1556-5068 |
| locations[0].source.is_core | False |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | SSRN Electronic Journal |
| locations[0].source.host_organization | https://openalex.org/I1318003438 |
| locations[0].source.host_organization_name | RELX Group (Netherlands) |
| locations[0].source.host_organization_lineage | https://openalex.org/I1318003438 |
| locations[0].license | |
| locations[0].pdf_url | |
| locations[0].version | acceptedVersion |
| locations[0].raw_type | posted-content |
| locations[0].license_id | |
| locations[0].is_accepted | True |
| locations[0].is_published | False |
| locations[0].raw_source_name | |
| locations[0].landing_page_url | https://doi.org/10.2139/ssrn.5401011 |
| indexed_in | crossref |
| authorships[0].author.id | https://openalex.org/A5106893161 |
| authorships[0].author.orcid | |
| authorships[0].author.display_name | F. F. Jing |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Feixiang Jing |
| authorships[0].is_corresponding | False |
| authorships[1].author.id | https://openalex.org/A5073698613 |
| authorships[1].author.orcid | https://orcid.org/0000-0003-0231-9093 |
| authorships[1].author.display_name | Di Huang |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Di Huang |
| authorships[1].is_corresponding | False |
| authorships[2].author.id | https://openalex.org/A5000386102 |
| authorships[2].author.orcid | https://orcid.org/0000-0001-8631-8455 |
| authorships[2].author.display_name | Yiliu He |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Yiliu He |
| authorships[2].is_corresponding | False |
| authorships[3].author.id | https://openalex.org/A5044155484 |
| authorships[3].author.orcid | https://orcid.org/0000-0002-9278-9218 |
| authorships[3].author.display_name | Yujian Ye |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Yujian Ye |
| authorships[3].is_corresponding | False |
| authorships[4].author.id | https://openalex.org/A5100653162 |
| authorships[4].author.orcid | https://orcid.org/0000-0003-1144-7804 |
| authorships[4].author.display_name | Hao Wang |
| authorships[4].author_position | last |
| authorships[4].raw_author_name | Hao Wang |
| authorships[4].is_corresponding | False |
| has_content.pdf | False |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://doi.org/10.2139/ssrn.5401011 |
| open_access.oa_status | green |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Optimizing Electric Transit Network and Fleet Transition: A Deep Reinforcement Learning Approach |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T11942 |
| primary_topic.field.id | https://openalex.org/fields/22 |
| primary_topic.field.display_name | Engineering |
| primary_topic.score | 0.9998999834060669 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/2203 |
| primary_topic.subfield.display_name | Automotive Engineering |
| primary_topic.display_name | Transportation and Mobility Innovations |
| related_works | https://openalex.org/W4310083477, https://openalex.org/W2328553770, https://openalex.org/W2920061524, https://openalex.org/W1977959518, https://openalex.org/W2038908348, https://openalex.org/W2107890255, https://openalex.org/W654272262, https://openalex.org/W603905442, https://openalex.org/W332282905, https://openalex.org/W2247480929 |
| cited_by_count | 0 |
| locations_count | 1 |
| best_oa_location.id | doi:10.2139/ssrn.5401011 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S4210172589 |
| best_oa_location.source.issn | 1556-5068 |
| best_oa_location.source.type | repository |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | 1556-5068 |
| best_oa_location.source.is_core | False |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | SSRN Electronic Journal |
| best_oa_location.source.host_organization | https://openalex.org/I1318003438 |
| best_oa_location.source.host_organization_name | RELX Group (Netherlands) |
| best_oa_location.source.host_organization_lineage | https://openalex.org/I1318003438 |
| best_oa_location.license | |
| best_oa_location.pdf_url | |
| best_oa_location.version | acceptedVersion |
| best_oa_location.raw_type | posted-content |
| best_oa_location.license_id | |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | False |
| best_oa_location.raw_source_name | |
| best_oa_location.landing_page_url | https://doi.org/10.2139/ssrn.5401011 |
| primary_location.id | doi:10.2139/ssrn.5401011 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S4210172589 |
| primary_location.source.issn | 1556-5068 |
| primary_location.source.type | repository |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | 1556-5068 |
| primary_location.source.is_core | False |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | SSRN Electronic Journal |
| primary_location.source.host_organization | https://openalex.org/I1318003438 |
| primary_location.source.host_organization_name | RELX Group (Netherlands) |
| primary_location.source.host_organization_lineage | https://openalex.org/I1318003438 |
| primary_location.license | |
| primary_location.pdf_url | |
| primary_location.version | acceptedVersion |
| primary_location.raw_type | posted-content |
| primary_location.license_id | |
| primary_location.is_accepted | True |
| primary_location.is_published | False |
| primary_location.raw_source_name | |
| primary_location.landing_page_url | https://doi.org/10.2139/ssrn.5401011 |
| publication_date | 2025-01-01 |
| publication_year | 2025 |
| referenced_works | https://openalex.org/W4388043388, https://openalex.org/W3011877742, https://openalex.org/W4282003066, https://openalex.org/W2811312949, https://openalex.org/W4397033052, https://openalex.org/W2612264206, https://openalex.org/W2582718034, https://openalex.org/W4321507170, https://openalex.org/W2983644131, https://openalex.org/W4389333949, https://openalex.org/W3040129181, https://openalex.org/W3126695353, https://openalex.org/W2930411951, https://openalex.org/W3011920635, https://openalex.org/W3026224272, https://openalex.org/W2889616553, https://openalex.org/W4211041802, https://openalex.org/W4290631490, https://openalex.org/W4392019515, https://openalex.org/W2145339207, https://openalex.org/W3210892103, https://openalex.org/W4308168019, https://openalex.org/W2888883156, https://openalex.org/W4381612171, https://openalex.org/W2039518099, https://openalex.org/W3091038948, https://openalex.org/W2995556201, https://openalex.org/W4293439293, https://openalex.org/W1969967095, https://openalex.org/W2166121475, https://openalex.org/W3130747578, https://openalex.org/W3093670558, https://openalex.org/W2976264609, https://openalex.org/W3177552305, https://openalex.org/W4405415459, https://openalex.org/W3000387162, https://openalex.org/W4323358975, https://openalex.org/W4290052333, https://openalex.org/W2887081459, https://openalex.org/W4399157111, https://openalex.org/W4214717370, https://openalex.org/W4393253095, https://openalex.org/W4396634364, https://openalex.org/W2804786528, https://openalex.org/W4408662850 |
| referenced_works_count | 45 |
| abstract_inverted_index | |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 5 |
| citation_normalized_percentile.value | 0.42358258 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |