A Deep Learning Approach for Estimating Traffic Density Using Data Obtained from Connected and Autonomous Probes Article Swipe
Daisik Nam
,
Riju Lavanya
,
R. Jayakrishnan
,
Inchul Yang
,
Woo Hoon Jeon
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.3390/s20174824
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.3390/s20174824
The focus of this research is on the estimation of traffic density from data obtained from Connected and Autonomous Probes (CAPs). CAPs pose an advantage over expensive and invasive infrastructure such as loop detectors. CAPs maneuver their driving trajectories, sensing the presence of adjacent vehicles and distances to them by means of several electronic sensors, whose data can be used for more sophisticated traffic density estimation techniques. Traffic density has a highly nonlinear nature during on-congestion and queue-clearing conditions. Closed-mathematical forms of the traditional density estimation techniques are incapable of dealing with complex nonlinearities, which opens the door for data-driven approaches such as machine learning techniques. Deep learning algorithms excel in data-rich contexts, which recognize nonlinear and highly situation-dependent patterns. Our research is based on an LSTM (Long short-term memory) neural network for the nonlinearity associated with time dynamics of traffic flow. The proposed method is designed to learn the input-output relation of Edie’s definition. At the same time, the method recognizes a temporally nonlinear pattern of traffic. We evaluate our algorithm by using a microscopic simulation program (PARAMICS) and demonstrate that our model accurately estimates traffic density in Free-flow, Transition, and Congested conditions.
Related Topics
Concepts
Nonlinear system
Queue
Computer science
Artificial neural network
Traffic flow (computer networking)
Deep learning
Artificial intelligence
Focus (optics)
Density estimation
Detector
Real-time computing
Relation (database)
Machine learning
Algorithm
Data mining
Mathematics
Optics
Programming language
Telecommunications
Physics
Quantum mechanics
Computer security
Statistics
Estimator
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/s20174824
- https://www.mdpi.com/1424-8220/20/17/4824/pdf?version=1598493498
- OA Status
- gold
- Cited By
- 19
- References
- 25
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3080432885
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W3080432885Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/s20174824Digital Object Identifier
- Title
-
A Deep Learning Approach for Estimating Traffic Density Using Data Obtained from Connected and Autonomous ProbesWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2020Year of publication
- Publication date
-
2020-08-26Full publication date if available
- Authors
-
Daisik Nam, Riju Lavanya, R. Jayakrishnan, Inchul Yang, Woo Hoon JeonList of authors in order
- Landing page
-
https://doi.org/10.3390/s20174824Publisher landing page
- PDF URL
-
https://www.mdpi.com/1424-8220/20/17/4824/pdf?version=1598493498Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.mdpi.com/1424-8220/20/17/4824/pdf?version=1598493498Direct OA link when available
- Concepts
-
Nonlinear system, Queue, Computer science, Artificial neural network, Traffic flow (computer networking), Deep learning, Artificial intelligence, Focus (optics), Density estimation, Detector, Real-time computing, Relation (database), Machine learning, Algorithm, Data mining, Mathematics, Optics, Programming language, Telecommunications, Physics, Quantum mechanics, Computer security, Statistics, EstimatorTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
19Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 3, 2024: 3, 2023: 5, 2022: 4, 2021: 3Per-year citation counts (last 5 years)
- References (count)
-
25Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W3080432885 |
|---|---|
| doi | https://doi.org/10.3390/s20174824 |
| ids.doi | https://doi.org/10.3390/s20174824 |
| ids.mag | 3080432885 |
| ids.pmid | https://pubmed.ncbi.nlm.nih.gov/32858983 |
| ids.openalex | https://openalex.org/W3080432885 |
| fwci | 1.79743805 |
| type | article |
| title | A Deep Learning Approach for Estimating Traffic Density Using Data Obtained from Connected and Autonomous Probes |
| biblio.issue | 17 |
| biblio.volume | 20 |
| biblio.last_page | 4824 |
| biblio.first_page | 4824 |
| topics[0].id | https://openalex.org/T11344 |
| topics[0].field.id | https://openalex.org/fields/22 |
| topics[0].field.display_name | Engineering |
| topics[0].score | 1.0 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/2215 |
| topics[0].subfield.display_name | Building and Construction |
| topics[0].display_name | Traffic Prediction and Management Techniques |
| topics[1].id | https://openalex.org/T10524 |
| topics[1].field.id | https://openalex.org/fields/22 |
| topics[1].field.display_name | Engineering |
| topics[1].score | 0.9995999932289124 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/2207 |
| topics[1].subfield.display_name | Control and Systems Engineering |
| topics[1].display_name | Traffic control and management |
| topics[2].id | https://openalex.org/T10698 |
| topics[2].field.id | https://openalex.org/fields/33 |
| topics[2].field.display_name | Social Sciences |
| topics[2].score | 0.9969000220298767 |
| topics[2].domain.id | https://openalex.org/domains/2 |
| topics[2].domain.display_name | Social Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/3313 |
| topics[2].subfield.display_name | Transportation |
| topics[2].display_name | Transportation Planning and Optimization |
| is_xpac | False |
| apc_list.value | 2400 |
| apc_list.currency | CHF |
| apc_list.value_usd | 2598 |
| apc_paid.value | 2400 |
| apc_paid.currency | CHF |
| apc_paid.value_usd | 2598 |
| concepts[0].id | https://openalex.org/C158622935 |
| concepts[0].level | 2 |
| concepts[0].score | 0.6943008303642273 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q660848 |
| concepts[0].display_name | Nonlinear system |
| concepts[1].id | https://openalex.org/C160403385 |
| concepts[1].level | 2 |
| concepts[1].score | 0.6544958353042603 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q220543 |
| concepts[1].display_name | Queue |
| concepts[2].id | https://openalex.org/C41008148 |
| concepts[2].level | 0 |
| concepts[2].score | 0.6134328842163086 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[2].display_name | Computer science |
| concepts[3].id | https://openalex.org/C50644808 |
| concepts[3].level | 2 |
| concepts[3].score | 0.5819113254547119 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q192776 |
| concepts[3].display_name | Artificial neural network |
| concepts[4].id | https://openalex.org/C207512268 |
| concepts[4].level | 2 |
| concepts[4].score | 0.5597231984138489 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q3074551 |
| concepts[4].display_name | Traffic flow (computer networking) |
| concepts[5].id | https://openalex.org/C108583219 |
| concepts[5].level | 2 |
| concepts[5].score | 0.4954746961593628 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q197536 |
| concepts[5].display_name | Deep learning |
| concepts[6].id | https://openalex.org/C154945302 |
| concepts[6].level | 1 |
| concepts[6].score | 0.48794978857040405 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[6].display_name | Artificial intelligence |
| concepts[7].id | https://openalex.org/C192209626 |
| concepts[7].level | 2 |
| concepts[7].score | 0.46147629618644714 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q190909 |
| concepts[7].display_name | Focus (optics) |
| concepts[8].id | https://openalex.org/C189508267 |
| concepts[8].level | 3 |
| concepts[8].score | 0.45770904421806335 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q17088227 |
| concepts[8].display_name | Density estimation |
| concepts[9].id | https://openalex.org/C94915269 |
| concepts[9].level | 2 |
| concepts[9].score | 0.4547944664955139 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q1834857 |
| concepts[9].display_name | Detector |
| concepts[10].id | https://openalex.org/C79403827 |
| concepts[10].level | 1 |
| concepts[10].score | 0.42618680000305176 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q3988 |
| concepts[10].display_name | Real-time computing |
| concepts[11].id | https://openalex.org/C25343380 |
| concepts[11].level | 2 |
| concepts[11].score | 0.41197580099105835 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q277521 |
| concepts[11].display_name | Relation (database) |
| concepts[12].id | https://openalex.org/C119857082 |
| concepts[12].level | 1 |
| concepts[12].score | 0.3447193503379822 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q2539 |
| concepts[12].display_name | Machine learning |
| concepts[13].id | https://openalex.org/C11413529 |
| concepts[13].level | 1 |
| concepts[13].score | 0.3364432752132416 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q8366 |
| concepts[13].display_name | Algorithm |
| concepts[14].id | https://openalex.org/C124101348 |
| concepts[14].level | 1 |
| concepts[14].score | 0.3264366388320923 |
| concepts[14].wikidata | https://www.wikidata.org/wiki/Q172491 |
| concepts[14].display_name | Data mining |
| concepts[15].id | https://openalex.org/C33923547 |
| concepts[15].level | 0 |
| concepts[15].score | 0.1132955551147461 |
| concepts[15].wikidata | https://www.wikidata.org/wiki/Q395 |
| concepts[15].display_name | Mathematics |
| concepts[16].id | https://openalex.org/C120665830 |
| concepts[16].level | 1 |
| concepts[16].score | 0.0 |
| concepts[16].wikidata | https://www.wikidata.org/wiki/Q14620 |
| concepts[16].display_name | Optics |
| concepts[17].id | https://openalex.org/C199360897 |
| concepts[17].level | 1 |
| concepts[17].score | 0.0 |
| concepts[17].wikidata | https://www.wikidata.org/wiki/Q9143 |
| concepts[17].display_name | Programming language |
| concepts[18].id | https://openalex.org/C76155785 |
| concepts[18].level | 1 |
| concepts[18].score | 0.0 |
| concepts[18].wikidata | https://www.wikidata.org/wiki/Q418 |
| concepts[18].display_name | Telecommunications |
| concepts[19].id | https://openalex.org/C121332964 |
| concepts[19].level | 0 |
| concepts[19].score | 0.0 |
| concepts[19].wikidata | https://www.wikidata.org/wiki/Q413 |
| concepts[19].display_name | Physics |
| concepts[20].id | https://openalex.org/C62520636 |
| concepts[20].level | 1 |
| concepts[20].score | 0.0 |
| concepts[20].wikidata | https://www.wikidata.org/wiki/Q944 |
| concepts[20].display_name | Quantum mechanics |
| concepts[21].id | https://openalex.org/C38652104 |
| concepts[21].level | 1 |
| concepts[21].score | 0.0 |
| concepts[21].wikidata | https://www.wikidata.org/wiki/Q3510521 |
| concepts[21].display_name | Computer security |
| concepts[22].id | https://openalex.org/C105795698 |
| concepts[22].level | 1 |
| concepts[22].score | 0.0 |
| concepts[22].wikidata | https://www.wikidata.org/wiki/Q12483 |
| concepts[22].display_name | Statistics |
| concepts[23].id | https://openalex.org/C185429906 |
| concepts[23].level | 2 |
| concepts[23].score | 0.0 |
| concepts[23].wikidata | https://www.wikidata.org/wiki/Q1130160 |
| concepts[23].display_name | Estimator |
| keywords[0].id | https://openalex.org/keywords/nonlinear-system |
| keywords[0].score | 0.6943008303642273 |
| keywords[0].display_name | Nonlinear system |
| keywords[1].id | https://openalex.org/keywords/queue |
| keywords[1].score | 0.6544958353042603 |
| keywords[1].display_name | Queue |
| keywords[2].id | https://openalex.org/keywords/computer-science |
| keywords[2].score | 0.6134328842163086 |
| keywords[2].display_name | Computer science |
| keywords[3].id | https://openalex.org/keywords/artificial-neural-network |
| keywords[3].score | 0.5819113254547119 |
| keywords[3].display_name | Artificial neural network |
| keywords[4].id | https://openalex.org/keywords/traffic-flow |
| keywords[4].score | 0.5597231984138489 |
| keywords[4].display_name | Traffic flow (computer networking) |
| keywords[5].id | https://openalex.org/keywords/deep-learning |
| keywords[5].score | 0.4954746961593628 |
| keywords[5].display_name | Deep learning |
| keywords[6].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[6].score | 0.48794978857040405 |
| keywords[6].display_name | Artificial intelligence |
| keywords[7].id | https://openalex.org/keywords/focus |
| keywords[7].score | 0.46147629618644714 |
| keywords[7].display_name | Focus (optics) |
| keywords[8].id | https://openalex.org/keywords/density-estimation |
| keywords[8].score | 0.45770904421806335 |
| keywords[8].display_name | Density estimation |
| keywords[9].id | https://openalex.org/keywords/detector |
| keywords[9].score | 0.4547944664955139 |
| keywords[9].display_name | Detector |
| keywords[10].id | https://openalex.org/keywords/real-time-computing |
| keywords[10].score | 0.42618680000305176 |
| keywords[10].display_name | Real-time computing |
| keywords[11].id | https://openalex.org/keywords/relation |
| keywords[11].score | 0.41197580099105835 |
| keywords[11].display_name | Relation (database) |
| keywords[12].id | https://openalex.org/keywords/machine-learning |
| keywords[12].score | 0.3447193503379822 |
| keywords[12].display_name | Machine learning |
| keywords[13].id | https://openalex.org/keywords/algorithm |
| keywords[13].score | 0.3364432752132416 |
| keywords[13].display_name | Algorithm |
| keywords[14].id | https://openalex.org/keywords/data-mining |
| keywords[14].score | 0.3264366388320923 |
| keywords[14].display_name | Data mining |
| keywords[15].id | https://openalex.org/keywords/mathematics |
| keywords[15].score | 0.1132955551147461 |
| keywords[15].display_name | Mathematics |
| language | en |
| locations[0].id | doi:10.3390/s20174824 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S101949793 |
| locations[0].source.issn | 1424-8220 |
| locations[0].source.type | journal |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | 1424-8220 |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | True |
| locations[0].source.display_name | Sensors |
| locations[0].source.host_organization | https://openalex.org/P4310310987 |
| locations[0].source.host_organization_name | Multidisciplinary Digital Publishing Institute |
| locations[0].source.host_organization_lineage | https://openalex.org/P4310310987 |
| locations[0].source.host_organization_lineage_names | Multidisciplinary Digital Publishing Institute |
| locations[0].license | cc-by |
| locations[0].pdf_url | https://www.mdpi.com/1424-8220/20/17/4824/pdf?version=1598493498 |
| locations[0].version | publishedVersion |
| locations[0].raw_type | journal-article |
| locations[0].license_id | https://openalex.org/licenses/cc-by |
| locations[0].is_accepted | True |
| locations[0].is_published | True |
| locations[0].raw_source_name | Sensors |
| locations[0].landing_page_url | https://doi.org/10.3390/s20174824 |
| locations[1].id | pmid:32858983 |
| locations[1].is_oa | False |
| locations[1].source.id | https://openalex.org/S4306525036 |
| locations[1].source.issn | |
| locations[1].source.type | repository |
| locations[1].source.is_oa | False |
| locations[1].source.issn_l | |
| locations[1].source.is_core | False |
| locations[1].source.is_in_doaj | False |
| locations[1].source.display_name | PubMed |
| locations[1].source.host_organization | https://openalex.org/I1299303238 |
| locations[1].source.host_organization_name | National Institutes of Health |
| locations[1].source.host_organization_lineage | https://openalex.org/I1299303238 |
| locations[1].license | |
| locations[1].pdf_url | |
| locations[1].version | publishedVersion |
| locations[1].raw_type | |
| locations[1].license_id | |
| locations[1].is_accepted | True |
| locations[1].is_published | True |
| locations[1].raw_source_name | Sensors (Basel, Switzerland) |
| locations[1].landing_page_url | https://pubmed.ncbi.nlm.nih.gov/32858983 |
| locations[2].id | pmh:oai:doaj.org/article:31eb8fe339df4ccc89bf2504fc1182c6 |
| locations[2].is_oa | True |
| locations[2].source.id | https://openalex.org/S4306401280 |
| locations[2].source.issn | |
| locations[2].source.type | repository |
| locations[2].source.is_oa | False |
| locations[2].source.issn_l | |
| locations[2].source.is_core | False |
| locations[2].source.is_in_doaj | False |
| locations[2].source.display_name | DOAJ (DOAJ: Directory of Open Access Journals) |
| locations[2].source.host_organization | |
| locations[2].source.host_organization_name | |
| locations[2].license | cc-by-sa |
| locations[2].pdf_url | |
| locations[2].version | submittedVersion |
| locations[2].raw_type | article |
| locations[2].license_id | https://openalex.org/licenses/cc-by-sa |
| locations[2].is_accepted | False |
| locations[2].is_published | False |
| locations[2].raw_source_name | Sensors, Vol 20, Iss 17, p 4824 (2020) |
| locations[2].landing_page_url | https://doaj.org/article/31eb8fe339df4ccc89bf2504fc1182c6 |
| locations[3].id | pmh:oai:escholarship.org:ark:/13030/qt2vs28131 |
| locations[3].is_oa | True |
| locations[3].source | |
| locations[3].license | cc-by |
| locations[3].pdf_url | |
| locations[3].version | submittedVersion |
| locations[3].raw_type | article |
| locations[3].license_id | https://openalex.org/licenses/cc-by |
| locations[3].is_accepted | False |
| locations[3].is_published | False |
| locations[3].raw_source_name | Sensors, vol 20, iss 17 |
| locations[3].landing_page_url | https://escholarship.org/uc/item/2vs28131 |
| locations[4].id | pmh:oai:mdpi.com:/1424-8220/20/17/4824/ |
| locations[4].is_oa | True |
| locations[4].source.id | https://openalex.org/S4306400947 |
| locations[4].source.issn | |
| locations[4].source.type | repository |
| locations[4].source.is_oa | True |
| locations[4].source.issn_l | |
| locations[4].source.is_core | False |
| locations[4].source.is_in_doaj | False |
| locations[4].source.display_name | MDPI (MDPI AG) |
| locations[4].source.host_organization | https://openalex.org/I4210097602 |
| locations[4].source.host_organization_name | Multidisciplinary Digital Publishing Institute (Switzerland) |
| locations[4].source.host_organization_lineage | https://openalex.org/I4210097602 |
| locations[4].license | cc-by |
| locations[4].pdf_url | |
| locations[4].version | submittedVersion |
| locations[4].raw_type | Text |
| locations[4].license_id | https://openalex.org/licenses/cc-by |
| locations[4].is_accepted | False |
| locations[4].is_published | False |
| locations[4].raw_source_name | Sensors; Volume 20; Issue 17; Pages: 4824 |
| locations[4].landing_page_url | https://dx.doi.org/10.3390/s20174824 |
| locations[5].id | pmh:oai:pubmedcentral.nih.gov:7506926 |
| locations[5].is_oa | True |
| locations[5].source.id | https://openalex.org/S2764455111 |
| locations[5].source.issn | |
| locations[5].source.type | repository |
| locations[5].source.is_oa | False |
| locations[5].source.issn_l | |
| locations[5].source.is_core | False |
| locations[5].source.is_in_doaj | False |
| locations[5].source.display_name | PubMed Central |
| locations[5].source.host_organization | https://openalex.org/I1299303238 |
| locations[5].source.host_organization_name | National Institutes of Health |
| locations[5].source.host_organization_lineage | https://openalex.org/I1299303238 |
| locations[5].license | other-oa |
| locations[5].pdf_url | |
| locations[5].version | submittedVersion |
| locations[5].raw_type | Text |
| locations[5].license_id | https://openalex.org/licenses/other-oa |
| locations[5].is_accepted | False |
| locations[5].is_published | False |
| locations[5].raw_source_name | Sensors (Basel) |
| locations[5].landing_page_url | https://www.ncbi.nlm.nih.gov/pmc/articles/7506926 |
| indexed_in | crossref, doaj, pubmed |
| authorships[0].author.id | https://openalex.org/A5061514913 |
| authorships[0].author.orcid | https://orcid.org/0000-0002-5059-4826 |
| authorships[0].author.display_name | Daisik Nam |
| authorships[0].countries | US |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I204250578, https://openalex.org/I4210140791 |
| authorships[0].affiliations[0].raw_affiliation_string | Department of Civil and Environmental Engineering, Institute of Transportation Studies, University of California, Irvine, CA 92697, USA |
| authorships[0].institutions[0].id | https://openalex.org/I4210140791 |
| authorships[0].institutions[0].ror | https://ror.org/04ysmca02 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I4210140791 |
| authorships[0].institutions[0].country_code | US |
| authorships[0].institutions[0].display_name | Irvine University |
| authorships[0].institutions[1].id | https://openalex.org/I204250578 |
| authorships[0].institutions[1].ror | https://ror.org/04gyf1771 |
| authorships[0].institutions[1].type | education |
| authorships[0].institutions[1].lineage | https://openalex.org/I204250578 |
| authorships[0].institutions[1].country_code | US |
| authorships[0].institutions[1].display_name | University of California, Irvine |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Daisik Nam |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | Department of Civil and Environmental Engineering, Institute of Transportation Studies, University of California, Irvine, CA 92697, USA |
| authorships[1].author.id | https://openalex.org/A5108224921 |
| authorships[1].author.orcid | |
| authorships[1].author.display_name | Riju Lavanya |
| authorships[1].countries | US |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I204250578, https://openalex.org/I4210140791 |
| authorships[1].affiliations[0].raw_affiliation_string | Department of Civil and Environmental Engineering, Institute of Transportation Studies, University of California, Irvine, CA 92697, USA |
| authorships[1].institutions[0].id | https://openalex.org/I4210140791 |
| authorships[1].institutions[0].ror | https://ror.org/04ysmca02 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I4210140791 |
| authorships[1].institutions[0].country_code | US |
| authorships[1].institutions[0].display_name | Irvine University |
| authorships[1].institutions[1].id | https://openalex.org/I204250578 |
| authorships[1].institutions[1].ror | https://ror.org/04gyf1771 |
| authorships[1].institutions[1].type | education |
| authorships[1].institutions[1].lineage | https://openalex.org/I204250578 |
| authorships[1].institutions[1].country_code | US |
| authorships[1].institutions[1].display_name | University of California, Irvine |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Riju Lavanya |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | Department of Civil and Environmental Engineering, Institute of Transportation Studies, University of California, Irvine, CA 92697, USA |
| authorships[2].author.id | https://openalex.org/A5085499435 |
| authorships[2].author.orcid | https://orcid.org/0000-0002-0155-3622 |
| authorships[2].author.display_name | R. Jayakrishnan |
| authorships[2].countries | US |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I204250578, https://openalex.org/I4210140791 |
| authorships[2].affiliations[0].raw_affiliation_string | Department of Civil and Environmental Engineering, Institute of Transportation Studies, University of California, Irvine, CA 92697, USA |
| authorships[2].institutions[0].id | https://openalex.org/I4210140791 |
| authorships[2].institutions[0].ror | https://ror.org/04ysmca02 |
| authorships[2].institutions[0].type | education |
| authorships[2].institutions[0].lineage | https://openalex.org/I4210140791 |
| authorships[2].institutions[0].country_code | US |
| authorships[2].institutions[0].display_name | Irvine University |
| authorships[2].institutions[1].id | https://openalex.org/I204250578 |
| authorships[2].institutions[1].ror | https://ror.org/04gyf1771 |
| authorships[2].institutions[1].type | education |
| authorships[2].institutions[1].lineage | https://openalex.org/I204250578 |
| authorships[2].institutions[1].country_code | US |
| authorships[2].institutions[1].display_name | University of California, Irvine |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | R. Jayakrishnan |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | Department of Civil and Environmental Engineering, Institute of Transportation Studies, University of California, Irvine, CA 92697, USA |
| authorships[3].author.id | https://openalex.org/A5032929028 |
| authorships[3].author.orcid | https://orcid.org/0000-0002-3882-0157 |
| authorships[3].author.display_name | Inchul Yang |
| authorships[3].countries | KR |
| authorships[3].affiliations[0].institution_ids | https://openalex.org/I3019177047 |
| authorships[3].affiliations[0].raw_affiliation_string | Korea Institute of Civil Engineering and Building Technology, Goyang-si 10223, Korea |
| authorships[3].institutions[0].id | https://openalex.org/I3019177047 |
| authorships[3].institutions[0].ror | https://ror.org/035enhp47 |
| authorships[3].institutions[0].type | government |
| authorships[3].institutions[0].lineage | https://openalex.org/I3019177047 |
| authorships[3].institutions[0].country_code | KR |
| authorships[3].institutions[0].display_name | Korea Institute of Civil Engineering and Building Technology |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Inchul Yang |
| authorships[3].is_corresponding | True |
| authorships[3].raw_affiliation_strings | Korea Institute of Civil Engineering and Building Technology, Goyang-si 10223, Korea |
| authorships[4].author.id | https://openalex.org/A5066222580 |
| authorships[4].author.orcid | |
| authorships[4].author.display_name | Woo Hoon Jeon |
| authorships[4].countries | KR |
| authorships[4].affiliations[0].institution_ids | https://openalex.org/I3019177047 |
| authorships[4].affiliations[0].raw_affiliation_string | Korea Institute of Civil Engineering and Building Technology, Goyang-si 10223, Korea |
| authorships[4].institutions[0].id | https://openalex.org/I3019177047 |
| authorships[4].institutions[0].ror | https://ror.org/035enhp47 |
| authorships[4].institutions[0].type | government |
| authorships[4].institutions[0].lineage | https://openalex.org/I3019177047 |
| authorships[4].institutions[0].country_code | KR |
| authorships[4].institutions[0].display_name | Korea Institute of Civil Engineering and Building Technology |
| authorships[4].author_position | last |
| authorships[4].raw_author_name | Woo Hoon Jeon |
| authorships[4].is_corresponding | False |
| authorships[4].raw_affiliation_strings | Korea Institute of Civil Engineering and Building Technology, Goyang-si 10223, Korea |
| has_content.pdf | True |
| has_content.grobid_xml | True |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://www.mdpi.com/1424-8220/20/17/4824/pdf?version=1598493498 |
| open_access.oa_status | gold |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | A Deep Learning Approach for Estimating Traffic Density Using Data Obtained from Connected and Autonomous Probes |
| has_fulltext | True |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T11344 |
| primary_topic.field.id | https://openalex.org/fields/22 |
| primary_topic.field.display_name | Engineering |
| primary_topic.score | 1.0 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/2215 |
| primary_topic.subfield.display_name | Building and Construction |
| primary_topic.display_name | Traffic Prediction and Management Techniques |
| related_works | https://openalex.org/W2366906938, https://openalex.org/W4375867731, https://openalex.org/W2349391998, https://openalex.org/W4205655149, https://openalex.org/W2000775715, https://openalex.org/W2795393339, https://openalex.org/W2611989081, https://openalex.org/W1982014942, https://openalex.org/W2375665878, https://openalex.org/W2053400739 |
| cited_by_count | 19 |
| counts_by_year[0].year | 2025 |
| counts_by_year[0].cited_by_count | 3 |
| counts_by_year[1].year | 2024 |
| counts_by_year[1].cited_by_count | 3 |
| counts_by_year[2].year | 2023 |
| counts_by_year[2].cited_by_count | 5 |
| counts_by_year[3].year | 2022 |
| counts_by_year[3].cited_by_count | 4 |
| counts_by_year[4].year | 2021 |
| counts_by_year[4].cited_by_count | 3 |
| counts_by_year[5].year | 2020 |
| counts_by_year[5].cited_by_count | 1 |
| locations_count | 6 |
| best_oa_location.id | doi:10.3390/s20174824 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S101949793 |
| best_oa_location.source.issn | 1424-8220 |
| best_oa_location.source.type | journal |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | 1424-8220 |
| best_oa_location.source.is_core | True |
| best_oa_location.source.is_in_doaj | True |
| best_oa_location.source.display_name | Sensors |
| best_oa_location.source.host_organization | https://openalex.org/P4310310987 |
| best_oa_location.source.host_organization_name | Multidisciplinary Digital Publishing Institute |
| best_oa_location.source.host_organization_lineage | https://openalex.org/P4310310987 |
| best_oa_location.source.host_organization_lineage_names | Multidisciplinary Digital Publishing Institute |
| best_oa_location.license | cc-by |
| best_oa_location.pdf_url | https://www.mdpi.com/1424-8220/20/17/4824/pdf?version=1598493498 |
| best_oa_location.version | publishedVersion |
| best_oa_location.raw_type | journal-article |
| best_oa_location.license_id | https://openalex.org/licenses/cc-by |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | True |
| best_oa_location.raw_source_name | Sensors |
| best_oa_location.landing_page_url | https://doi.org/10.3390/s20174824 |
| primary_location.id | doi:10.3390/s20174824 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S101949793 |
| primary_location.source.issn | 1424-8220 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | 1424-8220 |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | True |
| primary_location.source.display_name | Sensors |
| primary_location.source.host_organization | https://openalex.org/P4310310987 |
| primary_location.source.host_organization_name | Multidisciplinary Digital Publishing Institute |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310310987 |
| primary_location.source.host_organization_lineage_names | Multidisciplinary Digital Publishing Institute |
| primary_location.license | cc-by |
| primary_location.pdf_url | https://www.mdpi.com/1424-8220/20/17/4824/pdf?version=1598493498 |
| primary_location.version | publishedVersion |
| primary_location.raw_type | journal-article |
| primary_location.license_id | https://openalex.org/licenses/cc-by |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | Sensors |
| primary_location.landing_page_url | https://doi.org/10.3390/s20174824 |
| publication_date | 2020-08-26 |
| publication_year | 2020 |
| referenced_works | https://openalex.org/W3044850563, https://openalex.org/W2978157075, https://openalex.org/W2199895291, https://openalex.org/W2031346385, https://openalex.org/W1971967146, https://openalex.org/W2198009849, https://openalex.org/W966802791, https://openalex.org/W6671194128, https://openalex.org/W2796340745, https://openalex.org/W1967205480, https://openalex.org/W3022132953, https://openalex.org/W149016369, https://openalex.org/W1510279885, https://openalex.org/W2049952439, https://openalex.org/W2004353783, https://openalex.org/W2533328922, https://openalex.org/W2593182953, https://openalex.org/W6735809762, https://openalex.org/W1498436455, https://openalex.org/W1971757341, https://openalex.org/W4236396647, https://openalex.org/W4244532478, https://openalex.org/W2992393179, https://openalex.org/W2064675550, https://openalex.org/W2083402998 |
| referenced_works_count | 25 |
| abstract_inverted_index.a | 70, 162, 174 |
| abstract_inverted_index.At | 155 |
| abstract_inverted_index.We | 168 |
| abstract_inverted_index.an | 23, 125 |
| abstract_inverted_index.as | 31, 102 |
| abstract_inverted_index.be | 58 |
| abstract_inverted_index.by | 49, 172 |
| abstract_inverted_index.in | 110, 188 |
| abstract_inverted_index.is | 5, 122, 145 |
| abstract_inverted_index.of | 2, 9, 42, 51, 81, 89, 139, 152, 166 |
| abstract_inverted_index.on | 6, 124 |
| abstract_inverted_index.to | 47, 147 |
| abstract_inverted_index.Our | 120 |
| abstract_inverted_index.The | 0, 142 |
| abstract_inverted_index.and | 17, 27, 45, 76, 116, 179, 191 |
| abstract_inverted_index.are | 87 |
| abstract_inverted_index.can | 57 |
| abstract_inverted_index.for | 60, 98, 132 |
| abstract_inverted_index.has | 69 |
| abstract_inverted_index.our | 170, 182 |
| abstract_inverted_index.the | 7, 40, 82, 96, 133, 149, 156, 159 |
| abstract_inverted_index.CAPs | 21, 34 |
| abstract_inverted_index.Deep | 106 |
| abstract_inverted_index.LSTM | 126 |
| abstract_inverted_index.data | 13, 56 |
| abstract_inverted_index.door | 97 |
| abstract_inverted_index.from | 12, 15 |
| abstract_inverted_index.loop | 32 |
| abstract_inverted_index.more | 61 |
| abstract_inverted_index.over | 25 |
| abstract_inverted_index.pose | 22 |
| abstract_inverted_index.same | 157 |
| abstract_inverted_index.such | 30, 101 |
| abstract_inverted_index.that | 181 |
| abstract_inverted_index.them | 48 |
| abstract_inverted_index.this | 3 |
| abstract_inverted_index.time | 137 |
| abstract_inverted_index.used | 59 |
| abstract_inverted_index.with | 91, 136 |
| abstract_inverted_index.(Long | 127 |
| abstract_inverted_index.based | 123 |
| abstract_inverted_index.excel | 109 |
| abstract_inverted_index.flow. | 141 |
| abstract_inverted_index.focus | 1 |
| abstract_inverted_index.forms | 80 |
| abstract_inverted_index.learn | 148 |
| abstract_inverted_index.means | 50 |
| abstract_inverted_index.model | 183 |
| abstract_inverted_index.opens | 95 |
| abstract_inverted_index.their | 36 |
| abstract_inverted_index.time, | 158 |
| abstract_inverted_index.using | 173 |
| abstract_inverted_index.which | 94, 113 |
| abstract_inverted_index.whose | 55 |
| abstract_inverted_index.Probes | 19 |
| abstract_inverted_index.during | 74 |
| abstract_inverted_index.highly | 71, 117 |
| abstract_inverted_index.method | 144, 160 |
| abstract_inverted_index.nature | 73 |
| abstract_inverted_index.neural | 130 |
| abstract_inverted_index.(CAPs). | 20 |
| abstract_inverted_index.Traffic | 67 |
| abstract_inverted_index.complex | 92 |
| abstract_inverted_index.dealing | 90 |
| abstract_inverted_index.density | 11, 64, 68, 84, 187 |
| abstract_inverted_index.driving | 37 |
| abstract_inverted_index.machine | 103 |
| abstract_inverted_index.memory) | 129 |
| abstract_inverted_index.network | 131 |
| abstract_inverted_index.pattern | 165 |
| abstract_inverted_index.program | 177 |
| abstract_inverted_index.sensing | 39 |
| abstract_inverted_index.several | 52 |
| abstract_inverted_index.traffic | 10, 63, 140, 186 |
| abstract_inverted_index.Edie’s | 153 |
| abstract_inverted_index.adjacent | 43 |
| abstract_inverted_index.designed | 146 |
| abstract_inverted_index.dynamics | 138 |
| abstract_inverted_index.evaluate | 169 |
| abstract_inverted_index.invasive | 28 |
| abstract_inverted_index.learning | 104, 107 |
| abstract_inverted_index.maneuver | 35 |
| abstract_inverted_index.obtained | 14 |
| abstract_inverted_index.presence | 41 |
| abstract_inverted_index.proposed | 143 |
| abstract_inverted_index.relation | 151 |
| abstract_inverted_index.research | 4, 121 |
| abstract_inverted_index.sensors, | 54 |
| abstract_inverted_index.traffic. | 167 |
| abstract_inverted_index.vehicles | 44 |
| abstract_inverted_index.Congested | 192 |
| abstract_inverted_index.Connected | 16 |
| abstract_inverted_index.advantage | 24 |
| abstract_inverted_index.algorithm | 171 |
| abstract_inverted_index.contexts, | 112 |
| abstract_inverted_index.data-rich | 111 |
| abstract_inverted_index.distances | 46 |
| abstract_inverted_index.estimates | 185 |
| abstract_inverted_index.expensive | 26 |
| abstract_inverted_index.incapable | 88 |
| abstract_inverted_index.nonlinear | 72, 115, 164 |
| abstract_inverted_index.patterns. | 119 |
| abstract_inverted_index.recognize | 114 |
| abstract_inverted_index.(PARAMICS) | 178 |
| abstract_inverted_index.Autonomous | 18 |
| abstract_inverted_index.Free-flow, | 189 |
| abstract_inverted_index.accurately | 184 |
| abstract_inverted_index.algorithms | 108 |
| abstract_inverted_index.approaches | 100 |
| abstract_inverted_index.associated | 135 |
| abstract_inverted_index.detectors. | 33 |
| abstract_inverted_index.electronic | 53 |
| abstract_inverted_index.estimation | 8, 65, 85 |
| abstract_inverted_index.recognizes | 161 |
| abstract_inverted_index.short-term | 128 |
| abstract_inverted_index.simulation | 176 |
| abstract_inverted_index.techniques | 86 |
| abstract_inverted_index.temporally | 163 |
| abstract_inverted_index.Transition, | 190 |
| abstract_inverted_index.conditions. | 78, 193 |
| abstract_inverted_index.data-driven | 99 |
| abstract_inverted_index.definition. | 154 |
| abstract_inverted_index.demonstrate | 180 |
| abstract_inverted_index.microscopic | 175 |
| abstract_inverted_index.techniques. | 66, 105 |
| abstract_inverted_index.traditional | 83 |
| abstract_inverted_index.input-output | 150 |
| abstract_inverted_index.nonlinearity | 134 |
| abstract_inverted_index.on-congestion | 75 |
| abstract_inverted_index.sophisticated | 62 |
| abstract_inverted_index.trajectories, | 38 |
| abstract_inverted_index.infrastructure | 29 |
| abstract_inverted_index.queue-clearing | 77 |
| abstract_inverted_index.nonlinearities, | 93 |
| abstract_inverted_index.Closed-mathematical | 79 |
| abstract_inverted_index.situation-dependent | 118 |
| cited_by_percentile_year.max | 98 |
| cited_by_percentile_year.min | 89 |
| corresponding_author_ids | https://openalex.org/A5032929028 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 5 |
| corresponding_institution_ids | https://openalex.org/I3019177047 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/9 |
| sustainable_development_goals[0].score | 0.6499999761581421 |
| sustainable_development_goals[0].display_name | Industry, innovation and infrastructure |
| citation_normalized_percentile.value | 0.82646595 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |