Improving Transportation Information Resilience: Error Estimation for Networked Sensor Data Article Swipe
Yueyue Fan
,
Han Yang
,
Saurabh Maheshwari
,
Yudi Yang
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.7922/g2610xk9
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.7922/g2610xk9
Author(s): Fan, Yueyue; Yang, Han; Maheshwari, Saurabh; Yang, Yudi | Abstract: Nowadays, the effectiveness of any smart transportation management or control strategy would heavily depend on reliable traffic data collected by sensors. Two problems regarding sensor data quality have received attention: first, the problem of identifying malfunctioning sensors; second, reconstruction of traffic flow. Most existing studies concerned about identifying completely malfunctioning sensors whose data should be discarded. This project focuses on the problem of error detection and data recovery of partially malfunctioning sensors that could provide valuable information. By integrating a sensor measurement error model and a transportation network model, the authors propose a Generalized Method of Moments (GMM) based estimation approach to determine the parameters of systematic and random errors of traffic sensors in a road network. The proposed method allows flexible data aggregation that ameliorates identification and accuracy. The estimates regarding both systematic and random errors are utilized to conduct hypothesis test on sensor health and to estimate true traffic flows with observed counts. The results of three network examples with different scales demonstrate the applicability of the proposed method in a large variety of scenarios. This research improves fundamental knowledge on transportation data analytics as well as the effective management of data and information infrastructure in transportation practice.View the NCST Project Webpage
Related Topics
Concepts
Resilience (materials science)
Computer science
Data mining
Identification (biology)
Wireless sensor network
Intelligent transportation system
Data quality
Traffic flow (computer networking)
Real-time computing
Engineering
Transport engineering
Computer security
Computer network
Metric (unit)
Operations management
Botany
Biology
Physics
Thermodynamics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.7922/g2610xk9
- OA Status
- green
- Cited By
- 1
- Related Works
- 20
- OpenAlex ID
- https://openalex.org/W3083596311
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W3083596311Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.7922/g2610xk9Digital Object Identifier
- Title
-
Improving Transportation Information Resilience: Error Estimation for Networked Sensor DataWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2020Year of publication
- Publication date
-
2020-01-01Full publication date if available
- Authors
-
Yueyue Fan, Han Yang, Saurabh Maheshwari, Yudi YangList of authors in order
- Landing page
-
https://doi.org/10.7922/g2610xk9Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.7922/g2610xk9Direct OA link when available
- Concepts
-
Resilience (materials science), Computer science, Data mining, Identification (biology), Wireless sensor network, Intelligent transportation system, Data quality, Traffic flow (computer networking), Real-time computing, Engineering, Transport engineering, Computer security, Computer network, Metric (unit), Operations management, Botany, Biology, Physics, ThermodynamicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
-
2024: 1Per-year citation counts (last 5 years)
- Related works (count)
-
20Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W3083596311 |
|---|---|
| doi | https://doi.org/10.7922/g2610xk9 |
| ids.doi | https://doi.org/10.7922/g2610xk9 |
| ids.mag | 3083596311 |
| ids.openalex | https://openalex.org/W3083596311 |
| fwci | 0.0 |
| type | article |
| title | Improving Transportation Information Resilience: Error Estimation for Networked Sensor Data |
| biblio.issue | |
| biblio.volume | |
| biblio.last_page | |
| biblio.first_page | |
| 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 | 0.9872999787330627 |
| 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/T11357 |
| topics[1].field.id | https://openalex.org/fields/18 |
| topics[1].field.display_name | Decision Sciences |
| topics[1].score | 0.9490000009536743 |
| topics[1].domain.id | https://openalex.org/domains/2 |
| topics[1].domain.display_name | Social Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/1804 |
| topics[1].subfield.display_name | Statistics, Probability and Uncertainty |
| topics[1].display_name | Risk and Safety Analysis |
| topics[2].id | https://openalex.org/T11512 |
| topics[2].field.id | https://openalex.org/fields/17 |
| topics[2].field.display_name | Computer Science |
| topics[2].score | 0.942799985408783 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/1702 |
| topics[2].subfield.display_name | Artificial Intelligence |
| topics[2].display_name | Anomaly Detection Techniques and Applications |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C2779585090 |
| concepts[0].level | 2 |
| concepts[0].score | 0.6794884204864502 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q3457762 |
| concepts[0].display_name | Resilience (materials science) |
| concepts[1].id | https://openalex.org/C41008148 |
| concepts[1].level | 0 |
| concepts[1].score | 0.6633107662200928 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[1].display_name | Computer science |
| concepts[2].id | https://openalex.org/C124101348 |
| concepts[2].level | 1 |
| concepts[2].score | 0.6128872632980347 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q172491 |
| concepts[2].display_name | Data mining |
| concepts[3].id | https://openalex.org/C116834253 |
| concepts[3].level | 2 |
| concepts[3].score | 0.5668914318084717 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q2039217 |
| concepts[3].display_name | Identification (biology) |
| concepts[4].id | https://openalex.org/C24590314 |
| concepts[4].level | 2 |
| concepts[4].score | 0.5523307919502258 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q336038 |
| concepts[4].display_name | Wireless sensor network |
| concepts[5].id | https://openalex.org/C47796450 |
| concepts[5].level | 2 |
| concepts[5].score | 0.5000813007354736 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q508378 |
| concepts[5].display_name | Intelligent transportation system |
| concepts[6].id | https://openalex.org/C24756922 |
| concepts[6].level | 3 |
| concepts[6].score | 0.4489617943763733 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q1757694 |
| concepts[6].display_name | Data quality |
| concepts[7].id | https://openalex.org/C207512268 |
| concepts[7].level | 2 |
| concepts[7].score | 0.4119737148284912 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q3074551 |
| concepts[7].display_name | Traffic flow (computer networking) |
| concepts[8].id | https://openalex.org/C79403827 |
| concepts[8].level | 1 |
| concepts[8].score | 0.3221302032470703 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q3988 |
| concepts[8].display_name | Real-time computing |
| concepts[9].id | https://openalex.org/C127413603 |
| concepts[9].level | 0 |
| concepts[9].score | 0.20601284503936768 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q11023 |
| concepts[9].display_name | Engineering |
| concepts[10].id | https://openalex.org/C22212356 |
| concepts[10].level | 1 |
| concepts[10].score | 0.18177157640457153 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q775325 |
| concepts[10].display_name | Transport engineering |
| concepts[11].id | https://openalex.org/C38652104 |
| concepts[11].level | 1 |
| concepts[11].score | 0.16755038499832153 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q3510521 |
| concepts[11].display_name | Computer security |
| concepts[12].id | https://openalex.org/C31258907 |
| concepts[12].level | 1 |
| concepts[12].score | 0.08983898162841797 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q1301371 |
| concepts[12].display_name | Computer network |
| concepts[13].id | https://openalex.org/C176217482 |
| concepts[13].level | 2 |
| concepts[13].score | 0.0 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q860554 |
| concepts[13].display_name | Metric (unit) |
| concepts[14].id | https://openalex.org/C21547014 |
| concepts[14].level | 1 |
| concepts[14].score | 0.0 |
| concepts[14].wikidata | https://www.wikidata.org/wiki/Q1423657 |
| concepts[14].display_name | Operations management |
| concepts[15].id | https://openalex.org/C59822182 |
| concepts[15].level | 1 |
| concepts[15].score | 0.0 |
| concepts[15].wikidata | https://www.wikidata.org/wiki/Q441 |
| concepts[15].display_name | Botany |
| concepts[16].id | https://openalex.org/C86803240 |
| concepts[16].level | 0 |
| concepts[16].score | 0.0 |
| concepts[16].wikidata | https://www.wikidata.org/wiki/Q420 |
| concepts[16].display_name | Biology |
| concepts[17].id | https://openalex.org/C121332964 |
| concepts[17].level | 0 |
| concepts[17].score | 0.0 |
| concepts[17].wikidata | https://www.wikidata.org/wiki/Q413 |
| concepts[17].display_name | Physics |
| concepts[18].id | https://openalex.org/C97355855 |
| concepts[18].level | 1 |
| concepts[18].score | 0.0 |
| concepts[18].wikidata | https://www.wikidata.org/wiki/Q11473 |
| concepts[18].display_name | Thermodynamics |
| keywords[0].id | https://openalex.org/keywords/resilience |
| keywords[0].score | 0.6794884204864502 |
| keywords[0].display_name | Resilience (materials science) |
| keywords[1].id | https://openalex.org/keywords/computer-science |
| keywords[1].score | 0.6633107662200928 |
| keywords[1].display_name | Computer science |
| keywords[2].id | https://openalex.org/keywords/data-mining |
| keywords[2].score | 0.6128872632980347 |
| keywords[2].display_name | Data mining |
| keywords[3].id | https://openalex.org/keywords/identification |
| keywords[3].score | 0.5668914318084717 |
| keywords[3].display_name | Identification (biology) |
| keywords[4].id | https://openalex.org/keywords/wireless-sensor-network |
| keywords[4].score | 0.5523307919502258 |
| keywords[4].display_name | Wireless sensor network |
| keywords[5].id | https://openalex.org/keywords/intelligent-transportation-system |
| keywords[5].score | 0.5000813007354736 |
| keywords[5].display_name | Intelligent transportation system |
| keywords[6].id | https://openalex.org/keywords/data-quality |
| keywords[6].score | 0.4489617943763733 |
| keywords[6].display_name | Data quality |
| keywords[7].id | https://openalex.org/keywords/traffic-flow |
| keywords[7].score | 0.4119737148284912 |
| keywords[7].display_name | Traffic flow (computer networking) |
| keywords[8].id | https://openalex.org/keywords/real-time-computing |
| keywords[8].score | 0.3221302032470703 |
| keywords[8].display_name | Real-time computing |
| keywords[9].id | https://openalex.org/keywords/engineering |
| keywords[9].score | 0.20601284503936768 |
| keywords[9].display_name | Engineering |
| keywords[10].id | https://openalex.org/keywords/transport-engineering |
| keywords[10].score | 0.18177157640457153 |
| keywords[10].display_name | Transport engineering |
| keywords[11].id | https://openalex.org/keywords/computer-security |
| keywords[11].score | 0.16755038499832153 |
| keywords[11].display_name | Computer security |
| keywords[12].id | https://openalex.org/keywords/computer-network |
| keywords[12].score | 0.08983898162841797 |
| keywords[12].display_name | Computer network |
| language | en |
| locations[0].id | doi:10.7922/g2610xk9 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S7407050879 |
| locations[0].source.type | repository |
| locations[0].source.is_oa | False |
| locations[0].source.issn_l | |
| locations[0].source.is_core | False |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | UC Berkeley |
| locations[0].source.host_organization | |
| locations[0].source.host_organization_name | |
| locations[0].license | |
| locations[0].pdf_url | |
| locations[0].version | |
| locations[0].raw_type | article-journal |
| locations[0].license_id | |
| locations[0].is_accepted | False |
| locations[0].is_published | |
| locations[0].raw_source_name | |
| locations[0].landing_page_url | https://doi.org/10.7922/g2610xk9 |
| locations[1].id | mag:3083596311 |
| locations[1].is_oa | False |
| locations[1].source | |
| locations[1].license | |
| locations[1].pdf_url | |
| locations[1].version | |
| locations[1].raw_type | |
| locations[1].license_id | |
| locations[1].is_accepted | False |
| locations[1].is_published | |
| locations[1].raw_source_name | |
| locations[1].landing_page_url | https://escholarship.org/content/qt3t15p3cs/qt3t15p3cs.pdf?t=qe8dns |
| indexed_in | datacite |
| authorships[0].author.id | https://openalex.org/A5033144234 |
| authorships[0].author.orcid | https://orcid.org/0000-0002-6615-2029 |
| authorships[0].author.display_name | Yueyue Fan |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Yueyue Fan |
| authorships[0].is_corresponding | False |
| authorships[1].author.id | https://openalex.org/A5109214830 |
| authorships[1].author.orcid | https://orcid.org/0009-0006-8576-6698 |
| authorships[1].author.display_name | Han Yang |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Han Yang |
| authorships[1].is_corresponding | False |
| authorships[2].author.id | https://openalex.org/A5062103092 |
| authorships[2].author.orcid | https://orcid.org/0000-0003-3638-7545 |
| authorships[2].author.display_name | Saurabh Maheshwari |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Saurabh Maheshwari |
| authorships[2].is_corresponding | False |
| authorships[3].author.id | https://openalex.org/A5101091242 |
| authorships[3].author.orcid | |
| authorships[3].author.display_name | Yudi Yang |
| authorships[3].author_position | last |
| authorships[3].raw_author_name | Yudi Yang |
| authorships[3].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/10.7922/g2610xk9 |
| open_access.oa_status | green |
| open_access.any_repository_has_fulltext | False |
| created_date | 2020-09-11T00:00:00 |
| display_name | Improving Transportation Information Resilience: Error Estimation for Networked Sensor Data |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T06:51:31.235846 |
| 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 | 0.9872999787330627 |
| 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/W2955843591, https://openalex.org/W2399880423, https://openalex.org/W2750594111, https://openalex.org/W342925337, https://openalex.org/W611293336, https://openalex.org/W822479718, https://openalex.org/W3147948705, https://openalex.org/W2797047604, https://openalex.org/W3185869614, https://openalex.org/W3008621121, https://openalex.org/W2962706255, https://openalex.org/W802684990, https://openalex.org/W2056949014, https://openalex.org/W2782932266, https://openalex.org/W3047412441, https://openalex.org/W3211569424, https://openalex.org/W2735779099, https://openalex.org/W2921142444, https://openalex.org/W3112637509, https://openalex.org/W1864680547 |
| cited_by_count | 1 |
| counts_by_year[0].year | 2024 |
| counts_by_year[0].cited_by_count | 1 |
| locations_count | 2 |
| best_oa_location.id | doi:10.7922/g2610xk9 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S7407050879 |
| best_oa_location.source.type | repository |
| best_oa_location.source.is_oa | False |
| best_oa_location.source.issn_l | |
| best_oa_location.source.is_core | False |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | UC Berkeley |
| best_oa_location.source.host_organization | |
| best_oa_location.source.host_organization_name | |
| best_oa_location.license | |
| best_oa_location.pdf_url | |
| best_oa_location.version | |
| best_oa_location.raw_type | article-journal |
| best_oa_location.license_id | |
| best_oa_location.is_accepted | False |
| best_oa_location.is_published | False |
| best_oa_location.raw_source_name | |
| best_oa_location.landing_page_url | https://doi.org/10.7922/g2610xk9 |
| primary_location.id | doi:10.7922/g2610xk9 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S7407050879 |
| primary_location.source.type | repository |
| primary_location.source.is_oa | False |
| primary_location.source.issn_l | |
| primary_location.source.is_core | False |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | UC Berkeley |
| primary_location.source.host_organization | |
| primary_location.source.host_organization_name | |
| primary_location.license | |
| primary_location.pdf_url | |
| primary_location.version | |
| primary_location.raw_type | article-journal |
| primary_location.license_id | |
| primary_location.is_accepted | False |
| primary_location.is_published | False |
| primary_location.raw_source_name | |
| primary_location.landing_page_url | https://doi.org/10.7922/g2610xk9 |
| publication_date | 2020-01-01 |
| publication_year | 2020 |
| referenced_works_count | 0 |
| abstract_inverted_index.a | 90, 96, 103, 125, 183 |
| abstract_inverted_index.| | 9 |
| abstract_inverted_index.By | 88 |
| abstract_inverted_index.as | 197, 199 |
| abstract_inverted_index.be | 65 |
| abstract_inverted_index.by | 30 |
| abstract_inverted_index.in | 124, 182, 208 |
| abstract_inverted_index.of | 14, 44, 50, 73, 79, 106, 116, 121, 168, 178, 186, 203 |
| abstract_inverted_index.on | 25, 70, 154, 193 |
| abstract_inverted_index.or | 19 |
| abstract_inverted_index.to | 112, 150, 158 |
| abstract_inverted_index.The | 128, 140, 166 |
| abstract_inverted_index.Two | 32 |
| abstract_inverted_index.and | 76, 95, 118, 138, 145, 157, 205 |
| abstract_inverted_index.any | 15 |
| abstract_inverted_index.are | 148 |
| abstract_inverted_index.the | 12, 42, 71, 100, 114, 176, 179, 200, 211 |
| abstract_inverted_index.Fan, | 1 |
| abstract_inverted_index.Han; | 4 |
| abstract_inverted_index.Most | 53 |
| abstract_inverted_index.NCST | 212 |
| abstract_inverted_index.This | 67, 188 |
| abstract_inverted_index.Yudi | 8 |
| abstract_inverted_index.both | 143 |
| abstract_inverted_index.data | 28, 36, 63, 77, 133, 195, 204 |
| abstract_inverted_index.have | 38 |
| abstract_inverted_index.road | 126 |
| abstract_inverted_index.test | 153 |
| abstract_inverted_index.that | 83, 135 |
| abstract_inverted_index.true | 160 |
| abstract_inverted_index.well | 198 |
| abstract_inverted_index.with | 163, 172 |
| abstract_inverted_index.(GMM) | 108 |
| abstract_inverted_index.Yang, | 3, 7 |
| abstract_inverted_index.about | 57 |
| abstract_inverted_index.based | 109 |
| abstract_inverted_index.could | 84 |
| abstract_inverted_index.error | 74, 93 |
| abstract_inverted_index.flow. | 52 |
| abstract_inverted_index.flows | 162 |
| abstract_inverted_index.large | 184 |
| abstract_inverted_index.model | 94 |
| abstract_inverted_index.smart | 16 |
| abstract_inverted_index.three | 169 |
| abstract_inverted_index.whose | 62 |
| abstract_inverted_index.would | 22 |
| abstract_inverted_index.Method | 105 |
| abstract_inverted_index.allows | 131 |
| abstract_inverted_index.depend | 24 |
| abstract_inverted_index.errors | 120, 147 |
| abstract_inverted_index.first, | 41 |
| abstract_inverted_index.health | 156 |
| abstract_inverted_index.method | 130, 181 |
| abstract_inverted_index.model, | 99 |
| abstract_inverted_index.random | 119, 146 |
| abstract_inverted_index.scales | 174 |
| abstract_inverted_index.sensor | 35, 91, 155 |
| abstract_inverted_index.should | 64 |
| abstract_inverted_index.Moments | 107 |
| abstract_inverted_index.Project | 213 |
| abstract_inverted_index.Webpage | 214 |
| abstract_inverted_index.Yueyue; | 2 |
| abstract_inverted_index.authors | 101 |
| abstract_inverted_index.conduct | 151 |
| abstract_inverted_index.control | 20 |
| abstract_inverted_index.counts. | 165 |
| abstract_inverted_index.focuses | 69 |
| abstract_inverted_index.heavily | 23 |
| abstract_inverted_index.network | 98, 170 |
| abstract_inverted_index.problem | 43, 72 |
| abstract_inverted_index.project | 68 |
| abstract_inverted_index.propose | 102 |
| abstract_inverted_index.provide | 85 |
| abstract_inverted_index.quality | 37 |
| abstract_inverted_index.results | 167 |
| abstract_inverted_index.second, | 48 |
| abstract_inverted_index.sensors | 61, 82, 123 |
| abstract_inverted_index.studies | 55 |
| abstract_inverted_index.traffic | 27, 51, 122, 161 |
| abstract_inverted_index.variety | 185 |
| abstract_inverted_index.Saurabh; | 6 |
| abstract_inverted_index.approach | 111 |
| abstract_inverted_index.estimate | 159 |
| abstract_inverted_index.examples | 171 |
| abstract_inverted_index.existing | 54 |
| abstract_inverted_index.flexible | 132 |
| abstract_inverted_index.improves | 190 |
| abstract_inverted_index.network. | 127 |
| abstract_inverted_index.observed | 164 |
| abstract_inverted_index.problems | 33 |
| abstract_inverted_index.proposed | 129, 180 |
| abstract_inverted_index.received | 39 |
| abstract_inverted_index.recovery | 78 |
| abstract_inverted_index.reliable | 26 |
| abstract_inverted_index.research | 189 |
| abstract_inverted_index.sensors. | 31 |
| abstract_inverted_index.sensors; | 47 |
| abstract_inverted_index.strategy | 21 |
| abstract_inverted_index.utilized | 149 |
| abstract_inverted_index.valuable | 86 |
| abstract_inverted_index.Abstract: | 10 |
| abstract_inverted_index.Nowadays, | 11 |
| abstract_inverted_index.accuracy. | 139 |
| abstract_inverted_index.analytics | 196 |
| abstract_inverted_index.collected | 29 |
| abstract_inverted_index.concerned | 56 |
| abstract_inverted_index.detection | 75 |
| abstract_inverted_index.determine | 113 |
| abstract_inverted_index.different | 173 |
| abstract_inverted_index.effective | 201 |
| abstract_inverted_index.estimates | 141 |
| abstract_inverted_index.knowledge | 192 |
| abstract_inverted_index.partially | 80 |
| abstract_inverted_index.regarding | 34, 142 |
| abstract_inverted_index.Author(s): | 0 |
| abstract_inverted_index.attention: | 40 |
| abstract_inverted_index.completely | 59 |
| abstract_inverted_index.discarded. | 66 |
| abstract_inverted_index.estimation | 110 |
| abstract_inverted_index.hypothesis | 152 |
| abstract_inverted_index.management | 18, 202 |
| abstract_inverted_index.parameters | 115 |
| abstract_inverted_index.scenarios. | 187 |
| abstract_inverted_index.systematic | 117, 144 |
| abstract_inverted_index.Generalized | 104 |
| abstract_inverted_index.Maheshwari, | 5 |
| abstract_inverted_index.aggregation | 134 |
| abstract_inverted_index.ameliorates | 136 |
| abstract_inverted_index.demonstrate | 175 |
| abstract_inverted_index.fundamental | 191 |
| abstract_inverted_index.identifying | 45, 58 |
| abstract_inverted_index.information | 206 |
| abstract_inverted_index.integrating | 89 |
| abstract_inverted_index.measurement | 92 |
| abstract_inverted_index.information. | 87 |
| abstract_inverted_index.applicability | 177 |
| abstract_inverted_index.effectiveness | 13 |
| abstract_inverted_index.practice.View | 210 |
| abstract_inverted_index.identification | 137 |
| abstract_inverted_index.infrastructure | 207 |
| abstract_inverted_index.malfunctioning | 46, 60, 81 |
| abstract_inverted_index.reconstruction | 49 |
| abstract_inverted_index.transportation | 17, 97, 194, 209 |
| cited_by_percentile_year.max | 94 |
| cited_by_percentile_year.min | 90 |
| countries_distinct_count | 0 |
| institutions_distinct_count | 4 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/9 |
| sustainable_development_goals[0].score | 0.6600000262260437 |
| sustainable_development_goals[0].display_name | Industry, innovation and infrastructure |
| citation_normalized_percentile.value | 0.11574781 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |