{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:31:01Z","timestamp":1760146261751,"version":"build-2065373602"},"reference-count":33,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2024,10,18]],"date-time":"2024-10-18T00:00:00Z","timestamp":1729209600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100007664","name":"Georgia Department of Transportation, United States","doi-asserted-by":"publisher","award":["RP 20-28"],"award-info":[{"award-number":["RP 20-28"]}],"id":[{"id":"10.13039\/100007664","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>Traffic sensors are vital to the development and operation of Intelligent Transportation Systems, providing essential data for traffic monitoring, management, and transportation infrastructure planning. However, optimizing the placement of these sensors, particularly across large and complex statewide highway networks, remains a challenging task. In this research, we presented a novel search algorithm designed to address this challenge by leveraging information gradients from K-nearest neighbors within an embedding space. Our method enabled more informed and strategic sensor placement under budget and resource constraints, enhancing overall network coverage and data quality. Additionally, we incorporated spatial kriging analysis, harnessing spatial correlations of existing sensors to refine and reduce the search space. Our proposed approach was tested against the widely used Genetic Algorithm, demonstrating superior efficiency in terms of convergence time and producing more effective solutions with reduced information loss.<\/jats:p>","DOI":"10.3390\/info15100654","type":"journal-article","created":{"date-parts":[[2024,10,21]],"date-time":"2024-10-21T10:49:22Z","timestamp":1729507762000},"page":"654","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["An Information Gradient Approach to Optimizing Traffic Sensor Placement in Statewide Networks"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-2000-2361","authenticated-orcid":false,"given":"Yunxiang","family":"Yang","sequence":"first","affiliation":[{"name":"Smart Mobility and Infrastructure Laboratory, College of Engineering, University of Georgia, Athens, GA 30605, USA"}]},{"given":"Hao","family":"Zhen","sequence":"additional","affiliation":[{"name":"Smart Mobility and Infrastructure Laboratory, College of Engineering, University of Georgia, Athens, GA 30605, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4823-6322","authenticated-orcid":false,"given":"Jidong J.","family":"Yang","sequence":"additional","affiliation":[{"name":"Smart Mobility and Infrastructure Laboratory, College of Engineering, University of Georgia, Athens, GA 30605, USA"}]}],"member":"1968","published-online":{"date-parts":[[2024,10,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Cirianni, F.M.M., Comi, A., and Quattrone, A. (2023). Mobility Control Centre and Artificial Intelligence for Sustainable Urban Districts. Information, 14.","DOI":"10.3390\/info14100581"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Alonso, B., Musolino, G., Rindone, C., and Vitetta, A. (2023). Estimation of a Fundamental Diagram with Heterogeneous Data Sources: Experimentation in the City of Santander. ISPRS Int. J. Geo-Inf., 12.","DOI":"10.3390\/ijgi12100418"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2018\/6518329","article-title":"Passenger Mobility in a Discontinuous Space: Modelling Access\/Egress to Maritime Barrier in a Case Study","volume":"2018","author":"Birgillito","year":"2018","journal-title":"J. Adv. Transp."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"118134","DOI":"10.1016\/j.eswa.2022.118134","article-title":"Traffic Sensor Location Problem: Three decades of research","volume":"208","author":"Owais","year":"2022","journal-title":"Expert Syst. Appl."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"103","DOI":"10.3141\/2049-12","article-title":"Sensor locations for reliable travel time prediction and dynamic management of traffic networks","volume":"2049","author":"Viti","year":"2008","journal-title":"Transp. Res. Rec. J. Transp. Res. Board"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1301","DOI":"10.1007\/s40999-020-00537-0","article-title":"Distributing portable excess speed detectors in AL Riyadh city","volume":"18","author":"Mahmoud","year":"2020","journal-title":"Int. J. Civ. Eng."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1016\/j.trc.2012.01.004","article-title":"Locating sensors on traffic networks: Models, challenges and research opportunities","volume":"24","author":"Gentili","year":"2012","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"216","DOI":"10.1016\/j.trb.2019.01.004","article-title":"Optimization of traffic sensor location for complete link flow observability in traffic network considering sensor failure","volume":"121","author":"Salari","year":"2019","journal-title":"Transp. Res. Part B Methodol."},{"key":"ref_9","first-page":"100100","article-title":"Sensor location model for O\/D estimation: Multi-criteria meta-heuristics approach","volume":"6","author":"Owais","year":"2019","journal-title":"Oper. Res. Perspect."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1016\/j.trb.2023.02.008","article-title":"Submodularity of optimal sensor placement for Traffic Networks","volume":"171","author":"Li","year":"2023","journal-title":"Transp. Res. Part B Methodol."},{"key":"ref_11","unstructured":"(2024, August 17). Georgia\u2019s Traffic Monitoring Program, Available online: https:\/\/www.dot.ga.gov\/DriveSmart\/Data\/Documents\/Guides\/2018_Georgia_Traffic_Monitoring_Program.pdf."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"108174","DOI":"10.1016\/j.patcog.2021.108174","article-title":"Graph representation learning for road type classification","volume":"120","author":"Gharaee","year":"2021","journal-title":"Pattern Recognit."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Yang, Y., and Yang, J.J. (2023). Strategic Sensor Placement in Expansive Highway Networks: A Novel Framework for Maximizing Information Gain. Systems, 11.","DOI":"10.3390\/systems11120577"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"3797","DOI":"10.1109\/TIT.2014.2320500","article-title":"R\u00e9nyi Divergence and Kullback-Leibler Divergence","volume":"60","author":"Harremoes","year":"2014","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Grover, A., and Leskovec, J. (2016, January 13\u201317). Node2vec: Scalable Feature Learning for Networks. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA.","DOI":"10.1145\/2939672.2939754"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"McInnes, L., Healy, J., and Melville, J. (2018). UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction. arXiv.","DOI":"10.21105\/joss.00861"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"313","DOI":"10.1080\/02693799008941549","article-title":"Kriging: A method of interpolation for geographical information systems","volume":"4","author":"Oliver","year":"1990","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1061\/(ASCE)CP.1943-5487.0000118","article-title":"Comparison of linear and nonlinear kriging methods for characterization and interpolation of soil data","volume":"26","author":"Asa","year":"2012","journal-title":"J. Comput. Civ. Eng."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"594","DOI":"10.1016\/S1002-0160(10)60049-5","article-title":"Comparing ordinary kriging and regression kriging for soil properties in contrasting landscapes","volume":"20","author":"Zhu","year":"2010","journal-title":"Pedosphere"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"489","DOI":"10.1023\/A:1007577916868","article-title":"An alternative measure of the reliability of ordinary kriging estimates","volume":"32","author":"Yamamoto","year":"2000","journal-title":"J. Int. Assoc. Math. Geol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1109\/TIT.1967.1053964","article-title":"Nearest neighbor pattern classification","volume":"13","author":"Thomas","year":"1967","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_22","unstructured":"Sebastian, R. (2016). An overview of gradient descent optimization algorithms. arXiv."},{"key":"ref_23","unstructured":"Sutton, R.S., and Barto, A.G. (2018). Reinforcement Learning: An Introduction, MIT Press. A Bradford Book."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Talbi, E.G. (2009). Metaheuristics: From Design to Implementation, Wiley.","DOI":"10.1002\/9780470496916"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Eiben, A.E., and Smith, J.E. (2015). Introduction to Evolutionary Computing, Springer.","DOI":"10.1007\/978-3-662-44874-8"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Holland, J.H. (1992). Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence, MIT Press.","DOI":"10.7551\/mitpress\/1090.001.0001"},{"key":"ref_27","unstructured":"Goldberg, D.E. (1989). Genetic Algorithms in Search, Optimization, and Machine Learning, Addison-Wesley."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Michalewicz, Z. (1996). Genetic Algorithms + Data Structures = Evolution Programs, Springer.","DOI":"10.1007\/978-3-662-03315-9"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Nievergelt, J. (2000). Exhaustive search, combinatorial optimization and enumeration: Exploring the potential of raw computing power. International Conference on Current Trends in Theory and Practice of Computer Science, Springer.","DOI":"10.1007\/3-540-44411-4_2"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1007\/s00180-011-0237-5","article-title":"Search heuristics and the influence of non-perfect randomness: Examining Genetic Algorithms and Simulated Annealing","volume":"26","author":"Maucher","year":"2011","journal-title":"Comput. Stat."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"58029","DOI":"10.1007\/s11042-023-17167-y","article-title":"PyGAD: An intuitive genetic algorithm Python library","volume":"83","author":"Gad","year":"2024","journal-title":"Multimed. Tools Appl."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"225","DOI":"10.3758\/PBR.16.2.225","article-title":"Bayesian t tests for accepting and rejecting the null hypothesis","volume":"16","author":"Rouder","year":"2009","journal-title":"Psychon. Bull. Rev."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Comi, A., Rossolov, A., Polimeni, A., and Nuzzolo, A. (2021). Private car OD flow estimation based on automated vehicle monitoring data: Theoretical issues and empirical evidence. Information, 12.","DOI":"10.3390\/info12120493"}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/15\/10\/654\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:16:20Z","timestamp":1760112980000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/15\/10\/654"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,18]]},"references-count":33,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2024,10]]}},"alternative-id":["info15100654"],"URL":"https:\/\/doi.org\/10.3390\/info15100654","relation":{},"ISSN":["2078-2489"],"issn-type":[{"type":"electronic","value":"2078-2489"}],"subject":[],"published":{"date-parts":[[2024,10,18]]}}}