Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/80649
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dc.contributor.authorSalari, Men_US
dc.contributor.authorKattan, Len_US
dc.contributor.authorLam, WHKen_US
dc.contributor.authorLo, HPen_US
dc.contributor.authorEsfeh, MAen_US
dc.date.accessioned2019-04-23T08:16:43Z-
dc.date.available2019-04-23T08:16:43Z-
dc.date.issued2019-
dc.identifier.citationTransportation research. Part B, Methodological, 2019, v. 121, p. 216-251en_US
dc.identifier.issn0191-2615en_US
dc.identifier.urihttp://hdl.handle.net/10397/80649-
dc.description.abstractThe full link flow observability problem is to identify the minimum set of traffic sensors to be installed in links in a road traffic network. The sensors are used to both monitor the flow of observed links and to provide flow information for the link flow inference of unobserved links. Unavoidably, the traffic sensors deployed in a traffic network are subject to failure which leads to missing the link flow observation of observed links as well as the inability to infer the link flow of unobserved links. This study aims to identify the minimum set of links in a traffic network to be instrumented with two different types of counting sensors (basic and advanced sensors) to reach full link flow observability while minimizing the effect of sensor failure on the link flow inference of unobserved links. Mathematically, we formulate two objective functions including min-max and min-sum functions. The first function attempts to minimize the maximum effect of sensor failure on the link flow inference of unobserved links while the second one minimizes the expected number of unobserved links where flow cannot be inferred due to the failure of sensors. We select the genetic algorithm (GA) as a well-known heuristic to solve the proposed optimization model. The results recommend minimizing the number of sensors required for the link flow inference of each unobserved link as well as installing advanced sensors on links involved in the link flow inference of multiple unobserved links. We also develop a new objective function to reflect that links in a traffic network can be either minor or major roads with different levels of importance. The results suggest installing more advanced sensors on the major roads as well as minimizing the number of major roads included in the set of unobserved links. Concerning the availability of route flow information in a network, we consider the effect of this information on evaluating the sensor deployment in a network. To maintain full link flow observability of a traffic network if any sensor fails, we study the location and type of additional sensors introduced as redundant sensors, which are more than the minimum required for full link flow observability. Finally, we discuss the applicability of the proposed model for the partial observability problem in which the full link flow observability conditions are not satisfied.en_US
dc.description.sponsorshipDepartment of Civil and Environmental Engineeringen_US
dc.language.isoenen_US
dc.publisherPergamon Pressen_US
dc.relation.ispartofTransportation research. Part B, Methodologicalen_US
dc.rights© 2019 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/BY-NC-ND/4.0/).en_US
dc.rightsThe following publication Salari, M., Kattan, L., Lam, W. H., Lo, H. P., & Esfeh, M. A. (2019). Optimization of traffic sensor location for complete link flow observability in traffic network considering sensor failure. Transportation Research Part B: Methodological, 121, 216-251 is available at https://doi.org/10.1016/j.trb.2019.01.004en_US
dc.subjectFlow observationen_US
dc.subjectFull link flow observabilityen_US
dc.subjectGenetic algorithmen_US
dc.subjectNetwork sensor location problemen_US
dc.subjectPartial observabilityen_US
dc.subjectRedundant sensorsen_US
dc.subjectRoute flow informationen_US
dc.subjectSensor failureen_US
dc.titleOptimization of traffic sensor location for complete link flow observability in traffic network considering sensor failureen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage216en_US
dc.identifier.epage251en_US
dc.identifier.volume121en_US
dc.identifier.doi10.1016/j.trb.2019.01.004en_US
dc.identifier.isiWOS:000479182600011-
dc.identifier.scopus2-s2.0-85060980349-
dc.identifier.eissn1879-2367en_US
dc.description.validate201904 bcmaen_US
dc.description.oapublished_finalen_US
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