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Article: Exploring resilient observability in traffic-monitoring sensor networks: A study of spatial-temporal vehicle patterns

TitleExploring resilient observability in traffic-monitoring sensor networks: A study of spatial-temporal vehicle patterns
Authors
KeywordsSensor networks
Vehicle mobility
Resilience
Traffic-monitoring sensors
Spatial-temporal analysis
Issue Date2020
Citation
ISPRS International Journal of Geo-Information, 2020, v. 9, n. 4, article no. 247 How to Cite?
AbstractVehicle mobility generates dynamic and complex patterns that are associated with our day-to-day activities in cities. To reveal the spatial-temporal complexity of such patterns, digital techniques, such as traffic-monitoring sensors, provide promising data-driven tools for city managers and urban planners. Although a large number of studies have been dedicated to investigating the sensing power of the traffic-monitoring sensors, there is still a lack of exploration of the resilient performance of sensor networks when multiple sensor failures occur. In this paper, we reveal the dynamic patterns of vehicle mobility in Cambridge, UK, and subsequently, explore the resilience of the sensor networks. The observability is adopted as the overall performance indicator to depict the maximum number of vehicles captured by the deployed sensors in the study area. By aggregating the sensor networks according to weekday and weekend and simulating random sensor failures with different recovery strategies, we found that (1) the day-to-day vehicle mobility pattern in this case study is highly dynamic and decomposed journey durations follow a power-law distribution on the tail section; (2) such temporal variation significantly affects the observability of the sensor network, causing its overall resilience to vary with different recovery strategies. The simulation results further suggest that a corresponding prioritization for recovering the sensors from massive failures is required, rather than a static sequence determined by the first-fail-first-repair principle. For stakeholders and decision-makers, this study provides insightful implications for understanding city-scale vehicle mobility and the resilience of traffic-monitoring sensor networks.
Persistent Identifierhttp://hdl.handle.net/10722/301784

 

DC FieldValueLanguage
dc.contributor.authorTang, Junqing-
dc.contributor.authorWan, Li-
dc.contributor.authorNochta, Timea-
dc.contributor.authorSchooling, Jennifer-
dc.contributor.authorYang, Tianren-
dc.date.accessioned2021-08-19T02:20:44Z-
dc.date.available2021-08-19T02:20:44Z-
dc.date.issued2020-
dc.identifier.citationISPRS International Journal of Geo-Information, 2020, v. 9, n. 4, article no. 247-
dc.identifier.urihttp://hdl.handle.net/10722/301784-
dc.description.abstractVehicle mobility generates dynamic and complex patterns that are associated with our day-to-day activities in cities. To reveal the spatial-temporal complexity of such patterns, digital techniques, such as traffic-monitoring sensors, provide promising data-driven tools for city managers and urban planners. Although a large number of studies have been dedicated to investigating the sensing power of the traffic-monitoring sensors, there is still a lack of exploration of the resilient performance of sensor networks when multiple sensor failures occur. In this paper, we reveal the dynamic patterns of vehicle mobility in Cambridge, UK, and subsequently, explore the resilience of the sensor networks. The observability is adopted as the overall performance indicator to depict the maximum number of vehicles captured by the deployed sensors in the study area. By aggregating the sensor networks according to weekday and weekend and simulating random sensor failures with different recovery strategies, we found that (1) the day-to-day vehicle mobility pattern in this case study is highly dynamic and decomposed journey durations follow a power-law distribution on the tail section; (2) such temporal variation significantly affects the observability of the sensor network, causing its overall resilience to vary with different recovery strategies. The simulation results further suggest that a corresponding prioritization for recovering the sensors from massive failures is required, rather than a static sequence determined by the first-fail-first-repair principle. For stakeholders and decision-makers, this study provides insightful implications for understanding city-scale vehicle mobility and the resilience of traffic-monitoring sensor networks.-
dc.languageeng-
dc.relation.ispartofISPRS International Journal of Geo-Information-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.subjectSensor networks-
dc.subjectVehicle mobility-
dc.subjectResilience-
dc.subjectTraffic-monitoring sensors-
dc.subjectSpatial-temporal analysis-
dc.titleExploring resilient observability in traffic-monitoring sensor networks: A study of spatial-temporal vehicle patterns-
dc.typeArticle-
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.3390/ijgi9040247-
dc.identifier.scopuseid_2-s2.0-85083793706-
dc.identifier.volume9-
dc.identifier.issue4-
dc.identifier.spagearticle no. 247-
dc.identifier.epagearticle no. 247-
dc.identifier.eissn2220-9964-

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