FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3095785903> ?p ?o ?g. }

• W3095785903 abstract "The ever-increasing urban population and vehicular traffic without a corresponding expansion of infrastructure have been a challenge to transportation facilities managers and commuters. While some parts of transportation infrastructure have big data available, so many other locations have sparse data. This has posed a challenge in traffic state estimation and prediction for efficient and effective infrastructure management and route guidance. This research focused on traffic prediction problems and aims to develop novel spatial-temporal and robust algorithms, that can provide high accuracy in the presence of both big data and sparse data in a large urban road network.Intelligent transportation systems require the knowledge of current traffic state and forecast for effective implementation. The actual traffic state has to be estimated as the existing sensors do not capture the needed state. Sensor measurements often contain missing or incomplete data as a result of communication issues, faulty sensors or cost leading to incomplete monitoring of the entire road network. This missing data pose challenges to traffic estimation approaches. In this work, a robust spatio-temporal traffic imputation approach capable of withstanding high missing data rate is presented. A particle-based approach with Kriging interpolation is proposed. The performance of the particle-based Kriging interpolation for different missing data ratios was investigated for a large road network.A particle-based framework for dealing with missing data is also proposed. An expression of the likelihood function is derived for the case when the missing value is calculated based on Kriging interpolation. With the Kriging interpolation, the missing values of the measurements are predicted, which are subsequently used in the computation of likelihood terms in the particle filter algorithm.In the commonly used Kriging approaches, the covariance function depends only on the separation distance irrespective of the traffic at the considered locations. A key limitation of such an approach is its inability to capture well the traffic dynamics and transitions between different states. This thesis proposes a Bayesian Kriging approach for the prediction of urban traffic. The approach can capture these dynamics and model changes via the covariance matrix. The main novelty consists in representing both stationary and non-stationary changes in traffic flows by a discriminative covariance function conditioned on the observation at each location. An advantage is that by considering the surrounding traffic information distinctively, the proposed method is very likely to represent congested regions and interactions in both upstream and downstream areas." @default.
• W3095785903 created "2020-11-09" @default.
• W3095785903 creator A5048003864 @default.
• W3095785903 date "2020-01-01" @default.
• W3095785903 modified "2023-09-27" @default.
• W3095785903 title "Sensor Data Fusion for Improving Traffic Mobility in Smart Cities" @default.
• W3095785903 hasPublicationYear "2020" @default.
• W3095785903 type Work @default.
• W3095785903 sameAs 3095785903 @default.
• W3095785903 citedByCount "0" @default.
• W3095785903 crossrefType "dissertation" @default.
• W3095785903 hasAuthorship W3095785903A5048003864 @default.
• W3095785903 hasConcept C104114177 @default.
• W3095785903 hasConcept C119857082 @default.
• W3095785903 hasConcept C124101348 @default.
• W3095785903 hasConcept C137800194 @default.
• W3095785903 hasConcept C144024400 @default.
• W3095785903 hasConcept C149923435 @default.
• W3095785903 hasConcept C154945302 @default.
• W3095785903 hasConcept C2908647359 @default.
• W3095785903 hasConcept C33954974 @default.
• W3095785903 hasConcept C41008148 @default.
• W3095785903 hasConcept C58041806 @default.
• W3095785903 hasConcept C75684735 @default.
• W3095785903 hasConcept C79403827 @default.
• W3095785903 hasConcept C81692654 @default.
• W3095785903 hasConcept C9357733 @default.
• W3095785903 hasConceptScore W3095785903C104114177 @default.
• W3095785903 hasConceptScore W3095785903C119857082 @default.
• W3095785903 hasConceptScore W3095785903C124101348 @default.
• W3095785903 hasConceptScore W3095785903C137800194 @default.
• W3095785903 hasConceptScore W3095785903C144024400 @default.
• W3095785903 hasConceptScore W3095785903C149923435 @default.
• W3095785903 hasConceptScore W3095785903C154945302 @default.
• W3095785903 hasConceptScore W3095785903C2908647359 @default.
• W3095785903 hasConceptScore W3095785903C33954974 @default.
• W3095785903 hasConceptScore W3095785903C41008148 @default.
• W3095785903 hasConceptScore W3095785903C58041806 @default.
• W3095785903 hasConceptScore W3095785903C75684735 @default.
• W3095785903 hasConceptScore W3095785903C79403827 @default.
• W3095785903 hasConceptScore W3095785903C81692654 @default.
• W3095785903 hasConceptScore W3095785903C9357733 @default.
• W3095785903 hasLocation W30957859031 @default.
• W3095785903 hasOpenAccess W3095785903 @default.
• W3095785903 hasPrimaryLocation W30957859031 @default.
• W3095785903 hasRelatedWork W114251489 @default.
• W3095785903 hasRelatedWork W2739457923 @default.
• W3095785903 hasRelatedWork W2799791814 @default.
• W3095785903 hasRelatedWork W2800080636 @default.
• W3095785903 hasRelatedWork W2848293466 @default.
• W3095785903 hasRelatedWork W2902845002 @default.
• W3095785903 hasRelatedWork W2952348845 @default.
• W3095785903 hasRelatedWork W2989870471 @default.
• W3095785903 hasRelatedWork W2995485930 @default.
• W3095785903 hasRelatedWork W3011209807 @default.
• W3095785903 hasRelatedWork W3012020207 @default.
• W3095785903 hasRelatedWork W3012171964 @default.
• W3095785903 hasRelatedWork W3092125438 @default.
• W3095785903 hasRelatedWork W3140898540 @default.
• W3095785903 hasRelatedWork W3142057612 @default.
• W3095785903 hasRelatedWork W3157791740 @default.
• W3095785903 hasRelatedWork W3202235298 @default.
• W3095785903 hasRelatedWork W3203806024 @default.
• W3095785903 hasRelatedWork W3208506476 @default.
• W3095785903 hasRelatedWork W3112446002 @default.
• W3095785903 isParatext "false" @default.
• W3095785903 isRetracted "false" @default.
• W3095785903 magId "3095785903" @default.
• W3095785903 workType "dissertation" @default.