Data fusion and missing data estimation in road networks
Often in urban area, road users would like know the traffic condition and how long it would take to reach the destination. The focus of the project will be providing such information for traffic applications. This includes recovering low resolution large scale urban traffic data, predicting future t...
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2015
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sg-ntu-dr.10356-643632023-07-07T17:07:22Z Data fusion and missing data estimation in road networks Foo, Her Yiow Justin Dauwels School of Electrical and Electronic Engineering Centre for Transportation Studies DRNTU::Engineering::Electrical and electronic engineering Often in urban area, road users would like know the traffic condition and how long it would take to reach the destination. The focus of the project will be providing such information for traffic applications. This includes recovering low resolution large scale urban traffic data, predicting future traffic data and recovering missing traffic data using tensor methods which is essential for intelligence transport system (ITS) application. We developed a model for recovering of low resolution traffic data with the availability of higher resolution data in real-time, drivers can plan their journeys high low uncertainty. Partial least square regression method is used in estimating large scale urban traffic with fusion of traffic speed, flow and/or speed band as inputs. CANDECOMP/PARAFAC (CP) Tensor factorization specifically Bayesian CP and CP weight optimization (CPWOPT) will be used in estimating missing traffic data. The estimated results are compared with the actual speed for performance evaluation and demonstrated to be optimistic. Bachelor of Engineering 2015-05-26T05:22:24Z 2015-05-26T05:22:24Z 2015 2015 Final Year Project (FYP) http://hdl.handle.net/10356/64363 en Nanyang Technological University 54 p. application/pdf |
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DRNTU::Engineering::Electrical and electronic engineering Foo, Her Yiow Data fusion and missing data estimation in road networks |
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Often in urban area, road users would like know the traffic condition and how long it would take to reach the destination. The focus of the project will be providing such information for traffic applications. This includes recovering low resolution large scale urban traffic data, predicting future traffic data and recovering missing traffic data using tensor methods which is essential for intelligence transport system (ITS) application. We developed a model for recovering of low resolution traffic data with the availability of higher resolution data in real-time, drivers can plan their journeys high low uncertainty. Partial least square regression method is used in estimating large scale urban traffic with fusion of traffic speed, flow and/or speed band as inputs. CANDECOMP/PARAFAC (CP) Tensor factorization specifically Bayesian CP and CP weight optimization (CPWOPT) will be used in estimating missing traffic data. The estimated results are compared with the actual speed for performance evaluation and demonstrated to be optimistic. |
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Justin Dauwels |
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Justin Dauwels Foo, Her Yiow |
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Final Year Project |
author |
Foo, Her Yiow |
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Foo, Her Yiow |
title |
Data fusion and missing data estimation in road networks |
title_short |
Data fusion and missing data estimation in road networks |
title_full |
Data fusion and missing data estimation in road networks |
title_fullStr |
Data fusion and missing data estimation in road networks |
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Data fusion and missing data estimation in road networks |
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data fusion and missing data estimation in road networks |
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2015 |
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http://hdl.handle.net/10356/64363 |
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1772828716983910400 |