Hochschulschrift

Estimating traffic signal states by exploiting sparse low-frequency floating car data

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Location: Deutsche Nationalbibliothek Frankfurt am Main

ISBN: 9783844055139
3844055134

Dimensions: 21 cm

Extent: viii, iii, 170 Seiten

Edition: [1. Auflage]

Language: Englisch

Notes: Illustrationen
Danksagung und Kurzfassung auf Deutsch
Technische Universität Carolo-Wilhelmina zu Braunschweig, Dissertation, 2017

Bibliographic citation: Schriftenreihe / Technische Universität Braunschweig ; Heft 61

Keyword: Straßenverkehr
Kraftfahrzeug
Mobile Datenerfassung
Verkehrssignalanlage
Verkehrsablauf
Zustandsschätzung
Fahrerassistenzsystem

Event: Veröffentlichung

(where): Aachen

(who): Shaker Verlag

(when): 2017

Creator: Axer, Steffen

Contributor: Shaker Verlag

Table of contents: https://d-nb.info/1139183850/04

Rights: Bei diesem Objekt liegt nur das Inhaltsverzeichnis digital vor. Der Zugriff darauf ist unbeschränkt möglich.

Last update: 11.06.2025, 2:02 PM CEST

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Hochschulschrift

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2017

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Estimating Traffic Signal States by Exploiting Sparse Low-Frequency Floating Car Data

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OC6 Phase I: Investigating the underprediction of low-frequency hydrodynamic loads and responses of a floating wind turbine

Channel estimation for FDD massive MIMO system by exploiting the sparse structures in angular domain

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A map-matching algorithm with low-frequency floating car data based on matching path

Hochschulschrift

Traffic Pattern Analysis Framework with Emphasis on Floating Car Data (FCD)

Exploiting sensor mobility and covariance sparsity for distributed tracking of multiple sparse targets

Determining traffic congestion utilizing a fuzzy logic model and Floating Car Data (FCD)

OC6 Phase I: Investigating the underprediction of low-frequency hydrodynamic loads and responses of a floating wind turbine

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Estimating traffic signal states by exploiting sparse low-frequency floating car data

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Other Objects (12)

Estimating Traffic Signal States by Exploiting Sparse Low-Frequency Floating Car Data

Exploiting Deep Learning and Traffic Models for Freeway Traffic Estimation

Exploiting many-core architectures for dimensionally adaptive sparse grids

Structure-exploiting numerical methods for tree-sparse optimal control problems

Traffic Speed Estimation and Prediction Using Floating Car Data

Sparse Grids for Big Data: Exploiting Parsimony for Large-Scale Learning

A map-matching algorithm with low-frequency floating car data based on matching path

Traffic Pattern Analysis Framework with Emphasis on Floating Car Data (FCD)

Exploiting sensor mobility and covariance sparsity for distributed tracking of multiple sparse targets

Determining traffic congestion utilizing a fuzzy logic model and Floating Car Data (FCD)

OC6 Phase I: Investigating the underprediction of low-frequency hydrodynamic loads and responses of a floating wind turbine

Channel estimation for FDD massive MIMO system by exploiting the sparse structures in angular domain

Estimating Traffic Signal States by Exploiting Sparse Low-Frequency Floating Car Data

Exploiting Deep Learning and Traffic Models for Freeway Traffic Estimation

Exploiting many-core architectures for dimensionally adaptive sparse grids

Structure-exploiting numerical methods for tree-sparse optimal control problems

Traffic Speed Estimation and Prediction Using Floating Car Data

Sparse Grids for Big Data: Exploiting Parsimony for Large-Scale Learning

A map-matching algorithm with low-frequency floating car data based on matching path

Traffic Pattern Analysis Framework with Emphasis on Floating Car Data (FCD)

Exploiting sensor mobility and covariance sparsity for distributed tracking of multiple sparse targets

Determining traffic congestion utilizing a fuzzy logic model and Floating Car Data (FCD)

OC6 Phase I: Investigating the underprediction of low-frequency hydrodynamic loads and responses of a floating wind turbine

Channel estimation for FDD massive MIMO system by exploiting the sparse structures in angular domain

Estimating Traffic Signal States by Exploiting Sparse Low-Frequency Floating Car Data

Exploiting Deep Learning and Traffic Models for Freeway Traffic Estimation

Exploiting many-core architectures for dimensionally adaptive sparse grids

Structure-exploiting numerical methods for tree-sparse optimal control problems

Traffic Speed Estimation and Prediction Using Floating Car Data

Sparse Grids for Big Data: Exploiting Parsimony for Large-Scale Learning

A map-matching algorithm with low-frequency floating car data based on matching path

Traffic Pattern Analysis Framework with Emphasis on Floating Car Data (FCD)

Exploiting sensor mobility and covariance sparsity for distributed tracking of multiple sparse targets

Determining traffic congestion utilizing a fuzzy logic model and Floating Car Data (FCD)

OC6 Phase I: Investigating the underprediction of low-frequency hydrodynamic loads and responses of a floating wind turbine

Channel estimation for FDD massive MIMO system by exploiting the sparse structures in angular domain

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