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211007s2022 ne o 000 0 eng d |
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|a YDX
|b eng
|e pn
|c YDX
|d OPELS
|d OCLCO
|d UKAHL
|d OCLCF
|d OCLCO
|d SFB
|d OCLCQ
|d WAU
|d OCLCO
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| 019 |
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|a 1273913666
|a 1277046289
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| 020 |
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|a 9780128234334
|q (electronic bk.)
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|a 0128234334
|q (electronic bk.)
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| 020 |
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|z 9780128234327
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|a (OCoLC)1273667479
|z (OCoLC)1273913666
|z (OCoLC)1277046289
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| 050 |
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4 |
|a HE333
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| 082 |
0 |
4 |
|a 388.4131015118
|2 23
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| 245 |
0 |
0 |
|a Road traffic modelling and management :
|b using statistical monitoring and deep learning /
|c Fouzi Harrou [and more].
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| 260 |
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|a Amsterdam :
|b Elsevier,
|c 2022.
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| 300 |
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|a 1 online resource
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|a Front Cover -- Road Traffic Modeling and Management -- Copyright -- Contents -- 1 Introduction -- 1.1 Introduction -- 1.1.1 Types road traffic sensors -- 1.1.2 Key traffic features -- 1.1.3 Traffic flow modeling -- 1.2 Objectives and structure of the book -- References -- 2 Road traffic modeling -- 2.1 Introduction -- 2.2 Traffic detector types -- 2.2.1 Embedded traffic detectors -- 2.2.2 Intrusive detectors -- 2.2.3 Non-intrusive detectors -- 2.3 Traffic modeling model-based approaches -- 2.3.1 Microscopic approach -- 2.3.1.1 The microscopic variables -- 2.3.1.2 Microscopic models -- 2.3.2 Mesoscopic approach -- 2.3.2.1 Mesoscopic models -- 2.3.3 Macroscopic approach -- 2.3.3.1 The macroscopic variables -- 2.3.4 The macroscopic models -- 2.3.4.1 First-order macroscopic traffic models -- 2.3.4.2 The cell transmission model -- 2.3.5 PWSL traffic flow model -- 2.3.5.1 PWSL discrete dynamics -- 2.3.5.2 PWSL continuous dynamics -- 2.3.5.3 Boundary conditions -- 2.3.5.4 Insider road section traffic density dynamics -- 2.3.5.4.1 Simple link density dynamics -- 2.3.5.4.2 Merge and diverge links dynamics -- Merge links dynamics (the ON ramp configuration) -- Diverge links dynamics (the OFF ramp configuration) -- 2.3.5.5 PWSL parameter calibration -- Geometric and data traffic description -- Calibration methodology -- 2.3.6 Model validation and simulation results -- 2.3.6.1 Measurements of effectiveness -- 2.4 Conclusion -- References -- 3 Road traffic density estimation -- 3.1 Introduction -- 3.2 Observability of PWSL model -- 3.2.1 Generalities of observability study -- 3.2.2 PWSL observability study -- 3.3 PWSL-based observer -- 3.3.1 Principle and description of hybrid observer -- 3.3.2 Synthesis of PWSL-based hybrid observer -- 3.3.2.1 Discrete mode identification -- 3.3.2.2 Continuous state estimation.
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| 505 |
8 |
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|a 3.3.3 The PWSL-Kalman filter design for traffic state estimation -- 3.4 Application -- 3.5 Discussion -- References -- 4 Model-based techniques for traffic congestion detection -- 4.1 Introduction -- 4.2 The PWSL-KF design for traffic state estimation -- 4.2.1 PWSL approach -- 4.2.2 Kalman filter for traffic density estimation -- 4.3 kNN-based monitoring charts -- 4.3.1 Mixed kNN-Shewhart approach -- 4.3.2 Coupled kNN-EWMA approach -- 4.3.3 kNN-based schemes with nonparametric thresholds -- 4.3.3.1 KDE -- Kernel function selection -- Bandwidth selection -- 4.3.3.2 Nonparametric kNN-based detectors -- 4.4 Combining PWSL model with monitoring schemes -- 4.5 Results and case study -- 4.5.1 Model performance evaluation -- 4.5.2 Detection results -- 4.5.2.1 Performance evaluation -- 4.5.2.2 Detection of abrupt congestions -- 4.5.2.3 Scenarios with intermittent congestion -- 4.5.2.4 Detection of gradual congestion -- 4.6 Discussion -- References -- 5 Traffic congestion detection: data-based techniques -- 5.1 Introduction -- 5.2 Data preprocessing and denoising -- 5.2.1 Exponentially weighted moving-average (EWMA) filter -- 5.2.2 Wavelet-based multiscale filtering -- 5.3 Univariate statistical monitoring schemes -- 5.3.1 EWMA (exponentially weighted moving average) -- 5.3.2 Generalized likelihood ratio charts -- 5.4 Features extraction with principle component analysis (PCA) -- 5.4.1 Features extraction with PCA -- 5.4.2 Determination of model order -- optimal dimension of PC -- 5.4.2.1 Cumulative percent variance -- 5.4.2.2 Cross-validation -- 5.4.2.3 Scree test -- 5.4.2.4 Parallel analysis -- 5.4.2.5 Eigenvalue-one rule -- 5.5 PCA-based anomaly detection charts -- 5.5.1 SPE chart -- 5.5.2 Hotelling's T2 -- 5.5.3 The basic procedure for the PCA with Matlab� -- 5.5.4 A combined T2 and Q index.
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| 505 |
8 |
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|a 5.5.5 Anomaly detection using coupled EWMA-based SPE and T2 methods -- 5.5.5.1 The amalgamated Q-EWMA monitoring scheme -- 5.5.5.2 The amalgamated T2-EWMA monitoring scheme -- 5.5.6 Parametric and nonparametric PCA-based monitoring charts -- 5.6 Combining PCA with monitoring schemes for traffic congestion detection -- 5.7 Results and discussion -- 5.7.1 Data description -- 5.7.2 Data analysis -- 5.7.3 PCA modeling -- 5.7.4 Traffic anomalies detection -- 5.7.4.1 Detection of abrupt congestions -- 5.7.4.2 Scenarios with intermittent congestion -- 5.7.4.3 Detection of gradual congestion -- 5.8 Discussion -- References -- 6 Recurrent and convolutional neural networks for traffic management -- 6.1 Introduction -- 6.2 Recurrent neural network for traffic time series data -- 6.2.1 Data collection -- 6.2.2 Preprocessing the data before building a model -- 6.2.3 Data smoothing -- 6.2.3.1 Exponentially weighted moving average (EWMA) filter -- 6.2.3.2 Wavelet-based multiscale filter -- 6.2.4 Recurrent neural network (RNN)-based models -- 6.2.4.1 RNN model -- 6.2.4.2 LSTM model -- 6.2.4.3 LSTM implementation steps -- 6.2.4.4 Enhanced performance of LSTM models -- 6.2.5 Evaluating the model -- 6.2.6 GRU models -- 6.3 Road traffic forecasting using LSTM- and RNN-driven models -- 6.3.1 Proposed traffic forecasting methods -- 6.3.1.1 Forecasting results and discussion -- 6.3.1.2 Data description -- 6.3.2 Forecasting results -- 6.4 Detecting traffic congestion using deep learning-based monitoring charts -- 6.4.1 EWMA and DEWMA monitoring schemes -- 6.4.1.1 EWMA monitoring scheme -- 6.4.1.2 Double EWMA scheme -- 6.4.1.3 KDE-based DEWMA monitoring scheme -- 6.4.2 Traffic congestion detection using deep learning-based DEWMA schemes -- 6.4.3 Traffic congestion detection: case study -- 6.4.3.1 LSTM- and GRU-based traffic flow modeling -- 6.4.3.2 Detection results.
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| 505 |
8 |
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|a 6.4.3.2.1 Abrupt traffic congestion -- 6.4.3.2.2 Detection of gradual congestion -- 6.5 Vision-based traffic monitoring using CNN -- 6.5.1 Convolutional neural networks -- 6.5.1.1 Converting network traffic to images -- 6.5.1.2 CNN for network traffic prediction -- 6.5.2 TensorFlow integration with Python -- 6.5.2.1 Quickstart of TensorFlow Lite with Python -- 6.5.2.2 Installation of the TensorFlow Lite for Python -- 6.6 Discussion -- References -- 7 Conclusion and further research directions -- 7.1 Introduction -- References -- Index -- Back Cover.
|
| 650 |
|
0 |
|a Traffic engineering
|x Mathematical models.
|
| 650 |
|
0 |
|a Traffic engineering.
|
| 650 |
|
6 |
|a Technique de la circulation
|0 (CaQQLa)201-0003124
|x Mod�eles math�ematiques.
|0 (CaQQLa)201-0379082
|
| 650 |
|
6 |
|a Technique de la circulation.
|0 (CaQQLa)201-0003124
|
| 650 |
|
7 |
|a traffic engineering.
|2 aat
|0 (CStmoGRI)aat300054508
|
| 650 |
|
7 |
|a Traffic engineering
|2 fast
|0 (OCoLC)fst01154087
|
| 650 |
|
7 |
|a Traffic engineering
|x Mathematical models
|2 fast
|0 (OCoLC)fst01154109
|
| 700 |
1 |
|
|a Harrou, Fouzi,
|e editor.
|
| 776 |
0 |
8 |
|c Original
|z 0128234326
|z 9780128234327
|w (OCoLC)1257402451
|
| 856 |
4 |
0 |
|u https://sciencedirect.uam.elogim.com/science/book/9780128234327
|z Texto completo
|
