Verfügbarkeit: Machine Learning for Transportation Research and Applications

Machine Learning for Transportation Research and Applications:

Gespeichert in:

Bibliographische Detailangaben
1. Verfasser:	Wang, Yinhai (VerfasserIn)
Format:	Elektronisch E-Book
Sprache:	English
Veröffentlicht:	San Diego Elsevier 2023
Online-Zugang:	HWR01
Beschreibung:	Description based on publisher supplied metadata and other sources
Beschreibung:	1 Online-Ressource (254 Seiten)
ISBN:	9780323996808

Internformat

MARC


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505	8		\|a Front Cover -- Machine Learning for Transportation Research and Applications -- Copyright -- Contents -- About the authors -- 1 Introduction -- 1.1 Background -- 1.1.1 Importance of transportation -- 1.1.2 Motivation -- 1.2 ML is promising for transportation research and applications -- 1.2.1 A brief history of ML -- 1.2.2 ML for transportation research and applications -- 1.3 Book organization -- 2 Transportation data and sensing -- 2.1 Data explosion -- 2.2 ITS data needs -- 2.3 Infrastructure-based data and sensing -- 2.3.1 Traffic flow detection -- 2.3.2 Travel time estimation -- 2.3.3 Traffic anomaly detection -- 2.3.4 Parking detection -- 2.4 Vehicle onboard data and sensing -- 2.4.1 Traffic near-crash detection -- 2.4.2 Road user behavior sensing -- 2.4.3 Road and lane detection -- 2.4.4 Semantic segmentation -- 2.5 Aerial sensing for ground transportation data -- 2.5.1 Road user detection and tracking -- 2.5.2 Advanced aerial sensing -- 2.5.3 UAV for infrastructure data collection -- 2.6 ITS data quality control and fusion -- 2.7 Transportation data and sensing challenges -- 2.7.1 Heterogeneity -- 2.7.2 High probability of sensor failure -- 2.7.3 Sensing in extreme cases -- 2.7.4 Privacy protection -- 2.8 Exercises -- 3 Machine learning basics -- 3.1 Categories of machine learning -- 3.1.1 Supervised vs. unsupervised learning -- 3.1.2 Generative vs. discriminative algorithms -- 3.1.3 Parametric vs. nonparametric modeling -- 3.2 Supervised learning -- 3.2.1 Linear regression -- Problem setup -- Solving the optimization problem -- Vectorization -- 3.2.2 Logistic regression -- Softmax regression -- 3.3 Unsupervised learning -- 3.3.1 Principal component analysis -- 3.3.2 Clustering -- 3.4 Key concepts in machine learning -- 3.4.1 Loss -- 3.4.2 Regularization -- L1 vs. L2 -- 3.4.3 Gradient descent vs. gradient ascent
505	8		\|a 3.4.4 K-fold cross-validation -- 3.5 Exercises -- 3.5.1 Questions -- 4 Fully connected neural networks -- 4.1 Linear regression -- 4.2 Deep neural network fundamentals -- 4.2.1 Perceptron -- 4.2.2 Hidden layers -- 4.2.3 Activation functions -- Sigmoid function -- Tanh function -- ReLU function -- 4.2.4 Loss functions -- 4.2.5 Back-propagation -- Forward propagation -- Backward propagation -- 4.2.6 Validation dataset -- 4.2.7 Underfitting or overfitting? -- 4.3 Transportation applications -- 4.3.1 Traffic prediction -- 4.3.2 Traffic sign image classification -- 4.4 Exercises -- 4.4.1 Questions -- 5 Convolution neural networks -- 5.1 Convolution neural network fundamentals -- 5.1.1 From fully connected layers to convolutions -- 5.1.2 Convolutions -- 5.1.3 Architecture -- 5.1.4 AlexNet -- 5.2 Case study: traffic video sensing -- 5.3 Case study: spatiotemporal traffic pattern learning -- 5.4 Case study: CNNs for data imputation -- 5.4.1 CNN-based imputation approach -- 5.4.2 Experiment -- 5.5 Exercises -- 6 Recurrent neural networks -- 6.1 RNN fundamentals -- 6.2 RNN variants and related architectures -- 6.2.1 Long short-term memory (LSTM) and gated recurrent units (GRU) -- 6.2.2 Bidirectional RNN -- 6.2.3 Sequence to sequence -- 6.3 RNN as a building block for transportation applications -- 6.3.1 RNN for road traffic prediction -- Problem description -- Network-wide traffic prediction -- Traffic prediction algorithms -- 6.3.2 Traffic prediction with missing values -- Problem definition -- LSTM-based traffic prediction with missing values -- 6.4 Exercises -- 6.4.1 Questions -- 6.4.2 Project: predicting network-wide traffic using LSTM -- Problem definition -- Dataset preparation -- Implement and fine-tune model -- Model evaluation -- 7 Reinforcement learning -- 7.1 Reinforcement learning setting -- 7.1.1 Markov property
505	8		\|a 7.1.2 Goal of reinforcement learning -- 7.1.3 Categories and terms in reinforcement learning -- Model-free vs. model-based -- Stationary vs. nonstationary -- Deterministic policy vs. stochastic policy -- Offline learning vs. online learning -- Exploration vs. exploitation -- Off-policy learning vs. on-policy learning -- 7.2 Value-based methods -- 7.2.1 Q-learning -- 7.2.2 Deep Q-networks -- 7.3 Policy gradient methods for deep RL -- 7.3.1 Stochastic policy gradient -- 7.3.2 Deterministic policy gradient -- 7.4 Combining policy gradient and Q-learning -- 7.4.1 Actor-critic methods -- 7.5 Case study 1: traffic signal control -- 7.5.1 Agent formulation -- 7.6 Case study 2: car following control -- 7.6.1 Agent formulation -- 7.6.2 Model and simulation settings -- 7.7 Case study 3: bus bunching control -- 7.7.1 Agent formulation -- 7.8 Exercises -- 7.8.1 Questions -- 8 Transfer learning -- 8.1 What is transfer learning -- 8.2 Why transfer learning -- 8.3 Definition -- 8.4 Transfer learning steps -- 8.5 Transfer learning types -- 8.5.1 Domain adaptation -- 8.5.2 Multi-task learning -- 8.5.3 Zero-shot learning -- 8.5.4 Few-shot learning -- 8.6 Case study: vehicle detection enhancement through transfer learning -- 8.7 Case study: parking information management and prediction system by attribute representation learning -- 8.7.1 Background -- 8.7.2 Methods -- 8.7.3 Results -- 8.8 Case study: transfer learning for nighttime traffic detection -- 8.9 Case study: simulation to real-world knowledge transfer for driving be- havior recognition -- 8.10 Exercises -- 9 Graph neural networks -- 9.1 Preliminaries -- 9.2 Graph neural networks -- 9.2.1 Spectral GNN -- 9.2.2 Spatial GNN -- 9.2.3 Attention-based GNNs -- 9.3 Case study 1: traffic graph convolutional network for traffic prediction -- 9.3.1 Problem definition -- 9.3.2 Method: traffic graph convolutional LSTM.
505	8		\|a 9.3.3 Results -- 9.4 Case study 2: graph neural network for traffic forecasting with missing values -- 9.4.1 Problem definition -- 9.4.2 Method: graph Markov network -- 9.4.3 Results -- 9.5 Case study 3: graph neural network (GNN) for vehicle keypoints' cor- rection -- 9.5.1 Problem definition -- 9.5.2 Method: graph neural network for keypoints correction -- 9.5.3 Results -- 9.6 Exercises -- 9.6.1 Questions -- 10 Generative adversarial networks -- 10.1 Generative adversarial network (GAN) -- 10.1.1 Binary classification -- 10.1.2 Original GAN formulation as binary classification -- 10.1.3 Objective (loss) function -- 10.1.4 Optimization algorithm -- 10.2 Case studies: GAN-based roadway traffic state estimation -- 10.2.1 Problem formulation -- 10.2.2 Model: generative adversarial architecture for spatiotemporal traffic- state estimation -- 10.2.3 Results -- 10.3 Case study: conditional GAN-based taxi hotspots prediction -- 10.3.1 Problem formulation -- 10.3.2 Model: LSTM-CGAN-based-hotspot prediction -- 10.3.3 Results -- 10.4 Case study: GAN-based pavement image data transferring -- 10.4.1 Problem formulation -- 10.4.2 Model: CycleGAN-based image style transfer -- 10.4.3 Results -- 10.5 Exercises -- 11 Edge and parallel artificial intelligence -- 11.1 Edge computing concept -- 11.2 Edge artificial intelligence -- 11.3 Parallel artificial intelligence -- 11.4 Federated learning concept -- 11.5 Federated learning methods -- 11.5.1 Horizontal federated learning -- 11.5.2 Vertical federated learning -- 11.6 Case study 1: parallel and edge AI in multi-task traffic surveillance -- 11.6.1 Motivations -- 11.6.2 Parallel edge computing system architecture -- 11.6.3 Algorithms and results -- 11.7 Case study 2: edge AI in vehicle near-crash detection -- 11.7.1 Motivations -- 11.7.2 Relative motion patterns in camera views for near-crashes
505	8		\|a 11.7.3 Edge computing system architecture -- 11.7.4 Camera-parameter-free near-crash detection algorithm -- 11.7.5 Height or width -- 11.7.6 Modeling bounding box centers for horizontal motion pattern identi- fication -- 11.7.7 Experimental results -- 11.8 Case study 3: federated learning for vehicle trajectory prediction -- 11.8.1 Motivation -- 11.8.2 Methodology -- 11.8.3 Results -- 11.9 Exercises -- 12 Future directions -- 12.1 Future trends of deep learning technologies for transportation -- 12.2 The future of transportation with AI -- 12.3 Book extension and future plan -- Bibliography -- Index -- Back Cover
700	1		\|a Cui, Zhiyong \|e Sonstige \|4 oth
700	1		\|a Ke, Ruimin \|e Sonstige \|4 oth
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contents	Front Cover -- Machine Learning for Transportation Research and Applications -- Copyright -- Contents -- About the authors -- 1 Introduction -- 1.1 Background -- 1.1.1 Importance of transportation -- 1.1.2 Motivation -- 1.2 ML is promising for transportation research and applications -- 1.2.1 A brief history of ML -- 1.2.2 ML for transportation research and applications -- 1.3 Book organization -- 2 Transportation data and sensing -- 2.1 Data explosion -- 2.2 ITS data needs -- 2.3 Infrastructure-based data and sensing -- 2.3.1 Traffic flow detection -- 2.3.2 Travel time estimation -- 2.3.3 Traffic anomaly detection -- 2.3.4 Parking detection -- 2.4 Vehicle onboard data and sensing -- 2.4.1 Traffic near-crash detection -- 2.4.2 Road user behavior sensing -- 2.4.3 Road and lane detection -- 2.4.4 Semantic segmentation -- 2.5 Aerial sensing for ground transportation data -- 2.5.1 Road user detection and tracking -- 2.5.2 Advanced aerial sensing -- 2.5.3 UAV for infrastructure data collection -- 2.6 ITS data quality control and fusion -- 2.7 Transportation data and sensing challenges -- 2.7.1 Heterogeneity -- 2.7.2 High probability of sensor failure -- 2.7.3 Sensing in extreme cases -- 2.7.4 Privacy protection -- 2.8 Exercises -- 3 Machine learning basics -- 3.1 Categories of machine learning -- 3.1.1 Supervised vs. unsupervised learning -- 3.1.2 Generative vs. discriminative algorithms -- 3.1.3 Parametric vs. nonparametric modeling -- 3.2 Supervised learning -- 3.2.1 Linear regression -- Problem setup -- Solving the optimization problem -- Vectorization -- 3.2.2 Logistic regression -- Softmax regression -- 3.3 Unsupervised learning -- 3.3.1 Principal component analysis -- 3.3.2 Clustering -- 3.4 Key concepts in machine learning -- 3.4.1 Loss -- 3.4.2 Regularization -- L1 vs. L2 -- 3.4.3 Gradient descent vs. gradient ascent 3.4.4 K-fold cross-validation -- 3.5 Exercises -- 3.5.1 Questions -- 4 Fully connected neural networks -- 4.1 Linear regression -- 4.2 Deep neural network fundamentals -- 4.2.1 Perceptron -- 4.2.2 Hidden layers -- 4.2.3 Activation functions -- Sigmoid function -- Tanh function -- ReLU function -- 4.2.4 Loss functions -- 4.2.5 Back-propagation -- Forward propagation -- Backward propagation -- 4.2.6 Validation dataset -- 4.2.7 Underfitting or overfitting? -- 4.3 Transportation applications -- 4.3.1 Traffic prediction -- 4.3.2 Traffic sign image classification -- 4.4 Exercises -- 4.4.1 Questions -- 5 Convolution neural networks -- 5.1 Convolution neural network fundamentals -- 5.1.1 From fully connected layers to convolutions -- 5.1.2 Convolutions -- 5.1.3 Architecture -- 5.1.4 AlexNet -- 5.2 Case study: traffic video sensing -- 5.3 Case study: spatiotemporal traffic pattern learning -- 5.4 Case study: CNNs for data imputation -- 5.4.1 CNN-based imputation approach -- 5.4.2 Experiment -- 5.5 Exercises -- 6 Recurrent neural networks -- 6.1 RNN fundamentals -- 6.2 RNN variants and related architectures -- 6.2.1 Long short-term memory (LSTM) and gated recurrent units (GRU) -- 6.2.2 Bidirectional RNN -- 6.2.3 Sequence to sequence -- 6.3 RNN as a building block for transportation applications -- 6.3.1 RNN for road traffic prediction -- Problem description -- Network-wide traffic prediction -- Traffic prediction algorithms -- 6.3.2 Traffic prediction with missing values -- Problem definition -- LSTM-based traffic prediction with missing values -- 6.4 Exercises -- 6.4.1 Questions -- 6.4.2 Project: predicting network-wide traffic using LSTM -- Problem definition -- Dataset preparation -- Implement and fine-tune model -- Model evaluation -- 7 Reinforcement learning -- 7.1 Reinforcement learning setting -- 7.1.1 Markov property 7.1.2 Goal of reinforcement learning -- 7.1.3 Categories and terms in reinforcement learning -- Model-free vs. model-based -- Stationary vs. nonstationary -- Deterministic policy vs. stochastic policy -- Offline learning vs. online learning -- Exploration vs. exploitation -- Off-policy learning vs. on-policy learning -- 7.2 Value-based methods -- 7.2.1 Q-learning -- 7.2.2 Deep Q-networks -- 7.3 Policy gradient methods for deep RL -- 7.3.1 Stochastic policy gradient -- 7.3.2 Deterministic policy gradient -- 7.4 Combining policy gradient and Q-learning -- 7.4.1 Actor-critic methods -- 7.5 Case study 1: traffic signal control -- 7.5.1 Agent formulation -- 7.6 Case study 2: car following control -- 7.6.1 Agent formulation -- 7.6.2 Model and simulation settings -- 7.7 Case study 3: bus bunching control -- 7.7.1 Agent formulation -- 7.8 Exercises -- 7.8.1 Questions -- 8 Transfer learning -- 8.1 What is transfer learning -- 8.2 Why transfer learning -- 8.3 Definition -- 8.4 Transfer learning steps -- 8.5 Transfer learning types -- 8.5.1 Domain adaptation -- 8.5.2 Multi-task learning -- 8.5.3 Zero-shot learning -- 8.5.4 Few-shot learning -- 8.6 Case study: vehicle detection enhancement through transfer learning -- 8.7 Case study: parking information management and prediction system by attribute representation learning -- 8.7.1 Background -- 8.7.2 Methods -- 8.7.3 Results -- 8.8 Case study: transfer learning for nighttime traffic detection -- 8.9 Case study: simulation to real-world knowledge transfer for driving be- havior recognition -- 8.10 Exercises -- 9 Graph neural networks -- 9.1 Preliminaries -- 9.2 Graph neural networks -- 9.2.1 Spectral GNN -- 9.2.2 Spatial GNN -- 9.2.3 Attention-based GNNs -- 9.3 Case study 1: traffic graph convolutional network for traffic prediction -- 9.3.1 Problem definition -- 9.3.2 Method: traffic graph convolutional LSTM. 9.3.3 Results -- 9.4 Case study 2: graph neural network for traffic forecasting with missing values -- 9.4.1 Problem definition -- 9.4.2 Method: graph Markov network -- 9.4.3 Results -- 9.5 Case study 3: graph neural network (GNN) for vehicle keypoints' cor- rection -- 9.5.1 Problem definition -- 9.5.2 Method: graph neural network for keypoints correction -- 9.5.3 Results -- 9.6 Exercises -- 9.6.1 Questions -- 10 Generative adversarial networks -- 10.1 Generative adversarial network (GAN) -- 10.1.1 Binary classification -- 10.1.2 Original GAN formulation as binary classification -- 10.1.3 Objective (loss) function -- 10.1.4 Optimization algorithm -- 10.2 Case studies: GAN-based roadway traffic state estimation -- 10.2.1 Problem formulation -- 10.2.2 Model: generative adversarial architecture for spatiotemporal traffic- state estimation -- 10.2.3 Results -- 10.3 Case study: conditional GAN-based taxi hotspots prediction -- 10.3.1 Problem formulation -- 10.3.2 Model: LSTM-CGAN-based-hotspot prediction -- 10.3.3 Results -- 10.4 Case study: GAN-based pavement image data transferring -- 10.4.1 Problem formulation -- 10.4.2 Model: CycleGAN-based image style transfer -- 10.4.3 Results -- 10.5 Exercises -- 11 Edge and parallel artificial intelligence -- 11.1 Edge computing concept -- 11.2 Edge artificial intelligence -- 11.3 Parallel artificial intelligence -- 11.4 Federated learning concept -- 11.5 Federated learning methods -- 11.5.1 Horizontal federated learning -- 11.5.2 Vertical federated learning -- 11.6 Case study 1: parallel and edge AI in multi-task traffic surveillance -- 11.6.1 Motivations -- 11.6.2 Parallel edge computing system architecture -- 11.6.3 Algorithms and results -- 11.7 Case study 2: edge AI in vehicle near-crash detection -- 11.7.1 Motivations -- 11.7.2 Relative motion patterns in camera views for near-crashes 11.7.3 Edge computing system architecture -- 11.7.4 Camera-parameter-free near-crash detection algorithm -- 11.7.5 Height or width -- 11.7.6 Modeling bounding box centers for horizontal motion pattern identi- fication -- 11.7.7 Experimental results -- 11.8 Case study 3: federated learning for vehicle trajectory prediction -- 11.8.1 Motivation -- 11.8.2 Methodology -- 11.8.3 Results -- 11.9 Exercises -- 12 Future directions -- 12.1 Future trends of deep learning technologies for transportation -- 12.2 The future of transportation with AI -- 12.3 Book extension and future plan -- Bibliography -- Index -- Back Cover
ctrlnum	(ZDB-30-PQE)EBC7239072 (ZDB-30-PAD)EBC7239072 (ZDB-89-EBL)EBL7239072 (OCoLC)1376933383 (DE-599)BVBBV049019806
dewey-full	388.0285631
dewey-hundreds	300 - Social sciences
dewey-ones	388 - Transportation
dewey-raw	388.0285631
dewey-search	388.0285631
dewey-sort	3388.0285631
dewey-tens	380 - Commerce, communications, transportation
discipline	Wirtschaftswissenschaften
discipline_str_mv	Wirtschaftswissenschaften
format	Electronic eBook
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ind1=" " ind2="4"><subfield code="c">©2023</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">1 Online-Ressource (254 Seiten)</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">Description based on publisher supplied metadata and other sources</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">Front Cover -- Machine Learning for Transportation Research and Applications -- Copyright -- Contents -- About the authors -- 1 Introduction -- 1.1 Background -- 1.1.1 Importance of transportation -- 1.1.2 Motivation -- 1.2 ML is promising for transportation research and applications -- 1.2.1 A brief history of ML -- 1.2.2 ML for transportation research and applications -- 1.3 Book organization -- 2 Transportation data and sensing -- 2.1 Data explosion -- 2.2 ITS data needs -- 2.3 Infrastructure-based data and sensing -- 2.3.1 Traffic flow detection -- 2.3.2 Travel time estimation -- 2.3.3 Traffic anomaly detection -- 2.3.4 Parking detection -- 2.4 Vehicle onboard data and sensing -- 2.4.1 Traffic near-crash detection -- 2.4.2 Road user behavior sensing -- 2.4.3 Road and lane detection -- 2.4.4 Semantic segmentation -- 2.5 Aerial sensing for ground transportation data -- 2.5.1 Road user detection and tracking -- 2.5.2 Advanced aerial sensing -- 2.5.3 UAV for infrastructure data collection -- 2.6 ITS data quality control and fusion -- 2.7 Transportation data and sensing challenges -- 2.7.1 Heterogeneity -- 2.7.2 High probability of sensor failure -- 2.7.3 Sensing in extreme cases -- 2.7.4 Privacy protection -- 2.8 Exercises -- 3 Machine learning basics -- 3.1 Categories of machine learning -- 3.1.1 Supervised vs. unsupervised learning -- 3.1.2 Generative vs. discriminative algorithms -- 3.1.3 Parametric vs. nonparametric modeling -- 3.2 Supervised learning -- 3.2.1 Linear regression -- Problem setup -- Solving the optimization problem -- Vectorization -- 3.2.2 Logistic regression -- Softmax regression -- 3.3 Unsupervised learning -- 3.3.1 Principal component analysis -- 3.3.2 Clustering -- 3.4 Key concepts in machine learning -- 3.4.1 Loss -- 3.4.2 Regularization -- L1 vs. L2 -- 3.4.3 Gradient descent vs. gradient ascent</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">3.4.4 K-fold cross-validation -- 3.5 Exercises -- 3.5.1 Questions -- 4 Fully connected neural networks -- 4.1 Linear regression -- 4.2 Deep neural network fundamentals -- 4.2.1 Perceptron -- 4.2.2 Hidden layers -- 4.2.3 Activation functions -- Sigmoid function -- Tanh function -- ReLU function -- 4.2.4 Loss functions -- 4.2.5 Back-propagation -- Forward propagation -- Backward propagation -- 4.2.6 Validation dataset -- 4.2.7 Underfitting or overfitting? -- 4.3 Transportation applications -- 4.3.1 Traffic prediction -- 4.3.2 Traffic sign image classification -- 4.4 Exercises -- 4.4.1 Questions -- 5 Convolution neural networks -- 5.1 Convolution neural network fundamentals -- 5.1.1 From fully connected layers to convolutions -- 5.1.2 Convolutions -- 5.1.3 Architecture -- 5.1.4 AlexNet -- 5.2 Case study: traffic video sensing -- 5.3 Case study: spatiotemporal traffic pattern learning -- 5.4 Case study: CNNs for data imputation -- 5.4.1 CNN-based imputation approach -- 5.4.2 Experiment -- 5.5 Exercises -- 6 Recurrent neural networks -- 6.1 RNN fundamentals -- 6.2 RNN variants and related architectures -- 6.2.1 Long short-term memory (LSTM) and gated recurrent units (GRU) -- 6.2.2 Bidirectional RNN -- 6.2.3 Sequence to sequence -- 6.3 RNN as a building block for transportation applications -- 6.3.1 RNN for road traffic prediction -- Problem description -- Network-wide traffic prediction -- Traffic prediction algorithms -- 6.3.2 Traffic prediction with missing values -- Problem definition -- LSTM-based traffic prediction with missing values -- 6.4 Exercises -- 6.4.1 Questions -- 6.4.2 Project: predicting network-wide traffic using LSTM -- Problem definition -- Dataset preparation -- Implement and fine-tune model -- Model evaluation -- 7 Reinforcement learning -- 7.1 Reinforcement learning setting -- 7.1.1 Markov property</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">7.1.2 Goal of reinforcement learning -- 7.1.3 Categories and terms in reinforcement learning -- Model-free vs. model-based -- Stationary vs. nonstationary -- Deterministic policy vs. stochastic policy -- Offline learning vs. online learning -- Exploration vs. exploitation -- Off-policy learning vs. on-policy learning -- 7.2 Value-based methods -- 7.2.1 Q-learning -- 7.2.2 Deep Q-networks -- 7.3 Policy gradient methods for deep RL -- 7.3.1 Stochastic policy gradient -- 7.3.2 Deterministic policy gradient -- 7.4 Combining policy gradient and Q-learning -- 7.4.1 Actor-critic methods -- 7.5 Case study 1: traffic signal control -- 7.5.1 Agent formulation -- 7.6 Case study 2: car following control -- 7.6.1 Agent formulation -- 7.6.2 Model and simulation settings -- 7.7 Case study 3: bus bunching control -- 7.7.1 Agent formulation -- 7.8 Exercises -- 7.8.1 Questions -- 8 Transfer learning -- 8.1 What is transfer learning -- 8.2 Why transfer learning -- 8.3 Definition -- 8.4 Transfer learning steps -- 8.5 Transfer learning types -- 8.5.1 Domain adaptation -- 8.5.2 Multi-task learning -- 8.5.3 Zero-shot learning -- 8.5.4 Few-shot learning -- 8.6 Case study: vehicle detection enhancement through transfer learning -- 8.7 Case study: parking information management and prediction system by attribute representation learning -- 8.7.1 Background -- 8.7.2 Methods -- 8.7.3 Results -- 8.8 Case study: transfer learning for nighttime traffic detection -- 8.9 Case study: simulation to real-world knowledge transfer for driving be- havior recognition -- 8.10 Exercises -- 9 Graph neural networks -- 9.1 Preliminaries -- 9.2 Graph neural networks -- 9.2.1 Spectral GNN -- 9.2.2 Spatial GNN -- 9.2.3 Attention-based GNNs -- 9.3 Case study 1: traffic graph convolutional network for traffic prediction -- 9.3.1 Problem definition -- 9.3.2 Method: traffic graph convolutional LSTM.</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">9.3.3 Results -- 9.4 Case study 2: graph neural network for traffic forecasting with missing values -- 9.4.1 Problem definition -- 9.4.2 Method: graph Markov network -- 9.4.3 Results -- 9.5 Case study 3: graph neural network (GNN) for vehicle keypoints' cor- rection -- 9.5.1 Problem definition -- 9.5.2 Method: graph neural network for keypoints correction -- 9.5.3 Results -- 9.6 Exercises -- 9.6.1 Questions -- 10 Generative adversarial networks -- 10.1 Generative adversarial network (GAN) -- 10.1.1 Binary classification -- 10.1.2 Original GAN formulation as binary classification -- 10.1.3 Objective (loss) function -- 10.1.4 Optimization algorithm -- 10.2 Case studies: GAN-based roadway traffic state estimation -- 10.2.1 Problem formulation -- 10.2.2 Model: generative adversarial architecture for spatiotemporal traffic- state estimation -- 10.2.3 Results -- 10.3 Case study: conditional GAN-based taxi hotspots prediction -- 10.3.1 Problem formulation -- 10.3.2 Model: LSTM-CGAN-based-hotspot prediction -- 10.3.3 Results -- 10.4 Case study: GAN-based pavement image data transferring -- 10.4.1 Problem formulation -- 10.4.2 Model: CycleGAN-based image style transfer -- 10.4.3 Results -- 10.5 Exercises -- 11 Edge and parallel artificial intelligence -- 11.1 Edge computing concept -- 11.2 Edge artificial intelligence -- 11.3 Parallel artificial intelligence -- 11.4 Federated learning concept -- 11.5 Federated learning methods -- 11.5.1 Horizontal federated learning -- 11.5.2 Vertical federated learning -- 11.6 Case study 1: parallel and edge AI in multi-task traffic surveillance -- 11.6.1 Motivations -- 11.6.2 Parallel edge computing system architecture -- 11.6.3 Algorithms and results -- 11.7 Case study 2: edge AI in vehicle near-crash detection -- 11.7.1 Motivations -- 11.7.2 Relative motion patterns in camera views for near-crashes</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">11.7.3 Edge computing system architecture -- 11.7.4 Camera-parameter-free near-crash detection algorithm -- 11.7.5 Height or width -- 11.7.6 Modeling bounding box centers for horizontal motion pattern identi- fication -- 11.7.7 Experimental results -- 11.8 Case study 3: federated learning for vehicle trajectory prediction -- 11.8.1 Motivation -- 11.8.2 Methodology -- 11.8.3 Results -- 11.9 Exercises -- 12 Future directions -- 12.1 Future trends of deep learning technologies for transportation -- 12.2 The future of transportation with AI -- 12.3 Book extension and future plan -- Bibliography -- Index -- Back Cover</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Cui, Zhiyong</subfield><subfield code="e">Sonstige</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Ke, Ruimin</subfield><subfield code="e">Sonstige</subfield><subfield code="4">oth</subfield></datafield><datafield tag="776" ind1="0" ind2="8"><subfield code="i">Erscheint auch als</subfield><subfield code="n">Druck-Ausgabe</subfield><subfield code="a">Wang, Yinhai</subfield><subfield code="t">Machine Learning for Transportation Research and Applications</subfield><subfield code="d">San Diego : Elsevier,c2023</subfield><subfield code="z">9780323961264</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ZDB-30-PQE</subfield></datafield><datafield tag="999" ind1=" " 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id	DE-604.BV049019806
illustrated	Not Illustrated
index_date	2024-07-03T22:13:40Z
indexdate	2024-07-10T09:52:59Z
institution	BVB
isbn	9780323996808
language	English
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physical	1 Online-Ressource (254 Seiten)
psigel	ZDB-30-PQE ZDB-30-PQE HWR_PDA_PQE
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spelling	Wang, Yinhai Verfasser aut Machine Learning for Transportation Research and Applications San Diego Elsevier 2023 ©2023 1 Online-Ressource (254 Seiten) txt rdacontent c rdamedia cr rdacarrier Description based on publisher supplied metadata and other sources Front Cover -- Machine Learning for Transportation Research and Applications -- Copyright -- Contents -- About the authors -- 1 Introduction -- 1.1 Background -- 1.1.1 Importance of transportation -- 1.1.2 Motivation -- 1.2 ML is promising for transportation research and applications -- 1.2.1 A brief history of ML -- 1.2.2 ML for transportation research and applications -- 1.3 Book organization -- 2 Transportation data and sensing -- 2.1 Data explosion -- 2.2 ITS data needs -- 2.3 Infrastructure-based data and sensing -- 2.3.1 Traffic flow detection -- 2.3.2 Travel time estimation -- 2.3.3 Traffic anomaly detection -- 2.3.4 Parking detection -- 2.4 Vehicle onboard data and sensing -- 2.4.1 Traffic near-crash detection -- 2.4.2 Road user behavior sensing -- 2.4.3 Road and lane detection -- 2.4.4 Semantic segmentation -- 2.5 Aerial sensing for ground transportation data -- 2.5.1 Road user detection and tracking -- 2.5.2 Advanced aerial sensing -- 2.5.3 UAV for infrastructure data collection -- 2.6 ITS data quality control and fusion -- 2.7 Transportation data and sensing challenges -- 2.7.1 Heterogeneity -- 2.7.2 High probability of sensor failure -- 2.7.3 Sensing in extreme cases -- 2.7.4 Privacy protection -- 2.8 Exercises -- 3 Machine learning basics -- 3.1 Categories of machine learning -- 3.1.1 Supervised vs. unsupervised learning -- 3.1.2 Generative vs. discriminative algorithms -- 3.1.3 Parametric vs. nonparametric modeling -- 3.2 Supervised learning -- 3.2.1 Linear regression -- Problem setup -- Solving the optimization problem -- Vectorization -- 3.2.2 Logistic regression -- Softmax regression -- 3.3 Unsupervised learning -- 3.3.1 Principal component analysis -- 3.3.2 Clustering -- 3.4 Key concepts in machine learning -- 3.4.1 Loss -- 3.4.2 Regularization -- L1 vs. L2 -- 3.4.3 Gradient descent vs. gradient ascent 3.4.4 K-fold cross-validation -- 3.5 Exercises -- 3.5.1 Questions -- 4 Fully connected neural networks -- 4.1 Linear regression -- 4.2 Deep neural network fundamentals -- 4.2.1 Perceptron -- 4.2.2 Hidden layers -- 4.2.3 Activation functions -- Sigmoid function -- Tanh function -- ReLU function -- 4.2.4 Loss functions -- 4.2.5 Back-propagation -- Forward propagation -- Backward propagation -- 4.2.6 Validation dataset -- 4.2.7 Underfitting or overfitting? -- 4.3 Transportation applications -- 4.3.1 Traffic prediction -- 4.3.2 Traffic sign image classification -- 4.4 Exercises -- 4.4.1 Questions -- 5 Convolution neural networks -- 5.1 Convolution neural network fundamentals -- 5.1.1 From fully connected layers to convolutions -- 5.1.2 Convolutions -- 5.1.3 Architecture -- 5.1.4 AlexNet -- 5.2 Case study: traffic video sensing -- 5.3 Case study: spatiotemporal traffic pattern learning -- 5.4 Case study: CNNs for data imputation -- 5.4.1 CNN-based imputation approach -- 5.4.2 Experiment -- 5.5 Exercises -- 6 Recurrent neural networks -- 6.1 RNN fundamentals -- 6.2 RNN variants and related architectures -- 6.2.1 Long short-term memory (LSTM) and gated recurrent units (GRU) -- 6.2.2 Bidirectional RNN -- 6.2.3 Sequence to sequence -- 6.3 RNN as a building block for transportation applications -- 6.3.1 RNN for road traffic prediction -- Problem description -- Network-wide traffic prediction -- Traffic prediction algorithms -- 6.3.2 Traffic prediction with missing values -- Problem definition -- LSTM-based traffic prediction with missing values -- 6.4 Exercises -- 6.4.1 Questions -- 6.4.2 Project: predicting network-wide traffic using LSTM -- Problem definition -- Dataset preparation -- Implement and fine-tune model -- Model evaluation -- 7 Reinforcement learning -- 7.1 Reinforcement learning setting -- 7.1.1 Markov property 7.1.2 Goal of reinforcement learning -- 7.1.3 Categories and terms in reinforcement learning -- Model-free vs. model-based -- Stationary vs. nonstationary -- Deterministic policy vs. stochastic policy -- Offline learning vs. online learning -- Exploration vs. exploitation -- Off-policy learning vs. on-policy learning -- 7.2 Value-based methods -- 7.2.1 Q-learning -- 7.2.2 Deep Q-networks -- 7.3 Policy gradient methods for deep RL -- 7.3.1 Stochastic policy gradient -- 7.3.2 Deterministic policy gradient -- 7.4 Combining policy gradient and Q-learning -- 7.4.1 Actor-critic methods -- 7.5 Case study 1: traffic signal control -- 7.5.1 Agent formulation -- 7.6 Case study 2: car following control -- 7.6.1 Agent formulation -- 7.6.2 Model and simulation settings -- 7.7 Case study 3: bus bunching control -- 7.7.1 Agent formulation -- 7.8 Exercises -- 7.8.1 Questions -- 8 Transfer learning -- 8.1 What is transfer learning -- 8.2 Why transfer learning -- 8.3 Definition -- 8.4 Transfer learning steps -- 8.5 Transfer learning types -- 8.5.1 Domain adaptation -- 8.5.2 Multi-task learning -- 8.5.3 Zero-shot learning -- 8.5.4 Few-shot learning -- 8.6 Case study: vehicle detection enhancement through transfer learning -- 8.7 Case study: parking information management and prediction system by attribute representation learning -- 8.7.1 Background -- 8.7.2 Methods -- 8.7.3 Results -- 8.8 Case study: transfer learning for nighttime traffic detection -- 8.9 Case study: simulation to real-world knowledge transfer for driving be- havior recognition -- 8.10 Exercises -- 9 Graph neural networks -- 9.1 Preliminaries -- 9.2 Graph neural networks -- 9.2.1 Spectral GNN -- 9.2.2 Spatial GNN -- 9.2.3 Attention-based GNNs -- 9.3 Case study 1: traffic graph convolutional network for traffic prediction -- 9.3.1 Problem definition -- 9.3.2 Method: traffic graph convolutional LSTM. 9.3.3 Results -- 9.4 Case study 2: graph neural network for traffic forecasting with missing values -- 9.4.1 Problem definition -- 9.4.2 Method: graph Markov network -- 9.4.3 Results -- 9.5 Case study 3: graph neural network (GNN) for vehicle keypoints' cor- rection -- 9.5.1 Problem definition -- 9.5.2 Method: graph neural network for keypoints correction -- 9.5.3 Results -- 9.6 Exercises -- 9.6.1 Questions -- 10 Generative adversarial networks -- 10.1 Generative adversarial network (GAN) -- 10.1.1 Binary classification -- 10.1.2 Original GAN formulation as binary classification -- 10.1.3 Objective (loss) function -- 10.1.4 Optimization algorithm -- 10.2 Case studies: GAN-based roadway traffic state estimation -- 10.2.1 Problem formulation -- 10.2.2 Model: generative adversarial architecture for spatiotemporal traffic- state estimation -- 10.2.3 Results -- 10.3 Case study: conditional GAN-based taxi hotspots prediction -- 10.3.1 Problem formulation -- 10.3.2 Model: LSTM-CGAN-based-hotspot prediction -- 10.3.3 Results -- 10.4 Case study: GAN-based pavement image data transferring -- 10.4.1 Problem formulation -- 10.4.2 Model: CycleGAN-based image style transfer -- 10.4.3 Results -- 10.5 Exercises -- 11 Edge and parallel artificial intelligence -- 11.1 Edge computing concept -- 11.2 Edge artificial intelligence -- 11.3 Parallel artificial intelligence -- 11.4 Federated learning concept -- 11.5 Federated learning methods -- 11.5.1 Horizontal federated learning -- 11.5.2 Vertical federated learning -- 11.6 Case study 1: parallel and edge AI in multi-task traffic surveillance -- 11.6.1 Motivations -- 11.6.2 Parallel edge computing system architecture -- 11.6.3 Algorithms and results -- 11.7 Case study 2: edge AI in vehicle near-crash detection -- 11.7.1 Motivations -- 11.7.2 Relative motion patterns in camera views for near-crashes 11.7.3 Edge computing system architecture -- 11.7.4 Camera-parameter-free near-crash detection algorithm -- 11.7.5 Height or width -- 11.7.6 Modeling bounding box centers for horizontal motion pattern identi- fication -- 11.7.7 Experimental results -- 11.8 Case study 3: federated learning for vehicle trajectory prediction -- 11.8.1 Motivation -- 11.8.2 Methodology -- 11.8.3 Results -- 11.9 Exercises -- 12 Future directions -- 12.1 Future trends of deep learning technologies for transportation -- 12.2 The future of transportation with AI -- 12.3 Book extension and future plan -- Bibliography -- Index -- Back Cover Cui, Zhiyong Sonstige oth Ke, Ruimin Sonstige oth Erscheint auch als Druck-Ausgabe Wang, Yinhai Machine Learning for Transportation Research and Applications San Diego : Elsevier,c2023 9780323961264
spellingShingle	Wang, Yinhai Machine Learning for Transportation Research and Applications Front Cover -- Machine Learning for Transportation Research and Applications -- Copyright -- Contents -- About the authors -- 1 Introduction -- 1.1 Background -- 1.1.1 Importance of transportation -- 1.1.2 Motivation -- 1.2 ML is promising for transportation research and applications -- 1.2.1 A brief history of ML -- 1.2.2 ML for transportation research and applications -- 1.3 Book organization -- 2 Transportation data and sensing -- 2.1 Data explosion -- 2.2 ITS data needs -- 2.3 Infrastructure-based data and sensing -- 2.3.1 Traffic flow detection -- 2.3.2 Travel time estimation -- 2.3.3 Traffic anomaly detection -- 2.3.4 Parking detection -- 2.4 Vehicle onboard data and sensing -- 2.4.1 Traffic near-crash detection -- 2.4.2 Road user behavior sensing -- 2.4.3 Road and lane detection -- 2.4.4 Semantic segmentation -- 2.5 Aerial sensing for ground transportation data -- 2.5.1 Road user detection and tracking -- 2.5.2 Advanced aerial sensing -- 2.5.3 UAV for infrastructure data collection -- 2.6 ITS data quality control and fusion -- 2.7 Transportation data and sensing challenges -- 2.7.1 Heterogeneity -- 2.7.2 High probability of sensor failure -- 2.7.3 Sensing in extreme cases -- 2.7.4 Privacy protection -- 2.8 Exercises -- 3 Machine learning basics -- 3.1 Categories of machine learning -- 3.1.1 Supervised vs. unsupervised learning -- 3.1.2 Generative vs. discriminative algorithms -- 3.1.3 Parametric vs. nonparametric modeling -- 3.2 Supervised learning -- 3.2.1 Linear regression -- Problem setup -- Solving the optimization problem -- Vectorization -- 3.2.2 Logistic regression -- Softmax regression -- 3.3 Unsupervised learning -- 3.3.1 Principal component analysis -- 3.3.2 Clustering -- 3.4 Key concepts in machine learning -- 3.4.1 Loss -- 3.4.2 Regularization -- L1 vs. L2 -- 3.4.3 Gradient descent vs. gradient ascent 3.4.4 K-fold cross-validation -- 3.5 Exercises -- 3.5.1 Questions -- 4 Fully connected neural networks -- 4.1 Linear regression -- 4.2 Deep neural network fundamentals -- 4.2.1 Perceptron -- 4.2.2 Hidden layers -- 4.2.3 Activation functions -- Sigmoid function -- Tanh function -- ReLU function -- 4.2.4 Loss functions -- 4.2.5 Back-propagation -- Forward propagation -- Backward propagation -- 4.2.6 Validation dataset -- 4.2.7 Underfitting or overfitting? -- 4.3 Transportation applications -- 4.3.1 Traffic prediction -- 4.3.2 Traffic sign image classification -- 4.4 Exercises -- 4.4.1 Questions -- 5 Convolution neural networks -- 5.1 Convolution neural network fundamentals -- 5.1.1 From fully connected layers to convolutions -- 5.1.2 Convolutions -- 5.1.3 Architecture -- 5.1.4 AlexNet -- 5.2 Case study: traffic video sensing -- 5.3 Case study: spatiotemporal traffic pattern learning -- 5.4 Case study: CNNs for data imputation -- 5.4.1 CNN-based imputation approach -- 5.4.2 Experiment -- 5.5 Exercises -- 6 Recurrent neural networks -- 6.1 RNN fundamentals -- 6.2 RNN variants and related architectures -- 6.2.1 Long short-term memory (LSTM) and gated recurrent units (GRU) -- 6.2.2 Bidirectional RNN -- 6.2.3 Sequence to sequence -- 6.3 RNN as a building block for transportation applications -- 6.3.1 RNN for road traffic prediction -- Problem description -- Network-wide traffic prediction -- Traffic prediction algorithms -- 6.3.2 Traffic prediction with missing values -- Problem definition -- LSTM-based traffic prediction with missing values -- 6.4 Exercises -- 6.4.1 Questions -- 6.4.2 Project: predicting network-wide traffic using LSTM -- Problem definition -- Dataset preparation -- Implement and fine-tune model -- Model evaluation -- 7 Reinforcement learning -- 7.1 Reinforcement learning setting -- 7.1.1 Markov property 7.1.2 Goal of reinforcement learning -- 7.1.3 Categories and terms in reinforcement learning -- Model-free vs. model-based -- Stationary vs. nonstationary -- Deterministic policy vs. stochastic policy -- Offline learning vs. online learning -- Exploration vs. exploitation -- Off-policy learning vs. on-policy learning -- 7.2 Value-based methods -- 7.2.1 Q-learning -- 7.2.2 Deep Q-networks -- 7.3 Policy gradient methods for deep RL -- 7.3.1 Stochastic policy gradient -- 7.3.2 Deterministic policy gradient -- 7.4 Combining policy gradient and Q-learning -- 7.4.1 Actor-critic methods -- 7.5 Case study 1: traffic signal control -- 7.5.1 Agent formulation -- 7.6 Case study 2: car following control -- 7.6.1 Agent formulation -- 7.6.2 Model and simulation settings -- 7.7 Case study 3: bus bunching control -- 7.7.1 Agent formulation -- 7.8 Exercises -- 7.8.1 Questions -- 8 Transfer learning -- 8.1 What is transfer learning -- 8.2 Why transfer learning -- 8.3 Definition -- 8.4 Transfer learning steps -- 8.5 Transfer learning types -- 8.5.1 Domain adaptation -- 8.5.2 Multi-task learning -- 8.5.3 Zero-shot learning -- 8.5.4 Few-shot learning -- 8.6 Case study: vehicle detection enhancement through transfer learning -- 8.7 Case study: parking information management and prediction system by attribute representation learning -- 8.7.1 Background -- 8.7.2 Methods -- 8.7.3 Results -- 8.8 Case study: transfer learning for nighttime traffic detection -- 8.9 Case study: simulation to real-world knowledge transfer for driving be- havior recognition -- 8.10 Exercises -- 9 Graph neural networks -- 9.1 Preliminaries -- 9.2 Graph neural networks -- 9.2.1 Spectral GNN -- 9.2.2 Spatial GNN -- 9.2.3 Attention-based GNNs -- 9.3 Case study 1: traffic graph convolutional network for traffic prediction -- 9.3.1 Problem definition -- 9.3.2 Method: traffic graph convolutional LSTM. 9.3.3 Results -- 9.4 Case study 2: graph neural network for traffic forecasting with missing values -- 9.4.1 Problem definition -- 9.4.2 Method: graph Markov network -- 9.4.3 Results -- 9.5 Case study 3: graph neural network (GNN) for vehicle keypoints' cor- rection -- 9.5.1 Problem definition -- 9.5.2 Method: graph neural network for keypoints correction -- 9.5.3 Results -- 9.6 Exercises -- 9.6.1 Questions -- 10 Generative adversarial networks -- 10.1 Generative adversarial network (GAN) -- 10.1.1 Binary classification -- 10.1.2 Original GAN formulation as binary classification -- 10.1.3 Objective (loss) function -- 10.1.4 Optimization algorithm -- 10.2 Case studies: GAN-based roadway traffic state estimation -- 10.2.1 Problem formulation -- 10.2.2 Model: generative adversarial architecture for spatiotemporal traffic- state estimation -- 10.2.3 Results -- 10.3 Case study: conditional GAN-based taxi hotspots prediction -- 10.3.1 Problem formulation -- 10.3.2 Model: LSTM-CGAN-based-hotspot prediction -- 10.3.3 Results -- 10.4 Case study: GAN-based pavement image data transferring -- 10.4.1 Problem formulation -- 10.4.2 Model: CycleGAN-based image style transfer -- 10.4.3 Results -- 10.5 Exercises -- 11 Edge and parallel artificial intelligence -- 11.1 Edge computing concept -- 11.2 Edge artificial intelligence -- 11.3 Parallel artificial intelligence -- 11.4 Federated learning concept -- 11.5 Federated learning methods -- 11.5.1 Horizontal federated learning -- 11.5.2 Vertical federated learning -- 11.6 Case study 1: parallel and edge AI in multi-task traffic surveillance -- 11.6.1 Motivations -- 11.6.2 Parallel edge computing system architecture -- 11.6.3 Algorithms and results -- 11.7 Case study 2: edge AI in vehicle near-crash detection -- 11.7.1 Motivations -- 11.7.2 Relative motion patterns in camera views for near-crashes 11.7.3 Edge computing system architecture -- 11.7.4 Camera-parameter-free near-crash detection algorithm -- 11.7.5 Height or width -- 11.7.6 Modeling bounding box centers for horizontal motion pattern identi- fication -- 11.7.7 Experimental results -- 11.8 Case study 3: federated learning for vehicle trajectory prediction -- 11.8.1 Motivation -- 11.8.2 Methodology -- 11.8.3 Results -- 11.9 Exercises -- 12 Future directions -- 12.1 Future trends of deep learning technologies for transportation -- 12.2 The future of transportation with AI -- 12.3 Book extension and future plan -- Bibliography -- Index -- Back Cover
title	Machine Learning for Transportation Research and Applications
title_auth	Machine Learning for Transportation Research and Applications
title_exact_search	Machine Learning for Transportation Research and Applications
title_exact_search_txtP	Machine Learning for Transportation Research and Applications
title_full	Machine Learning for Transportation Research and Applications
title_fullStr	Machine Learning for Transportation Research and Applications
title_full_unstemmed	Machine Learning for Transportation Research and Applications
title_short	Machine Learning for Transportation Research and Applications
title_sort	machine learning for transportation research and applications
work_keys_str_mv	AT wangyinhai machinelearningfortransportationresearchandapplications AT cuizhiyong machinelearningfortransportationresearchandapplications AT keruimin machinelearningfortransportationresearchandapplications

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