Transportation Big Data

Transportation Big Data: Theory and Methods is centered on the big data theory and methods. Big data is now a key topic in transportation, simply because the volume of data has increased exponentially due to the growth in the amount of traffic (all modes) and detectors. This book provides a structured analysis of the commonly used methods for handling transportation big data; it is supported by a wealth of transportation engineering examples, together with codes. It offers a concise, yet comprehensive, description of the key techniques and important tools in transportation big data analysis.

Title of Book
Cover image
Title page
Table of Contents
Copyright
Preface
Chapter 1. Introduction
1.1 Aims and scopes
1.1.1 Background
1.1.2 Features of this book
1.1.3 Objectives of this book
1.1.4 Contant summary
1.2 Foundations of this book
1.2.1 What is data mining?
1.2.2 Typical process for data analysis and modeling
1.2.2.1 Data selection
1.2.2.2 Data preprocessing
1.2.2.3 Exploratory data analysis
1.2.2.4 Data modeling
1.2.3 Introduction to types of traffic data
1.2.3.1 Static data
1.2.3.2 Fixed detector data
1.2.3.3 Mobile detector data
1.2.3.4 Operational data
1.3 Introduction to the example datasets
1.4 Introduction to chapters
Chapter 2. Data analysis in Python
2.1 Setting up the Python environment
2.1.1 Basic Python environment configuration
2.1.2 Anaconda environment configuration
2.1.3 Common interactive tools - Jupyter Notebook
2.2 Python basics
2.2.1 Basic data types
2.2.1.1 Integer
2.2.1.2 Floating point number
2.2.1.3 Character string
2.2.1.4 Boolean value
2.2.1.5 Null
2.2.2 Variables and assignment
2.2.2.1 String operators
2.2.2.2 Frequently used functions for string processing
2.2.3 Indentation and comments
2.2.3.1 Indentation
2.2.3.2 Single-line comment
2.2.3.3 Multiline comments
2.3 Container in Python
2.3.1 List
2.3.1.1 The definition of list
2.3.1.2 Taking and slicing lists
2.3.1.3 Addition and deletion of elements
2.3.1.4 List generation
2.3.2 Tuple
2.3.3 Dict
2.3.3.1 Definition of dictionary
2.3.3.2 Dictionary values
2.3.3.3 Addition and deletion of dictionary elements
2.3.3.4 Taking out the dictionary keys
2.3.4 Set
2.3.4.1 Definition of set
2.3.4.2 Addition and deletion of collection elements
2.3.4.3 Operations on sets
2.4 Control flow statements
2.4.1 Conditional statements
2.4.2 Loop
2.4.2.1 Two types of loops
2.4.2.2 Loop control
2.5 Function definition and invocation
2.5.1 Function definition
2.5.2 Function invocation
2.6 Exception handling
2.7 Anonymous functions
2.8 Python modules
2.8.1 Module usage
2.8.1.1 Module retrieval
2.8.1.2 Module installation
2.8.1.3 Module usage
2.8.2 Introduction to Python standard library
2.8.2.1 Math module
2.8.2.2 Time module
2.8.2.3 Random module
2.8.3 Introduction to NumPy
2.8.3.1 Installing NumPy
2.8.3.2 Ndarray object
2.8.3.3 Indexing and slicing of multidimensional arrays
2.8.3.4 Array operations
2.8.3.5 Random number generation
2.8.3.6 Further knowledge
2.8.4 Introduction to pandas
2.8.4.1 Installing pandas
2.8.4.2 DataFrame data type
2.8.4.3 File reading and writing
2.8.4.4 Column operations
2.8.4.5 Grouping operations
2.8.4.6 Data concatenation
2.8.4.7 Statistical functions in pandas
2.8.4.8 Advanced statistical functions
2.8.5 Introduction matplotlib
2.8.5.1 Matplotlib installation
2.8.5.2 Introduction to matplotlib
2.8.5.3 Introduction to seaborn
2.8.6 Introduction to scikit-learn
2.8.6.1 Scikit-learn installation
2.8.6.2 Introduction to scikit-learn
2.8.6.3 Data preprocessing
2.8.6.4 Common models
2.8.7 Introduction to TensorFlow
2.8.7.1 TensorFlow introduction
2.8.7.2 TensorFlow 2 installation
2.8.7.3 Colaboratory usage
2.8.7.4 Introduction to TensorFlow high-level API—Keras
2.9 Chapter conclusion
2.10 Chapter exercises
Chapter 3. Data preprocessing and exploratory data analysis
3.1 Data preprocessing
3.1.1 Data quality analysis
3.1.1.1 Missing value check
3.1.1.2 Anomaly check
3.1.2 Handling of missing values
3.1.2.1 Deleting records
3.1.2.2 Imputation of missing values
3.1.3 Handling of outliers
3.1.4 Data standardization
3.1.4.1 Min-max normalization
3.1.4.2 Zero-mean normalization
3.1.4.3 Decimal scaling normalization
3.2 Basics of spatiotemporal data analysis
3.2.1 Spatial coordinate system transformation
3.2.2 Spatiotemporal unit partitioning
3.2.3 Spatiotemporal feature extraction
3.2.3.1 Calculation of individual features
3.2.3.2 Individual feature aggregation
3.2.4 Ride-hailing trajectory data after gridding
3.3 Exploratory data analysis
3.3.1 Analysis of data distribution characteristics
3.3.2 Statistical analysis
3.3.2.1 Measures of central tendency
3.3.2.2 Measures of dispersion
3.3.3 Comparative analysis
3.3.4 Cyclical analysis
3.3.5 Correlation analysis
3.3.5.1 Direct plot of scatter plots
3.3.5.2 Plotting a scatter plot matrix
3.3.5.3 Calculating the correlation coefficient
3.4 Chapter conclusion
3.5 Chapter exercises
Chapter 4. Data visualization
4.1 Setting chart elements
4.1.1 Axis configuration
4.1.2 Grid configuration
4.1.3 Text configuration in the plot
4.1.4 Adding legends and annotations
4.1.5 Adjusting figure size, resolution, and saving
4.2 Common visualization charts
4.2.1 Line plots
4.2.2 Bar charts
4.2.3 Boxplots
4.2.4 Pie charts
4.2.5 Histogram
4.2.6 Scatter plots
4.2.7 Faceted graphs
4.3 Fundamentals of interactive data visualization
4.3.1 Introduction to scatter plots and interactive operations
4.3.2 Overlay of interactive graphs
4.3.3 Interactive faceted charts
4.3.4 Saving interactive plots
4.4 Basic of point-line network plotting
4.4.1 Generating graphs in NetworkX
4.4.2 Network analysis for graphs
4.4.3 Drawing point-line networks
4.5 Chapter conclusion
4.6 Chapter exercises
Chapter 5. Machine learning basics
5.1 Fundamental concepts of machine learning
5.1.1 Common concepts
Samples and features
Data sets (training, validation, and test sets)
Features and feature space
Labeled data and unlabeled data
Model
Hypothesis space
Hyperparameters
5.1.2 The subject of machine learning
5.1.3 The main task of machine learning
5.2 The introduction of tasks in machine learning
5.2.1 Regression
5.2.2 Classification
5.2.3 Clustering
5.2.4 Dimensionality reduction
5.3 The basic process of machine learning
5.4 Model evaluation and selection
5.4.1 Error and loss function
Error
Loss function
The expected risk and empirical risk of an error
5.4.2 Performance metrics of classification problem
Accuracy
Precision
Recall
P–R curves and ROC curves
F1-score
5.4.3 Underfitting and overfitting
The introduction to underfitting and overfitting
Bias and variance
Learning curve
5.5 The methods for improving model performance
5.5.1 Regularization
5.5.2 Cross-validation
S-fold cross-validation
Leave-one-out cross-validation
5.5.3 Conclusion for improving model performance
5.6 Techniques in machine learning training
5.6.1 Methods to improve model performance using data
Artificially augmenting the dataset
Resample
Rescale
Data transformation
5.6.2 The techniques of feature selection
5.6.3 The methods of hyperparameters tuning
Grid search
Random search
Bayesian optimization
Other hyperparameter tuning methods
5.7 Chapter conclusion
5.8 Chapter exercises
Chapter 6. Linear models
6.1 Linear regression
6.1.1 Fundamental concepts
Guidance 1: Data generation
6.1.2 Least squares method
Guidance 2: Data splitting and standardization
Guidance 3: Learning linear regression
6.1.3 Multiple linear regression
Guidance 4: Model evaluation and validation
Guidance 5: Model visualization
6.1.4 Maximum likelihood estimation
6.1.5 Overfitting in linear regression
Overfitting phenomenon
Ridge regression
6.1.6 Application of linear regression
Variable selection
Fitting using the linear model
Prediction using linear model
6.2 Logistic regression
6.2.1 Generalized linear model
6.2.2 Logistic regression
6.2.3 Logistic regression parameter learning rules
6.2.4 Logistic regression loss: Cross-entropy
Batch gradient descent algorithm
Stochastic gradient descent algorithm
Mini-batch SGD algorithm
6.2.5 Multiclass problems
Building multiple binary classifiers [18]
Softmax classification
6.2.6 Example application of logistic regression
Dataset introduction
Data preprocessing
Model training
Visualization of classification results
Analysis of classification accuracy
Summary
6.3 Chapter conclusion
6.4 Chapter exercises
Chapter 7. Support vector machine
7.1 Basic concepts in support vector machine
7.1.1 Linear separability and hyperplanes
7.1.2 Margins and support vectors
7.2 Linearly separable support vector machine
7.2.1 Basic form of support vector machine
7.2.2 Dual problem of linearly separable support vector machine
7.3 Soft margin linear support vector machine
7.3.1 Hard margin and soft margin
7.3.2 Dual problem of the soft margin linear support vector machine
7.4 Nonlinear support vector machine
7.4.1 Kernel trick
7.4.2 Commonly used kernel functions
7.4.2.1 Linear kernel
7.4.2.2 Polynomial kernel
7.4.2.3 Gaussian kernel
7.4.2.4 Sigmoid kernel
7.5 Solving support vector machine models
7.5.1 Sequential minimal optimization algorithm
7.5.2 Bivariate quadratic programming
7.5.3 Choice of optimization parameters
7.6 Example application of SVM model
7.6.1 Dataset import
7.6.2 Exploratory analysis of the dataset
7.6.3 Model training
7.6.4 Summary
7.7 Chapter conclusion
7.8 Chapter exercises
Chapter 8. Decision tree
8.1 Decision tree model
8.1.1 The structure of decision tree
8.1.2 Feature selection
8.1.2.1 Information gain
8.1.2.2 Information gain ratio
8.1.2.3 Gini index
8.2 Decision tree generation
8.2.1 ID3 algorithm based on information gain
8.2.1.1 ID3 tree construction rules
8.2.1.2 ID3 algorithm procedure
8.2.2 C4.5 algorithm based on information gain ratio
8.2.2.1 C4.5 tree construction rules
8.2.2.2 Discretization of continuous variables
8.2.3 Classification and regression trees model
8.2.3.1 Branching rules for CART classification trees
8.2.3.2 CART classification trees algorithm procedure
8.2.3.3 CART regression tree
8.2.4 Decision boundary
8.3 Decision tree pruning
8.3.1 The introduction of pruning
8.3.2 Pessimistic error pruning
8.3.3 Complexity pruning method
8.4 The application examples of decision tree
8.4.1 Introduction to the dataset
8.4.2 Modeling preprocessing
8.4.3 Model training
8.4.4 Classification accuracy and visualization
8.4.5 Conclusion
8.5 Conclusion
8.6 Exercises
Chapter 9. Clustering analysis
9.1 General modeling process of cluster analysis
9.1.1 Modeling principles
9.1.2 Performance metrics of cluster analysis algorithms
9.1.3 Similarity calculation
9.2 K-means clustering algorithms
9.2.1 Algorithm process
9.2.2 Objective function
9.2.3 Algorithm variant: K-medoids
9.2.4 Example application of the K-means algorithm
9.2.4.1 Dataset introduction
9.2.4.2 Model training
9.2.4.3 Cluster results analysis and visualization
9.3 Gaussian mixture clustering
9.3.1 Basic concepts
9.3.1.1 Derivation of key parameters
9.3.2 EM algorithm procedure
9.3.3 Example application of Gaussian mixture clustering algorithm
9.4 Hierarchical clustering algorithm
9.4.1 Basic idea of the algorithm
9.4.2 Algorithm procedure
9.4.3 An example application of hierarchical clustering algorithm
9.5 DBSCAN clustering method based on density
9.5.1 Basic concepts
9.5.2 Algorithm procedure
9.5.3 Algorithm variant: ST-DBSCAN
9.5.4 Example 9.4: Using the DBSCAN algorithm to determine grid congestion levels
9.6 Chapter conclusion
9.7 Chapter exercises
Chapter 10. Ensemble learning
10.1 Classification of ensemble learning
10.2 Boosting
10.2.1 Introduction to boosting
10.2.2 AdaBoost
10.2.3 Boosting trees
10.2.4 Gradient boosting trees
10.3 Bagging
10.3.1 Introduction to bagging
10.3.2 Random forest
10.4 Diversity of individual learners
10.5 Example applications of ensemble learning models
10.5.1 Introduction to the data set
10.5.2 Model preprocessing
10.5.3 Model training
10.5.4 Model accuracy comparison and visualization
10.6 Chapter conclusion
10.7 Chapter exercises
Chapter 11. Artificial neural networks
11.1 Basic structure of neural networks
11.1.1 Basic concepts of the neuron model
11.1.2 Generalized representation of the neuron model
11.1.3 Multilayer neural network models
11.1.4 Neural network fitting ability
11.2 Activation functions and forward propagation
11.2.1 Activation function
11.2.1.1 Sigmoid function
11.2.1.2 Tanh function
11.2.1.3 ReLU function
11.2.1.4 Softmax function
11.2.2 Forward propagation
11.2.3 Forward propagation example
11.3 Backpropagation
11.4 Solutions to common problems in neural networks
11.4.1 Gradient vanishing and gradient explosion
11.4.2 Normalization and standardization of data
11.4.3 Overfitting
11.4.3.1 Early-stopping
11.4.3.2 Dropout
11.5 Application of neural networks to transportation problems
11.5.1 Introduction to the dataset
11.5.2 Read data and generate features
11.5.3 Data set partitioning and normalization
11.5.4 Building neural networks
11.5.5 Model training and validation
11.5.6 Summary and reflections
11.6 Chapter conclusion
11.7 Chapter exercises
Chapter 12. Deep learning
12.1 Convolutional neural network
12.1.1 Convolution operation
12.1.2 Convolutional layer
12.1.3 Downsampling layer
12.1.4 Architectural design
12.1.4.1 Sequential design of convolution–pooling
12.1.4.2 Multiconvolutional kernel design
12.1.4.3 Skip/shortcut connection
12.1.5 Application to ride-hailing traffic prediction
12.1.5.1 Application scenario analysis
12.1.5.2 Data preprocessing
12.1.5.3 Model building
12.1.5.4 Model training and performance evaluation
12.2 Recurrent neural network
12.2.1 Basic structure
12.2.2 Backpropagation
12.2.3 Gates
12.2.4 Application to ride-hailing traffic prediction
12.2.4.1 Modeling preprocessing
12.2.4.2 Model establishment
12.2.4.3 Model training and performance evaluation
12.3 Graph neural networks
12.3.1 Graph convolution
12.3.2 Attention mechanism
12.3.3 Gating mechanism
12.3.4 Residual connections
12.4 Chapter conclusion
12.5 Chapter exercises
Notation
Index

Life Sciences

Physical Sciences & Engineering

Social Sciences & Humanities

Health

Transportation Big Data

Theory and Methods

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Zhiyuan Liu

Ziyuan Gu

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