Machine Learning for Transportation Research and Applications / Yinhai Wang, Zhiyong Cui, and Ruimin Ke.

Format:

Book

Author/Creator:

Wang, Yinhai, author.

Cui, Zhiyong, 1989- author.

Ke, Ruimin, author.

Language:

English

Subjects (All):

Machine learning.

Transportation--Data processing.

Transportation.

Physical Description:

1 online resource (254 pages)

Place of Publication:

Amsterdam, Netherlands : Elsevier Inc., [2023]

Summary:

Transportation is a combination of systems that presents a variety of challenges often too intricate to be addressed by conventional parametric methods. Increasing data availability and recent advancements in machine learning provide new methods to tackle challenging transportation problems. This textbook is designed for college or graduate-level students in transportation or closely related fields to study and understand fundamentals in machine learning. Readers will learn how to develop and apply various types of machine learning models to transportation-related problems. Example applications include traffic sensing, data-quality control, traffic prediction, transportation asset management, traffic-system control and operations, and traffic-safety analysis.

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

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.

Notes:

Description based on print version record.

Includes bibliographical references and index.

Other Format:

Print version: Wang, Yinhai Machine Learning for Transportation Research and Applications

ISBN:

9780323996808

OCLC:

1376933383