AIPrimer.AI
  • 🚦AI Primer In Transportation
  • CHAPTER 1 - INTRODUCTION TO MACHINE LEARNING
    • Machine Learning in Transportation
    • What is Machine Learning?
    • Types of Machine Learning
      • Supervised Learning
      • Unsupervised Learning
      • Semi-supervised Learning
      • Reinforced Learning
    • Fundamental concepts of machine learning
      • Model Training and Testing
      • Evaluating the Model’s Prediction Accuracy
      • The Underfitting and Overfitting Problems
      • Bias-Variance Tradeoff in Overfitting
      • Model Validation Techniques
      • Hyperparameter Tuning
      • Model Regularization
      • The Curse of Ddimensionality
    • Machine Learning versus Statistics
  • CHAPTER 2 - SUPERVISED METHODS
    • Supervised Learning_Complete Draft
    • K-Nearest Neighbor (KNN) Algorithm
    • Tree-Based Methods
    • Boosting
    • Support Vector Machines (SVMs)
  • CHAPTER 3 - UNSUPERVISED LEARNING
    • Principal Component Analysis
      • How Does It Work?
      • Interpretation of PCA result
      • Applications in Transportation
    • CLUSTERING
      • K-MEANS
      • SPECTRAL CLUSTERING
      • Hierarchical Clustering
    • REFERENCE
  • CHAPTER 4 - NEURAL NETWORK
    • The Basic Paradigm: Multilayer Perceptron
    • Regression and Classification Problems with Neural Networks
    • Advanced Topologies
      • Modular Network
      • Coactive Neuro–Fuzzy Inference System
      • Recurrent Neural Networks
      • Jordan-Elman Network
      • Time-Lagged Feed-Forward Network
      • Deep Neural Networks
  • CHAPTER 5 - DEEP LEARNING
    • Convolutional Neural Networks
      • Introduction
      • Convolution Operation
      • Typical Layer Structure
      • Parameters and Hyperparameters
      • Summary of Key Features
      • Training of CNN
      • Transfer Learning
    • Recurrent Neural Networks
      • Introduction
      • Long Short-Term Memory Neural Network
      • Application in transportation
    • Recent Development
      • AlexNet, ZFNet, VggNet, and GoogLeNet
      • ResNet
      • U-Net: Full Convolutional Network
      • R-CNN, Fast R-CNN, and Faster R-CNN
      • Mask R-CNN
      • SSD and YOLO
      • RetinaNet
      • MobileNets
      • Deformable Convolution Networks
      • CenterNet
      • Exemplar Applications in Transportation
    • Reference
  • CHAPTER 6 - REINFORCEMENT LEARNING
    • Introduction
    • Reinforcement Learning Algorithms
    • Model-free v.s. Model-based Reinforcement Learning
    • Applications of Reinforcement Learning to Transportation and Traffic Engineering
    • REFERENCE
  • CHAPTER 7 - IMPLEMENTING ML AND COMPUTATIONAL REQUIREMENTS
    • Data Pipeline for Machine Learning
      • Introduction
      • Problem Definition
      • Data Ingestion
      • Data Preparation
      • Data Segregation
      • Model Training
      • Model Deployment
      • Performance Monitoring
    • Implementation Tools: The Machine Learning Ecosystem
      • Machine Learning Framework
      • Data Ingestion tools
      • Databases
      • Programming Languages
      • Visualization Tools
    • Cloud Computing
      • Types and Services
    • High-Performance Computing
      • Deployment on-premise vs on-cloud
      • Case Study: Data-driven approach for the implementation of Variable Speed Limit
      • Conclusion
  • CHAPTER 8 - RESOURCES
    • Mathematics and Statistics
    • Programming, languages, and software
    • Machine learning environments
    • Tools of the Trade
    • Online Learning Sites
    • Key Math Concepts
  • REFERENCES
  • IMPROVEMENT BACKLOG
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  1. CHAPTER 5 - DEEP LEARNING
  2. Recurrent Neural Networks

Application in transportation

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Last updated 1 year ago

Traffic forecasting involves prediction of future traffic state based on historical traffic data. Accurate traffic forecasting is one of the major challenges in transportation. In general, the complexities in traffic forecasting incurs due to presence of sharp non-linearities caused by transitions among free flow, breakdown, recovery, and congestion [82]. To deal such discontinuities researchers are relying on deep learning models particularly LSTM model. LSTM models have created a new opportunity to capture the sharp discontinuities present in traffic flows using a combination of non-linear functions (tanh, sigmoid etc.) with gating operation to store valuable information for long data sequences. Hence, applications of LSTM model in transportation has allowed us to deal with more complex problems and big data challenges.

Recently, LSTM and different variants of LSTM models have been used in several short-term traffic state prediction problems, such as, traffic speed prediction, ([83], [84], [80]) travel time prediction [85] , traffic flow prediction, ([86], [82], [87]) vehicular queue length prediction ([88], [89]). In short-term traffic prediction problem, the model learns to predict traffic variation over a short period of time.

Figure 2-68 Traffic speed Prediction [81]

Figure 2-69 Vehicle queue length prediction [89]

In addition to regular traffic condition, studies have also explored the application of LSTM for traffic management during a major event such as hurricane evacuation [81] and traffic incidents (accidents) [90]. During such events, traffic forecasting becomes more challenging due to irregular traffic flow variation induced by sudden surge in traffic demand, so we need a more robust model. However, LSTM model has successfully predicted traffic speed in such condition with reasonable accuracy. Such application has potential benefits in incident management such as evacuation traffic routing.

The applications of LSTM in transportation are not limited to traffic state prediction only. Other studies have also utilized the model in real time crash prediction [91] and crash severity analysis [92]. In case of real time crash prediction, the model learns to predict the crash risk for given location based on traffic state variation (speed, volume, occupancy) for short interval of time. In most of the cases LSTM perform better than tradition model in crash risk prediction. State-of-the-art crash prediction methods have gone beyond a zone wise crash risk analysis; researchers are exploiting LSTM models for network-level real time crash risk analysis [93]. These methods will help traffic and safety engineers to develop proactive measures to reduce the number of crashes.

On a final note for traffic forecasting, emerging methods are those that use a combination of different approaches to build a more effective model including the mixture of CNNs and LSTMs for short term traffic prediction [94]. These models have been shown to be able to utilize the capabilities of different methods simultaneously to explore and learn different spatiotemporal features in the training data to achieve more accurate results with better efficiency.