Traffic Forecasting with Python: LSTM & Graph Neural Network

Name: Traffic Forecasting with Python: LSTM & Graph Neural Network
Rating: 3.8 (8 reviews)

Python-driven traffic forecasting with Keras: LSTM and Graph Convolutional Networks for spatiotemporal data modeling

Created byKarthik Karunakaran, Ph.D.

Last updated 11/2024

English

What you'll learn

Understand and analyze real-world traffic data using Python.
Implement and apply Graph Convolutional Networks (GCNs) for traffic data.
Combine LSTM networks with GCNs for time series forecasting.
Preprocess and normalize large datasets for machine learning.
Build, train, and evaluate predictive models using TensorFlow and Keras.
Visualize and interpret model results for traffic prediction.

Course content

2 sections • 38 lectures • 1h 8m total length

Introduction1:10
About this Project0:21
Applications3:24
Apply time series forecasting with graph neural networks and LSTM to transport and traffic management, enabling smarter congestion control, routing, and city planning.
Job Opportunities3:28
Explore the job opportunities that follow mastery of time series forecasting with graph convolutional neural networks and LSTM, from data scientists to AI researchers across finance, healthcare, and urban planning.
Why Python, Keras, and Google Colab?1:45

Set up the working directory0:58
What is inside dataset folder?1:25
What is inside code.ipynb?0:29
Explore how a Jupyter notebook builds a traffic forecasting model with deep learning, focusing on temporal and spatial traffic data, with sections and step-by-step operations in upcoming lectures.
Launch Project0:30
Activate GPU0:29
Open the code dot ipynb file in Google Colab and activate the GPU by changing the runtime type to GPU, enabling acceleration for deep learning models in this project.
Mounting Google Drive2:08
Upgrading the Keras library1:22
Importing necessary libraries2:40
Defining the directory1:12
Reading route distances and speeds data2:17
Shapes of our data1:52
Selecting a subset of routes1:52
Shapes of our data (again)0:25
Data visualization-11:44
Visualize speed data from speeds array by plotting with a configured figure and legend to compare route zero and route 25, revealing trends, patterns, and anomalies for traffic analysis.
Data visualization-21:59
Visualizes the correlation matrix of speed data to reveal relationships between routes, identify groups with similar patterns, and detect data quality issues and outliers for traffic analysis and forecasting.
Data preprocessing and splitting2:54
Data preprocessing and its outcomes2:12
Defining parameters2:12
Creating TensorFlow datasets1:59
Creating train, validation, and test datasets1:48
Create train, validation, and test TensorFlow datasets from numpy arrays using the create_tf_dataset function with input sequence length, forecast horizon, batch size, and shuffle set to false for test.
Compute the adjacency matrix1:58
Store graph information1:47
Compute the adjacency matrix for a graph2:14
Compute the adjacency matrix for a graph using sigma2 and epsilon, extract edges with numpy's npoi function, and encapsulate nodes and edges in a graph info object for analysis.
Implement a graph convolutional layer2:48
Combine a graph convolutional layer with an LSTM layer2:32
Model parameters2:19
Instance of the LSTMGC model1:35
Initialize an LSTM GC model with 1 input feature, 10 output features, 64 LSTM units, 12-step inputs, and a 3-step forecast horizon, to learn from spatial and temporal dependencies.
Inputs and outputs of the model1:42
Define the model's inputs and outputs with an input layer and shape, including input sequence length, graph num nodes, and node features, using St GCN–LSTM for training and inference.
Compiling the model1:29
Training the model1:41
Train the model using the training dataset with input-output pairs, validate after each epoch to monitor generalization and prevent overfitting with early stopping based on validation loss.
Evaluating the model1:25
Evaluate the trained traffic forecasting model on the test dataset to assess generalization to unseen data, comparing predictions to ground truth with accuracy and error metrics to gauge deployment readiness.
Visualizing the actual and forecasted values1:32
Visualize actual and forecasted values from the model by plotting them on the same graph, with a legend showing actual and forecast lines to compare predictions against ground truth.
Mean absolute errors (MAE)2:43

Requirements

Basic proficiency in Python programming.
Access to a computer with an internet connection for coding and data analysis.

Description

This course offers an in-depth journey into the world of advanced time series forecasting, specifically tailored for traffic data analysis using Python. Throughout the course, learners will engage with the PeMSD7 dataset, a real-world traffic speed dataset, to develop predictive models that can forecast traffic conditions with high accuracy. The course focuses on integrating Long Short-Term Memory (LSTM) networks with Graph Convolutional Networks (GCNs), enabling learners to understand and apply cutting-edge techniques in spatiotemporal data analysis.

Key topics include data preprocessing, feature engineering, model building, and evaluation, with hands-on coding in Python to solidify understanding. Learners will also gain practical experience in using popular libraries such as TensorFlow and Keras for deep learning applications.

This course is ideal for those looking to advance their careers in data science, machine learning, or AI-driven industries. The practical skills acquired will be highly valuable for roles in smart city planning, transportation analysis, and any field that relies on predictive modeling. By the end of the course, learners will not only have a strong grasp of advanced forecasting techniques but will also be well-prepared for job opportunities in data science and related fields, where they can contribute to innovative solutions in traffic management and urban development.

Who this course is for:

Data scientists and machine learning engineers interested in time series forecasting.
Python programmers looking to enhance their skills in deep learning and graph-based models.
Researchers and students in the fields of transportation, urban planning, or smart cities.
Professionals working with traffic data or other spatiotemporal datasets.
AI enthusiasts seeking to understand and implement advanced neural network architectures like LSTM and graph convolutional networks.
Individuals with a background in data analysis who want to apply machine learning to real-world datasets.

Traffic Forecasting with Python: LSTM & Graph Neural Network

What you'll learn

Explore related topics

Course content

Fundamentals5 lectures • 10min

Building and Training Model33 lectures • 58min

Requirements

Description

Who this course is for: