TRAFFIC FORECASTING USING GRAPH NEURAL NETWORKS AND LSTM

Inspiration

The inspiration behind "Traffic Forecasting Using Graph Neural Networks and LSTM" comes from a desire to contribute to solving the real-world problem of traffic congestion. The project was motivated by the goal of making a positive impact on people's daily commutes by harnessing data and technology to improve traffic prediction.

What it does

This project is designed to predict future traffic speeds for different road segments by considering not only each segment's historical data but also how they influence one another. It leverages graph neural networks and LSTM to capture the complex interdependencies between road segments, leading to more accurate traffic speed predictions.

How we built it

We built this project by following a structured approach. We collected historical traffic data and transformed it into a graph representation, treating road segments as nodes and their interactions as edges. We designed a neural network model that combined graph convolution and LSTM layers to handle spatial and temporal dependencies. We preprocessed the data, trained the model, and optimized it for better predictions.

Challenges we ran into

Several significant challenges were faced during the project, including data quality and availability issues, the complexity of modeling interdependencies between road segments, and the optimization of the model's performance. Managing computational resources, training times, and real-world integration were also challenging aspects.

Accomplishments that we're proud of

Key accomplishments include achieving accurate predictions, effectively considering complex interactions between road segments, applying the project to real-world traffic management, and innovatively combining graph networks and LSTM for traffic forecasting.

What we learned

Through this project, we gained expertise in advanced machine learning techniques, data quality management, spatial-temporal modeling, hyperparameter optimization, and graph-based thinking. We also learned about the transition from model development to practical application.

What's next for TRAFFIC FORECASTING USING GRAPH NEURAL NETWORKS AND LSTM

The future holds the potential for further improvements, such as enhancing model performance, integrating real-time data sources, scaling the model, developing user-friendly applications, and exploring cross-domain applications. The project is poised to continue evolving and making a positive impact on transportation systems and urban planning.

Built With

• lstm
• machine-learning
• python