Summary of Urban Traffic Forecasting with Integrated Travel Time and Data Availability in a Conformal Graph Neural Network Framework, by Mayur Patil et al.
Urban Traffic Forecasting with Integrated Travel Time and Data Availability in a Conformal Graph Neural Network Framework
by Mayur Patil, Qadeer Ahmed, Shawn Midlam-Mohler
First submitted to arxiv on: 17 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This study proposes a novel framework for traffic flow prediction, which incorporates travel times between stations into a weighted adjacency matrix of a Graph Neural Network (GNN) architecture. The framework utilizes information from traffic stations based on their data availability to handle uncertainties. To validate the results, a microscopic traffic scenario is modeled and a Monte-Carlo simulation is performed to get a travel time distribution for a Vehicle Under Test (VUT), which is compared against real-world data. The proposed model outperforms the next-best model by approximately 24% in MAE and 8% in RMSE. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Traffic flow prediction helps plan better infrastructure, but state-of-the-art models struggle to handle data and uncertainties. This study solves this problem by using a special type of neural network called Graph Neural Network (GNN) that looks at travel times between stations. It also uses a special method called Adaptive Conformal Prediction (ACP) that adjusts prediction intervals based on real-time validation residuals. The researchers tested their model with a simulated traffic scenario and compared the results to real-world data. Their model performed well, beating the next-best model by 24% in one way and 8% in another. |
Keywords
» Artificial intelligence » Gnn » Graph neural network » Mae » Neural network
