Summary of Predicting Traffic Congestion at Urban Intersections Using Data-driven Modeling, by Tara Kelly et al.
Predicting Traffic Congestion at Urban Intersections Using Data-Driven Modeling
by Tara Kelly, Jessica Gupta
First submitted to arxiv on: 12 Apr 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 The paper presents a predictive model for traffic congestion at intersections in major US cities. It uses a dataset of trip-logging metrics from commercial vehicles across 4,800 intersections, featuring 27 variables such as intersection coordinates, street names, time of day, and traffic metrics. The authors incorporate additional features like rainfall/snowfall percentage, distance from downtown/outskirts, and road types to enhance the model’s predictive power. They employ data exploration, feature transformation, and missing value handling through low-rank models and label encoding. This model can assist city planners in anticipating traffic hotspots, optimizing operations, and identifying infrastructure challenges. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study creates a special computer program that helps predict when roads will get congested at busy intersections. The program uses lots of information about the roads, like their location and what kind of roads they are. It also looks at things that might affect traffic, like how much rain or snow there is. This tool can help city planners figure out where the worst traffic jams will be, so they can make changes to keep the roads moving smoothly. |
