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Improving Accuracy and Precision of Bicycle Volume Estimates Using Advanced Machine Learning Approaches
Principal Investigator:
Sirisha Kothuri, Portland State University
Co-Investigators:
- Banafsheh Rekabdar, Portland State University
Summary:
This study presents a novel approach to bicycle volume estimation using data fusion techniques that integrate crowdsourced data (e.g., Strava) with traditional count data. We aim to improve the accuracy and robustness of bicycle volume estimates in different regions by using deep learning algorithms, especially Long Short-Term Memory networks and Deep Neural Networks. Our method involves combining...
This study presents a novel approach to bicycle volume estimation using data fusion techniques that integrate crowdsourced data (e.g., Strava) with traditional count data. We aim to improve the accuracy and robustness of bicycle volume estimates in different regions by using deep learning algorithms, especially Long Short-Term Memory networks and Deep Neural Networks. Our method involves combining static variables, such as network characteristics, demographics, and land use dynamic crowdsourced data, count data, and static data from different regions. The results show that our models, particularly when using Monthly Average Daily Bicyclists (MADB) as the target variable, provide superior generalization and transferability compared to traditional Annual Average Daily Bicyclists (AADB) models. This increased accuracy has significant implications for urban planning and infrastructure development, enabling more effective allocation of resources to improve bicycle infrastructure.
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Project Details
| Project Type: | Technology Transfer |
|---|---|
| Project Status: | Completed |
| End Date: | September 30, 2024 |
| UTC Funding: | $40,000 |