Combining Crowdsourcing and Machine Learning to Collect Sidewalk Accessibility Data at Scale
| 项目名称： | Combining Crowdsourcing and Machine Learning to Collect Sidewalk Accessibility Data at Scale |
| 摘要： | Sidewalks significantly impact the mobility and quality of life of millions of Americans. In the proposal, the research team described new, scalable methods for collecting data on sidewalk accessibility using machine learning, crowdsourcing, and online map imagery as well as new interactive visualizations aimed at providing novel insights into urban accessibility. As with the team's prior research, the team will work closely with key stakeholders, including local governments and transit departments, mobility-impaired individuals and caretakers, and walkability advocates to help shape and evaluate the design of the team's tools. |
| 状态： | Active |
| 资金： | 100000 |
| 资助组织： | United States Department of Transportation - FHWA - LTAP<==>Office of the Assistant Secretary for Research and Technology |
| 执行机构： | University of Washington, Seattle |
| 开始时间： | 20190916 |
| 预计完成日期： | 20210915 |
| 主题领域： | Data and Information Technology；Pedestrians and Bicyclists |
相关文献
- Enabling a New Data Science for Urban Accessibility for All
- Combining Virtual Reality and Machine Learning for Enhancing the Resiliency of Transportation Infrastructure in Extreme Events
- Combining Virtual Reality and Machine Learning for Intelligent Sustainable Traffic Management
- Towards a Smart, Safe, and Sustainable Sidewalk: A Quantitative Analysis on How Sidewalk Infrastructure Affect Personal Delivery Devices
- Statistical Inference Using Stochastic Gradient Descent
- Where the Sidewalk Ends: Equity Disparities with Respect to Municipal Maintenance Policy
检索历史
应用推荐
