Sidewalk Walkability Assessment via Machine Learning Scoring
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Solution Overview
Problem
Current walkability assessment tools, such as Walk Score, primarily measure proximity to destinations but fail to inform users about the quality and character of sidewalks, which affects the walking experience in urban areas.
Innovation Solution
A system utilizing machine learning and data mining to collect and analyze micro and macro data on sidewalk attributes, applying a supervised learning algorithm to score walkability based on criteria like natural beauty, safety, and utility, and providing recommendations through a user interface for improved route navigation and urban planning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If current walkability assessment tools like Walk Score are used, then proximity to destinations can be measured, but the quality and character of sidewalks cannot be assessed
Solution Approach 1:
The sidewalk assessment system is segmented into multiple independent components: data collection module (capturing sidewalk attributes), machine learning module (processing and analyzing data), and recommendation module (providing walkability scores). This segmentation allows the system to comprehensively assess sidewalk quality without requiring a single complex assessment tool.
Solution Approach 2:
A machine learning model serves as an intermediary between raw sidewalk data and walkability assessment results. The model processes diverse data inputs (sidewalk width, surface quality, lighting, etc.) and transforms them into meaningful walkability scores, bridging the gap between data collection and quality assessment.
2Measurement precision
If a comprehensive machine learning system is implemented to assess sidewalk quality, then detailed walkability information can be provided, but system complexity increases
Solution Approach 1:
The system performs preliminary actions by pre-collecting and organizing sidewalk attribute data before actual walkability assessment is needed. Data collection instruments capture sidewalk characteristics in advance, and the machine learning model is pre-trained with extensive data, enabling rapid and precise assessment when queries are made without requiring complex real-time processing.
Solution Approach 2:
The system uses digital copies and representations of sidewalk attributes rather than requiring physical measurement during assessment. Machine learning models process copied data from various sources (municipal databases, crowd-sourced information, remote sensing) to generate walkability scores, eliminating the need for complex physical measurement equipment during actual assessments.
Data Source
AI summary
A computer learning, assessment and recommendation system to generate new information about the quality and degrees of walkability of sidewalks and walking routes through the collection, assessment and manipulation of large amounts of visual and geographic data, a scoring system, a rule-based computational methods and selected data representation are provided. Micro and macro data, methods and systems for sidewalk assessment generate various characteristics and qualities of routes creating general, locational and navigational functions using attributes and categories identified as important to walkability or to the experience of walking. Computed scores and recommendations displayed in maps and other visual tools and apps can be used by multiple client and sector groups including navigation, real estate, fitness, tourism and urban and rural development planning. This abstract complies with rules requiring abstract submission but does not limit the scope, interpretation or full meaning of the claims.