Commute Prediction System Using Segmented Data Sources
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods struggle to accurately predict commute times due to numerous variables affecting commuter behavior, such as weather, road conditions, and holidays, which complicates traffic flow management and productivity optimization for businesses and governments.
Innovation Solution
A computer-implemented system that accesses payroll databases and external sources to analyze individual commute data, predicting travel times and adjusting traffic routing in real-time by leveraging machine intelligence and data from various sources, including weather and traffic reports.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple data sources and variables are considered to improve prediction accuracy, then the system complexity increases
Solution Approach 1:
The system segments the commute prediction problem into multiple independent data sources (payroll databases, traffic reports, weather reports, holiday information) that can be accessed and processed separately. Each data source contributes specific variables to the overall prediction model, allowing the system to maintain high accuracy while managing complexity through modular data collection and processing.
2Loss of time
If real-time traffic flow control is implemented to reduce commute times, then the extent of automation increases
Solution Approach 1:
The system implements feedback loops where predicted commute times and actual traffic conditions are continuously monitored and used to adjust traffic flow control decisions. The machine intelligence application analyzes predicted variables and provides feedback to traffic control mechanisms, enabling automated real-time adjustments that reduce commute times while maintaining manageable automation levels through iterative optimization.
Data Source
AI summary
A method, system and apparatus for accessing an internal database that includes at least one payroll database, identifying an individual, a first location, a second location, a future date, and a time of day. Responsive to identifying the individual, the first location, the second location, the date, and the time of day, determining a predicted travel time for the individual between the first location and the second location at the time of day on the future date.