Crowdsourced Transit Routing Using Game Theory
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Solution Overview
Problem
Current public transportation routing systems primarily focus on individual commuter needs, neglecting the broader impact on other commuters and the overall efficiency of the public transportation system, failing to consider factors like urgency levels, alternative choices, and cooperativeness.
Innovation Solution
A decision-support system that generates and displays public transit route options based on decision factors influencing other commuters, using game theory algorithms to optimize routes for all users, incorporating commuter behavior models and profiles to recommend routes that balance individual and collective interests.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If routing systems focus on individual commuter needs, then individual commuter satisfaction is improved, but overall system efficiency and impact on other commuters deteriorates
Solution Approach 1:
The system introduces an intermediary layer (the decision-support system with game theory algorithms) between individual commuters and the routing decision. This intermediary considers both individual preferences and system-wide impacts, mediating between self-interest and collective good to recommend routes that balance personal convenience with overall system efficiency.
Solution Approach 2:
The system changes the parameters used for routing decisions from purely individual-based metrics (time, distance) to include system-wide parameters (impact on other commuters, urgency levels, alternative choices). This multi-parameter approach allows optimization of both individual satisfaction and overall system efficiency simultaneously.
2Measurement precision
If the system provides detailed decision factors and multiple route options, then commuter decision quality is improved, but system complexity increases
Solution Approach 1:
The system segments the complex decision-making process into distinct components: (1) collecting commuter profiles and preferences, (2) generating multiple route options, (3) calculating decision factors for each option, (4) presenting options with clear visual indicators. This segmentation makes the complex system more manageable and understandable for both users and operators.
Solution Approach 2:
The game theory algorithm acts as an intermediary that processes complex system-wide data and transforms it into simplified decision factors and visual indicators. This intermediary handles the computational complexity while presenting simplified, actionable information to commuters, maintaining high decision quality without overwhelming users with complexity.
3Productivity
If the system uses game theory algorithms to optimize for all commuters, then collective welfare is improved, but computational requirements and processing time increase
Solution Approach 1:
The system performs preliminary actions by pre-calculating commuter profiles, preferences, and behavior patterns before routing decisions are needed. Commuter profiles and historical data are stored and prepared in advance, allowing the game theory algorithm to work with pre-processed data during actual routing decisions, reducing real-time computational burden while maintaining collective welfare optimization.
Data Source
AI summary
A decision-support system for a public transportation system includes a computing system programmed to generate public transit route options for the commuter based on at least one decision factor that estimates an expected impact on other commuters resulting from the commuter choosing each of the public transit route options, and output, for display, values associated with the at least one decision factor to influence the commuter in making a route selection.