Traffic Routing System with Evolved State Prediction
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Solution Overview
Problem
Existing vehicle navigation systems fail to provide time-efficient route plans as they rely on static traffic state information, neglecting potential future traffic conditions that may evolve, leading to missed opportunities for optimizing routes and activities during delays.
Innovation Solution
A vehicle navigation system that identifies candidate route segments and obtains speed metrics for both current and future traffic states, allowing for a route plan that navigates segments in more favorable traffic conditions, potentially including intermediate destinations for activities like refueling or recharging, based on projected traffic evolution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If navigation systems use static traffic state information for route calculation, then the system complexity remains low, but the route planning efficiency and time-optimization capability deteriorate
Solution Approach 1:
The system performs preliminary actions by predicting future traffic states before the vehicle actually reaches those road segments. The prediction engine anticipates traffic conditions at future times, allowing the route planning to account for evolving traffic patterns rather than reacting to current conditions only. This enables the system to proactively identify optimal routes that will be favorable at the time of traversal.
Solution Approach 2:
The system transitions from static traffic information to dynamic traffic state prediction. The prediction engine continuously updates traffic state forecasts based on current traffic data and historical patterns, creating a time-varying model of traffic conditions. This dynamic approach allows route calculations to adapt to changing traffic patterns, improving planning efficiency while managing complexity through structured prediction models.
2Loss of time
If navigation systems recalculate routes based on current traffic conditions, then the route may avoid present congestion, but opportunities to utilize future favorable traffic states are lost
Solution Approach 1:
The system performs preliminary prediction of future traffic states to identify favorable time windows for route traversal. By anticipating when traffic conditions will improve, the system can plan routes that strategically traverse congested segments at optimal future times, potentially reducing overall travel time while utilizing otherwise lost information about future traffic evolution.
Solution Approach 2:
The system adds a time dimension to traditional route planning by incorporating future traffic state predictions. Instead of evaluating only current traffic conditions, the system assesses traffic states across multiple future time points, allowing route optimization to consider when to traverse segments rather than just which segments to take. This temporal dimension transforms static route selection into dynamic timing-aware planning.
Data Source
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AI summary
An improved vehicle navigation system and method provides a route plan or alternative route based at least in part on evolved state information (e.g., a second traffic state, occurring some time after a first traffic state) from which time-efficiencies may be realized relative to navigation in a previous traffic state. Optionally, the route plan may identify an intermediate destination (e.g., such as a refueling or recharging station) and the schedule may allocate time for customer activities at the intermediate destination.