Dynamic Routing via Historical Traffic Data Aggregation
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Solution Overview
Problem
Conventional wireless communication systems fail to accurately determine travel time and routing for long distances due to reliance on current traffic conditions, which become irrelevant by the time they are reached, and do not account for historical traffic patterns and varying conditions throughout the day.
Innovation Solution
A method that collects and aggregates historical location and speed data to calculate travel times based on route segments, combining real-time and historical information to provide dynamic travel time and routing determinations, allowing for prediction of traffic conditions at specific times and locations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If routing is performed based on current traffic conditions, then routing information reflects real-time status, but the information becomes irrelevant by the time it is reached for long-distance travel
Solution Approach 1:
The system pre-calculates routing information based on historical traffic patterns and anticipated conditions rather than relying solely on current traffic data. This preliminary action allows the routing system to provide relevant guidance for future time periods when the traveler will actually reach those segments, resolving the temporal mismatch between data collection and usage.
Solution Approach 2:
The system dynamically adjusts routing recommendations by combining real-time traffic conditions with historical patterns and predicted future states. Rather than static routing based on current conditions alone, the system adapts routing suggestions based on when and where the traveler will be, making the information remain relevant throughout the journey.
2Measurement precision
If routing is performed during off-peak hours using current traffic conditions, then the recommended routing reflects low traffic density, but the opposite conditions prevail when actually reaching that segment
Solution Approach 1:
The system performs preliminary routing calculations that anticipate future traffic conditions based on historical patterns. Instead of simply reflecting current off-peak conditions, the system pre-predicts what conditions will be like when the traveler actually reaches each segment, ensuring accurate travel time estimates regardless of when the query is made.
Solution Approach 2:
The system changes the temporal parameter of traffic condition assessment by shifting from current-time snapshots to time-shifted predictions. By adjusting which time period's traffic data is applied to which route segment based on anticipated arrival times, the system maintains measurement precision across varying traffic conditions throughout the day.
Data Source
AI summary
Aspects relate to automatically providing updated route and predicted travel time to allow a user to travel a shortest route between a first point and a second point. A route can be planned based on a multitude of route segments, wherein historical data related to speed is known for each of the route segments. Further, the historical data is categorized based on temporal aspects, such as time of day, day of week, as well as other aspects, such as known events that can have an influence on the speed at which each route segment can be traveled. As the user moves along the route, the planned route, as well as an anticipated travel time, are almost continually updated to provide the most up-to-date and accurate data.