Travel Time Prediction Using Cached Corrective Factors
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Solution Overview
Problem
Current systems for predicting travel time between geographical locations are inefficient due to frequent access to large, remote, or third-party databases, leading to delays, bottlenecks, and increased costs, as they do not effectively account for traffic variations and require multiple queries to GIS services.
Innovation Solution
A system that assigns geographic locations to clusters based on proximity, using a cache to store corrective factors for tuples of clusters, allowing for efficient calculation of predictive travel time by applying a ratio between nominal and actual travel times, reducing the need for frequent GIS queries and minimizing communication overhead.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If frequent queries are submitted to remote GIS servers for travel time estimation, then accurate travel time data can be obtained, but system response time increases and communication overhead increases
Solution Approach 1:
The system performs preliminary actions by caching nominal travel times from GIS servers and pre-calculating corrective factors based on historical traffic patterns. When a travel time query is received, the system retrieves cached data and applies corrections locally without querying the remote GIS server, thus maintaining accuracy while reducing response time.
Solution Approach 2:
The system introduces an intermediary mechanism - a local cache with corrective factors - that mediates between the client and the remote GIS server. This intermediary stores pre-computed adjustments for traffic conditions, allowing the system to estimate actual travel times by combining cached nominal times with cached corrective factors, thereby avoiding frequent remote queries.
2Measurement precision
If multiple queries are submitted to GIS services to account for traffic variations, then accurate travel time prediction is achieved, but communication overhead and costs increase
Solution Approach 1:
The system performs preliminary actions by pre-calculating and caching corrective factors that capture traffic pattern variations. Instead of querying the GIS server multiple times for different traffic conditions, the system computes these corrective factors once based on historical data and reuses them for multiple travel time estimations, significantly reducing communication overhead.
Solution Approach 2:
The system creates local copies of travel time data and corrective factors in a cache. By copying and storing this information locally, the system eliminates the need for repeated remote queries, reducing communication overhead while maintaining the ability to provide accurate travel time predictions through local computation.
3Reliability
If remote server queries are performed for each travel time estimation, then up-to-date traffic information is obtained, but processing delays occur
Solution Approach 1:
The system performs preliminary actions by caching nominal travel times and pre-computing corrective factors during off-peak periods or when traffic conditions are stable. This allows the system to quickly respond to travel time queries using cached data, improving route planning efficiency while maintaining reasonable accuracy through the corrective factors that account for typical traffic patterns.
Solution Approach 2:
The cache system acts as an intermediary between real-time travel time requests and the remote GIS server. It provides quickly accessible travel time estimates using cached data and corrective factors, improving response speed and route planning efficiency while still maintaining reliability by using pre-computed corrections that capture traffic variations.
Data Source
AI summary
Disclosed embodiments are a computing system and a computer-implemented method related to predicting travel time by using adjacent travel time. The prediction may be based on correcting the nominal predictive travel time estimation using a relation between the predictive travel time estimation and the nominal predictive travel time estimation of a nearby commuting instance in a similar direction. This allows a reduced number of queries to the geographic information system, which may save time, costs and conserve communication resources.