Route Computation System Using Geographical Expansion and Cache Search
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Solution Overview
Problem
Current systems for travel routing and time computation, particularly for multi-stop routes, are inefficient and costly due to frequent reliance on third-party services, leading to high expenses and performance delays.
Innovation Solution
A computer-implemented method and system that geographically expands origin and destination coordinates, searches a cache for precomputed travel times, and optimizes routing sequences by using regional average speeds and minimizing third-party service calls, thereby reducing costs and improving performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If third-party services are used for travel time computation, then accuracy of travel time data is improved, but cost and computation time increase
Solution Approach 1:
The system pre-computes travel times between all pairs of locations and stores them in a cache before they are needed. When a routing request comes in, the system first checks the cache for pre-computed values before calling expensive third-party services, thereby reducing both cost and computation time while maintaining accuracy for frequently requested routes.
Solution Approach 2:
The system maintains different quality levels of travel time data for different locations based on their importance and access frequency. High-traffic locations have pre-computed and cached data for quick access, while less important locations may use estimation or on-demand computation, optimizing the balance between accuracy and resource consumption.
2Measurement precision
If third-party services are used for travel time computation, then accuracy of travel time data is improved, but computation time increases
Solution Approach 1:
The system pre-computes travel times between all pairs of locations and stores them in a cache before they are needed. When a routing request comes in, the system first checks the cache for pre-computed values before calling expensive third-party services, thereby reducing both cost and computation time while maintaining accuracy for frequently requested routes.
Solution Approach 2:
The system combines multiple data sources including pre-computed cached values, real-time third-party service data, and historical patterns to provide accurate travel time estimates. This merging allows the system to use faster cached data when available while supplementing with third-party data only when necessary, reducing overall computation time.
3Measurement precision
If exact travel time computation is performed for all location pairs, then routing accuracy is improved, but system complexity and computation cost increase
Solution Approach 1:
The system segments the computation task by dividing locations into different categories (frequently accessed, occasionally accessed, rarely accessed) and applying different computation strategies to each segment. High-priority locations receive exact pre-computed values, while lower-priority locations use approximation or on-demand computation, reducing overall system complexity while maintaining routing accuracy for critical paths.
Solution Approach 2:
The system pre-computes and caches travel times for frequently accessed location pairs, so that during normal operation, these values are retrieved quickly without complex real-time computation. This preliminary action simplifies the runtime system while maintaining high routing accuracy for the most important routes.
Data Source
AI summary
A system and method efficiently computes travel times between an origin and destination, minimizing expensive calls to a third party service by first geographically expanding both origin and destination and then searching a cache of previously computed or obtained travel times for any route satisfying the expanded origin and destination. A further embodiment concerns a system and method to prepare an optimized routing sequence to travel to a set of geographical task sites, in satisfaction of applicable conditions for one or more of the task sites. Advantageously, optimized routing may employ the disclosed method of computing travel times between origin and destination.