Route Efficiency Metrics for Real-Time Alternative Route Selection
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Solution Overview
Problem
Existing transportation systems face inefficiencies and inflexibilities in implementing computer systems due to reliance on rigid temporal factors for route selection, leading to increased user interactions, resource waste, and computational demands.
Innovation Solution
A navigation routing system that utilizes dynamic route efficiency metrics combining temporal and non-temporal factors to identify and rank optimal alternative routes, reducing the need for additional user interactions and computational resources by considering distance, access, congestion, risk, and match-based efficiency metrics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the system provides multiple alternative routes with detailed efficiency metrics, then the user interface becomes more informative and flexible, but the computational load and bandwidth consumption increase significantly
Solution Approach 1:
The system segments route efficiency into distinct metric categories (temporal factors like travel time, non-temporal factors like distance and congestion, risk factors, match factors). By breaking down the overall route evaluation into manageable segments, the system can process and present information in an organized manner that reduces computational overhead while maintaining comprehensive route analysis capabilities.
Solution Approach 2:
The system provides a limited set of pre-calculated route efficiency metrics rather than all possible metrics. This partial action approach allows the system to present sufficient information for informed route selection without the excessive computational burden of calculating every conceivable route parameter, thereby balancing user needs with resource consumption.
2Adaptability or versatility
If the system generates and displays multiple alternative routes, then users have more options for route selection, but the number of user interactions and interface complexity increases
Solution Approach 1:
The system merges multiple route evaluation criteria (temporal, non-temporal, risk, and match factors) into a unified route efficiency metric framework. This consolidation allows the system to present multiple alternative routes in an integrated manner, reducing interface complexity by showing all factors together rather than requiring separate interfaces for each metric type.
Solution Approach 2:
The route efficiency metric framework serves multiple functions simultaneously: it evaluates temporal efficiency, non-temporal efficiency, risk, and match potential within a single unified system. This multi-functionality allows the user interface to display comprehensive route information without requiring multiple separate evaluation interfaces, thereby reducing overall system complexity.
3Reliability
If the system continuously monitors and adjusts routes based on real-time data, then route optimization improves, but the computational bandwidth and processing power requirements increase
Solution Approach 1:
The system pre-calculates and stores route efficiency metrics for alternative routes before they are needed. By performing this computational work in advance, the system can quickly present optimized routes without requiring intensive real-time processing power during actual route selection, thereby maintaining high route optimization reliability while reducing instantaneous processing requirements.
Data Source
AI summary
The present disclosure relates to systems, non-transitory computer-readable media, and methods that improve efficiency and flexibility of implementing computer devices by providing efficient user interfaces to provider devices that include optimal digital routes selected based on dynamic route efficiency metrics. In particular, the disclosed systems can identify and surface an optimal alternative route from a pickup location to a drop-off location associated with a transportation match based on a variety of dynamic non-temporal factors. The disclosed systems can utilize a variety of computer implemented models to determine non-temporal factors, such as distance efficiency metrics, route-segment access efficiency metrics, congestion efficiency metrics, risk efficiency metrics, and match-based efficiency metrics. The disclosed systems can then combine these non-temporal factors utilizing a common efficiency framework to determine and surface an optimal alternative route.