Transportation Matching Using Network Coverage Metrics
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Solution Overview
Problem
Conventional transportation matching systems face inefficiencies due to rigid matching algorithms that fail to adapt to device imbalances and network coverage fluctuations, leading to wasted resources, long wait times, and excessive computational processing.
Innovation Solution
The system employs machine-learning generated network coverage efficiency metrics and Markov decision processes to determine optimal assignment of provider devices, considering metrics like provider device utilization scores and unmatched requester device efficiency, to improve matching efficiency and network coverage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If conventional transportation matching systems immediately assign provider devices to transportation requests, then request fulfillment speed is improved, but system resource efficiency deteriorates due to wasted provider device opportunities and excessive computational processing
Solution Approach 1:
The system dynamically adjusts matching decisions by evaluating real-time network coverage metrics and device utilization scores. Instead of static immediate assignment, the system adapts its matching behavior based on current system state, determining whether to accept or reject requests based on predicted network coverage improvement and provider device opportunity costs.
Solution Approach 2:
The system performs preliminary evaluation of network coverage metrics and device utilization before making assignment decisions. By pre-calculating these efficiency metrics and using them to guide matching decisions, the system avoids wasteful assignments before they occur, improving overall resource efficiency while maintaining fulfillment speed.
2Ease of operation
If rigid matching algorithms are used to quickly assign provider devices, then operational simplicity is improved, but adaptability to device imbalances and network coverage fluctuations deteriorates
Solution Approach 1:
The system changes key parameters including network coverage improvement metrics, device utilization scores, and request efficiency metrics to dynamically adjust matching behavior. These parameter changes enable the system to adapt to varying device imbalances and network conditions while maintaining a unified matching framework that preserves operational simplicity.
Solution Approach 2:
The system implements feedback mechanisms by continuously evaluating network coverage metrics and device utilization scores after each matching decision. This feedback loop allows the system to learn from past assignments and adjust future matching behavior to better adapt to device imbalances and network coverage fluctuations.
3Productivity
If provider devices are assigned to all transportation requests, then request fulfillment rate is improved, but computational resource efficiency deteriorates due to excessive processing of low-value matches
Solution Approach 1:
The system applies partial action by selectively accepting only those transportation requests that meet efficiency thresholds based on network coverage improvement metrics. Rather than assigning all requests, the system performs excessive evaluation of each request's potential value and only proceeds with assignments that demonstrate sufficient efficiency, reducing computational waste on low-value matches.
Solution Approach 2:
The system introduces intermediary efficiency metrics (network coverage improvement, device utilization scores, request efficiency metrics) that mediate between raw request data and assignment decisions. These intermediary metrics filter out low-value matches before they consume computational resources, enabling the system to maintain high fulfillment rates for valuable requests while rejecting inefficient ones.
4Productivity
If machine-learning models evaluate multiple metrics for each request, then matching efficiency is improved, but system complexity increases
Solution Approach 1:
The system employs universal machine-learning models that perform multiple functions: evaluating network coverage improvement, assessing device utilization, calculating request efficiency metrics, and predicting assignment outcomes. This multi-functionality consolidates what would otherwise require multiple separate systems into a unified framework, improving matching efficiency while managing complexity through shared model infrastructure.
Data Source
AI summary
Methods, systems, and non-transitory computer readable storage media are disclosed for generating transportation matches for transportation requests utilizing one or more efficiency metrics based on network coverage in a region. For example, the systems utilize regional and sub-regional network coverage features to generate a predicted network coverage improvement metric resulting from not assigning a particular provider device to a transportation request according to the regional network coverage features. Additionally, the systems utilize regional network coverage features to determine possible request states for a transportation request. The systems then utilize a Markov decision model policy based on the request states to generate an unmatched requester device efficiency metric associated with leaving the request unassigned for a time period. The systems also utilize the network coverage improvement metric and/or the unmatched requester device efficiency metric to generate a transportation match for the transportation request.