Roadside Assistance Provider Selection Using Predictive Boost Values
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Solution Overview
Problem
Current roadside assistance systems often face challenges in efficiently selecting the most suitable provider due to auto-assignment based solely on proximity, leading to rejection of requests and prolonged wait times for drivers and passengers, as they may not consider factors like provider availability, service capabilities, and estimated arrival time effectively.
Innovation Solution
Implement a method that utilizes predictive models to calculate a boost value for each roadside assistance provider based on predicted acceptance probabilities and service aspects, allowing for the selection of the most suitable provider and adjusting these values based on feedback for improved service efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If roadside assistance providers are auto-assigned based solely on proximity, then the selection process is simple and fast, but the acceptance rate is low and wait times are prolonged
Solution Approach 1:
The system changes the selection parameters from simple proximity-based metrics to a comprehensive scoring system that incorporates multiple factors including provider availability, service capabilities, estimated arrival time, and historical performance data. This transforms the selection criteria to balance ease of operation with improved acceptance rates.
Solution Approach 2:
The patent replaces the mechanical/proximity-based assignment system with an intelligent algorithm that uses predictive modeling and machine learning to evaluate provider suitability. This substitution enables more accurate predictions of provider acceptance while maintaining automated selection.
2Reliability
If multiple factors are considered for provider selection, then the acceptance rate improves, but the selection process becomes more complex
Solution Approach 1:
The complex selection process is segmented into distinct functional modules: data collection module, predictive modeling module, scoring module, and selection module. Each module handles specific aspects of the evaluation, making the overall complex system manageable and maintainable while achieving high acceptance rates.
Solution Approach 2:
The patent introduces an intermediary scoring system that translates multiple complex factors into a unified provider suitability score. This intermediary layer simplifies the decision-making process by consolidating diverse inputs (availability, capabilities, arrival time, history) into a single comparable metric.
3Productivity
If providers are selected without considering service capabilities, then the selection is faster, but the service quality and matching accuracy decrease
Solution Approach 1:
The system performs preliminary actions by pre-evaluating and storing provider service capabilities, availability, and performance history in databases before actual assistance requests occur. This advance preparation enables fast real-time selection without sacrificing matching accuracy, as the heavy computational work is done beforehand.
Solution Approach 2:
Providers self-report and update their service capabilities, availability status, and performance data, which the system automatically incorporates into the selection algorithm. This self-service approach reduces the burden on the selection system while maintaining high matching accuracy through up-to-date provider information.
Data Source
AI summary
Implementations include providing assistance services, and more specifically for selecting a roadside assistance provider from a plurality of providers using one or more predictive models of aspects of a roadside assistance request. Selection of the roadside assistance provider may be based on ranking of roadside assistance providers associated with a service area, the ranking based on one or more predicted output values from one or more predictive models of aspects of a roadside assistance service, such as estimated arrival time and an estimated probability of acceptance of the request by a roadside assistance provider. One or more of the predicted output values may be adjusted based on a boost value as determined boost value predictive model to increase a likelihood that a selected roadside assistance provider accepts a request to provide the assistance.