Multi-leg Transportation Itinerary Quality Adjustment
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current transportation service systems lack real-time monitoring and adjustment capabilities to improve user experience and efficiency across multiple transportation modalities, leading to suboptimal service quality and user satisfaction.
Innovation Solution
A computer-implemented method that generates a multi-modal transportation itinerary, monitors user states, computes real-time quality measurements, and initiates adjustments to subsequent transportation legs based on these measurements to enhance the overall service score.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If real-time monitoring and adjustment capabilities are implemented across multiple transportation modalities, then service quality and user satisfaction improve, but system complexity increases
Solution Approach 1:
The system segments the multi-modal transportation service into distinct transportation legs (first leg, second leg, etc.), each with its own quality measurement and state monitoring. This allows the complex system to be managed through modular, independent components that can be monitored and adjusted separately, reducing overall system complexity while maintaining comprehensive service quality control.
Solution Approach 2:
The system implements continuous feedback loops by computing quality measurements for each transportation leg based on user state, comparing these measurements against thresholds, and automatically initiating adjustment actions when quality deteriorates. This closed-loop feedback mechanism enables real-time service quality improvement through automated responses to detected issues.
2Adaptability or versatility
If dynamic adjustments are made to subsequent transportation legs based on real-time quality measurements, then user experience improves, but computational requirements increase
Solution Approach 1:
The system pre-computes and stores quality thresholds for different user states and transportation legs before service delivery. These predetermined thresholds enable rapid real-time decision-making without requiring complex computational analysis during active service, reducing computational energy requirements while maintaining adaptive personalization capabilities.
Solution Approach 2:
The system applies different quality measurement criteria and adjustment strategies to different transportation legs and user states. Each leg has its own quality threshold and appropriate adjustment actions, allowing computational resources to be focused on specific local issues rather than processing the entire service itinerary uniformly, thereby improving energy efficiency.
3Productivity
If the system monitors user state and computes quality measurements for each transportation leg, then service optimization improves, but data processing requirements increase
Solution Approach 1:
The system extracts and monitors only the critical user state information necessary for quality assessment of each transportation leg, rather than processing all possible data. By focusing on essential state variables and quality metrics, the system achieves effective service optimization while minimizing data processing requirements and preventing information overload.
Data Source
AI summary
Example aspects of the present disclosure relate to dynamic tracking and coordination of multi-leg transportation. The example method includes receiving a request for a transportation service. The method includes computing a multi-modal transportation itinerary for the user based on the request. The method includes accessing data associated with the multi-modal transportation service and data associated with a state of the user relative to the multi-modal transportation itinerary. The method includes computing a quality measurement of the multi-modal transportation service based on the data associated with the multi-modal transportation service and the data associated with the state of the user. And, the method includes initiating an adjustment action associated with the multi-modal transportation itinerary for the user based on the quality measurement.