Transportation Change Restriction via ML Signal Analysis
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Solution Overview
Problem
Conventional on-demand transportation matching systems lack computational speed and accuracy to detect opportunistic or faulty destination changes in real-time, leading to inefficient resource utilization and potential financial losses due to inaccurate detection of changes.
Innovation Solution
A dynamic transportation change system that analyzes real-time signals using a machine-learning model to determine whether to restrict or approve changes in destination, route, or waypoints, providing notifications to vehicles based on safety conditions and payment integrity checks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional rule-based heuristics are used to detect transportation changes, then the system is simple to implement, but the detection accuracy is low and cannot distinguish between earnest and opportunistic changes
Solution Approach 1:
The patent transforms the detection approach from rule-based to machine-learning-based, changing the fundamental parameter of how transportation changes are analyzed. The ML model processes multiple signals (route deviations, timing patterns, communication sequences) to detect opportunistic changes with high accuracy, resolving the contradiction between detection precision and system complexity.
Solution Approach 2:
The patent replaces the mechanical rule-based heuristic system with an intelligent machine-learning system. Instead of following predetermined rules, the ML model learns patterns from historical data and signals to accurately distinguish between legitimate and opportunistic transportation changes, achieving high detection accuracy.
2Productivity
If the system processes hundreds or thousands of transportation changes per minute using conventional methods, then it can handle high volume, but it lacks computational speed and models to detect faulty changes in real-time
Solution Approach 1:
The patent replaces conventional processing methods with a machine-learning-based system that can analyze high-volume transportation change signals in real-time. The ML model processes multiple signals simultaneously (route data, timing information, communication patterns) to reliably detect opportunistic changes even at thousands of changes per minute.
Solution Approach 2:
The patent adds multiple analytical dimensions to the processing system by analyzing various signals simultaneously (spatial route deviations, temporal patterns, communication sequences). This multi-dimensional analysis enables reliable detection of opportunistic changes while handling high processing volumes that conventional single-dimension methods cannot manage.
3Ease of operation
If rigid rule-based heuristics are applied to grant or deny transportation changes, then the system is easy to operate, but it inflexibly ignores relevant real-time data
Solution Approach 1:
The patent changes the operational parameter from rigid rule-based decisions to flexible machine-learning-based decisions. The ML model continuously adapts to real-time signals and patterns, allowing the system to respond flexibly to changing conditions while maintaining ease of operation through automated intelligent decision-making.
Solution Approach 2:
The patent transforms the static rule-based system into a dynamic adaptive system. The machine-learning model continuously learns from incoming signals and adjusts its detection criteria in real-time, enabling the system to adapt to new patterns of opportunistic behavior while maintaining simple operation through automation.
4Device complexity
If conventional systems fail to detect opportunistic changes, then processing is simpler, but computing resources are wasted in processing communications and payment requests
Solution Approach 1:
The patent applies preliminary detection using the machine-learning model to identify and filter out opportunistic transportation changes before they proceed to full processing. By detecting suspicious patterns early in the signal analysis stage, the system prevents wasteful processing of communications and payment requests for fraudulent changes, reducing computational resource waste.
Data Source
AI summary
The present disclosure relates to systems, non-transitory computer-readable media, and methods for intelligently restricting transportation changes based on signals in a dynamic transportation matching system. For example, the disclosed systems can analyze signals associated with transportation of a requestor by a vehicle of a provider to determine whether a transportation-change request is indicated. In response to determining that a transportation-change request is indicated, the disclosed systems can prevent a transportation change to a destination, route, or waypoint and generate a restriction notification. The disclosed system can also provide the restriction notification to at least a provider client device in response to determining that it is safe to do so.