Transport Route Optimization Using Reusable ML Predictions
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Solution Overview
Problem
Current transport network route design for tourist areas relies heavily on local expertise and fixed, handcrafted models that do not adapt to changing dynamics, struggles with Non-deterministic Polynomial hard problems at large scales, and fails to consider the impact of tourists on transport networks.
Innovation Solution
A computer-implemented method using machine learning to analyze transport needs and tourist hotspots, training models to determine optimal routes by combining probabilistic predictions and tourist impact assessment, reducing dependency on local knowledge and improving computational efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional optimization methods are used for BRTRDP, then the approach is straightforward and easy to implement, but it cannot effectively handle large-scale Non-deterministic Polynomial hard problems and fails to adapt to changing dynamics
Solution Approach 1:
The patent implements a dynamic route optimization system that continuously adapts to changing tourist patterns and transport conditions. The system uses real-time data from mobile devices to detect changes in tourist density and preferences, then dynamically adjusts recommended routes accordingly. This allows the system to handle non-stationary patterns where tourist behavior changes over time, rather than relying on fixed historical patterns.
Solution Approach 2:
The system employs machine learning models that automatically learn and improve from data without requiring manual intervention or local expertise. The models self-adjust to changing conditions by processing real-time data from the transport network, eliminating the need for continuous manual route planning while adapting to new patterns autonomously.
2Measurement precision
If machine learning models are trained for each subset of probabilistic predictions, then the solution becomes highly adaptive and accurate, but the computational time and resources increase significantly
Solution Approach 1:
The patent divides the large-scale route optimization problem into smaller, manageable subsets based on geographic regions, transport modes, or time periods. Multiple specialized machine learning models are trained on these segmented datasets in parallel, then their predictions are aggregated to form the final route recommendations. This segmentation reduces the computational burden on individual models while maintaining overall system accuracy.
Solution Approach 2:
The system performs preliminary training of machine learning models on historical data and common scenarios before actual operation. Pre-computed route options and probabilistic predictions are stored for frequently occurring patterns, allowing the system to quickly retrieve and adapt pre-analyzed solutions rather than performing full computations in real-time.
3Ease of manufacture
If fixed handcrafted models are used for route design, then the implementation is simple and requires less computational resources, but the models do not adapt to changing tourist patterns and preferences
Solution Approach 1:
The patent replaces traditional mechanical route planning methods (manual expert systems and fixed algorithms) with data-driven machine learning models. These models automatically analyze tourist behavior patterns from mobile device data and transport network information, then generate optimized routes based on learned patterns rather than pre-programmed rules. This substitution enables automatic adaptation to changing tourist preferences while maintaining ease of implementation through automated processes.
4Manufacturing precision
If comprehensive data analysis is performed for all nodes and edges in the transport network, then the route optimization becomes highly accurate, but the computational complexity increases for large-scale networks
Solution Approach 1:
The patent applies different levels of analysis granularity to different parts of the transport network based on their importance and characteristics. High-traffic areas and critical routes receive more detailed analysis with multiple probabilistic predictions, while lower-traffic areas use simplified models. This localized approach maintains high accuracy for important routes while reducing overall computational complexity.
Solution Approach 2:
The system dynamically adjusts the level of analysis depth and model complexity based on parameters such as tourist density, time of day, and network congestion. During peak periods or for critical routes, the system performs comprehensive analysis with high precision. During off-peak periods or for less critical routes, it uses simplified models with fewer computations, thereby adapting the system complexity to actual operational needs.
Data Source
AI summary
Computer-implemented methods for optimising routes in a transport network for a geographical region are disclosed. Methods may include obtaining an analysis of the geographical region; generating probabilistic predictions; and checking whether at least a subset of the generated probabilistic predictions have previously been solved by a trained machine learning model. When at least a first subset of the generated probabilistic predictions have previously been solved, retrieving the trained machine learning model previously used to solve the at least a first subset of the generated probabilistic predictions and executing the trained machine learning model to determine a first plurality of suggested routes for the transport network. When at least a second subset of the generated probabilistic predictions have not previously been solved, training a new machine learning model to solve the at least a second subset of generated probabilistic predictions to determine a second plurality of suggested routes for the transport network.