Transport Route Graph Partitioning for Tourist Demand Optimization
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Solution Overview
Problem
Current transport network route design relies heavily on local expertise and fixed, handcrafted models, failing to consider the impact of tourism on transport systems and struggling with large-scale Non-deterministic Polynomial hard problems, leading to inefficient and time-consuming solutions.
Innovation Solution
An AI-driven system that learns to optimize transport routes by combining transport and tourist data, using graph embeddings and machine learning to assess tourist impact and generate optimal routes without human intervention, enabling fast and scalable solutions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional optimization methods are used to solve BRTRDP, then the problem can be addressed with established algorithms, but the computational complexity increases significantly for large-scale problems
Solution Approach 1:
The patent divides the large-scale transport network into multiple subgraphs or clusters based on spatial proximity and demand patterns. Each subgraph is processed independently through parallel computation, transforming a single complex NP-hard problem into multiple smaller manageable subproblems that can be solved more efficiently while maintaining solution quality.
Solution Approach 2:
The patent introduces graph embedding techniques that map the discrete graph structure into a continuous vector space. This dimensional transformation allows the application of machine learning algorithms to optimize route design, converting a combinatorial optimization problem into a continuous optimization problem that can be solved more efficiently.
2Manufacturing precision
If handcrafted models based on local expertise are used, then the routes can be optimized with domain knowledge, but the process becomes time-consuming and requires human intervention
Solution Approach 1:
The patent implements self-service through automated machine learning models that learn optimal routing strategies from historical transport data and tourist patterns. The system automatically generates and refines route solutions without requiring manual intervention from experts, eliminating the time-consuming iterative process of handcrafted model development while maintaining high optimization quality.
Solution Approach 2:
The system incorporates feedback mechanisms where the machine learning model continuously learns from actual transport usage data, tourist behavior patterns, and route performance metrics. This feedback loop enables the model to automatically improve route optimization quality over time without requiring repeated human expert intervention.
3Ease of manufacture
If fixed models are used for route design, then the implementation is straightforward, but the system cannot adapt to changing tourist patterns and transport needs
Solution Approach 1:
The patent transforms fixed static routing models into dynamic adaptive systems using machine learning. The model continuously updates its routing recommendations based on real-time and historical data about tourist movements, transport capacity, and demand patterns. This enables the system to automatically adapt to changing conditions while maintaining ease of implementation through automated decision-making.
Solution Approach 2:
The system dynamically adjusts routing parameters such as bus frequencies, route paths, and capacity allocations based on learned patterns from data. By changing these parameters automatically in response to observed demand variations, the system achieves high adaptability without requiring complex manual reconfiguration.
Data Source
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AI summary
Computer-implemented methods for optimising routes in a transport network for a geographical region are disclosed. Methods comprise obtaining transport data and tourist data, generating a graph based on the transport data and the tourist data, and determining a relationship between tourist hotspots and current transport needs. The determining comprises: dividing the graph into a plurality of subgraphs; assessing each subgraph in parallel to determine the relationship between the tourist hotspots and the current transport needs; and combining the assessed subgraphs to produce an updated graph representing the relationship between the tourist hotspots and the current transport needs for the geographical region.