Deep Learning Itinerary Planning Model for Route Optimization
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Solution Overview
Problem
Existing methods for planning travel itineraries are inefficient and require significant human-machine interaction, making it difficult for travelers to create personalized and optimized routes based on their demands.
Innovation Solution
A method and apparatus using a deep learning model to build an itinerary-planning model, which trains on user travel demands and scenic spot data to generate optimized travel routes by maximizing feedback values and minimizing regression errors, incorporating features like route theme, heat, and travel time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If traditional manual itinerary planning methods are used, then travelers can understand scenic spots and plan routes, but the process requires significant time investment and multiple human-machine interaction steps
Solution Approach 1:
The system enables self-service itinerary planning by automatically generating travel routes based on user input. The deep learning model autonomously processes travel demands, selects scenic spots, and optimizes routing without requiring manual intervention at each step, allowing the system to serve itself in creating personalized itineraries.
Solution Approach 2:
The patent replaces the mechanical manual planning process with an intelligent automated system. Instead of travelers manually researching and arranging routes, a deep learning-based itinerary planning model automatically generates optimized travel plans, substituting human cognitive and manual operations with machine intelligence.
2Productivity
If automated route generation is implemented, then planning time is reduced, but personalized customization according to user demands becomes difficult
Solution Approach 1:
The system handles personalized customization by dynamically adjusting multiple parameters including travel preferences, budget constraints, time availability, and scenic spot priorities. The deep learning model processes these varying parameters to generate customized itineraries that adapt to individual user demands while maintaining high planning efficiency.
Solution Approach 2:
The itinerary planning model achieves universality by being capable of handling diverse travel requirements through a single unified system. It can accommodate different travel types, destinations, and user preferences while maintaining consistent high-performance automated planning, making the system adaptable to various personalized needs.
3Reliability
If deep learning models are used for itinerary planning, then personalized and optimized routes can be generated, but the model training and processing require computational resources and time
Solution Approach 1:
The system performs preliminary action by pre-training the deep learning model with extensive travel data before actual use. This upfront computational investment creates a robust model that can then quickly generate high-quality itineraries with minimal real-time processing, reducing the energy cost during actual deployment while maintaining reliable planning quality.
Data Source
AI summary
The present disclosure provides a method and apparatus for building an itinerary-planning model and planning a traveling itinerary, wherein the method for building the itinerary-planning model comprises: obtaining a travel route with a known travel demand; training a deep learning model by regarding the travel demand, a set of candidate scenic spots determined by using the travel demand and the travel route corresponding to the travel demand as training samples, to obtain the itinerary-planning model; the itinerary-planning model is configured to use the travel demand to obtain a corresponding travel route. The method of planning a travelling itinerary comprises: obtaining the user's travel demand; according to the user's travel demand, obtaining a set of candidate scenic spots corresponding to the travel demand; inputting the user's travel demand and the set of candidate scenic spots into an itinerary-planning model, to obtain a travel route obtained by the itinerary-planning model.