Trip Plan Engine Optimizing Routes and Modes via User Intent
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Solution Overview
Problem
Conventional navigation systems fail to optimally determine destinations, routes, and modes of transportation, leading to wasted time, money, and fuel, as users must already know their destinations and travel details, and lack tools for accessing vehicles with appropriate cargo capabilities for their needs.
Innovation Solution
A machine learning-trained trip plan engine identifies destinations, determines routes, and selects transportation modes based on user intents, preferences, and context information, generating a reservation plan to facilitate efficient trips.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional navigation systems are used, then users can obtain basic routing information, but the trip planning is far from optimal leading to wasted time, money, and fuel
Solution Approach 1:
The system automatically analyzes user intent from natural language input and generates optimized trip plans without requiring users to manually search for destinations or configure trip parameters. The machine learning model self-adjusts routing recommendations based on learned user preferences and contextual factors.
Solution Approach 2:
The system performs preliminary analysis of user intent and pre-generates multiple optimized trip plan options before the user needs to make a decision. Contextual factors such as weather, traffic patterns, and user preferences are pre-processed to prepare optimal routing recommendations in advance.
2Adaptability or versatility
If conventional navigation systems are used, then basic routing information is provided, but users lack access to tools for accessing vehicles with appropriate cargo capabilities
Solution Approach 1:
The trip plan engine integrates multiple functions including destination identification, routing optimization, transportation mode selection, and vehicle availability checking into a single unified system. This multi-functional approach provides comprehensive trip planning capabilities that adapt to diverse user needs.
Solution Approach 2:
The system acts as an intermediary between users and transportation resources, translating user intent into specific vehicle requirements and matching users with appropriate available vehicles. The machine learning model mediates the complex matching process between user needs and transportation options.
3Measurement precision
If users manually determine destinations and travel details, then they have control over trip parameters, but the establishment identified and trip details are far from optimal
Solution Approach 1:
The system replaces manual trip planning processes with an automated machine learning-based engine. The mechanical process of users manually searching and selecting destinations is substituted with an intelligent system that automatically analyzes intent and generates optimized trip plans using learned patterns and contextual understanding.
Solution Approach 2:
The machine learning model dynamically adjusts trip plan parameters such as routing, timing, and transportation mode selection based on learned user preferences and real-time contextual factors. The system optimizes multiple parameters simultaneously to achieve overall trip optimization while adapting to changing conditions.
Data Source
AI summary
An apparatus, for example, obtains a trip intent associated with a user; and causes a trip intent engine to determine a trip plan based at least in part on the trip intent using a trip intent model. The trip intent engine comprises the trip intent model, which is a machine learning-trained model. Determining the trip plan comprises identifying a destination based at least in part on point of interest data and the trip intent, determining a route from a current location of the user to the destination, determining a time for beginning a trip to the destination, and/or determining one or more modes of transportation for use in traversing one or more portions of the route. The apparatus causes at least a portion of the trip plan to be provided to the user via a user interface of a user apparatus.