AI Multi-Modal Route Generation with Safety and Cost Optimization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional techniques for generating transportation routes are inefficient and ineffective, particularly in urban environments, as they do not seamlessly integrate multiple transportation types, require manual planning and payment, and lack real-time adjustments based on user preferences and safety analysis.
Innovation Solution
A computer-based system using AI to generate efficient transportation routes by analyzing historical trip data, user preferences, and real-time contextual data, which automatically selects and purchases transportation services, and provides safety scores, while also collecting telematics data for model retraining and cost determination.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional route planning techniques are used, then users can obtain basic transportation directions, but the system cannot seamlessly integrate multiple transportation types or provide real-time optimizations
Solution Approach 1:
The system integrates multiple transportation types (public transit, rideshare, bike-share, walking) into a single unified route planning platform. The AI model evaluates and combines different transportation modes to generate optimized multi-modal routes, allowing users to access diverse transportation options through one interface rather than separate applications for each mode.
Solution Approach 2:
The system continuously collects real-time data from mobile devices including location, travel behavior, and user preferences. This feedback is processed by the AI model to dynamically optimize routes during transit and update future route recommendations, enabling real-time adaptations based on actual travel conditions and user responses.
2Ease of operation
If manual route planning and payment processes are used, then users can control their travel choices, but the process becomes time-consuming and complex
Solution Approach 1:
The system pre-calculates and stores optimized routes using AI models trained on historical trip data before users need them. When a user requests a route, the system quickly retrieves and presents pre-processed route options with associated costs and times, eliminating the need for users to manually plan each aspect of their journey.
Solution Approach 2:
The system automatically handles payment processing for multiple transportation types through integrated payment methods. Users select their preferred payment option once, and the system autonomously manages transactions across different service providers including public transit, rideshare companies, and bike-share programs without requiring manual intervention for each payment.
3Reliability
If basic route information is provided, then users receive simple directions, but safety analysis and real-time adjustments are not available
Solution Approach 1:
The system divides the route into discrete segments, each associated with a specific transportation type and safety profile. The AI model evaluates safety factors for each segment independently (such as crime rates, traffic conditions, and well-lit areas) and provides detailed safety information for each portion of the journey, allowing users to make informed decisions about each segment.
Solution Approach 2:
The system provides dynamic safety monitoring and real-time route adjustments based on changing conditions. The AI model continuously updates safety assessments during transit using real-time data from mobile devices and external sources, automatically suggesting alternative routes if safety conditions deteriorate, and providing push notifications about safety concerns along the current route.
4Measurement precision
If historical trip data is collected, then the AI model can be trained for better route generation, but data privacy and security concerns arise
Solution Approach 1:
The system implements differential privacy and data anonymization techniques that preserve the utility of historical trip data for AI training while protecting individual user identities. Sensitive personal information is obscured or aggregated in ways that maintain route optimization accuracy without exposing specific users' travel patterns to unauthorized parties.
Data Source
AI summary
A computer system may be provided. The computer system may include at least one processor. The at least one processor may be programmed to: (i) receive, from a user device, a trip request including a destination location for a trip; (ii) generate, based upon the trip request using an AI model, a route including a plurality of route segments, each of the plurality of route segments associated with a respective type of transportation, wherein the AI model is trained using historical trip records including historical trip data associated with historical trips; (iii) generate a user interface, the user interface including instructions associated with the generated route, the instructions indicating the respective type of transportation to be used for each route segment; and/or (iv) cause the user device to display the generated user interface.