AI Route Optimization for Inner-City Emissions and Cost
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Solution Overview
Problem
Current navigation systems fail to dynamically incorporate real-time data on road emissions levels, variable emissions-based pricing, and multi-objective user preferences, leading to suboptimal routing from an environmental and economic perspective, especially in inner-cities with high congestion and emissions levels.
Innovation Solution
An AI-based system that acquires real-time nitrogen dioxide data, integrates with vehicle databases for emissions profiles, and uses a genetic algorithm economic and environmental dispatch (GA-EED) optimizer to generate optimized routes based on user-selected criteria such as time, emissions, penalty, and cost.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If navigation systems use traditional routing algorithms based on limited criteria (distance, time, traffic), then the system complexity remains low and ease of operation is maintained, but the routing optimization from environmental and economic perspectives deteriorates
Solution Approach 1:
The patent transforms the routing optimization problem by changing the parameters considered beyond traditional distance and time. It incorporates emissions data (NO2 concentrations), dynamic pricing information, and multi-objective criteria (environmental impact, cost, time) to create a comprehensive optimization framework that resolves the contradiction between system complexity and optimization performance
Solution Approach 2:
The patent introduces an AI-based intermediary system that acts as a mediator between multiple data sources (emissions data, pricing data, traffic data) and the routing algorithm. This intermediary processes and integrates diverse parameters, enabling complex multi-objective optimization without requiring direct complexity in the core routing logic, thus maintaining ease of operation while improving optimization performance
2Reliability
If navigation systems incorporate real-time emissions data and dynamic pricing, then environmental awareness and economic optimization improve, but the device complexity and data processing requirements increase
Solution Approach 1:
The patent creates a universal AI-based platform that handles multiple functions: acquiring emissions data, processing dynamic pricing information, analyzing traffic conditions, and generating optimized routes. This multi-functional system consolidates various data processing tasks into a single integrated architecture, improving environmental and economic optimization while managing device complexity through functional consolidation
Solution Approach 2:
The patent segments the complex optimization system into distinct functional modules: data acquisition module (for emissions and pricing data), data processing module (for integrating multiple parameters), and route generation module (for producing optimized routes). This segmentation allows each component to handle specific tasks independently, improving overall system reliability while making the architecture more manageable and less complex
3Adaptability or versatility
If navigation systems integrate comprehensive vehicle databases with emissions profiles, then vehicle-specific optimization capability improves, but the quantity of data to be processed and stored increases
Solution Approach 1:
The patent applies local quality by storing and processing only the specific vehicle-related data that is relevant to emissions optimization (vehicle type, emissions profile, fuel consumption characteristics) rather than comprehensive vehicle databases. This selective data approach enables vehicle-specific optimization capability while minimizing the quantity of data that needs to be processed and stored
4Productivity
If navigation systems use advanced AI techniques like genetic algorithms for multi-objective optimization, then routing efficiency and environmental performance improve, but the computational power and processing time required increase
Solution Approach 1:
The patent applies preliminary action by pre-processing and organizing emissions data, pricing data, and vehicle profiles before the actual route optimization is performed. The AI system pre-calculates emissions for different route segments and pre-processes pricing information, so that when genetic algorithms or other optimization techniques are applied, they work with already-processed data. This reduces the computational energy required during real-time optimization while maintaining high routing efficiency
Data Source
AI summary
A system and method for AI-based dynamic pricing, mapping, and emissions control to optimize inner-city mobility. The system acquires real-time nitrogen dioxide data across a geographic area from sources including satellites, feeds this data into a decision support system along with road-specific weight matrices from authorities, and updates a dynamic penalty matrix for roads in the area. Automatic number plate recognition tracks vehicle movement, queries vehicle databases for emissions profiles, and updates the platform. Users input journey parameters and the system, using AI tools including a genetic algorithm economic and environmental dispatch (GA-EED) optimizer, generates an optimized route based on user-selected criteria such as time, emissions, penalty, and cost. The system enables dynamic, environmentally-aware route optimization for inner-city mobility.