Multimodal Routing System with Parking and Safety Integration
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Solution Overview
Problem
Current vehicle navigation systems do not effectively integrate parking availability and safety into route calculations, nor do they balance travel time and monetary costs, leading to suboptimal routing solutions for users.
Innovation Solution
A multimodal vehicle routing system that incorporates parking availability, safety, and cost factors into route calculations, using algorithms like Dijkstra and A* to determine optimal routes that consider multiple transportation modes and parking locations, allowing users to set preferences for time, money, and safety.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional vehicle routing systems calculate routes based only on travel time and distance, then the routing calculation is simple and fast, but the route quality is suboptimal because it does not consider parking availability and safety
Solution Approach 1:
The routing system is segmented into multiple independent modules: a routing module that handles basic path calculation, a parking availability module that assesses parking options, a safety module that evaluates route safety, and an integration module that combines all factors. This segmentation allows each module to specialize in one aspect while maintaining overall system manageability and improving route quality through comprehensive evaluation
Solution Approach 2:
The patent merges traditionally separate routing functions with parking assessment and safety evaluation into a unified multimodal routing system. By combining these previously independent considerations into a single integrated framework, the system achieves comprehensive route optimization without proportionally increasing complexity, as shared infrastructure and data structures are utilized across all modules
2Reliability
If the system incorporates multiple factors (parking, safety, cost, time) into route calculations, then the route optimization improves, but the calculation complexity and processing time increase
Solution Approach 1:
The system performs preliminary assessments of parking availability and safety conditions for potential routing areas before final route calculation. By pre-evaluating these factors and caching results where applicable, the system reduces the computational burden during actual route optimization, allowing comprehensive multi-factor analysis without proportional increases in calculation time
Solution Approach 2:
The routing system dynamically adjusts the level of detail and depth of analysis for different route segments based on real-time conditions and user preferences. For high-priority segments, more detailed analysis is performed, while for less critical segments, simplified assessments are used. This dynamic approach optimizes the balance between route quality and calculation efficiency
3Ease of operation
If users set detailed preferences for time, money, and safety, then the routing results are more personalized and accurate, but the system complexity and data processing requirements increase
Solution Approach 1:
The system implements a universal preference framework that handles multiple user preferences (time, money, safety) through a unified interface and processing mechanism. This multi-functional approach allows the same core routing engine to accommodate various preference combinations without requiring separate processing paths, thereby reducing overall system complexity while maintaining high ease of operation
Solution Approach 2:
User preferences are implemented as adjustable parameters that modify the weighting and prioritization of different route evaluation criteria. By changing parameters rather than restructuring the system for different preference types, the system achieves high customization capability with minimal increase in complexity, as the underlying architecture remains consistent across all preference configurations
Data Source
AI summary
A system and method is provided that creates a multimodal transportation routing that originates using a road-based vehicle which then requires parking. Parking is not necessarily near the destination, but is determined based on user preferences and specification of the relative importance of speed of travel and monetary cost and/or safety. Other factors can also influence the route selection and comprise: parking availability, user preference for parking type, vehicle restrictions, maximum distance a user is willing to walk or bike and what types of public transportation a user is willing to use. Monetary factors include: fuel costs, parking costs, tolls, and public transportation costs.