Intermodal Routing Optimization via Precomputed Availability Models
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Solution Overview
Problem
The increasing complexity of intermodal routing computations due to the growing number of shared vehicles and transport options poses a challenge for service providers in optimizing route calculations efficiently, leading to increased computational complexity and resource usage.
Innovation Solution
A method and system that pre-compute shared vehicle availability patterns for geographic areas, generating an intermodal vehicle availability model to selectively exclude areas with unavailable transport modes, thereby optimizing intermodal route computations by considering all possible transport modes and reducing computational resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the number of shared vehicles and transport options increases to provide more routing choices, then the versatility and quality of intermodal routing services improve, but the computational complexity and resource requirements for route computations increase
Solution Approach 1:
The system pre-computes vehicle availability patterns for different geographic areas and time periods before actual routing requests are processed. This preliminary action creates lookup tables and availability models that capture historical and predictive vehicle availability data, allowing the routing system to quickly query pre-analyzed data rather than performing complex computations in real-time when routing requests arrive.
Solution Approach 2:
The geographic service area is divided into multiple discrete geographic areas or zones, each with its own vehicle availability pattern. This segmentation allows the system to manage computational complexity by treating each zone independently, analyzing vehicle availability patterns at a granular level, and combining results efficiently when generating intermodal routes that may span multiple zones.
2Measurement precision
If real-time computation of all possible intermodal routes is performed to ensure accurate routing, then the precision and reliability of route recommendations improve, but the time consumption and computational resources increase
Solution Approach 1:
The system performs preliminary analysis of vehicle availability patterns across different geographic areas and time periods, storing this pre-computed information in accessible data structures. When a routing request arrives, the system queries this pre-analyzed data to quickly determine which transport modes are available in which areas, eliminating the need for real-time computation of vehicle availability and enabling fast, accurate route generation.
3Reliability
If comprehensive vehicle availability data is collected for all geographic areas to improve routing decisions, then the quality and reliability of intermodal route recommendations improve, but the data processing burden and storage requirements increase
Solution Approach 1:
The system extracts and stores only the essential vehicle availability pattern information from comprehensive vehicle data, rather than retaining all raw data. By analyzing historical and real-time vehicle availability data and extracting key patterns (such as typical availability by geographic area and time period), the system maintains routing reliability while significantly reducing data storage requirements and processing burdens.
Data Source
AI summary
An approach is provided for optimizing intermodal route computations. A routing platform retrieves shared vehicle availability data associated with one or more modes of transport for geographic areas. The routing platform further processes the shared vehicle availability data to determine a shared vehicle availability pattern for each geographic area. The routing platform further generates, based on the shared vehicle availability pattern, an intermodal vehicle availability model indicating a combination of modes of transport for each geographic area, wherein the geographic areas are selectively excluded from computations to generate an intermodal route. The routing platform further provides the intermodal vehicle availability model as an output for generating the intermodal route.