Automated Transit Routing Using Simulated Annealing and Degree of Circuity
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Solution Overview
Problem
Current transit network designs fail to efficiently route vehicles to move passengers and packages between their start or final destinations and central transportation hubs, leading to increased traffic congestion and operational inefficiencies, despite advancements in autonomous and connected vehicles.
Innovation Solution
An automated system using the Simulated Annealing algorithm and Degree of Circuity to determine optimal vehicle routing, considering maximum acceptable travel times for passengers and packages, which minimizes total costs and vehicle operating times by dynamically rerouting and relocating feeder buses between multiple transportation hubs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional fixed-route transit network design is used, then operational simplicity is maintained, but traffic congestion increases and operational efficiency decreases
Solution Approach 1:
The patent implements dynamic routing where vehicle paths are continuously adjusted based on real-time conditions. The system determines optimal routes dynamically rather than using fixed schedules, allowing transit vehicles to adapt to changing traffic patterns, passenger demand, and environmental conditions, thereby improving operational efficiency while managing complexity through automated decision-making
Solution Approach 2:
The patent replaces manual transit planning and mechanical scheduling systems with an automated computational system. The processor-based system uses algorithms to optimize routes, replacing traditional mechanical approaches to transit management with intelligent software that can process multiple constraints and objectives simultaneously, improving efficiency without requiring proportional increases in human operational complexity
2Loss of energy
If automated routing optimization is implemented, then operational costs decrease, but system complexity increases
Solution Approach 1:
The patent creates a multi-functional automated routing system that simultaneously optimizes for multiple objectives: minimizing operating costs, reducing travel time, lowering carbon emissions, and improving passenger convenience. This universal system handles diverse optimization criteria within a single integrated framework, reducing the need for multiple separate systems and thereby managing complexity while achieving cost reductions across multiple operational dimensions
Solution Approach 2:
The patent changes key operational parameters dynamically, including vehicle speed, route selection, and scheduling based on real-time conditions. By adjusting these parameters continuously rather than maintaining fixed values, the system optimizes operating costs adaptively. The automated system manages the complexity of tracking and adjusting multiple parameters through centralized computational control
3Ease of operation
If maximum travel time constraints are applied for each passenger, then user convenience increases, but routing optimization difficulty increases
Solution Approach 1:
The patent segments the transit network into discrete paths between origins and destinations, allowing individual travel time constraints to be applied to each segment. This segmentation enables the system to handle multiple passenger-specific constraints by breaking down the overall routing problem into manageable path segments, making the optimization task more tractable while still providing personalized service that improves user convenience
Data Source
AI summary
A system and method for automated routing of people and materials from one location to another based on automated vehicle technology are applied to bus transit, ridesharing and car sharing, and on multiple modes of delivery, including rail, water, road and air. An optimal transit algorithm uses Degree of Circuity (DOC) and Maximum Degree of Circuity (Max DOC) to refine transit network design and scheduling. Max DOC and computed shortest travel times are used to define the maximum acceptable travel time for each passenger or package. Using those maximum acceptable travel times for passengers and/or packages as constraints, optimal routings are developed for each primary transport hub, using a Simulated Annealing (SA) algorithm. The SA algorithm may be used as a basis for optimal flexible feeder bus routing, which considers relocation of buses for multiple primary transport hubs and multiple primary transport vehicles.