Route Optimization Algorithm for Dynamic Traffic
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Solution Overview
Problem
Current smart trip planning services face challenges in efficiently processing large requests in real-time, especially with dynamic traffic changes and time-critical trip planning, due to the high computational complexity of the Traveling Salesman Problem (TSP), which is NP-hard, making it difficult to optimize routes quickly and adapt to changing conditions such as traffic, weather, and agenda updates.
Innovation Solution
A method that utilizes a low-complexity algorithm for real-time optimization of routes by recalculating travel times based on dynamic changes, employing a matrix of context changes transformed by principal component analysis, and adjusting the route by recalculating parameters such as destination sequence, time schedule, and refueling/charging stops, with thresholds to determine when to rerun the optimization for improved route planning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If a traditional TSP algorithm is used to optimize routes visiting multiple destinations, then the route optimization accuracy is improved, but the computational complexity becomes NP-hard making real-time processing impossible
Solution Approach 1:
The patent divides the complete TSP route into multiple segments between consecutive destinations. Instead of optimizing the entire route at once, the system optimizes each segment independently using dynamic programming, reducing the computational complexity from NP-hard to polynomial time while maintaining optimization accuracy for each segment.
Solution Approach 2:
The patent combines multiple optimization techniques including dynamic programming for time-window constraints, Dijkstra's algorithm for shortest path calculation, and heuristic methods for initial solution generation. These algorithms work together in an integrated framework to achieve both computational efficiency and optimization quality.
2Adaptability or versatility
If the route optimization is recalculated frequently to adapt to dynamic traffic changes, then the adaptability to changing conditions is improved, but the processing time and computational load increase
Solution Approach 1:
The system implements periodic recalculation of route optimizations at predetermined intervals or when specific triggers occur (e.g., significant traffic changes, time window violations). This allows the system to adapt to dynamic conditions while avoiding continuous recalculation that would consume excessive processing time.
Solution Approach 2:
The system continuously monitors traffic conditions, arrival times, and time window constraints, and uses this feedback to determine when recalculation is necessary. The feedback mechanism enables adaptive recalculation frequency, recalculating only when conditions warrant it rather than at fixed intervals.
3Productivity
If the system processes large numbers of destinations and requests, then the service coverage and user capacity are improved, but the computational complexity and processing speed deteriorate
Solution Approach 1:
The patent segments the routing problem into independent destination pairs, allowing parallel processing of multiple requests. Each request is handled as a separate optimization problem that can be solved independently using the same efficient dynamic programming approach, enabling the system to scale to large numbers of destinations and users.
Solution Approach 2:
The system changes the parameter representation from complete routes to segment-based time-cost matrices. This parameter transformation enables more efficient computation and storage, allowing the system to handle larger datasets with reduced computational complexity.
Data Source
AI summary
Embodiments relate to a method, a computer program and a device for controlling a route connecting a plurality of destinations in a road network. The method for controlling a route connecting a plurality of destinations in a road network comprises receiving input information. The input information comprises information about a situation with impact on the route. Furthermore, the method comprises recalculating a plurality of travel times between the plurality of destinations of the route, based on the received input information and distances between the plurality of destinations. Also, the method comprises providing output information about the recalculation, which comprises information about a dynamic change of the route.