Route Planning Using Segmented Graph Search for Real-Time Traffic
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Solution Overview
Problem
Existing route planning methods struggle to account for real-time traffic conditions in large-scale dynamic road networks, as speed-up algorithms require precomputed arc costs and Dijkstra-based searches at query time become unacceptably long when incorporating real-time data.
Innovation Solution
A two-step route planning methodology is implemented, where an initial graph search using real-time traffic data is executed until relevant, and then precomputed data based on traffic prediction is used to calculate optimal routes, leveraging algorithms like Dijkstra's or contraction hierarchies to balance flexibility and speed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If speed-up algorithms like contraction hierarchies are used with precomputed arc costs, then query computation speed is improved, but the ability to account for real-time traffic conditions deteriorates
Solution Approach 1:
The route planning process is divided into two distinct phases: a precomputation phase that builds the graph structure with predicted traffic costs, and a query phase that uses real-time traffic data. This segmentation allows the system to maintain the speed benefits of precomputation while incorporating real-time traffic information when needed.
Solution Approach 2:
The system performs preliminary computation of the graph structure and shortcuts using predicted traffic data before queries are made. This precomputation establishes a foundation for fast querying while allowing real-time traffic data to adjust costs during the actual route calculation, combining the benefits of both approaches.
2Adaptability or versatility
If Dijkstra-based search is performed at query time with real-time traffic data, then adaptability to real-time conditions is improved, but query computation time deteriorates
Solution Approach 1:
The search process is segmented into an initial phase using precomputed shortcuts with predicted traffic costs, followed by a refinement phase that incorporates real-time traffic data only where necessary. This reduces the overall computation time while maintaining adaptability to current conditions.
Solution Approach 2:
Real-time traffic data is applied locally to specific arcs and regions of the graph rather than uniformly across the entire network. This localized application of real-time data maintains query speed by avoiding unnecessary recomputation in areas where traffic conditions have not changed.
3Productivity
If precomputed shortcuts are used, then scalability to large graphs is improved, but the ability to reflect dynamic traffic conditions deteriorates
Solution Approach 1:
The system precomputes shortcuts and graph structure using predicted traffic data to enable scalable handling of large graphs. During query processing, real-time traffic data is then applied to adjust costs dynamically, allowing the system to scale to large networks while still reflecting current traffic conditions.
Solution Approach 2:
The system maintains a dynamic approach by allowing real-time traffic data to modify arc costs during query processing, even though the underlying graph structure is precomputed. This dynamic cost adjustment enables the system to handle large graphs efficiently while adapting to changing traffic conditions.
Data Source
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AI summary
A computer-implemented route planning method comprises determining source and destination nodes in a graph data structure based on a route planning query, executing an initial graph search on the graph data structure using graph costs based on real-time traffic data, wherein the initial graph search starts at the source node and settles nodes until it stops, and computing one or more routes to the destination node from one or more of said settled nodes using precomputed data based on traffic prediction data, thereby to determine a route from the source node to the destination node via one of said settled nodes.