Route Planning Algorithm Balancing Safety and Distance
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Solution Overview
Problem
Existing route planning systems for bike or foot travel typically require a predefined destination, failing to generate open random routes that balance distance and safety while adhering to time constraints.
Innovation Solution
A computer-implemented method and system using multi-objective bio-inspired optimization algorithms to calculate routes starting from a known location, randomly assigning destination coordinates and preferences for safety, inclination, and duration, ensuring routes comply with time constraints and optimizing for Pareto trade-offs between safety and distance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a predefined destination is required for route planning, then the route calculation is straightforward and efficient, but the system cannot generate open random routes that allow users to explore without a fixed endpoint
Solution Approach 1:
The patent applies dynamics by making the destination flexible rather than fixed. The system generates random destinations dynamically during route calculation, allowing the endpoint to change based on exploration needs while still maintaining efficient pathfinding algorithms. This resolves the contradiction by enabling open route generation without requiring completely new complex systems.
Solution Approach 2:
The system performs preliminary actions by pre-calculating and storing route segments, safety metrics, and environmental data before actual route generation. This preprocessing allows the system to handle open routes efficiently by combining pre-computed elements with random destination selection, avoiding the need for entirely new complex calculation systems.
2Reliability
If multiple objectives (safety, distance, time) are optimized simultaneously, then the route quality improves, but the computational complexity increases significantly
Solution Approach 1:
The patent segments the multi-objective optimization problem into separate evaluatable components: safety metrics, distance calculations, and time constraints are computed independently and then combined. This segmentation allows each objective to be optimized separately using appropriate algorithms, reducing overall computational complexity while maintaining route quality.
Solution Approach 2:
The system changes parameters by converting multiple objectives into a unified evaluation framework where safety, distance, and time are transformed into comparable metric types. This parameter transformation enables simultaneous optimization without requiring complex multi-objective algorithms, as all objectives are expressed in compatible computational forms.
3Productivity
If strict time constraints are imposed on route generation, then the user experience is improved with predictable travel times, but the system cannot explore diverse route options adequately
Solution Approach 1:
The patent applies partial action by generating multiple candidate routes that exceed the minimum time constraint requirement, then selecting the optimal subset. This allows the system to explore diverse route options beyond strict time limits during calculation, then filter results to meet user time preferences, thereby maintaining both efficiency and diversity.
Solution Approach 2:
The system performs preliminary generation of multiple potential routes with varying characteristics before applying time constraint filtering. This preliminary exploration phase allows diverse route options to be created without time pressure, then the best options are selected based on time constraints, resolving the contradiction between efficiency and diversity.
Data Source
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AI summary
A computer-implemented method for computing open routes and a route planning system, comprising: providing the initial location coordinates of the user and a maximum time duration for a route, digitally assigning in a random way destination location coordinates, a value of safety preference, inclination preference and duration preference, applying a heuristic algorithm for digitally calculating a candidate route by means of a route planning engine, said candidate route starting at the initial location coordinates, ending at the destination location coordinates, fulfilling said values of safety preference, inclination preference and duration preference, and for digitally checking whether the calculated candidate route fulfils the time constraints regarding said maximum time duration and a time duration tolerance value and, if the time constraints are fulfilled, selecting said route and calculating a security value and a distance value associated to the route, and if the time constraints are not fulfilled, discarding the candidate route, repeating the steps of digitally assigning in a random way destination location coordinates, a value of safety preference, a value of inclination preference and a value of duration preference, digitally calculating a candidate route and digitally checking the fulfilment of time constraints in the calculated route and if applicable, calculating a security value and a distance value for the route, until a set of selected routes has been obtained by balancing the Pareto trade-off between the route safety level and the distance of the route.