Route-Based Starting Point Selection for Balanced Arrival Times
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Solution Overview
Problem
Existing methods for determining a meeting or delivery starting point fail to ensure that entities reach the location equally quickly or fairly, considering varying travel modes and real-time traffic conditions.
Innovation Solution
A method that iteratively determines an optimal starting point by calculating routes from each entity's location, adjusting the starting point within a circle encompassing all entities to minimize total travel time and distance variance, and accounting for different travel modes and traffic conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the starting point is determined as the geometric center of a circle enclosing all entities, then the calculation is simple and fast, but the travel time and distance distribution among entities is not optimized
Solution Approach 1:
The starting point is determined dynamically through iterative optimization rather than a static geometric calculation. The algorithm repeatedly adjusts the starting point position based on route calculations and travel time data, converging on an optimal position that minimizes maximum travel time across all entities.
Solution Approach 2:
The method implements feedback by calculating actual travel times for routes from the current starting point estimate to each entity, using this feedback information to adjust the starting point position in subsequent iterations until convergence is achieved.
2Loss of substance
If the starting point is determined by minimizing the sum of all travel distances, then the total distance traveled is reduced, but the fairness of travel distribution among entities is not ensured
Solution Approach 1:
The algorithm seeks to achieve equipotentiality in terms of travel time by adjusting the starting point position so that all entities experience approximately equal travel times. This is done by minimizing the maximum travel time among all entities rather than just the sum of distances.
Solution Approach 2:
The method changes the optimization parameter from minimizing total distance to minimizing maximum travel time. This parameter change allows the system to account for different travel modes and speeds of individual entities, ensuring fairer travel distribution while still reducing overall travel time.
3Device complexity
If the route calculation does not consider traffic conditions and travel mode, then the calculation is simpler, but the accuracy of arrival time prediction is reduced
Solution Approach 1:
The route calculation incorporates additional parameters such as traffic conditions and travel mode-specific speed characteristics. The algorithm adjusts route estimation by considering entity-specific travel modes (e.g., walking, cycling, driving) and real-time traffic data to produce accurate arrival time predictions.
Data Source
AI summary
A method is provided for determining a starting point between entities. The locations of the entities are determined in a map, a circle enclosing all entities is determined and an optimum position of the starting point is determined iteratively. The starting point initially coincides with a center point of the circle enclosing the entities. To find an optimum position of the starting point for each entity, a route is determined between the respective location and the starting point. In order to shift the position of the starting point, a sum of all of the temporal or physical distances of the routes between entities and the starting point falls below a defined maximum distance, and/or a temporal or physical distance difference between a shortest and a longest route falls below a specified maximum difference.