Dynamic Transportation Routing Mode Switching
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Solution Overview
Problem
Current location-based services (LBS) lack the ability to dynamically adjust transportation routes based on real-time changes in traffic conditions and user preferences, particularly in scenarios where a user transitions between passenger vehicles and auxiliary vehicles, such as bicycles or scooters, to optimize route efficiency and comfort.
Innovation Solution
A system that determines the current transportation mode of a user by analyzing data from both passenger and auxiliary vehicles, evaluates routes based on predicted performance using AI-driven predictive models, and dynamically updates the route to switch between passenger vehicle and auxiliary vehicle modes to optimize travel time and comfort.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a user switches from passenger vehicle to auxiliary vehicle mode, then travel comfort is improved, but route planning complexity increases
Solution Approach 1:
The system dynamically adjusts the route plan based on real-time detection of user transportation mode changes. When the user switches from passenger vehicle to auxiliary vehicle mode, the system automatically re-evaluates and re-plans the route, transitioning from a static route plan to a dynamic, adaptive planning approach that responds to changing user needs and conditions.
Solution Approach 2:
The system incorporates feedback mechanisms by continuously monitoring user transportation mode through data from passenger vehicle and auxiliary vehicle systems. This feedback loop enables the route planning system to detect mode changes and adjust routes accordingly, improving comfort while managing complexity through automated response protocols.
2Loss of time
If real-time route adjustment is implemented, then travel time is minimized, but system computational load increases
Solution Approach 1:
The system performs preliminary route evaluation and pre-computes alternative routes based on predicted performance metrics. By preparing multiple potential routes in advance and ranking them according to predicted criteria such as travel time and comfort, the system can quickly select and execute optimal routes without requiring intensive real-time computational analysis, thus reducing immediate computational load while maintaining time efficiency.
Solution Approach 2:
The system changes evaluation parameters dynamically based on user transportation mode and real-time conditions. By adjusting the weightings and criteria used for route evaluation according to current user needs (e.g., prioritizing comfort for auxiliary vehicle mode), the system optimizes travel time while managing computational resources through targeted parameter adjustment rather than comprehensive re-evaluation of all possible routes.
Data Source
AI summary
Methods, computer program products, and systems are presented. The method computer program products, and systems can include, for instance: determining a current transportation mode of a user with use of data of a user passenger vehicle associated to the user and data of a user auxiliary passenger vehicle associated to the user, wherein the user passenger vehicle is capable of carrying the user auxiliary vehicle, and wherein the user auxiliary vehicle is configured to be hand carried by the user; evaluating a current route of the user in dependence on the current transportation mode of the user as determined by the determining; and providing one or more output in dependence on the evaluating.