Mobility Point Layout Using Time-Series Pedestrian Route Analysis
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Solution Overview
Problem
Conventional navigation systems fail to consider real-time pedestrian environments and variability of traffic conditions, particularly for mobility-vulnerable persons, and lack the ability to predict optimal boarding and alighting points through time-series pattern analysis.
Innovation Solution
A method and system that perform time-series analysis on pedestrian environment and dynamic data to derive optimal pick-up and drop-off points, considering user-specific conditions and temporal changes, and visualize these points on user terminals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional navigation systems use static map information and vehicle-oriented traffic data to calculate routes, then the routing calculation is simple and fast, but the system fails to reflect actual pedestrian environments and temporal variability of traffic conditions
Solution Approach 1:
The system transitions from static route calculation to dynamic route optimization by continuously collecting real-time pedestrian environment data, traffic conditions, and user feedback. The routing algorithm adapts to changing conditions by recalculating optimal paths based on current data, making the system responsive to temporal variability in pedestrian environments and traffic patterns.
Solution Approach 2:
The system implements feedback mechanisms by collecting user evaluations of recommended routes and using this information to continuously improve route recommendations. User feedback on factors such as walkability, safety, and convenience is integrated into the routing algorithm, creating a closed-loop system that learns and adapts from actual user experiences.
2Measurement precision
If conventional systems calculate routes based on single-point-in-time data, then the calculation is computationally efficient, but the system cannot predict or design boarding and alighting points by reflecting temporal changes
Solution Approach 1:
The system performs preliminary actions by pre-collecting and storing pedestrian environment data, traffic patterns, and historical route information in databases. This pre-processing allows the system to quickly retrieve and analyze relevant data when calculating routes, reducing real-time computation requirements while maintaining high temporal accuracy in route recommendations.
3Adaptability or versatility
If conventional route guidance technologies focus on general routing, then the system is simple to implement, but it fails to comprehensively consider user-specific conditions such as ramps, tactile paving, walkway width, or illumination
Solution Approach 1:
The system applies local quality by tailoring route recommendations to specific user needs and conditions. Different user profiles (e.g., pedestrians with mobility impairments, elderly users, travelers with luggage) receive customized routes that prioritize locally relevant features such as ramp accessibility, tactile paving continuity, walkway width, and illumination levels at specific segments of the route.
Data Source
AI summary
The present invention relates to a method and system for designing mobility points (MPs) that support boarding, alighting, and transfers between various modes of transportation by performing time-series analysis on transportation, walking, and environmental data, and for visually providing the design results, thereby improving the mobility convenience of all users including mobility-vulnerable persons.