Dynamic Walking Profile Adjustment for Accurate Time Estimation
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Solution Overview
Problem
Existing scheduling systems for ride-sharing and delivery services do not accurately account for users' walking times, leading to inefficiencies and waiting times for both users and service providers due to the lack of consideration for individual walking speeds and environmental conditions.
Innovation Solution
A networked system that dynamically maintains and utilizes walking profiles by correlating users' walking pace data with environmental conditions to adjust parameters and provide accurate time estimates for service scheduling, including generating geofences and determining optimal pickup and drop-off locations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a fixed time estimation is used for service scheduling, then the scheduling system is simple to operate, but it does not account for individual walking speeds and environmental conditions, leading to waiting times and inefficiencies
Solution Approach 1:
The patent implements dynamic time estimation by continuously updating walking profiles based on actual walking data collected from users. The system adjusts walking speeds and time estimates in real-time based on environmental conditions, terrain, and individual user performance, transforming a static scheduling system into a dynamic one that adapts to changing conditions.
Solution Approach 2:
The system collects actual walking data from users during their journeys and uses this feedback to continuously refine and update their walking profiles. This feedback loop enables the system to improve time estimation accuracy over time by comparing predicted arrival times with actual arrival times and adjusting parameters accordingly.
2Loss of time
If individual walking profiles are maintained and dynamically updated, then time estimation accuracy is improved, but data processing and system complexity increase
Solution Approach 1:
The system performs preliminary actions by pre-calculating and storing walking profiles for each user based on their historical walking data. These pre-computed profiles include typical walking speeds, preferred routes, and response to environmental conditions, enabling the system to quickly generate accurate time estimates without performing complex real-time calculations during service provisioning.
Solution Approach 2:
The system changes parameters by adjusting walking speeds and time estimates based on environmental conditions such as weather, terrain, and time of day. The walking profile includes multiple parameter sets that can be selectively applied depending on current conditions, allowing the system to optimize time estimates without requiring complete recalculation from scratch.
3Reliability
If walking pace data is correlated with environmental conditions, then time estimation precision is improved, but measurement and data processing difficulty increases
Solution Approach 1:
The patent segments the walking profile into distinct components: base walking speed, environmental condition modifiers, terrain adjustments, and user-specific factors. Each component is measured and processed separately, making the overall system more manageable. The environmental conditions are also segmented into discrete categories (weather types, terrain types, time of day) that can be independently tracked and correlated.
Data Source
AI summary
Systems and methods for dynamically maintaining and utilizing walking profiles for time estimations in service scheduling are provided. A networked system detects usage of an application on a user device. In response to the detecting, the networked system accesses environmental condition data at a location associated with a user of the user device, whereby the environmental condition data comprises one or more environmental condition affecting the location. The networked system accesses walking pace data from the user device, whereby the walking pace data represents a current walking pace of the user and correlates the walking pace data with the environmental condition data. Using the correlated data, the networked system adjusts a parameter in a walking profile of the user. The walking profile is then used to determine time estimates that are used to schedule services that require walking by a user.