Pedestrian Probe Data for Ridesharing Resource Allocation
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Solution Overview
Problem
Ridesharing service providers face challenges in automatically assessing demand for ridesharing resources across geographic areas, as existing methods lack effective tools to utilize pedestrian probe data for optimizing resource allocation and service deployment.
Innovation Solution
The method involves receiving and processing pedestrian probe data to identify common origins and destinations, which are then used to determine optimal ridesharing locations and resource allocations, leveraging this data to enhance the efficiency of ridesharing services by focusing on high-demand areas.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If ridesharing service providers manually assess demand for ridesharing resources, then they can make informed decisions about resource allocation, but the process is time-consuming and labor-intensive
Solution Approach 1:
The patent replaces manual demand assessment with an automated computer-based system that processes probe data from mobile devices. The system automatically identifies high-demand geographic areas and time periods by analyzing aggregated location data, eliminating the need for manual market research while providing precise, data-driven insights for resource allocation decisions.
Solution Approach 2:
The system enables ridesharing providers to self-serve by automatically generating demand assessments without requiring external consultants or manual analysis. The computer system autonomously collects, processes, and interprets probe data to produce actionable intelligence about optimal vehicle deployment locations and timing.
2Area of stationary object
If ridesharing resources are deployed across all geographic areas, then service coverage is maximized, but resource utilization efficiency decreases due to overserving low-demand areas
Solution Approach 1:
The patent applies local quality by tailoring resource deployment to specific geographic areas based on their individual demand characteristics. The system identifies high-demand zones through probe data analysis and recommends concentrated resource allocation to those specific locations, rather than uniform distribution. This allows service coverage to be optimized locally in high-demand areas while avoiding waste in low-demand areas.
Solution Approach 2:
The system dynamically adjusts resource allocation parameters (vehicle locations, deployment timing) based on analyzed probe data patterns. By changing these parameters according to actual demand measurements rather than fixed deployment schedules, the system achieves both broad service coverage and high resource utilization efficiency simultaneously.
3Productivity
If ridesharing resources are concentrated in specific high-demand areas, then resource utilization efficiency improves, but service coverage to other areas decreases
Solution Approach 1:
The patent implements dynamic resource allocation where vehicle deployment locations and timing are continuously adjusted based on real-time and historical probe data analysis. The system identifies temporal patterns in pedestrian movement and recommends deploying resources to different geographic areas at different times, allowing concentrated service in high-demand areas during peak periods while maintaining broader coverage across different time windows.
4Device complexity
If manual methods are used to identify demand patterns, then implementation complexity is low, but the precision and reliability of demand assessment is insufficient
Solution Approach 1:
The patent introduces probe data from mobile devices as an intermediary that objectively captures actual pedestrian movement patterns. This data intermediary provides reliable, empirical evidence of demand patterns without requiring complex manual surveys or subjective assessments. The system processes this intermediary data through automated algorithms to produce reliable demand insights while keeping the overall implementation complexity manageable.
Data Source
AI summary
An approach is provided for using pedestrian probe data for ridesharing. The approach involves, for example, receiving pedestrian probe data from a search zone associated with a designated location. The pedestrian probe data is collected, for instance, from one or more location sensors of at least one device associated with at least one pedestrian. The approach also involves processing the pedestrian probe data to determine one or more pedestrian paths terminating at the designated location. The approach further involves identifying at least one common origin, at least one common destination, or a combination thereof of the one or more pedestrian paths and providing the least one common origin, the at least one common destination, or a combination thereof as an output.