Rideshare Vehicle Deployment via Transaction Data Prediction
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Solution Overview
Problem
Conventional rideshare systems lack the ability to accurately anticipate the number of vehicles needed for a given geographic area, leading to oversaturation in some areas and insufficient vehicle deployment in others, resulting in longer wait times for users.
Innovation Solution
A system and method that utilize transaction data from facilities to predict the number of rideshare vehicles required by analyzing metrics such as user transaction history, proximity to facilities, and current location, allowing for a more accurate estimation of vehicle deployment through a computing system that interfaces with rideshare computing systems via APIs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional rideshare systems deploy vehicles based on general availability rather than predicted demand, then vehicle deployment is simple and does not require complex analysis, but this leads to oversaturation in some areas and insufficient vehicle deployment in others, resulting in longer wait times
Solution Approach 1:
The system performs preliminary analysis of transaction data, user behavior patterns, and historical rideshare requests to predict future vehicle demand before deployment decisions are made. This advance prediction allows the system to prepare appropriate vehicle allocations in advance, resolving the contradiction by enabling accurate demand forecasting through pre-processing and analysis of multiple data sources.
Solution Approach 2:
The patent introduces a rideshare computing system as an intermediary between the organization's transaction data and the vehicle deployment decisions. This intermediary processes transaction data, analyzes user behavior, generates predictions, and communicates deployment recommendations to drivers through the rideshare application, thereby enabling accurate prediction without requiring the organization's system to directly manage complex deployment logistics.
2Productivity
If more rideshare vehicles are deployed to high-demand areas, then user wait times are reduced and service quality improves, but this increases operational costs and creates oversaturation in low-demand areas
Solution Approach 1:
The system applies local quality by tailoring vehicle deployment to specific geographic boundaries and facilities based on predicted demand. Instead of uniform deployment, the system analyzes transaction data and user behavior at each location to determine optimal vehicle allocation, ensuring high service efficiency in high-demand areas while avoiding oversaturation in low-demand areas, thus resolving the contradiction between service efficiency and operational cost.
Solution Approach 2:
The patent implements dynamic vehicle deployment that adjusts to changing demand patterns in real-time. The system continuously monitors transaction data, updates predictions, and modifies deployment recommendations accordingly, allowing the organization to optimize service efficiency while minimizing operational costs by deploying vehicles only where and when they are actually needed.
3Measurement precision
If the system analyzes detailed transaction history and user behavior data to predict rideshare demand, then deployment accuracy improves, but this increases data processing requirements and system complexity
Solution Approach 1:
The system extracts only the most relevant features and patterns from transaction data and user behavior that are predictive of rideshare demand, rather than processing all raw data. By identifying and extracting key predictive indicators from the organization's transaction history and user profiles, the system achieves accurate demand estimation while minimizing data processing energy requirements.
Data Source
AI summary
Embodiments disclosed herein generally related to a system and method for rideshare vehicle routing. A computing system receives, from one or more facilities, one or more transaction requests associated with one or more accounts of an organization associated with the computing system. The computing system maps one or more customers to a respective transaction request. For each facility of the one or more facilities, the computing device identifies a geographic location thereof. The computing system categorizes each of the one or more facilities into one or more boundaries. For each boundary, the computing system determines an estimated number of rideshare vehicles to deploy, based at least on a transaction history of each customer of the one or more customers. The computing system transmits the estimated number of rideshare vehicles to be deployed to each boundary to a rideshare computing system.