Ride Sharing Incentive Matching for First Mile Last Mile Access

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Solution Overview

Problem

Conventional car sharing platforms face challenges in user adoption due to the 'first mile/last mile' problem, where users need to travel to and from central vehicle locations, often requiring additional transportation modes that can be inconvenient and lengthy.

Innovation Solution

A computer-implemented method and system that uses trained machine learning models to analyze user profiles and route data, determining incentives to encourage first-mile/last-mile transfers between car sharing users, thereby facilitating vehicle sharing and reducing the need for additional transportation.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Ease of operation

If users travel to central vehicle locations for car sharing, then vehicle access is enabled, but additional transportation time and complexity increase

Engineering Contradiction:
Improveease of vehicle accessVSAvoidfirst mile/last mile travel time
Core Design Contradiction:
Ease of operationVSLoss of time

Solution Approach 1:

The patent introduces ride-sharing intermediaries (other car sharing users) who act as mediators to transport users between their locations and central vehicle locations. This intermediary service eliminates the need for users to independently travel to centralized locations, resolving the contradiction between enabling vehicle access and reducing additional transportation time.

Inventive Principle:
Principle #24Intermediary (Mediator)

Solution Approach 2:

The system enables car sharing users to provide ride-sharing services to each other, allowing the community to self-service the first-mile/last-mile problem. Users with available time and vehicles offer rides to other users, eliminating the need for centralized locations and reducing overall transportation time and complexity for the community.

Inventive Principle:
Principle #25Self-service

2Adaptability or versatility

If users travel to central vehicle locations, then vehicle retrieval is enabled, but trip complexity and additional transportation modes increase

Engineering Contradiction:
Improveflexibility of vehicle accessVSAvoidcomplexity of transportation arrangement
Core Design Contradiction:
Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The platform introduces digital intermediaries (mobile applications and matching algorithms) that coordinate ride-sharing arrangements between users. These digital mediators match riders with drivers, manage trip details, and facilitate communication, enabling flexible vehicle access while reducing the complexity of arranging first-mile/last-mile transportation through automated matching and coordination.

Inventive Principle:
Principle #24Intermediary (Mediator)

3Extent of automation

If car sharing uses centralized locations, then vehicle monitoring is simplified, but user convenience decreases due to mandatory trips to these locations

Engineering Contradiction:
Improveautomation of vehicle monitoringVSAvoidconvenience of vehicle access
Core Design Contradiction:
Extent of automationVSEase of operation

Solution Approach 1:

The patent makes car sharing users multi-functional by enabling them to serve both as vehicle users and as ride providers. Users maintain full access to the car sharing system's automated monitoring and management features while simultaneously providing transportation services to others, eliminating the need for centralized locations and improving convenience without reducing automation.

Inventive Principle:
Principle #6Universality (Multi-functionality)

Data Source

PatentUS20250076060A1First Mile and Last Mile Ride Sharing Method and System
Publication Date: 2025.03.06 STATE FARM MUTAL AUTOMOBILE INSURANCE COMPANY
  • US20250076060A1 patent drawing
  • US20250076060A1 patent drawing
  • US20250076060A1 patent drawing

AI summary

A method of facilitating first mile/last mile transfer of a vehicle includes: analyzing a route to determine, respectively, an incentive to be offered to a prospective user; analyzing a plurality of user profiles using a trained machine learning model to determine at least one potential user likely to accept the incentive, wherein the trained machine learning model is trained using at least one of a first historical data set of ride sharing data indicating a price that was paid, profile information about a vehicle operator, or a second historical data set of ride sharing data indicating geographical details of a ride that was given; and causing a message including the incentive to be displayed to the at least one potential user via an electronic device of the at least one potential user.