Mobility Management System for Fleet Behavior Modification
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Solution Overview
Problem
Mobility operators face challenges in adjusting vehicle behavior in real-time due to the lack of access to aggregated vehicle event data, particularly for large fleets, and are unable to modify behavior based on data from other fleets, hindering effective policy implementation by governing entities.
Innovation Solution
A mobility management system acts as an intermediary, processing data from mobility operators and governing entities to identify geographic areas of interest, apply vehicle rules, and generate behavior modifications by analyzing GPS data and historical vehicle behavior, using machine-learned models to predict vehicle behavior and adjust rules accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual analysis of fleet vehicle event data is performed, then behavior modification can be achieved, but the process is time-consuming and cannot be done in real-time
Solution Approach 1:
The patent introduces a mobility management system as an intermediary between governing entities and mobility operators. This system automatically aggregates vehicle event data from multiple fleets, analyzes compliance with governing policies, and generates behavior modifications, eliminating the need for manual real-time analysis while enabling automated decision-making.
Solution Approach 2:
The patent replaces manual mechanical analysis processes with automated electronic data processing. The system uses electronic aggregation of GPS data, automated parsing of vehicle events, and algorithmic analysis to substitute human manual review, achieving real-time processing capability.
2Device complexity
If a mobility operator analyzes only its own fleet data, then operational simplicity is maintained, but comprehensive behavior adjustment based on aggregated fleet insights is not possible
Solution Approach 1:
The mobility management system serves multiple mobility operators simultaneously, aggregating data across entire fleets rather than individual vehicles. This universal approach allows the system to identify broad behavioral patterns and generate fleet-wide behavior modifications that individual operators could not detect from isolated data.
Solution Approach 2:
The system merges data from multiple mobility operators' fleets into a unified dataset. By combining GPS data, vehicle events, and policy compliance information across fleets, the system creates comprehensive insights that enable more effective behavior adjustments than any single operator could achieve alone.
3Productivity
If real-time vehicle behavior analysis is implemented, then policy compliance improves, but data processing requirements and system complexity increase
Solution Approach 1:
The system segments the complex task of fleet management into distinct modular components: GPS data reception modules, vehicle event parsing modules, policy compliance analysis modules, and behavior modification generation modules. This segmentation allows each component to handle specific processing tasks independently, reducing overall system complexity while maintaining real-time capability.
Data Source
AI summary
Systems and methods are disclosed herein for a mobility management system that serves as a data processing intermediary between mobility operators and a governing entity that defines rules for vehicle parking events occurring in a geographic region. The system receives from mobility operators vehicle behavior data indicating GPS locations and associated durations for vehicles in a fleet. For each vehicle behavior event, the system maps the GPS data to governing data to identify a geographic area of interest, such as delivery zones and on-street parking zones, associated with the GPS location and determines the applicability of one or more vehicle rules to the vehicle behavior events. Event durations and associated rule data for fleet vehicles are aggregated and periodically transmitted to the mobility operator.