Self-Learning Mechanism for Dynamic Vehicle Utilization

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Solution Overview

Problem

Conventional vehicle utilization and optimization systems lack fully automated solutions, requiring manual checks and failing to account for practical conditions and location-specific regulations, leading to inefficiencies and inaccuracies, especially during peak hours and dynamic planning scenarios.

Innovation Solution

A self-learning based mechanism that uses a processor-implemented method to receive travel requests, query a database for vehicle information, identify optimal vehicle assignments based on occupancy capacity, social constraints, and generate trip schedules, dynamically updating the system to optimize vehicle utilization and reduce underutilization.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If manual checks by transport administrators are used, then local knowledge and social constraints can be considered, but process efficiency deteriorates due to delays

Engineering Contradiction:

Improveability to consider local knowledge and social constraintsVSAvoidprocess efficiency

Core Design Contradiction:

Adaptability or versatilityVSProductivity

Solution Approach 1:

The system performs self-learning by automatically analyzing travel requests, location patterns, and social constraints without requiring manual administrator intervention. The machine learning model autonomously optimizes vehicle allocation while considering local knowledge and social factors, eliminating the need for manual checks while maintaining adaptability.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent replaces the manual mechanical process of administrator checks with an automated machine learning-based system. The ML model processes travel requests, analyzes location frequencies, and allocates vehicles automatically, substituting human manual operations with intelligent automated processing that maintains both efficiency and adaptability.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Ease of operation

If conventional ride-sharing algorithms focusing on traveler preferences are used, then traveler satisfaction is improved, but system accuracy deteriorates in dynamic planning scenarios

Engineering Contradiction:

Improvetraveler satisfactionVSAvoidaccuracy in dynamic scenarios

Core Design Contradiction:

Ease of operationVSMeasurement precision

Solution Approach 1:

The system dynamically adapts to changing conditions by continuously analyzing travel requests and updating vehicle allocations in real-time. The machine learning model adjusts to dynamic planning scenarios by learning from incoming requests and modifying routing decisions, maintaining both traveler satisfaction and accuracy in changing conditions.

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The system incorporates feedback mechanisms where the machine learning model continuously learns from travel request patterns, location frequencies, and allocation outcomes. This feedback loop enables the system to improve accuracy in dynamic scenarios while maintaining traveler satisfaction through adaptive optimization.

Inventive Principle:

Principle #23Feedback

3Productivity

If linear programming and geo code based route planning are used, then map based routing is provided, but practical conditions and location specific regulations are not considered

Engineering Contradiction:

Improverouting capabilityVSAvoidability to consider practical conditions and regulations

Core Design Contradiction:

ProductivityVSAdaptability or versatility

Solution Approach 1:

The system changes the parameters considered in routing by incorporating location frequency data, social constraints, and practical conditions into the optimization process. Instead of relying solely on geo-code based routing, the ML model adjusts routing parameters based on learned patterns from historical data and current travel requests, enabling consideration of practical conditions and location-specific regulations.

Inventive Principle:

Principle #35Parameter changes

4Quantity of substance

If conventional systems are used during peak hours, then basic transportation is provided, but vehicle utilization efficiency deteriorates due to inability to handle huge volume

Engineering Contradiction:

Improvevolume of travel requests handledVSAvoidvehicle utilization efficiency

Core Design Contradiction:

Quantity of substanceVSProductivity

Solution Approach 1:

The system performs preliminary analysis of travel request patterns and location frequencies before peak hours occur. The machine learning model pre-processes incoming requests, identifies high-frequency locations, and prepares optimized vehicle allocations in advance, enabling efficient handling of huge volumes during peak hours while maintaining high utilization efficiency.

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS12190402B2Self-learning based mechanism for vehicle utilization and optimization

Publication Date: 2025.01.07 TATA CONSULTANCY SERVICES LTD

AI summary

There is no mechanism for vehicle utilization and optimization through continuous and incremental planning which ensures that transportation plans are based on real-time conditions. The present invention discloses systems and methods for vehicle utilization and optimization based on self-learning mechanism. A machine learning model for dynamic association of users to vehicles is provided that learns previously clubbed patterns of users with their corresponding locations. The learnt previously clubbed patterns are utilized for determining association between previously clubbed locations which is further utilized to obtain an optimal set of locations. The users are dynamically associated to vehicles allocated for the obtained optimal set of locations by honoring one or more social and vehicle constraints. The proposed system has self-learning capability which ensures effective vehicle utilization and optimization in real time.