Page 253 - Emerging Trends and Innovations in Web-Based Applications and Technologies

International Journal of Trend in Scientific Research and Development (IJTSRD) @ www.ijtsrd.com eISSN: 2456-6470
Ø Machine Learning for Recommendations: Ø Recommendation Engine: Apply machine learning
A recommendation engine will be incorporated to suggest algorithms to suggest garages based on user
the best garages based on user preferences, past preferences, past history, and reviews.
interactions, and service history.
C. Service Provider Integration
Ø Cloud-Based Architecture: Ø Garage Partner Platform: Develop a dashboard for
The backend will use cloud infrastructure to ensure garages to manage their profiles, update service details,
scalability and handle real-time data processing efficiently. and monitor customer bookings.
Expected Outcomes: Ø Automated Notifications: Include alerts for garages
The proposed GarageLocator platform will create a robust, about new service requests and status updates for
interconnected ecosystem for vehicle owners and local ongoing bookings.
garages. By providing a real-time, transparent, and user- Ø Partnership Onboarding: Create a structured
friendly solution, the platform is expected to improve
customer satisfaction, reduce the time and effort required to onboarding process for local garages to join the
platform.
locate reliable services, and empower local service providers
to grow their businesses. Ultimately, this innovative D. Performance Metrics
approach will redefine how vehicle owners and garages Ø Efficiency: Measure the average time taken to connect
interact, setting a new standard for convenience and trust in users to suitable garages.
the auto service industry.
Ø User Satisfaction: Evaluate customer feedback on ease
IV. PROPOSED RESEARCH MODEL of use, reliability, and overall experience.
The proposed research model for "GarageLocator: Bridging
the Gap between Vehicle Owners and Local Auto Services Ø Garage Engagement: Analyze the impact on garage
with Real-Time Technology" is designed to develop and visibility, customer inflow, and revenue growth.
validate a comprehensive framework that bridges the gap 3. Research Methodology
between vehicle owners and auto service providers through A. Phase 1: Data Collection and Analysis
the use of real-time technology. This model is centered Ø Conduct a market survey with vehicle owners to identify
around key components, including user needs, technological pain points.
architecture, and service provider integration, while
incorporating both theoretical and practical dimensions. Ø Interview garage owners to understand their
operational challenges and willingness to adopt new
1. Research Objectives technologies.
The research aims to:
Ø Analyze the pain points and requirements of vehicle Ø Review existing solutions and their limitations.
owners seeking auto services. B. Phase 2: System Development
Ø Identify the gaps in current solutions and determine the Ø Design and prototype the GarageLocator platform using
agile development methodology.
value addition of real-time technology.
Ø Develop and test individual components, including the
Ø Develop a real-time technology-based platform that
efficiently connects users and local garages. user interface, real-time tracking module, and
recommendation engine.
Ø Evaluate the effectiveness of the proposed system in C. Phase 3: Pilot Testing
improving service accessibility, transparency, and user
satisfaction. Ø Deploy the prototype in a small region or city for a trial
run.
2. Components of the Research Model
A. User-Centric Design Ø Collect feedback from both users and service providers
Ø Requirement Analysis: Conduct surveys and during the pilot phase.
interviews with vehicle owners to understand their D. Phase 4: Evaluation
challenges and expectations when searching for auto Ø Analyze the performance of the platform using key
services. metrics such as response time, user satisfaction, and
service adoption rates.
Ø User Features: Define key features such as real-time
garage tracking, service availability, pricing Ø Refine the system based on feedback and re-evaluate.
transparency, and emergency assistance.
4. Expected Contributions
B. Technological Framework Ø Theoretical Contribution: The research will contribute
Ø Real-Time Location Services: Use GPS and mapping to the understanding of how real-time technology can
APIs (e.g., Google Maps, Mapbox) to enable precise transform service-based industries by addressing
garage location tracking. specific pain points and bridging existing gaps.
Ø Cloud-Based Infrastructure: Implement a cloud-based Ø Practical Contribution: The development of
backend to handle large-scale data processing and GarageLocator will demonstrate how technology can
storage, ensuring scalability. improve service accessibility, enhance user satisfaction,
and boost the efficiency of service providers.
Ø Dynamic Data Updates: Integrate with garage
management systems for real-time updates on service
availability, queue status, and wait times.

IJTSRD | Special Issue on Emerging Trends and Innovations in Web-Based Applications and Technologies Page 243