AI Driven IoT Fleet Management and Dispatch Solution
Challenge
Empire Limousine Services manages a premium fleet of high-end vehicles used for corporate travel, special events, and private luxury transfers. The company faced operational challenges due to manual dispatch processes, limited real-time visibility into fleet status, and inefficient maintenance scheduling. In order to deliver the superior service expected by their clientele, the client needed an integrated system that would streamline fleet management, enhance dispatch operations, and improve customer satisfaction. Project had following business goals.
Streamline dispatch operations by automating scheduling and vehicle assignment using real-time data.
Enhance fleet visibility by tracking vehicle real-time location and performance continuously.
Elevate customer experience to provide accurate booking, tracking, and communication through integrated customer and driver interfaces.
Reduce operational costs by optimizing routes to reduce fuel consumption and vehicle maintenance costs.
Proactive vehicle maintenance based on real-time diagnostics to keep vehicles in top condition.
Empire Limousine Services manages a premium fleet of high-end vehicles used for corporate travel, special events, and private luxury transfers. The company faced operational challenges due to manual dispatch processes, limited real-time visibility into fleet status, and inefficient maintenance scheduling. In order to deliver the superior service expected by their clientele, the client needed an integrated system that would streamline fleet management, enhance dispatch operations, and improve customer satisfaction. Project had following business goals.
Streamline dispatch operations by automating scheduling and vehicle assignment using real-time data.
Enhance fleet visibility by tracking vehicle real-time location and performance continuously.
Elevate customer experience to provide accurate booking, tracking, and communication through integrated customer and driver interfaces.
Reduce operational costs by optimizing routes to reduce fuel consumption and vehicle maintenance costs.
Proactive vehicle maintenance based on real-time diagnostics to keep vehicles in top condition.
Solution
* Serverless architecture design on AWS cloud leveraging eventbridge to handle varying loads and rapid growth.
* AWS Lambda functions implementtation for handling event-driven logic, automatically triggering IoT rules, route optimizations, and dispatch notifications.
* Real-time data ingestion of telemetry data leveraging AWS IoT Core, including GPS coordinates, engine diagnostics, and fuel consumption.
* RESTful API driven integrations with third-party systems using AWS API Gateway.
IoT Device Connectivity
* Seamless connectivity over MQTT protocol for secure and persistent communication between IoT cloud and IoT sensors.
Fleet Management
* Vehicle inventory management, including details like make, model, year, features.
* Real-time status updates
* Maintenance scheduling and service history tracking of vehicle.
Vehicle Health Monitoring
* Implemented vehicle health monitoring features using OBD-II sensors to monitor engine revolutions, tire pressure, vehicle speed, and fuel usage.
Rental and Booking Management
* Automated booking confirmations, cancellation, and rescheduling functionalities.
* Integrated rental agreements, pricing management, and promotions.
Automated Dispatch and Scheduling
* Developed dynamic dispatch engine for assigning vehicles based on location, demand, and availability in different states.
Real-Time Vehicle Tracking and Monitoring
* GPS tracking for real-time vehicle monitoring on map.
* Integrated Amazon location service to implement geofencing and vehicle tracking.
Customer and Driver App
* Developed react-native mobile app and responsive web portal for customers to book, track, and manage rentals.
Payment & Billing
* Integrated payment gateway with dispatch solution for secure payment processing via Paypal and stripe.
Customer Communication
* Automated notifications leveraging Amazon SNS for booking confirmations, ETAs, and status updates.
Predictive Vehicle Maintenance
(i).Data Preparation
* Normalized and cleanse sensor data to remove outliers and fill missing values.
(ii).Model Training and Validation
* Trained model on historical data using AWS SageMaker platform.
* Validated model using cross-validation and performance metrics.
(iii).Real-Time Inference
* Established real-time data ingestion pipelines for incoming IoT data from sensors to train model.
* Implemented alert triggers for anomalies or predicted failures detection via dashboards, emails.
* Serverless architecture design on AWS cloud leveraging eventbridge to handle varying loads and rapid growth.
* AWS Lambda functions implementtation for handling event-driven logic, automatically triggering IoT rules, route optimizations, and dispatch notifications.
* Real-time data ingestion of telemetry data leveraging AWS IoT Core, including GPS coordinates, engine diagnostics, and fuel consumption.
* RESTful API driven integrations with third-party systems using AWS API Gateway.
IoT Device Connectivity
* Seamless connectivity over MQTT protocol for secure and persistent communication between IoT cloud and IoT sensors.
Fleet Management
* Vehicle inventory management, including details like make, model, year, features.
* Real-time status updates
* Maintenance scheduling and service history tracking of vehicle.
Vehicle Health Monitoring
* Implemented vehicle health monitoring features using OBD-II sensors to monitor engine revolutions, tire pressure, vehicle speed, and fuel usage.
Rental and Booking Management
* Automated booking confirmations, cancellation, and rescheduling functionalities.
* Integrated rental agreements, pricing management, and promotions.
Automated Dispatch and Scheduling
* Developed dynamic dispatch engine for assigning vehicles based on location, demand, and availability in different states.
Real-Time Vehicle Tracking and Monitoring
* GPS tracking for real-time vehicle monitoring on map.
* Integrated Amazon location service to implement geofencing and vehicle tracking.
Customer and Driver App
* Developed react-native mobile app and responsive web portal for customers to book, track, and manage rentals.
Payment & Billing
* Integrated payment gateway with dispatch solution for secure payment processing via Paypal and stripe.
Customer Communication
* Automated notifications leveraging Amazon SNS for booking confirmations, ETAs, and status updates.
Predictive Vehicle Maintenance
(i).Data Preparation
* Normalized and cleanse sensor data to remove outliers and fill missing values.
(ii).Model Training and Validation
* Trained model on historical data using AWS SageMaker platform.
* Validated model using cross-validation and performance metrics.
(iii).Real-Time Inference
* Established real-time data ingestion pipelines for incoming IoT data from sensors to train model.
* Implemented alert triggers for anomalies or predicted failures detection via dashboards, emails.
Results
Dispatch Time
Before Implementation: 10-15 Minutes (Manual)
After Implementation: <1 minute (Automated)
Improvement: 90% Faster
Fleet Utilization
Before Implementation: 65%
After Implementation: 88%
Improvement: 35% Increase
Customer Wait Time
Before Implementation: ~20 minutes
After Implementation: 6-8 minutes
Improvement: 60% Reduced
Operational Costs
Before Implementation: High due to 24/7 server uptime
After Implementation: 40% Reduction (serverless pay-per-use model)
Improvement: Significant savings
Unexpected Maintenance Costs
Before Implementation: Unnecessary maintenance before time
After Implementation: 35% reduction with predictive maintenance
Improvement: Better vehicle longevity
Customer Satisfaction
Before Implementation: 3.8/5
After Implementation: 4.7/5
Improvement: Better customer experience and higher retention
Dispatch Time
Before Implementation: 10-15 Minutes (Manual)
After Implementation: <1 minute (Automated)
Improvement: 90% Faster
Fleet Utilization
Before Implementation: 65%
After Implementation: 88%
Improvement: 35% Increase
Customer Wait Time
Before Implementation: ~20 minutes
After Implementation: 6-8 minutes
Improvement: 60% Reduced
Operational Costs
Before Implementation: High due to 24/7 server uptime
After Implementation: 40% Reduction (serverless pay-per-use model)
Improvement: Significant savings
Unexpected Maintenance Costs
Before Implementation: Unnecessary maintenance before time
After Implementation: 35% reduction with predictive maintenance
Improvement: Better vehicle longevity
Customer Satisfaction
Before Implementation: 3.8/5
After Implementation: 4.7/5
Improvement: Better customer experience and higher retention
