Overview
Request Mis-bookings is an AI-powered feature that automatically identifies potential booking errors to improve rider satisfaction and system efficiency. The system analyzes accepted trip requests and flags issues that may lead to poor rider experiences or operational challenges.
What Problems Does It Solve?
Rider Experience Issues
Wrong locations: Prevents riders from being dropped off at incorrect addresses due to typos
Incorrect times: Catches AM/PM confusion that could leave riders stranded
Missing accessibility features: Ensures vehicles arrive with the proper equipment for riders with disabilities
Duplicate bookings: Identifies overlapping trips that could cause confusion
Operational Efficiency
Reduces no-shows: Flags trips that should likely be cancelled based on previous cancellations
Prevents double dispatching: Avoids sending multiple vehicles when booking errors occur
Improves on-time performance: Reduces system disruptions caused by booking mistakes
The Cost of Uncaught Mis-bookings
Without this feature, booking errors create significant operational and service impacts:
Operational Waste
Empty vehicle dispatches: Drivers arrive at locations where no rider is present due to address errors or timing mistakes
Double vehicle assignments: When accessibility requirements are missed, a second vehicle must be dispatched with proper equipment, often requiring constraint overrides
Resource inefficiency: Vehicles tied up on incorrect trips cannot serve legitimate requests, reducing overall system capacity
Service Disruption
Cascading delays: Wrong bookings force emergency re-dispatching, disrupting schedules and impacting on-time performance across the system
Rider abandonment: Passengers left waiting due to AM/PM confusion or wrong pickup locations
Emergency interventions: Staff must manually coordinate corrections, pulling resources from other critical tasks
Financial Impact
Wasted fuel and driver time: Vehicles traveling to incorrect locations or with wrong equipment
Overtime costs: Extended operations needed to recover from booking errors
Customer service overhead: Increased support calls and complaint resolution
How It Works
Automatic Analysis: When a request moves to "Accepted" status, AI analyzes the booking details
Pattern Recognition: The system compares new requests against the rider's historical travel patterns
Issue Detection: Five types of problems are automatically identified:
Inaccurate locations (obvious address typos)
Duplicate or overlapping bookings
Inaccurate times (clear AM/PM confusion)
Missing accessibility features
Missed cancellations (return trips not cancelled when outbound was a no-show)
Using the Feature
Finding Mis-bookings
Live Requests Filter: Use the "Mis-booked" column to quickly identify flagged requests
Request List: Apply the "Detected Mis-bookings" filter to view all flagged trips
Request Details: A prominent banner appears at the top of flagged request pages
Resolving Mis-bookings
Review the flagged issue details in the banner
Make necessary corrections to the request (time, location, accessibility features, etc.)
Click "Dismiss" to dismiss the flag
Auto-resolution: If you edit the request and the system no longer detects issues, mis-bookings are automatically resolved
Key Benefits
✅ Proactive Quality Control: Catch errors before they impact riders
✅ Improved Rider Satisfaction: Ensure accurate pickups and drop-offs
✅ Better Resource Utilization: Prevent unnecessary vehicle dispatches
✅ Enhanced System Performance: Maintain optimal on-time performance
✅ Cost Savings: Eliminate wasted trips and reduce emergency interventions
Important Notes
The feature uses AI analysis, so occasional false positives may occur
Only requests in "Accepted" status are analyzed
Historical mis-booking details cannot be viewed once resolved
The system learns from rider travel patterns to improve accuracy over time
This feature helps maintain the high-quality service your riders expect while optimizing your operational efficiency. For questions about enabling Request Mis-bookings, contact your Spare representative.