Intelligent Transportation Scheduling & Driver Assignment Algorithm
Project description.
We designed and developed an intelligent transportation scheduling solution to automate the assignment of daily trips across a fleet of drivers. The system considers driver starting locations, existing assignments, trip timing, workload, minimum earning requirements, distance, and traffic conditions when determining suitable assignments.
The solution integrates Google Places and distance APIs to support location-based scheduling and uses backend automation to distribute available trips while avoiding conflicts with already scheduled rides.
Project Highlights
Industry
- Transportation
- Mobility
- Passenger Transportation
- Fleet Operations
Project Duration
Team Composition
- 1 Engineer
Project Type
- Intelligent Transportation Scheduling System
- Automated Driver Assignment Engine
- Ride Scheduling & Optimization Platform
- Backend Automation Solution
Services Included
- Custom Scheduling Algorithm Development
- Transportation Software Development
- Automated Driver Assignment
- Ride Scheduling Automation
- Route & Distance Optimization
- Google Places API Integration
- Google Distance/Routes API Integration
- Backend Algorithm Development
- Transportation Workflow Automation
Technology Stack
- Backend Development
- Scheduling Algorithms
- Google Places API
- Google Distance/Routes APIs
- Traffic Data Integration
- REST APIs
- Database
The Business Challenge & Our Solution
Automating Daily Trip Assignment
The company managed a large number of daily trips that needed to be distributed across drivers. Manually reviewing every trip against driver schedules, locations, and workload was time-consuming.
We developed a backend scheduling algorithm that evaluates available trips against multiple business rules and automatically determines suitable driver assignments.
Starting From Driver Home Locations
Drivers were required to start their day from home at 5:00 AM, making their starting location an important factor in the first assignment of the day.
The algorithm considers each driver's home location as their starting point and evaluates the distance and travel requirements to available trips.
Avoiding Conflicts With Existing Trips
Some trips were already assigned manually by the administration team. New automated assignments could not interfere with those existing schedules.
Before assigning a new trip, the algorithm checks the driver's existing schedule, trip timing, and required travel time to ensure the new assignment can be completed without conflict.
Balancing Driver Loads & Minimum Earnings
The goal was not simply to assign every trip to the nearest driver. The system also needed to distribute workloads and consider minimum earning requirements for drivers.
The algorithm considers existing driver workload and earnings alongside distance and availability, allowing assignments to be distributed more intelligently instead of relying solely on proximity.
Considering Real Travel Distance & Traffic
Geographic proximity alone does not always represent the actual travel required between a driver and a trip.
We integrated Google Places and distance-related APIs to evaluate locations, travel distances, and traffic conditions, allowing the algorithm to make assignments based on practical travel requirements.
Technology & Development Approach
The core of the solution was a backend-based multi-constraint scheduling algorithm designed around the transportation company's operational rules.
The scheduling engine processes available trips and evaluates potential drivers using factors such as:
Driver starting location
Existing assigned trips
Trip timing
Travel time
Schedule conflicts
Driver workload
Minimum earning requirements
Distance between locations
Traffic conditions
Google Places and distance-related APIs provide the location and travel information required by the scheduling engine. The algorithm then combines this information with driver and trip data to determine the most suitable available assignment.
The important aspect of the implementation was that no single factor determines the assignment. The algorithm evaluates the relationship between the trip and the driver's complete schedule before making a decision.
The Result
The automated scheduling solution transformed a highly manual driver assignment process into an algorithm-driven workflow capable of handling approximately 200–300 trips per day.
| Metric | Results |
|---|---|
| Daily Trips | 200–300 trips scheduled |
| Scheduling Accuracy | 90% |
| Manual Scheduling | Replaced the workload of 2 physical schedulers |
| Driver Assignment | Automated using multiple operational constraints |
| Driver Workload | Better balanced across available drivers |
| Schedule Conflicts | Reduced through schedule-aware assignment |
| Location & Traffic | Integrated into assignment decisions |
What Our Client Says About Working With CumulativeApps
We needed a better way to manage our transportation operations without having different processes handled separately. CumulativeApps took the time to understand how our team works and built a solution around our actual workflow. It has made our day-to-day operations much easier to manage and given us better control over the entire process.
James Walker
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