Smarter routing, built around every student

By enhancing student transportation planning with an ML-powered route optimization engine, a US-based passenger transportation management company reduced trip distance and travel time by up to 20–30%, provided required transportation accommodations for 100% of students, and reduced late school arrivals by over 90%.

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Industry

Transportation

Region

United States

Solution

AI-powered route optimization

About the client

A transportation provider built around specialized mobility needs

A US-based passenger transportation management company, the organization serves transit agencies across Colorado and provides specialized transportation services, including non-emergency medical transportation. Its operations require transportation planning to balance efficiency and flexibility with driver safety, accountability, and the individual needs of passengers. As those requirements evolve, the organization continues to explore technology-driven ways to improve how trips are planned and delivered.

20-30%

Reduction in total trip distance and time.

AI/ML-powered route optimization reduced overall trip distance and travel time, improving transportation efficiency.

100%

Of students received personalized transportation support

Student-specific requirements, including wheelchair access, booster seats, and pet allowances, were incorporated into transportation planning.

90%+

Reduction in late school arrivals,

Optimized route planning significantly reduced late school arrivals, improving on-time performance across student transportation services.

At scale

Dynamic route adjustment

Routes can adapt to changing student requirements and operational constraints, enabling more flexible transportation planning.

The business challenge

Scaling personalized student transportation without compromise

The client already had a software system for assigning student pick-up and drop-off trips. The next opportunity was to use AI and ML to automatically calculate optimal routes for transporting multiple students at once while accounting for operational constraints and each student's specific transportation requirements.

The goal was to improve route efficiency, reduce travel time and distance, and ensure personalized transportation support while maintaining reliable, on-time service.

Operational adaptability

The organization needed a planning approach that could support more dynamic transportation operations and create a foundation for future mobility initiatives.

Scaling beyond trip assignment

The organization wanted to evolve from assigning individual trips to automatically optimizing routes for multiple students at once.

Balancing efficiency and personalization

The goal was to reduce travel distance and time while continuing to provide the transportation support required by each student.

Where friction was building

Complex multi-stop routing

Coordinating pickups and drop-offs for multiple students across different locations required route planning that could balance numerous operational constraints simultaneously.

Individual accommodation needs

Transportation plans needed to account for requirements such as wheelchair access, booster seats, monitors, and specialized equipment.

Strict scheduling requirements

School bell schedules required routes to be planned with precise timing, leaving little flexibility for delays and making punctuality a critical operational requirement.

Dynamic operational constraints

Changes in driver availability, vehicle capacity, and other transportation constraints could affect planned routes, requiring a faster and more effective reassignment approach.

Personalization at scale

Meeting individual student requirements while maintaining route efficiency added complexity to transportation planning.

An intelligent optimization engine

Delivering personalized school transportation at scale required a solution capable of balancing multiple operational and student-specific constraints simultaneously. Nagarro developed an ML-based route optimization solution that transformed transportation requirements into efficient, constraint-aware transportation plans.

Heuristic student grouping and driver assignment

A heuristic-based approach groups students and assigns drivers by factoring in driver availability, vehicle capacity, bell times, and each student's specific transportation requirements. This helps ensure transportation plans are aligned with operational constraints while meeting individual student needs.

Multi-Objective Linear Programming (MOLP) for route planning

MOLP-based route optimization balances competing priorities, including timing precision, bell schedules, monitor requirements, and specialized equipment needs. The approach generates efficient routes while accommodating individual transportation requirements and constraints.

Driver recommendation module for disruption management

When disruptions occur, the solution uses real-time data on driver availability, distance, vehicle capacity, and other operational constraints to recommend suitable driver reassignment options. This supports faster decision-making and helps maintain continuity of transportation services.

Smarter routes.
Personalized journeys.
Built to adapt.

What it enabled

Precision at the individual level

Student-specific transportation requirements, including wheelchair access, booster seats, monitors, specialized equipment, and other accommodation needs, were systematically incorporated into route planning, helping ensure personalized transportation support was consistently delivered.

Operational agility

The ability to dynamically adjust routes and recommend driver reassignments based on real-time operational data gave the organization greater flexibility in responding to changing transportation requirements and constraints.

Scalable transportation planning

The solution enabled transportation planning to adapt to changing student requirements while maintaining efficiency, punctuality, and accommodation needs across routing operations.

Trip efficiency
20–30%

reduction in total trip distance and travel time, improving transportation efficiency across daily operations.

Student accommodation
100% of students

received required personalized transportation support, including wheelchair access, booster seats, and other specialized requirements.

On-time performance
90%+ reduction

in late school arrivals, improving reliability for schools, parents, and students.

Scalability and flexibility
Dynamic route adjustment

enabled routes to adapt to real-time student requirements and operational constraints.

Optimization engine

Multi-Objective Linear Programming (MOLP) for multi-constraint route planning across student populations.

Heuristic algorithms

Rule-based grouping and assignment logic incorporating availability, capacity, bell times, and special requirements.

Machine learning

Real-time driver recommendation module leveraging live operational data for disruption management.

From trip assignment to intelligent route optimization

The client built its existing trip assignment system with an AI/ML-based optimization approach that accounts for driver availability, vehicle capacity, bell times, and individual student requirements when planning routes.

Personalized transportation at scale

Student-specific requirements, including accommodation needs and specialized transportation support, were systematically incorporated into route planning, helping ensure personalized service delivery across transportation operations.

Improved disruption management

The driver recommendation module supports reassignment decisions during disruptions by leveraging real-time information on driver availability, distance, vehicle capacity, and operational constraints.

Higher service reliability

Optimized route planning and improved scheduling precision contributed to a reduction of more than 90% in late school arrivals, improving reliability for schools, parents, and students.

Greater operational flexibility

The solution enables route adjustments based on real-time student requirements and operational constraints, helping the organization respond more effectively to changing transportation needs.

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