MOVE
Optimization of Public Transport Networks
Optimize public transit operations including vehicle scheduling, crew rostering, route planning, and timetabling to reduce costs, improve service quality, and support sustainable urban mobility.
Understanding the Problem
Public transport optimization addresses complex problems in vehicle scheduling, route planning, crew management, and infrastructure deployment. Transit agencies face mounting pressure to deliver efficient, cost-effective services while managing driver shortages, budget constraints, and evolving passenger expectations in growing urban environments.
THE CHALLENGE
What Makes it Hard
Coordinating thousands of vehicles, crews, and routes across multi-modal transit networks while satisfying labor regulations, demand variability, infrastructure constraints, and service quality requirements.
WHO FACES IT
Driver shortages and complex labor regulations create difficult crew scheduling constraints
Demand variability across peak/off-peak periods and routes requires dynamic resource allocation
Electric bus transition introduces charging infrastructure planning and range constraints
BUSINESS IMPACT
Timetable optimization reduces missed stations by 37%, real-time systems cut wait times by 63%, and demand-responsive transit boosts ridership by 40%.
Timetable Optimization
37%
Service Improvement[1]
Bus timetable optimization reduced missed stations and waiting passengers by 37% while reducing departure frequency (AIMS 2024).
Passenger Wait Time
63%
Reduction[2]
Real-time information systems reduced average transit stop wait time from 4:37 to 1:43 minutes.
Ridership Growth
40%
Increase[3]
Replacing fixed-route services with demand-responsive transit increased ridership while reducing operating costs per trip by 30%.
How We Solve It
Mixed-integer linear programming with column generation and branch-and-price techniques handles vehicle and crew scheduling. Metaheuristics (genetic algorithms, ant colony optimization, adaptive large neighborhood search) address dynamic routing problems. Quantum annealing shows promise for traffic signal control and network design problems.
Hybrid Compute
What We Bring
Column generation for large-scale vehicle scheduling and crew rostering problems
Constraint programming for highly constrained scheduling with labor regulations
Multi-objective optimization balancing cost, service quality, and environmental impact
Real-time demand-responsive transit routing with queue-based heuristic A* algorithms
FUTURE POSSIBILITIES
The
Quantum Horizon
Quantum annealing demonstrates significant advantages for traffic signal control, network design, and routing problems. Real-world pilots show quantum approaches can match or exceed classical methods for specific transit optimization challenges.
Exploratory Work
Quantum annealing research for transportation is moving from proof-of-concept toward practical pilots. The German Q-GRID project identifies peer-to-peer energy trading and decentralized grid optimization as promising quantum applications. Current limitations include QUBO formulation challenges and quantum hardware scale constraints, but digital annealing provides near-term alternative on classical hardware. Experts predict practical deployment in 2027-2030 timeframe.
Current Research Directions
Traffic signal optimization using quantum annealing on 2,500-intersection simulations with global control advantages
Quantum annealing for transport network design showing quadratic to exponential speedup over classical methods
EV parking and charging optimization achieving 72% power loss reduction and 87.5% voltage deviation reduction
Volkswagen-D-Wave Quantum Shuttle: real-time event-based transit optimization for traffic congestion management
Interested in quantum research?
Explore proof-of-concept implementations with our team.
Ready to solve this problem?
Talk to our experts about how Strangeworks can help with optimization of public transport networks.