applied mathematics course
This course equips you with applied mathematics techniques to analyse and optimise bike-sharing operations. Delve into demand forecasting using Poisson and time-series models, queueing theory for capacity planning, linear programming for fleet allocation, and simulation for policy evaluation, enabling data-backed decisions for efficient city mobility services.

from 4 to 360h flexible workload
valid certificate in your country
What will I learn?
Gain practical skills in applied mathematics to model bike-sharing demand, determine station capacities, and assess system performance with data-driven approaches. Master data cleaning, probabilistic modelling, optimisation for allocations, and rebalancing policy testing to boost service reliability in urban transport.
Elevify advantages
Develop skills
- Develop bike-share demand models using Poisson and time-series methods.
- Analyse capacity and queues to calculate shortages, overflows, and service levels.
- Optimise fleet allocation with linear and mixed-integer programming models.
- Test rebalancing policies and scenarios under peak demand conditions.
- Validate models through discrete-event simulation and stress-testing.
Suggested summary
Before starting, you can change the chapters and workload. Choose which chapter to start with. Add or remove chapters. Increase or decrease the course workload.What our students say
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