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 and integer programming for fleet allocation, and simulation for policy evaluation, enabling data-backed decisions for efficient urban mobility services.

flexible workload of 4 to 360h
valid certificate in your country
What will I learn?
Gain practical skills in applied mathematics to model bike-sharing demand, optimise station capacities, and assess system performance with data-driven approaches. Master data cleaning, probabilistic modelling, daily allocation optimisation, and rebalancing policy testing to enhance service reliability in urban transport systems.
Elevify advantages
Develop skills
- Develop bike-share demand models using Poisson and time-series techniques.
- Perform capacity and queue analysis to calculate shortages 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 the 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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