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Predictive Modelling of Bicycle Availability for Bicycle-Sharing Systems

Student: Mariia Golovina

Supervisor: Ivan Stankevich

Faculty: Faculty of Economic Sciences

Educational Programme: Economics (Bachelor)

Final Grade: 8

Year of Graduation: 2018

Station-based bicycle-sharing systems allow customers to pick up a bicycle from one of the stations distributed across the city and return it to another. Effective management of these systems accounts for the major part of their operational costs and gives rise to various optimization problems, including predicting station-level demand in order to understand where bicycle rebalancing should be done and how many bicycles should be relocated in order to meet the demand for bikes and empty docks at the stations. However, measuring actual demand at the stations might be a challenging task due to their finite capacity. When the station is empty or completely full the demand for either bicycles or docks is not observed. This study focuses on predicting unobserved station-level demand for bicycles and docks using both data mining techniques and stochastic modelling. We simulate behavior of a station by modelling spatiotemporal arrival and departure rates as Poisson processes with piecewise-constant intensity rates. We justify using an adjusted formula that counts only points in time when both rates are observed to estimate arrival and departure rates. Obtained estimates of intensity rates are used to predict unobserved demand of a simulated station. Performance analysis of this model in comparison to results yielded by several popular machine learning algorithms show that proposed model outperforms them on a time horizon of 30 minutes, but does not beat Random Forest algorithm on time horizons of 1 hour and 2 hours in terms of RMSE.

Full text (added May 15, 2018)

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