Bike sharing is a sustainable and environmentally friendly transportation mode that offers bikes ‘‘on-demand’’ to improve daily urban mobility. However, although bikesharing systems potentially offer a viable alternative for enhancing urban mobility, they suffer from the effects of fluctuating spatial and temporal demand. In other words, the number of bikes and docks available in any given station heavily depends on its location and the time of day, this dynamics introduce inefficiencies in the system, such as having empty or full stations for long periods of time.
For example, in this plot we can see the mean number of bikes for different days of the week and times of day over a period of 3 months. Note how the demand patterns are different on weekdays and weekends and that during the day there is a sudden increase on arrivals at around 8AM and departures at 16 PM.
The issue of this demand pattern is that during a day, some stations will be empty and some will be full, degrading the level of service. Furthermore, there is a cascade effect, where when a station becomes full, the nearby stations will also get full quickly, as riders that arrive to the full station are looking for docks to park their bikes. As a result, bike sharing operators are forced to “rebalance” the system by repositioning bikes in real time, and they usually do so by loading and unloading bikes to a fleet of vans that travel around the city. In New York City, due to congestion, it is more efficient to do it by using a rickshaw-like trailer.
You may wonder if this also occurs in other vehicle sharing systems, such as car sharing, for example. The simple answer is that it only occurs on systems where one-way trips are allowed, meaning that you can pick a vehicle in one station and drop it to another.
Most of car sharing systems do not allow for one-way trips. Look at ZipCar for example. Well, after all, repositioning a bike is much easier than repositioning a vehicle, but, what will happen if the autonomous vehicle becomes mainstream?
Efficiently solving the bike sharing rebalancing problem has been my main research focus. To solve it, you need to answer the 3 following questions:
- Which stations will be empty or full?
- How many bikes do we need to add or remove?
- Which is the optimal route of the repositioning vehicle?