Data is key to understanding the underlying problem and build models for better decision making. Oslo Bysykkel share a lot of interesting and useful data from it’s system on their developer pages: https://developer.oslobysykkel.no/
The two maps below are examples of what type of information you can get from the Oslo data. Each circle in the map represents a city bike station in the system. The colours from dark red to dark blue is a measure of the difference between the number of pick-ups (bike demand) and the number of deliveries (lock demand) on each station. A station with a dark blue colour has a high number of pick-ups compared to the number of deliveries. When moving from blue to red, the number of deliveries compared to pick-ups increases.
The size of the circles represent the number of pick-ups and deliveries at the station in the time period.
The first map shows workday mornings in August 2017.
We see that the stations outside of the city center has a higher demand for bikes than locks in this timeperiod, and vice verca, stations within the city center has a higher demand for locks than bikes. This is not ground-breaking analysis, as the vast majority of users ride the bikes from the residential
areas down to the city center during morning hours.
Shifting to the afterwork hours in the map below, we see the opposite trend.
When people get off work, the demand for bikes in the city center increases together with the increase in demand for locks in the residential areas outside the city center.
These patterns are common in bike sharing systems and drive the need for redistribution cars that pick up bikes at stations with high demand for locks and drive them to stations with high demand for bikes.
This type of information is of great importance to the people that redistribute bikes in the system and key input to the model we are building for operational planning.
The FT today has an interesting and pretty utopian take on the transformation of Paris as Europe’s (potential) first Future City. All driven by the development of shared transport – metro, driverless cars, bike sharing. We couldn’t agree more. And of course, it needs some pretty hefty algorithms to make it work – making sure that car you are thinking about ordering is where you need it, when you need it. That’s where Optimeering comes in – contact us to learn more about out Future Cities project.
While our focus at the moment is firmly on city bikes, driverless cars – and what they mean for shared transport services in future cities – are really not that far away. An important requirement here is dedicated chips for running the ML algorithms used in self driving systems. These need to cope with high velocity data, like that coming from high-definition cameras, in real time. You don’t want to collide with a lamp post due to chip latency issues. Chip makers look to be taking some significant steps forward here, lead by Nvidia and following on from its lead in the GPU space for training ML systems. Reuters has a good overview – definitely worth reading.
A quick shout-out to www.bikesharingmap.com – mapping all city bike projects, worldwide. There’s quite a few…
On August 24th, the Optimeering FutureCities team welcomed project partners to a successful kickoff workshop for our new Bike Sharing project.
The project is funded by Oslo Kommune and Akershus Fylkeskommune as part of their RFF Hovedstaden program and will aim to create innovative algorithms for more efficient bike sharing programs.
Together with our project partners Urban Infrastructure Partner (UIP), Oslo Kommune and the Norwegian University of Science and Technology (NTNU) we will investigate how recent developments within areas such as operations research and machine learning can be used to build intelligent solutions for bike sharing planners on both operational, tactical and strategic level.