Credit: Unsplash

Where are the low-hanging fruits?

Mapping potential consumers of shared e-mobility to help decarbonise cities

Francisco Macedo , urban planner at the Rebel Group, Rotterdam; Clint de Keizer, data scientist at the Rebel Group, Rotterdam.

[Left] Daily average of shared e-scooter trips in Rotterdam, Summer of 2020] | [Right] Concentration of shops ‘Winkels’ in Rotterdam, BAG (2020). Produced by the author.

How to find low-hanging fruits?

In this article, an approach is proposed to help providers and cities find them. The approach is operationalised in the context of the Netherlands, a country where shared e-mobility is spreading quickly. Some use cases of this exercise can be applied to: (i) size potential market for expansions [e.g. deployment of vehicles or installation of facilities]; (ii) size potential impacts of modal shift on city-wide Co2 emissions; (iii) design subsidies that encourage providers to deploy assets in certain areas; (iv) change fees depending on the potential to attract former private vehicle users; (v) investigate reasons behind the existence of avoidable car trips; among others [the more, the better!].The steps are operationalised as follows:

  1. Mapping where the avoidable car trips are produced. Many countries keep their Household Travel Surveys up to date so that city planners can use that information to have a ‘region-wide idea’ of travel habits [desire lines, purpose, mode choice, etc];
  2. Labelling locations in regard to their likelihood of having more or less low-hanging fruits. In this step, I apply Unsupervised Learning (k-means) to find probable clusters of low-hanging fruits. It’s a combination of avoidable car trips with relevant census variables.

Datasets used

In order to achieve (1), we used an anonymous, ‘privately acquired’ shared mobility OD travel matrix produced in 2020 by a third party mobility company. This OD refers to trips done by e-scooter users of Rotterdam during the summer of 2020 (June, July and August), and looks like this:

Credit: Origin-Destination matrix acquired by Rebel from Fluctuo.
E-scooter trips aggregated by H3 hexagon and week (summer of 2020). Produced by the author.
Reported Origin-Destination lines from the Dutch HTS (2020). Produced by the author.

1. Profile of shared e-mobility trips

To define the profile of shared e-mobility consumers, we explored how far users travel. To do that, the acquired OD was transformed in geographic desire lines, from which simplified euclidean distances were calculated. In the histogram below, it’s possible to see that the majority of trips made on e-scooters (around 80%) are between 500m and 2,5km. A better approximation of the histogram to reality would be a curve shifted a bit to the right, due to network effects on distance, however this information was not available.

Euclidean distance from e-scooter trips. Produced by author.
Reported distances of ‘trip legs’ (ODiN, 2020). Produced by the author.

2. Mapping clusters of ‘avoidable’ car trips

Scheme of a ‘full trip’ and a ‘trip leg’. Produced by the author.
Zip codes that produce journeys with at least one ‘avoidable’ car trip leg. Produced by the author.

3. Finally, where are the ‘low-hanging fruits’?

The Elbow method. Produced by the author.
Generated clusters. Produced by the author.
Generated clusters, including the ‘low-hanging fruits’. Produced by the author.


We propose and operationalise an approach to help cities and mobility providers identify potential users of shared mobility probably willing to replace private vehicles with more sustainable options due to their trip profile and socioeconomic characteristics. If shared mobility could seduce more low-hanging fruits, more significant environmental impacts from modal shift could be achieved. This is not intended to look like an ‘ultimate solution’, but just contribute to the discussion of how to better insert shared e-mobility in our cities and cause significant impact.