Where are the low-hanging fruits? Strategic mapping of potential consumers of shared e-mobility to help decarbonise mobility of our cities.

Abstract

In marketing research, the concept of ‘low-hanging fruits’ refers to those consumers who are easiest to attract to a business. Focusing efforts on this group maximizes the effectiveness of a marketing campaign. In mobility planning, this concept could be adopted by city planners more often to achieve sustainability goals.Imagine that a start-up just launched a new model of shared e-scooter in a busy town like Rotterdam. It is natural to expect that, for the sake of financial sustainability, a significant part of the revenue should come from neighbourhoods that cluster factors of success for potential usage (e.g. commercial activities, jobs, good infrastructure). However, if shared e-mobility is meant to cause significant and positive impact on sustainability, helping cities achieve their goals, further structural changes in travel habits are certainly necessary. In short, part of the ‘unnecessary’ car trips should be more often replaced by more sustainable modes, like shared e-mobility. ‘Unnecessary’ is interpreted in this study as a car trip that has a similar profile (e.g. length, travel time, socioeconomics) of a shared e-mobility trip, and therefore could be ‘avoided’ or ‘replaced’ by more sustainable alternatives. The individuals making those trips are called ‘low-hanging fruits’, but are ‘not yet consuming the product’. How to map low-hanging fruits? In this study, an approach is proposed to help providers and cities strategically map them. The approach is operationalised in the context of the Netherlands, a country where shared e-mobility is spreading quickly. The approach can be divided in 3 major phases: 1) Characterising a typical ‘avoidable’ car trip in the context of a given population (city, region, country), through the investigation of how current users of shared mobility travel (e.g. trip distance, duration) and their characteristics (e.g. age, gender, income); 2) Mapping where the avoidable car trips are generated, since countries like the Netherlands keep their Household Travel Surveys up to date so that city planners can use that information to extract insights of travel habits (desire lines, purpose, mode, etc); 3) Labelling locations in regard to their likelihood of having more or less low-hanging fruits, through the application of unsupervised learning (k-means) to find probable clusters of low-hanging fruits. 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. For (2), we explored the latest Dutch Household Travel Survey (2020) and combined it with (1). This type of survey provided annual information about daily travel patterns of more than 60.000 people. The Dutch HTS can also be expanded to mitigate negative impacts of data collection biases and be a reasonable representation of how the whole population travels on a daily basis. In (3), we combined insights extracted from (2) with Census information to perform the unsupervised classification of locations. We propose and operationalise a pragmatic approach to help cities and mobility providers identify potential users of shared mobility. If shared mobility could seduce more low-hanging fruits, significant environmental impacts from modal shift could be achieved. 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.