Urban Mobility and Commercial Intelligence

Estimating transportation costs per person, calculating the floating population that travels through a certain sector and establishing their socioeconomic level, is essential to know exactly what should be the right location for a new station to serve customers who demand mobility services within the cities.

Wednesday, April 28, 2021

Every day there are more and more work teams and data driven companies that have found in Commercial Intelligence and Geomarketing solutions a possibility to reduce risks in new investments associated with expansion plans and opening of new service points.

In different cities of the region there are companies that offer bicycle, electric scooter or scooter rental services for short distances. As these companies expand their operations, there is a growing need to make sound decisions based on data, which in many cases are related to the start-up of new stations.

In this sense, Rodrigo Morales, Data Analyst at CentralAmericaData, developed a project that aimed to answer the following question: What is the best location for a new station for a bicycle rental station?


(Image from the report. Shows data on floating population and consumers classified by socioeconomic level).

To answer this question, Morales carried out an analysis that took into account data on the floating population, empty spaces between existing stations, spending on public transportation, young floating population, areas where the company's stations have a greater influx of bicycles, and areas with a greater presence of people of different socioeconomic levels.

Another important variable in this type of analysis is public transportation expenditure, a figure that must be calculated for each quadrant of interest. For this purpose, Morales mixed information on floating population, classification of people by socioeconomic level and structure of consumer spending.

As a result, Morales was able to establish that there are a total of 17 proposals for the opening of new bicycle stations. These proposed items are aimed at reducing the risk of new investments, increasing the company's revenues, improving customer service and attracting new customers.

Morales explained that "... using different machine learning models it was possible to select the three best locations out of the 17 proposed, then a deeper analysis was made to confirm if the three chosen locations were the most suitable."

Nowadays, the analyzed company, dedicated to offering micromobility services, has 480 stations and about 7,000 bicycles.


(Image from the report. Shows the 17 locations proposed for the opening of new bicycle stations). 

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