Shared Mobility - South America

  • South America
  • South America is projected to witness a significant increase in revenue in the Shared Mobility market.
  • According to the latest projections, revenue in this market is expected to reach US$62,170.00m by 2024, with an annual growth rate of 3.12% between 2024 and 2029.
  • By 2029, the projected market volume in the region is expected to be US$72,500.00m.
  • Flights is the largest market, with a projected market volume of US$28,520.00m in 2024.
  • The number of users in Public Transportation is expected to reach 272.30m users by 2029.
  • The user penetration is expected to increase from 75.6% in 2024 to 81.1% by 2029 in the Shared Mobility market.
  • The average revenue per user (ARPU) is expected to be US$199.00.
  • Online sales are projected to account for 64% of total revenue in the Shared Mobility market by 2029.
  • In global comparison, China is expected to generate the highest revenue of US$365bn in 2024.
  • In Brazil, the shared mobility market is rapidly growing due to the high demand for affordable transportation options in urban areas.

Key regions: United States, Saudi Arabia, Germany, Malaysia, India

 
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Analyst Opinion

Shared Mobility services have been rapidly gaining popularity in South America, transforming the way people move around in the region.

Customer preferences:
Customers in South America are increasingly looking for convenient and cost-effective transportation options, which has fueled the growth of Shared Mobility services. The younger demographic, in particular, values flexibility and sustainability, making services like ride-sharing, bike-sharing, and scooter-sharing appealing.

Trends in the market:
In Brazil, ride-sharing services have seen significant growth, with major players expanding their operations to meet the rising demand for convenient urban transportation. Similarly, bike-sharing services have gained traction in cities like Buenos Aires, Argentina, where initiatives to promote cycling and reduce traffic congestion have been well-received.

Local special circumstances:
One of the key factors driving the growth of Shared Mobility in South America is the region's rapidly urbanizing population. As cities become more crowded, there is a growing need for efficient and sustainable transportation solutions. Additionally, the prevalence of smartphone usage and the increasing penetration of mobile internet have made it easier for people to access and use Shared Mobility services.

Underlying macroeconomic factors:
Economic factors such as rising income levels and changing consumer behavior have also contributed to the expansion of Shared Mobility in South America. As disposable incomes increase, more people are willing to spend on convenient transportation options rather than owning a vehicle. Furthermore, government initiatives to promote sustainable transportation and reduce carbon emissions have created a favorable environment for Shared Mobility services to thrive.

Methodology

Data coverage:

The data encompasses B2C enterprises. Figures are based on bookings, revenues, and online shares of car rentals, ride-hailing, taxi, car-sharing, bike-sharing, e-scooter-sharing, moped-sharing, trains, buses, public transportation, and flights.

Modeling approach:

Market sizes are determined through a bottom-up approach, building on a specific rationale for each market. As a basis for evaluating markets, we use financial reports, third-party studies and reports, federal statistical offices, industry associations, and price data. To estimate the number of users and bookings, we furthermore use data from the Statista Consumer Insigths Global survey. In addition, we use relevant key market indicators and data from country-specific associations, such as demographic data, GDP, consumer spending, internet penetration, and device usage. This data helps us estimate the market size for each country individually.

Forecasts:

In our forecasts, we apply diverse forecasting techniques. The selection of forecasting techniques is based on the behavior of the relevant market. For example, ARIMA, which allows time series forecasts, accounting for stationarity of data and enabling short-term estimates. Additionally, simple linear regression, Holt-Winters forecast, the S-curve function and exponential trend smoothing methods are applied.

Additional notes:

The data is modeled using current exchange rates. The market is updated twice a year in case market dynamics change.

Overview

  • Revenue
  • Sales Channels
  • Analyst Opinion
  • Users
  • Global Comparison
  • Methodology
  • Key Market Indicators
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