Shared Mobility - Latvia

  • Latvia
  • It is projected that the Shared Mobility market in Latvia will generate a revenue of US$620.10m in 2024.
  • Moreover, it is expected to demonstrate an annual growth rate (CAGR 2024-2029) of 2.32%, resulting in a projected market volume of US$695.60m by 2029.
  • The largest market in this domain is Flights, which is expected to reach a market volume of US$247.20m in 2024.
  • By 2029, the number of users in the Public Transportation market is expected to amount to 1,251.00k users.
  • The user penetration rate is anticipated to increase from 90.2% in 2024 to 95.0% by 2029.
  • The average revenue per user (ARPU) is projected to be US$379.70.
  • In Latvia's Shared Mobility market, 57% of the total revenue is expected to be generated through online sales by 2029.
  • It is worth noting that in comparison to other countries, China is expected to generate the most revenue in this market, amounting to US$365bn in 2024.
  • Latvia's shared mobility market is rapidly expanding, with the introduction of new bike-sharing schemes and on-demand car rental services in major cities.

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

 
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Analyst Opinion

The Shared Mobility market in Latvia is experiencing a surge in popularity driven by changing customer preferences, technological advancements, and unique local circumstances.

Customer preferences:
Customers in Latvia are increasingly looking for convenient and cost-effective transportation solutions, leading to a growing demand for shared mobility services. The younger generation, in particular, values flexibility and sustainability, opting for shared modes of transportation over traditional car ownership.

Trends in the market:
The Shared Mobility market in Latvia is witnessing a rise in on-demand services such as ride-hailing, car-sharing, and bike-sharing. This trend is fueled by the ease of access to mobile applications, which allow users to book and pay for rides seamlessly. Additionally, the integration of electric vehicles in shared fleets is gaining traction, aligning with the country's focus on environmental sustainability.

Local special circumstances:
Latvia's relatively small size and well-connected urban areas make it conducive for shared mobility services to thrive. The capital city, Riga, with its dense population and traffic congestion, presents a lucrative market for shared transportation options. Moreover, the country's growing tourism sector contributes to the demand for flexible and efficient mobility solutions for visitors.

Underlying macroeconomic factors:
The steady economic growth in Latvia has increased disposable income levels, enabling more individuals to explore alternative transportation modes like shared mobility. Furthermore, government initiatives promoting sustainable urban development and reducing carbon emissions are driving the adoption of eco-friendly shared transportation options in the country.

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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