Shared Mobility - Central Asia

  • Central Asia
  • Central Asia is expected to witness an impressive growth in the Shared Mobility market.
  • The market's revenue is estimated to reach US$3,583.00m by 2024, with an annual growth rate (CAGR 2024-2029) of 6.00%, resulting in a projected market volume of US$4,795.00m by 2029.
  • The Public Transportation market is the largest in the market, with a projected market volume of US$1,774.00m in 2024.
  • By 2029, the number of users in the Public Transportation market is expected to reach 49.47m users.
  • The user penetration rate is expected to rise from 77.3% in 2024 to 93.1% by 2029.
  • The average revenue per user (ARPU) is projected to be US$58.46.
  • Furthermore, in the Shared Mobility market, 48% of total revenue is expected to be generated through online sales by 2029.
  • It is worth noting that in terms of global comparison, China is predicted to generate the most revenue in the Shared Mobility market, with US$365bn in 2024.
  • Despite challenges in infrastructure and regulations, the growth of shared mobility services like car-sharing and bike-sharing is slowly gaining traction in Central Asia.

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

 
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Analyst Opinion

The Shared Mobility market in Central Asia is experiencing a significant growth trajectory driven by changing customer preferences, emerging trends, local special circumstances, and underlying macroeconomic factors.

Customer preferences:
Customers in Central Asia are increasingly seeking cost-effective and convenient transportation solutions, leading to a rising demand for shared mobility services. The preference for on-demand transportation options that offer flexibility and affordability is fueling the adoption of ride-hailing, car-sharing, and bike-sharing services in the region.

Trends in the market:
In Central Asia, the Shared Mobility market is witnessing a surge in the popularity of ride-hailing services, with major players expanding their operations to cater to the growing demand. Additionally, the integration of technology such as mobile apps for booking and payment convenience is enhancing the overall customer experience. Moreover, the emergence of electric scooters and bikes as sustainable transportation alternatives is gaining traction among environmentally conscious users in the region.

Local special circumstances:
Central Asia's unique geographical and cultural landscape presents both opportunities and challenges for the Shared Mobility market. The region's diverse terrain and varying urban infrastructures require tailored transportation solutions to meet the needs of different cities and populations. Furthermore, cultural norms and regulatory frameworks influence the adoption and operation of shared mobility services in Central Asia.

Underlying macroeconomic factors:
The economic development and urbanization in Central Asia are key macroeconomic factors driving the growth of the Shared Mobility market. As cities expand and populations increase, there is a growing need for efficient transportation systems to alleviate traffic congestion and improve mobility. Additionally, favorable government policies and investments in transportation infrastructure are further propelling the expansion of shared mobility services across the region.

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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