Shared Mobility - China

  • China
  • China is expected to generate the highest revenue in the Shared Mobility market, with projected revenue of US$411,700.00m in 2024.
  • This revenue is expected to grow at an annual rate of 6.92%, resulting in a projected market volume of US$538,000.00m by 2028.
  • The largest market China is Flights, with a projected market volume of US$136,300.00m in 2024.
  • The number of users in Public Transportation is expected to reach 894.80m users by 2028.
  • According to the analysis, the user penetration rate will be 87.0% in 2024 and is expected to increase to 93.5% by 2028.
  • The average revenue per user (ARPU) is projected to be US$330.50.
  • Online sales will generate 71% of total revenue in the Shared Mobility market by 2028.
  • When compared globally, China is expected to generate the highest revenue in the Shared Mobility market.
  • China's shared mobility market is dominated by bike-sharing companies, with millions of users and fierce competition.

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

Region comparison

Analyst Opinion

The Shared Mobility market in China has been experiencing significant growth and evolution in recent years.

Customer preferences:
Chinese consumers are increasingly valuing convenience, affordability, and sustainability when it comes to transportation options. Shared mobility services such as ride-hailing, bike-sharing, and car-sharing have gained popularity due to their cost-effectiveness and ease of use. Customers appreciate the flexibility and convenience offered by these services, allowing them to travel seamlessly within cities.

Trends in the market:
One notable trend in the Shared Mobility market in China is the fierce competition among major players. Domestic companies, as well as international giants, have been vying for market share by offering innovative services and expanding their presence to cater to a wider customer base. Additionally, the integration of advanced technologies such as artificial intelligence and big data analytics has enhanced the overall user experience, making shared mobility services more efficient and personalized.

Local special circumstances:
China's unique urban landscape, characterized by densely populated cities and increasing traffic congestion, has created a conducive environment for the growth of shared mobility services. The government's support for sustainable transportation solutions and efforts to reduce pollution have also played a significant role in shaping the Shared Mobility market in the country. Moreover, the rise of the digital economy and smartphone penetration has made it easier for consumers to access and utilize shared mobility services seamlessly.

Underlying macroeconomic factors:
The rapid urbanization and growing middle-class population in China have fueled the demand for convenient and affordable transportation options, driving the expansion of the Shared Mobility market. Additionally, the changing lifestyles and preferences of Chinese consumers, especially the younger generation, who prioritize experiences over ownership, have contributed to the popularity of shared mobility services. Furthermore, the government's regulatory framework and policies aimed at promoting sustainable transportation practices have provided a supportive environment for the development of the Shared Mobility market in China.


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.


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.


  • Revenue
  • Sales Channels
  • Analyst Opinion
  • Users
  • Mode of Transportation
  • User Demographics
  • Global Comparison
  • Methodology
  • Key Market Indicators
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