Shared Mobility - United States

  • United States
  • The Shared Mobility market in the United States is expected to generate a revenue of €291.50bn by 2024.
  • This revenue is projected to grow annually at a rate of 2.15%, resulting in a market volume of €324.20bn by 2029.
  • The largest market in this market is Flights, which is expected to have a market volume of €132.40bn by 2024.
  • By 2029, the number of users in the Public Transportation market is expected to be 133.00m users.
  • The user penetration rate, which is 81.7% in 2024, is projected to rise to 85.5% by 2029.
  • The average revenue per user (ARPU) is expected to reach €1,044.00.
  • By 2029, 77% of the total revenue in the Shared Mobility market will be generated through online sales.
  • In a global comparison, China is projected to generate the most revenue, with an estimated revenue of €338bn in 2024.
  • The United States is experiencing a surge in demand for shared mobility services, driven by urbanization and the need for cost-effective transportation options.

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

 
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Analyst Opinion

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

Customer preferences:
Customers in the United States are increasingly valuing convenience, flexibility, and cost-efficiency when it comes to transportation options. Shared Mobility services such as ride-hailing, bike-sharing, and car-sharing have gained popularity due to their ability to provide on-demand transportation solutions tailored to individual needs.

Trends in the market:
One prominent trend in the Shared Mobility market in the United States is the integration of technology. Companies are investing in advanced mobile applications, GPS tracking, and digital payment systems to enhance the overall user experience. Additionally, there is a growing focus on sustainability, with more emphasis on electric vehicles and eco-friendly practices within shared transportation services.

Local special circumstances:
The United States has a vast and diverse transportation landscape, with varying levels of infrastructure and regulations across different states. This diversity has led to the emergence of region-specific Shared Mobility solutions, catering to the unique needs of urban, suburban, and rural areas. For example, densely populated cities like New York have seen a surge in ride-hailing services, while bike-sharing programs have gained traction in more bike-friendly regions like California.

Underlying macroeconomic factors:
Several macroeconomic factors contribute to the development of the Shared Mobility market in the United States. The increasing urbanization rate, coupled with rising traffic congestion in major cities, has driven the demand for alternative transportation options. Moreover, changing consumer behaviors, especially among the younger demographic, favor the sharing economy and on-demand services, further propelling the growth of Shared Mobility 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.

Vue d’ensemble

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