Shared Mobility - India

India

By 2024, the Shared Mobility market in India is expected to generate a revenue of US$91,070.00m.
It is further projected to grow annually at a rate of 3.13% resulting in a market volume of US$48,310.00m by 2028.
The market's largest market is Flights, which is expected to grow to a market volume of US$48,310.00m by 2024.
By 2028, the number of users in the Public Transportation market is expected to reach 983.20m users.
In 2024, user penetration is projected to be 74.5%, which is expected to increase to 72.5% by 2028.
The average revenue per user (ARPU) is expected to be US$84.83.
Furthermore, it is projected that 69% of the total revenue generated in the Shared Mobility market will be generated through online sales by 2028.
When compared globally, China is projected to generate the most revenue in the Shared Mobility market, with a projected revenue of US$412bn by 2024.
India's shared mobility market is experiencing rapid growth due to increasing urbanization and a shift towards sustainable transportation options.

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

Market

Region

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Currency

Analyst Opinion

The Shared Mobility market in India has been witnessing significant growth and evolution in recent years.

Customer preferences: Customers in India are increasingly looking for convenient and cost-effective transportation options, leading to a rise in demand for shared mobility services. The younger population, especially in urban areas, prefers on-demand services that offer flexibility and affordability.

Trends in the market: One of the key trends in the Shared Mobility market in India is the rapid adoption of ride-hailing services and bike-sharing platforms. Companies offering these services have been expanding their presence to cater to a larger customer base. Additionally, carpooling and shared bike services are gaining popularity as people look for sustainable and eco-friendly transportation options.

Local special circumstances: India's unique demographic and infrastructural challenges have shaped the Shared Mobility market in the country. The presence of congested roads in major cities has led to a growing need for efficient transportation solutions. Moreover, the government's push towards reducing carbon emissions and promoting electric vehicles is influencing the market dynamics.

Underlying macroeconomic factors: The growing middle-class population with increasing disposable income levels is driving the demand for shared mobility services in India. Additionally, the rise of digital technology and smartphone penetration has made it easier for customers to access and use shared mobility platforms. Moreover, changing lifestyle preferences and a shift towards shared ownership models are contributing to the market growth.

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.