Shared Mobility - Albania

Albania

In Albania, the Shared Mobility market is expected to experience significant growth in the coming years.
By 2024, revenue is projected to reach US$353.90m and show an annual growth rate of 2.90%, resulting in a market volume of US$396.70m by 2028.
The largest market in the market is Flights, which is projected to generate revenue of US$155.00m in 2024.
By 2028, the number of users in the Public Transportation market is expected to amount to 2,046.00k users.
The user penetration rate is expected to be 95.0% in 2024 and 95.0% by 2028.
The average revenue per user (ARPU) is expected to be US$131.80.
By 2028, 59% of the total revenue in the Shared Mobility market is expected to be generated through online sales.
It is worth noting that in global comparison, China is projected to generate the most revenue in this market, with US$412bn in 2024.
Shared mobility solutions, such as car sharing and bike sharing, are slowly gaining popularity in Albania, especially in urban areas.

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

Market

Region

Region comparison

Currency

Analyst Opinion

The Shared Mobility market in Albania has been experiencing notable growth in recent years.

Customer preferences: Customers in Albania 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 are becoming popular choices among consumers looking for flexible and cost-effective ways to travel within cities.

Trends in the market: One of the key trends in the Shared Mobility market in Albania is the rise of ride-hailing services, which are gaining traction due to their ease of use and availability through mobile apps. Additionally, the increasing awareness of environmental issues is driving the demand for eco-friendly transportation options, leading to the growth of bike-sharing services in urban areas.

Local special circumstances: Albania's unique geographical features, including its mountainous terrain and coastal cities, present both challenges and opportunities for Shared Mobility providers. In cities like Tirana and Durres, where traffic congestion is a common issue, Shared Mobility services offer a practical solution for residents and tourists alike to navigate the streets more efficiently.

Underlying macroeconomic factors: The growth of the Shared Mobility market in Albania is also influenced by macroeconomic factors such as rising urbanization rates, increasing disposable income levels, and a growing tech-savvy population. As more people move to urban centers and seek convenient transportation options, the demand for Shared Mobility services is expected to continue on an upward trajectory.

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.