Key regions: China, Germany, Thailand, Saudi Arabia, India
The E-Scooter-sharing market in Romania has experienced significant growth in recent years, driven by changing customer preferences, emerging trends, and local special circumstances.
Customer preferences: In Romania, customers have shown a growing preference for convenient and eco-friendly transportation options. E-Scooter-sharing services offer a convenient solution for short-distance travel, allowing users to easily navigate through congested urban areas and avoid traffic jams. The affordability and flexibility of these services have also attracted customers, as they can rent an e-scooter for a short period of time without the need for long-term commitments or ownership.
Trends in the market: One of the key trends in the E-Scooter-sharing market in Romania is the increasing number of market players and the expansion of their service areas. As more companies enter the market, competition intensifies, leading to improved service quality and lower prices for customers. Additionally, companies are expanding their operations beyond major cities to reach a wider customer base, including suburban areas and tourist destinations. Another trend in the market is the integration of e-scooter-sharing services into existing transportation networks. This integration allows users to seamlessly combine e-scooter rides with other modes of transportation, such as buses or trains, to complete their journeys. This trend not only enhances the convenience of e-scooter-sharing services but also promotes sustainable and multimodal transportation options.
Local special circumstances: Romania's urban centers, such as Bucharest and Cluj-Napoca, face challenges related to traffic congestion and limited parking spaces. E-Scooter-sharing services provide a solution to these issues by offering a compact and efficient mode of transportation that can be easily parked and accessed in crowded areas. The relatively flat terrain of many Romanian cities also makes e-scooters a practical and enjoyable mode of transportation for residents and tourists alike.
Underlying macroeconomic factors: The growth of the E-Scooter-sharing market in Romania is also influenced by underlying macroeconomic factors. The country has experienced steady economic growth in recent years, leading to increased disposable income and a higher standard of living for many Romanians. This economic prosperity has contributed to the rising demand for convenient and sustainable transportation options, including e-scooter-sharing services. In conclusion, the E-Scooter-sharing market in Romania is developing rapidly due to changing customer preferences, emerging trends, local special circumstances, and underlying macroeconomic factors. As customers increasingly prioritize convenience, affordability, and sustainability, the market is expected to continue its growth trajectory in the coming years.
Data coverage:
The data encompasses B2C enterprises. Figures are based on bookings and revenues of e-scooter-sharing services.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.
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