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Lu - vi, 9:30 - 17:00 h (CET)
Lu - vi, 9:00 - 18:00 h (EST)
Lu - vi, 9:00 - 17:00 h (SGT)
Lu - vi, 10:00 - 18:00 h (JST)
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Key regions: South America, Malaysia, India, Indonesia, Saudi Arabia
The Bike-sharing market in Singapore has experienced significant growth in recent years, driven by changing customer preferences, market trends, and local special circumstances.
Customer preferences: Customers in Singapore have shown a strong preference for convenient and eco-friendly transportation options, which has contributed to the rise of bike-sharing services. With increasing concerns about traffic congestion and environmental sustainability, many people are opting for alternative modes of transportation, such as cycling. Bike-sharing offers a flexible and affordable solution for short-distance travel, allowing users to easily access bikes at various locations across the city.
Trends in the market: One of the key trends in the Bike-sharing market in Singapore is the adoption of dockless bike-sharing systems. Unlike traditional docked systems, where bikes are picked up and returned at designated stations, dockless systems allow users to park and lock bikes anywhere within a specified area. This flexibility has made bike-sharing more convenient and accessible, as users are not restricted by the availability of docking stations. Another trend in the market is the integration of bike-sharing services with mobile apps and digital platforms. Many bike-sharing companies in Singapore have developed user-friendly apps that allow customers to locate and unlock bikes, track their usage, and make payments seamlessly. This digital integration has enhanced the overall user experience and made bike-sharing more convenient and efficient.
Local special circumstances: Singapore's compact size and well-developed infrastructure make it an ideal market for bike-sharing. The city-state has a comprehensive network of cycling paths and park connectors, providing safe and convenient routes for cyclists. Additionally, Singapore's warm and tropical climate encourages outdoor activities, including cycling.
Underlying macroeconomic factors: The growth of the Bike-sharing market in Singapore is also influenced by underlying macroeconomic factors. The city-state has a high population density and a large number of residents who rely on public transportation for their daily commute. Bike-sharing offers a first and last-mile solution, bridging the gap between public transport stations and final destinations. Furthermore, the government's efforts to promote sustainable transportation and reduce car ownership have created a favorable environment for bike-sharing companies to thrive. In conclusion, the Bike-sharing market in Singapore has experienced significant growth due to changing customer preferences, market trends, local special circumstances, and underlying macroeconomic factors. The convenience, affordability, and eco-friendly nature of bike-sharing services have made them increasingly popular among Singaporeans. With the continued development of dockless systems and digital integration, the Bike-sharing market in Singapore is expected to further expand in the coming years.
Data coverage:
The data encompasses B2C enterprises. Figures are based on bookings, revenues, and online shares of bike-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.Lu - vi, 9:30 - 17:00 h (CET)
Lu - vi, 9:00 - 18:00 h (EST)
Lu - vi, 9:00 - 17:00 h (SGT)
Lu - vi, 10:00 - 18:00 h (JST)
Lu - vi, 9:30 - 17:00 h (GMT)
Lu - vi, 9:00am-6:00pm (EST)