4.4
31 条评分
★★★★☆
2022年4月11日
2018年4月24日
更新内容
v3.6The latest enhancements in this update include: - Bug fixes and performance improvements
应用信息
- 开发者
- Micromobility.com, Inc.
- 分类
- Travel
- 价格
- 免费
- 版本
- 3.6
- App ID
- 1258546477
简介
MiMoto is the first electric Scooter Sharing made in Italy with a Smart&Green philosophy. MiMoto, the eco-friendly scooter sharing to revolutionise urban mobility. MiMoto eSharing is the first electric Scooter Sharing service Made in Italy. Sharing becomes green, smart, convenient and user-friendly. The service gives you a whole fleet of Italian designed and built electric scooters available for rental through an app on your smartphone. It will significantly reduce travel times through city traffic and cut fuel and maintenance costs. Traffic queues and looking for parking are a distant memory/thing of the past. At the end of the rental process, after reaching your destination, you will be able to leave the scooter anywhere within the allowed service area and in respect of the traffic laws. You will finally be free of finding charging stations! MiMoto esharing will always ensure fully charged vehicles and guarantee the free floating service. MiMoto esharing combines the advantages of shared mobility with the vision of a cleaner and livable city. Reduced air and sound pollution, reduced traffic, flexible and improved travel times are just some of the benefits of our service. No need to worry about trying to balance expenses anymore. Forget about refuelling, maintenance, vehicle license fees and insurance, we will take care of them. MiMoto esharing will provide fully charged batteries to all scooters and making them available again in no time. MiMoto offers not only a dynamic and useful service but also a strong alliance to any city administration in the battle against pollution and traffic – creating greener and smarter cities. Eco-sustainability, the respect of the environment and integrated mobility are a the core of MiMoto company vision.
下载量预测
预估总下载量
16
低 / 月
23
预估 / 月
45
高 / 月
基于评分数量 ÷ 类别评分率估算,实际下载量误差可达 ±50%,与 Sensor Tower 方法一致。
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