Micromobility providers collect a myriad of data, principally through IoT and various sensors, including GPS, accelerometers, and gyroscopes. This data encompasses ride duration, route details, vehicle status, and even user feedback. Harnessing IoT technology, they gather real-time information about vehicle performance, environmental conditions, and user behaviour, offering a treasure trove of insights into urban mobility patterns.
Data is the heart of micromobility rental schemes. It informs profitability strategies, enhances user experiences, and fuels dynamic responses to fluctuating demand. Data companies crunch these enormous datasets, generating predictive models to anticipate demand at city, national, and global levels, optimising fleet management and deployment.
Moreover, data can also unveil the impact of local conditions—such as weather or infrastructure—on user choices, providing guidance on what kind of hardware to use in different regions. These analytics offer invaluable intelligence to micromobility brands, helping them stay agile and user-centric.
For example in Rome electric scooters have caught on ahead of push bikes because Rome’s hills are too steep for most cyclists.
Interestingly, although all micromobility tends to see a drop in usage in winter months, scooters are preferred over bikes in more adverse or colder weather climates as it is easier to dress for the conditions when riding a scooter. Looking ahead a spokesperson at Tier Mobility has said they will be deploying a new model with a bigger front wheel, brighter lights, and rear-wheel drive, to cope better with adverse weather
Data plays a pivotal role in optimising route planning. Through capturing, crowdsourcing, collating, and analysing data, route planning applications can recommend the most efficient routes, predict congestion, and offer alternative paths, enhancing user experiences and promoting more sustainable urban commuting.
For example, micromobility platforms can take a leaf out of Citymapper’s book, which already offers route options based on users' preferences—quiet, regular, or fast. Or, from Pointz, a safety-focused micromobility mapping app which uses crowdsourcing to improve route planning and so that its results can follow real-time conditions.
Despite the immense potential, micromobility data collection faces challenges. Regulatory constraints, such as the recent ban on electric scooters in Paris, which hampers data collection efforts in a major European city. Additionally, public concern over data privacy is a significant hurdle. Clear communication and robust PR strategies, articulating the benefits of data collection and robust privacy measures, can help assuage these concerns.
The vast datasets collected by micromobility services are emerging as goldmines for urban planners. They offer granular insights into mobility patterns, congestion hotspots, popular routes, and peak usage times. Such information can directly influence city designs, infrastructure development, traffic management, and public transport planning.
In the future, this data will help create 'smart' cities, where infrastructure is dynamically adapted to real-time conditions and predicted trends, making urban spaces more efficient, sustainable, and livable.
Data-informed urban planning promises a future where commuting is frictionless, and urban spaces are more responsive to their residents' needs. It will significantly influence daily life, fostering convenient mobility, improving public spaces, and potentially even reducing pollution. For the construction industry, data-informed planning signifies a shift towards more adaptable, intelligent designs that account for evolving mobility trends.