The Rise of Crowdsourced Mobility Data

With the advent of the smart city, much is being made about plans and strategies to gather data through sensor networks. Cities are looking for the best and most affordable ways to deploy an army of data generating sensors to streamline their operations and save money. However, those deployments often come with large price tags. When adding on the recurring networking, server, and analytics costs, dedicated sensor networks may not always pencil out for municipal authorities.

Crowdsourcing to the Rescue

There is an alternative emerging. Crowdsourced data efforts are starting to materialize in ways that drive forward smart city policy. In these types of systems, you the resident are technically the sensor. Or, rather, your car, your phone, and any other connected device may be serving as a data node contributing to your city’s decision-making. While not necessarily a direct replacement for municipal infrastructure, this type of data crowdsourcing can serve as a powerful complement to smart city efforts.

Anyone familiar with the ability of Google Maps or Waze to display traffic slowdowns and propose alternative routes should be familiar with this model. Speed and location data from all the other phones using the app are anonymized, aggregated, and used to draw that red line telling you there is a backup ahead. Each driver is both a node in the sensor network and a user of it. If harnessed effectively, crowdsourcing data can drive the smart city forward.

Data, From and For Everyone

As described in a recent Guidehouse Insights report, smart parking systems that are light on municipally deployed infrastructure or use entirely crowdsourced data are growing in popularity. Bosch is working to deploy its community-based parking system that utilizes the side-mounted sensors on connected vehicles. Data from these sensors, commonly used for automated parallel parking, are anonymized and combined with curb mapping data to report curbside parking availability through onboard navigation. A process that had previously required a municipal authority to sign a contract and deploy hundreds of sensors can now be achieved with no municipally deployed infrastructure at all.

Cities are also looking to work with the data generated by users of ridesharing apps to better understand movement patterns by car. While such data sharing agreements are still uncommon, the City of Chicago was able to negotiate such an arrangement and even made that data open-source for easier public and governmental access. The City of Boston, in a precocious move, developed its own app called Street Bump which uses the accelerometer in volunteers’ phones to feed the city data on street surface conditions. Elsewhere, cities like Seattle and Portland are working with fitness app Strava to better understand how walkers, joggers, and cyclists move through urban environments.

Using Data to Build a Better Future

Data is the beating heart of the smart city. But before it can be used to make our cities function better, it has to be collected somehow. While the mass deployment of sensors to directly capture these data may be the most direct answer, more and more cities are finding ways to rely on their own citizens for data collection. While these models are relatively young, they present an emerging way to accomplish smart city goals while reducing the financial burden to those same places.