Qrowd piloted its solutions at two key points in the mobility value chain: cities or other public authorities that are ultimately responsible for implementing new policies and services and data and technology vendors in relevant areas, including traffic management, route management, and maps.
We have delivered six co-designed use cases:
We pilot them in the city of Trento. Trento has been selected by the IEEE as one of the 10 core cities involved in the IEEE Smart cities Initiative. The initiative supports participating cities in addressing their strategy and improving their smartness by leveraging the experience and knowledge of world- class technology experts. The initiative ecourages cities to collaborate with one another and with world-renowned smart city technicians and scientists.
To design effective mobility policies, the first step is to understand how citizens and commuters are currently moving around the city. The main indicator for this purpose is the “Modal Split”, that estimates the percentage of people that travelled using what mode (or modes) of transport.
Traditionally, modal split is estimated with paper or telephone surveys that are expensive to set up and can only be executed every now and then. Using the QROWD platform, we developed a hybrid-human machine workflow to implement agile modal split estimation.
An essential piece of information for the design and implementation of mobility policies is knowledge of the location, characteristics and state of the current mobility infrastructure. This data is often expensive to generate, maintain and update for Cities. Physically send municipality employees to locate the required items does not scale in the area of the city, as to be certain that coverage is complete and no items have been over-sighted, several employees need to be sent. In some cities, engaged communities of volunteers contribute via external platforms such as Open Street Maps, however, there is no control on what their contributions are, when they make them, an on which area of the city they will focus.
To tackle this problem, we developed a hybrid human-machine workflow that combines crowdsourcing and on-the-field approaches to generate
To reduce traffic congestion in peak tourist season, the Touristic Network suggests travelers which touristic hotspots to visit based on current travel time. Information is displayed using various dynamic visualizations based on a schematic map, summarizing the strategic routes which are essential to reach the touristic hotspots, and displaying current travel time and eventual delays for each segment of road.
TomTom data on car navigation is used to estimate the distance to the hotspots. The connection with i-Log can be used to complement this data by either contributing with their location data, and/or answering questions about how crowded the hotspots are.
For drivers, parking after reaching a destination can be a stressful experience that also increases pollution. We implemented a Parking Probabilities service that computes the expected parking availability on public streets in certain city areas.The service can be interfaced directly to end users, or used as a component of a routing app that suggests a parking spot near the final location. The service is based on the TomTom data base, complemented with data from QROWDDB, collected and consented via the i-Log app, e.g., as part of a Modal Split computation campaign (See Automating Modal Split Computation)
TomTom’s Road Event Reporter service allows cities and road authorities, event organizers, and fleet managers to collaborate and announce roadwork or stoppages to millions of users at once. Trusted partners can report road closures, accidents and other potential traffic disruptions using a web-based application. With the service’s easy-to-use interface, users can identify potential
issues by visualizing current traffic flow, quickly create and edit road events on the screen and view current, upcoming, and expired events, enabling easy communication of disruption to road users
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