Digitizing the last mile: Portland Edition
Being able to provide detailed curb information for various use cases like private, bus or delivery service parking, or car sharing, requires large amounts of data from a multitude of sources that are different in every city. Therefore we’d like to give you a glimpse into how we went about doing that in Portland.
Understanding Portland’s Context
The first step of our team when approaching a new city, is to get a detailed understanding of how parking works in that city. This process includes surveying available digital information, mostly provided by cities themselves, reading laws to understand implicit rules that are not written on street signs and in many cases direct communication with cities to clarify ambiguous or conflicting rules and regulations. Portland, like many other cities, is offering pretty detailed information here. Their parking data comes in form of a point dataset showing every parking space, including additional regulatory information such as eligible rates and restrictions (e.g. if parking spots are reserved for the disabled, loading zones or solely for motorcycles). Furthermore, Portland does also provide data for distinct no-parking spots. This allows for an already very detailed look at the parking situation overall.
Enhancing Data
But, needless to say: That data set does not contain all the information to create a complete and comprehensive picture of parking in Portland. Fortunately, the city provides other data sets, that now have to be combined with the data already ingested into our GIS database: Portland is also offering a dataset on street markings, which are categorized by line types. It contains the locations of crosswalks and other areas strictly off-limits for vehicles, as well as the spaces between parking stalls and much more.
Using a number of geospatial algorithms, the data sets are normalized and combined into one dataset, which now is a complete representation of the real situation on the street, without ever having set foot on the city’s streets ourselves.
At this point, the data is transformed and refined to be used in our Rules and Restrictions API. The beauty of combining all this data into one comprehensive data set is that now data can be rendered out in a context aware way. The users of our APIs can now get information based on their specific properties (e.g. which permits they are holding, etc.) on any location of the city and at any time. Furthermore, concepts like complicated time restrictions are broken down to give only the information relevant for that specific user / use case.
Use Cases
Having that detailed curb information, which is then altered in terms of design for optimal user-friendliness and further enhanced with predictive, real-time imagery data, gives us the possibility to create extremely valuable information for several use cases. Thereby, we create a hassle-free parking experience with location-based parking information. This information can be accessed with our App, but more importantly by in-car systems, where they help to create a smooth and hassle free end-to-end navigation experience, including the parking process.
But there is more:
· Global package delivery services can benefit from enhanced routing and alerts for their drivers to avoid potential parking rule violations.
· The challenge of bus parking can be made a lot easier by enabling bus and coach drivers to find dedicated bus parking zones and helping them to avoid fines.
· The different usage of parking spaces by car hailing and ride sharing services as pick-up and/or drop-off points can be taken into consideration as well — the respective data integrated into used navigation apps by the company.
Want to know more? Visit the services-section on our website or get in touch with us directly in order to explore the possibilities of a cooperation!
