It’s amazing how quickly the curb became a public infrastructure mapping and digitizing focus. And it stands to reason why would it. Ridesharing and autonomous driving companies are often cited as one of the biggest motivators in digitizing curb space. Improved efficiency in drop offs and pickups dictated by available curb space not only improves productivity and customer experience but also keeps private companies on the good side of cities (and the parking authority). Mapping curb rules may also help us improve traffic, mobility and productivity in cities. An analysis of various parking ‘cruising’ studies by Donald Shoup concluded that on average around 30% of road traffic could be attributed to people finding a place to park on the street. Another study in Washington DC revealed that the City was losing approximately $650 million a year because of the lack of loading zones for delivery trucks. Trucks were double parking in passenger vehicle locations or just in the middle of the street.
It makes sense why some see the curb as an opportunity. And so, companies like Coord, a spin-off of Sidewalk Labs, have been developing tools to help cites map curb rules in detail, releasing the data as part of the curb API – of course at a price. On the other hand SharedStreets, a project of the non-profit Open Transport Partnership is taking a more open data/open-source approach. Their recent flurry of open source releases have paved the way for cities to use their own existing regulation or curb inventory data with SharedStreets linear referencing tools and curb rule specification, creating curb data which can then be shared and consumed by third parties.
To understand the potential opportunity in mapping curb rules I wanted to see how curb data in my own city could be used. In this post, I explore the process of collecting on-street parking data, cleaning the data, creating a curb rules API, and finally a visualization.