In modern cities, data can be found almost everywhere, for just about anything. Thanks to this abundance of data, driverless cars know their way around, the arrival of the next bus can be shown on the signboard at the bus stops, and the routes taken by shared rides and micromobility options can be stored on the cloud (along with endless more applications). We now have access to tons of data, however, we do not necessarily know what to do with it.
When it comes to managing the curb, cities often struggle with their curb data. Either they have too much data, incomplete data, information scattered everywhere, or no relevant data at all. Even if a city has a lot of data, not all of them may be suitable. Every city is in a different state of digitizing their curb, which is having a digital version of their curb inventory so that they can plan and manage their curbs in more innovative ways. Some may have too much data that they do not know how to deal with, whereas some cities might be new to the concept of digitizing the curbs and hence do not have any relevant data to begin with. Having a digital curb inventory is one of the very first steps for urban planning and city development, but how can cities achieve that?
Most of the time, cities have the exact data they need to create a digital curb inventory but do not realize it. For example, cities often have:
It is actually relatively common these days for a city to have a myriad of data from various sources. In a recent project completed for the City of Columbus, we planned to run a manual survey to map out the entire project area at first. However, we then realized that Columbus had sufficient data for us to digitize the curb. Upon discussion, we discovered that the city has the curb segment survey data that was dated a few years back in the line format, the curb asset dataset in the point format, parking meters dataset from the online map server, plus other datasets that are either dated or updated, and in point, line or polygon geometry format.
How do we deal with datasets that come in different forms? And how do we deal with the conflicts between a dated and more recent dataset? There is not a one-size-fit-all solution, but fortunately, there can be generalized processes that deal with those datasets. For example, data that comes in point form can be mapped to the closest segment according to its side of street and activity attributes in an automated process. Data in line format can also be converted to polygon in accordance with the Curb Data Specification (CDS).
Other than dealing with the geometry of the dataset, there can also be a system set up to deal with different types of signs. For example, the accessible permits and taxicab stand signs indicate that only that section of the street is designated for that purpose, whereas the no stopping and free parking signs typically apply to the whole block. There may also be times when the regulation only applies to a street segment between the two signs with the same regulation. Many cities have similar curb-related datasets, so processes can be generalized and applied to different cities with customizable additions as needed.
There are also times when the dataset is incomplete. Maybe the data only exists in certain parts of the city, only the location is identified but not the type of the signs, or even where a parking meter is identified but the parking rates and max stay are not determined on the dataset. Sometimes, we just have to make do with what we have. Even if data only exists on certain streets, we can still use that as a test pilot area.
This is even better as the city can have a taste of how having a digitized curb inventory would help in their curb maintenance and management before deciding to digitize the whole city. Even without detailed information about the street sign or the regulation, the geographical location of the data is still very valuable as that can be served as a starting point. Cities can then have a base map to begin with and add on the details later or even update the curb inventory on a monthly or yearly basis. Otherwise, they could choose to carry out a partial survey on the area where data is missing to save both time and money. This approach may not be ideal, but can still provide an effective way for municipalities to complete a digital curb inventory without spending money upfront to capture the entire city.
Even if no relevant data is available, there is still another way around it. It would take a long time to collect a digital curb inventory through a manual survey on foot, but mobile mapping can help solve this problem. The process uses machine learning algorithms that can quickly identify signs and their texts. From the data processing point of view, this is the more straightforward path as data obtained through mobile mapping can be easily fed into our existing data processing automation process and produce an industry standard format. There is already a process set up to take the data straight from the survey tool, upload them to the cloud, process them in a series of automated data processing and output the file in the desired industry format, CDS. This is especially great for any city who wants to start fresh. The city can even pick a particular issue to focus on, such as tackling all loading zones or congestion around pick-up and drop-off locations.
Regardless of whether a city has too little or too much data, there is always an option to produce a digital curb inventory which can help provide clarity on curbside management and urban development. Yes, some assumptions and regulation priority conditions might need to be set up first, but we can utilize everything that was given and start setting up a curb layer that the city can work on and update. With that in hand, cities can then understand, manage, and optimize their curb, work with the different mobility providers, provide live data occupancy status on the map and do much more. All these will then help cities achieve their sustainability goals and create a more vibrant and people-oriented city. CurbIQ’s Curb Converter data processing works well with any type of curb-related data and even when you have too much or too little of them. No matter what your current state of affairs is with your data, there is a path to better curbside management!
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