Urban mobility is evolving rapidly, with curb management becoming a critical element for efficient city planning. The Curb Data Specification (CDS) provides cities with the structure needed to collect, share, and analyze curb usage data. Although CDS has proven to be helpful to standardize data and outline an architecture, it is ultimately up to cities and vendors to make use of this framework to actually improve curbside operations. This case study explores how custom API queries using CDS provide an effective process for using curb data and CDS to address urban curb management challenges, offering insights valuable to city planners and transportation officials.
Problem: Navigating Curb Data
Cities face numerous challenges in managing curb space, including congestion, inefficient use of resources, and conflicts among various users such as delivery services, ride-sharing vehicles, and personal cars. The lack of standardized data collection and analysis methods exacerbates these issues, making it difficult for city planners to make informed decisions. We know CDS can help solve this by removing data silos, enabling automated processes to maintain curb data, and giving the ability to dynamically manage regulations. However, some problems still remain with using this data:
- Data Overload: a complete curbside inventory can be a valuable resource of information, but it can also be overwhelming to sift through all the different curb zones and policies in a given city.
- Filtering Challenges: CDS stores data in universal metrics, such as latitude and longitude coordinates, as well as unique UUIDs (universal unique identifier). This is helpful for standardization but can make it difficult to filter to relevant information.
- Redundant Data: knowing about curbside closures that happened last month or what permit holders are allowed to park on a given curb segment could be useful to some users but not others.
CDS has basic queries and endpoints to search through CDS data. However, these queries are introductory and may require prior knowledge (for example, knowing a given curb zone’s UUID). This is no fault of CDS; it is meant to be a framework. This framework is in place so that custom applications can be built on CDS to make the most use of this data.
Solution: Leveraging Custom API Queries with CDS
First, what do we even mean by custom API queries, or queries in general? API queries are commands sent to an application programming interface (API) to request specific data or perform operations. They allow users to extract, manipulate, and interact with data in a standardized and automated way, tailored to their specific needs. Custom queries are an effective way to help users get what they want from APIs and are very common in data integrations.
How does this relate to the curbside? Well, a delivery driver only cares where the loading zones are, and a private driver in a rush only cares where they can park at 1:55 PM, 5 minutes before their important meeting. By providing custom queries to get this exact type of information, users can quickly and effectively get the relevant data they need from CDS, which then improves their curbside experience.
Some specific examples of custom API queries that the CurbIQ team has created can be seen below. This is just a sample – more use cases grow each day as more users start to adopt CDS.
- Nearest Curb Block Query: provides regulation data for the nearest curb block based on the location provided, at a specific time and date. Used to get a quick snapshot of curbside regulations on a given block face.
- Nearest Regulation Query: provides the nearest location of a specified regulation type (e.g., EV Parking or Loading Zone) based on a provided time, date, and current location. The nearest available location can also be provided if utilization data is available.
- Regulation Count Query: provides a count of a specific type of curb space in a given area based on the polygon provided, at a specific time and date. Used to quickly understand the overall supply of curb inventory in a given neighborhood or area.
What does this look like in practice? Using the same syntax as CDS is key – a sample of one of a custom API query that provides the closest delivery zone at 3 PM can be seen below. This query provide a direct way for a logistics driver (or app) to quickly identify where they should route their trip to. More examples like this can be seen here on one of your sample API documentation pages.
GET https://api.curbiq.io/api/v1/{CITYNAME}/policy/by_position?
coordinates=53.3402,-6.2642
&activity=loading
&time=15:00
Real Life Examples: Connecting Users to CDS
These custom queries being discussed aren’t some hypothetical concept: the CurbIQ team is using these in several different applications, some of which are highlighted below:
- City of Dublin and Loading Zones: the CurbIQ team recently helped Smart Dublin map out the digital kerb (aka the European curb) inventory for the Temple Bar neighbourhood. One application for this data was passing information on loading zones to delivery drivers to improve logistics on crowded streets. The CurbIQ team created a custom API that returned the three nearest loading zone spaces to a given location at a given time, so drivers can know exactly where to head when completing a delivery. This is being implemented into their logistics program. A sample of the APIs we provided can be seen in this video (0:40 seconds in).
- Arlington County and Parking Rate Changes: the County of Arlington is undergoing a performance parking program where they are updating rate changes on a quarterly basis to help change user behaviour and better allocate the curb space based on who needs it. CurbIQ has mapped out all the paid parking spaces and is populating all demand data in CDS. However, if a user wants to just know about the latest rate changes, they would need to filter through all the policies and zones to find the relevant info. The CurbIQ team is working on a custom API to isolate the latest rate changes for a specific time and place, so users can quickly know where rates have been updated. More details on this project here.
- Real Time Parking Maps with HotSpot Parking: CurbIQ has created real time availability maps in over 10 Canadian municipalities, including Edmonton and Halifax, by using HotSpot transaction data to display what spaces have been paid for directly in the app. CurbIQ hosts all this transaction data in CDS but set up a custom API to pull the active event sessions so the map can quickly populate what spaces are available, with all past sessions being filtered out. See here for more information on how your city can get more out of utilization data like this.
Conclusion: Increasing Access to the Curb
As you can quickly see, there are many examples of the potential of custom API queries using CDS to enhance urban curb management. By leveraging these queries, cities can improve the efficiency and effectiveness of their curb space allocation by enabling users to make informed decisions. The lessons learned from this implementation can serve as a valuable guide for other cities facing similar challenges, while also maximizing the use of CDS. Don’t hesitate to reach out with any new custom applications of curb data that would be useful in your city; you likely aren’t the only one looking for a way to build on CDS to solve your curbside problems.
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