Use Case: Pricing the Curb Correctly

Note: this is a specific implementation of use case from the CDS Examples page.

This example supports the CDS use case "Price the curb correctly." It shows how the Curb Data Specification (CDS) can be used to monitor curb utilization, evaluate parking demand, and adjust pricing policies using occupancy aggregates derived from curb events. Street names and occupancy values are illustrative.

CDS describes the curb with six objects, starting with the parent grouping:

• Curb Area: the parent grouping of zones, here "Business Corridor"
• Curb Zone: one stretch of curb on one block face under one set of rules. The zone references policies and is associated with curb activity.
• Curb Space: one marked parking space inside a zone.
• Curb Policy: the rules, meaning who may do what, when, and at what price. A zone references one or more policies.
• Curb Events: records describing activity at the curb, such as vehicle arrivals, departures, parking sessions, and payments.
• Aggregates: summarized metrics derived from curb events over a defined period, such as occupancy, revenue, turnover, and dwell time.

The Two Corridors

Corridor A (Main Street) consists of several paid parking curb zones with a parking rate of $2/hour. Occupancy aggregates generated from curb events show an average occupancy of 90%.

Corridor B (Broadway Avenue) consists of similar paid parking curb zones operating under the same parking policy. The parking rate is also $2/hour, but occupancy aggregates show an average occupancy of only 50%. This corridor is adjacent to Corridor A.

Despite having identical parking policies and pricing, demand is concentrated on Corridor A, while Corridor B has substantial unused capacity.

Policy Goal: Balancing Utilization

The city seeks to maintain occupancy levels that maximize access while preserving availability. Using occupancy aggregates generated from curb events, staff identify that Corridor A is operating well above the desired occupancy range while Corridor B is operating below it.

These aggregate metrics help the city understand how curb demand is distributed and where policy changes may improve utilization.

Policy Adjustment: Rate Increases

To better balance demand, the city updates the curb policies associated with Corridor A.

• Corridor A: parking rate increases from $2/hour to $3/hour
• Corridor B: parking rate remains $2/hour

The updated pricing information is represented through CDS curb policies and published to curb management systems and applications.

As drivers respond to the pricing change, new curb events continue to be collected and summarized into occupancy aggregates.

Result

Several weeks after the policy change, the city reviews updated occupancy aggregates.

• Corridor A: average occupancy decreases from 90% to 78%
• Corridor B: average occupancy increases from 50% to 72%

The utilization of both corridors becomes more balanced. Drivers experience improved parking availability on Corridor A, while previously underutilized spaces on Corridor B receive greater use.

This example demonstrates how CDS supports data-driven curb pricing. By combining Curb Policies, Curb Events, and Aggregates, cities can monitor demand, evaluate pricing strategies, and make policy adjustments that improve curb access, utilization, and revenue outcomes.

Sample API responses

Each example is a response from the CDS server. Identifiers, locations, and times are consistent across all of them, so the zones, spaces, and policies cross-reference.

1. GET /curbs/zones: both corridors in one collection

2. GET /curbs/policies: applied across both corridors

2. GET /metrics/aggregates: for the past month across both corridors for comparison

2. GET /curbs/policies: generate new policy with adjusted rate for Corridor B, as well as removing the current policy on those zones.

2. GET /metrics/aggregates: for the latest month across both corridors for comparison to see how rate changes adjusted behaviour.

Other Applications

CDS can support other approaches to pricing the curb correctly, including:

• Time-of-day pricing: Cities can use curb events and aggregates to identify peak and off-peak demand periods, then apply different curb policies by time of day. For example, a zone may have a higher rate during midday commercial activity and a lower rate in the evening.

• Dynamic pricing: Cities can use regularly updated occupancy or revenue aggregates to adjust prices over time. If a zone is consistently overutilized, rates can increase; if it is underutilized, rates can decrease.

• Adding or removing paid spaces: Cities can compare demand across curb zones and adjust how much curb space is allocated to paid parking. If occupancy is high and turnover is low, additional curb spaces may be converted to paid parking. If demand is low, spaces may be reassigned to loading, pickup/drop-off, parklets, or other uses.

• Comparing policy outcomes: Cities can evaluate how different curb policies affect utilization, turnover, and revenue across areas. This helps determine whether pricing changes are achieving the desired policy outcome.

• Revenue management: Cities can combine payment events and occupancy aggregates to understand whether pricing changes are increasing curb access, improving turnover, or generating revenue in line with policy goals.

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