// Solution
Parking Optimisation
We model demand across your parking facilities and hand back a routing and pricing strategy that puts drivers in a space instead of circling for one.
Who it’s for
Operators of multiple parking facilities, and the municipal transport teams who answer for the congestion they create.
Cruising for parking is a measurable share of urban traffic, and it is the share that produces nothing. Drivers circle because they cannot see occupancy ahead of arriving, so demand piles onto the two or three facilities everybody already knows about while capacity sits unused a block away.
Most operators already hold the data needed to fix this — entry and exit counts, season-ticket records, tariff history — but hold it per site, which is exactly the wrong shape for a problem that is about choosing between sites. This solution models the portfolio as one system and returns the routing and pricing decisions that follow from it.
Scope and what you get
Parking Optimisation
Fixed scope, fixed price. Portfolios of any size are quoted on the same terms.
- Scale
- Any portfolio size — quoted on facility and space count
- Timeline
- Scoped at kickoff — you get an indicative date in our first reply
- Pricing
- Fixed quote after a scoping call
- You provide
- Occupancy or entry/exit history per facility, and current tariffs
Deliverables
- Demand model across the whole portfolio, by hour and by day type
- Routing strategy: which facility to send arriving drivers to, and when
- Tariff recommendations, with the utilisation change each one buys
- Simulation results comparing current operation against the proposal
- Summary report and handover walkthrough with your operations team
Method
Underneath this sits a parking mechanism designed to reduce cruising time across multiple facilities, using an infinite-horizon look-ahead strategy. The distinction matters: a greedy allocation sends each arriving driver to whichever space is nearest and free right now, which works until it does not — it fills the convenient facility first and leaves the next hour’s arrivals with nothing.
Look-ahead evaluates an assignment against the arrivals still to come rather than against the current instant, so a driver may be routed slightly further to keep the portfolio balanced. Over a day, that trades a small amount of individual walking distance for a large reduction in aggregate circling. Infinite-horizon means the policy is evaluated over an unbounded future rather than a fixed window, so it does not degrade at the edge of its planning period.
We calibrate the model on your own occupancy history, then simulate it against that history so the reported gain is measured against how your facilities actually behaved, not against a synthetic baseline. Pricing is treated as part of the same mechanism: tariff is one of the levers that moves demand between sites, and it is usually the cheapest one to change.
This builds on our Urban Analytics & Smart City service, which is where the simulation and demand-modelling work comes from.
Tell us how many facilities you run.
Send the facility count, the space count and whatever occupancy history you hold, and we will come back with a duration and a price.