In the late 1980s, running a closed car park required no internet or computer experts — a single parking attendant was often sufficient. The attendant learned the site’s rhythms: which employees left for lunch, who worked late, who came only on certain days. That human knowledge allowed flexible decisions about admitting occasional cars without displacing monthly permit holders. Today’s parking management systems are attempting to replicate that local know‑how with data, cameras and predictive software.
Technology took over gradually: first barriers at the entrance, then pay machines, paper tickets and later card payments. Licence‑plate recognition (ANPR) cameras automated entry and exit logging so that, in many cases, barriers open automatically on departure. The next step has been to move beyond recording the present state and to analyse historical entry–exit patterns to forecast future occupancy.
A concrete illustration from the article: consider a 100‑space car park with 50 spaces reserved for permit holders. Traditionally operators would limit casual admissions to 50 cars to guarantee a spot for every permit holder. In practice, however, the 50 issued permits rarely correspond to 50 cars present simultaneously: some users arrive only in the morning, others only in the evening, some work from home on certain days, people take holidays or use public transport on particular days.
The old parking attendant knew these subtleties and could safely admit extra casual cars when attendance was expected to be low, while reclaiming spaces in time for returning permit holders. Modern systems aim to do the same using large datasets and longer time series: they learn which times of day or weekdays are busier, when the lot typically clears, and when groups of permit holders return. If the model predicts that not all permit holders will be present in the next hour, it can admit more casual cars; if a return wave is imminent, it reserves spaces proactively.
This approach does not increase the physical capacity — a 100‑space lot still fits at most 100 cars at once — but it raises the number of distinct users per day. That raises utilisation and revenue for operators and reduces the amount of cruising traffic searching for spaces, lowering congestion and fuel waste.
Users benefit too: systems can provide forecasts of expected traffic, suggest optimal arrival times, or allow booking of individual spaces in advance. Some implementations already apply these ideas in practice: for example, EPS Global’s CityZen platform was used in a Cypriot project to estimate expected occupancy from previous parking habits, enabling more efficient sharing of the same physical space without adding new stalls.
The story closes with the retired parking attendant watching the automated system that has replaced his booth: the barrier rises silently, cameras read plates, and software predicts how many spaces will be needed. The attendant’s experiential knowledge has been translated into algorithms — the ‘‘thinking’’ about the lot no longer sits in one person’s head but in software that helps use existing capacity more efficiently.
Why it matters
- Better land use: the same area serves more users across a day.
- Lower environmental impact: fewer cars circling for parking means less congestion and fuel burned.
- Improved user experience: forecasts, arrival recommendations and booking increase convenience.
Rather than building new garages, improved operational intelligence can be enough to significantly increase effective parking capacity and reduce the negative side‑effects of parking scarcity.
