As we move into a new decade, it’s safe to say that “real-time data” has become one of the key phrases of the 2000’s and 2010’s. Certainly with traffic information, real-time data has proven to be invaluable, giving drivers updates around potential traffic jams and providing options to change their route before running into heavy traffic.
But is it always best to rely purely on real-time data, to the exclusion of other sources?
In parking, one of the main sources of on-street real-time availability data is sensors (either electro-magnetic or infra-red), which are installed in the middle of a space to sense if there is a vehicle on top of it occupying the space. However, if just one sensor breaks in a group of 5, the data for all 5 is reported inaccurately due to aggregation across all 5 spaces. There is also the cost of installing and maintaining sensors in the street environment that can be prohibitive. In the UK, Westminster City Council have turned off their much-lauded smart parking street sensor scheme, though it is unclear if this decision was due to maintenance costs or another factor.
So is “real-time” really real-time? In reality, there is a minimum lag-time of at least 1 minute between the source system reporting an available space to the cloud, the data linking with a digital parking services provider such as Parkopedia, and then getting that data into the car system of connected vehicles in the vicinity who are searching for spaces at that time. Unlike off-street parking, it is impossible to reserve a street parking space, so the likelihood of the reported available space being taken by the time the driver arrives can be extremely high.
So what are the other options for parking availability data?
- One potential source is car sensor data. However, whilst this data is perceived as accurate in general, connected vehicle data has the same time lag issues as with infrastructure-based sensor data, and additionally many suffer from GPS error. With a “parking event” in an urban environment (where parking pressure occurs), the GPS location is generally accurate to 1-2 metres. As the urban environment becomes increasingly built up with tall buildings and skyscrapers, line of sight to the GPS satellite is increasingly compromised, meaning the data inaccuracy and error count will only increase. Inside a city where errors can be up to dozens of metres, the result is car sensor data that reports a parked location as being in a different road section or an adjoining road. In testing with some of our automotive partners, we’ve seen results showing vehicles that have apparently driven “off-road” and through solid buildings!
- Transactions data is another option. However this too is flawed if used as the sole source of truth. In many cities, there are multi-space meters that cover several streets, which means you can’t tell which street has actually been parked on, and therefore where spaces are available. Even with a system able to identify individual spaces, the data reported is often not fully correct, as drivers may over-pay, resulting in a free space that is not reported as such, or under-pay, where the space is still occupied even after the paid time-limit has expired. Transactions data also doesn’t take into account anyone with a permit (eg residents or disable badge holders) who are exempt from the payment, but may still occupy a paid parking space nor does transaction data cover hours of operations when parking is free.
At Parkopedia, we fuse multiple data sources - static parking data, ground surveys, real-time parking sensors, car sensors, transactions and imagery to overcome potential errors or gaps, and combine these with machine learning models, to provide predicted parking availability.
With a combination of historical behaviour patterns and real-time data, these predictions are able to take into account events (such as football games, festivals, etc) which change normal behaviour and may impact parking availability.
This approach offers Parkopedia’s B2B customers an industry-leading solution that they can provide to their drivers with confidence. Parkopedia predictions are rigorously tested, as we run a continual programme of ground surveys to manually collect and check all relevant information including the accuracy of our prediction models globally.
In tests using transactions as the sole data source, Parkopedia’s predictions models based on fusing multiple data sources excluding transactions perform up to 2x better, and we see an additional 15-20% improvement in accuracy prediction when transactions are included.