Most local authorities should be actively reducing parking provision. Particularly if this creates opportunities to regenerate town centres through new housing on brownfield sites and supporting new, more sustainable business models.
Having said that, car parks will be a both a feature of our urban landscapes and a vital community resource for the foreseeable future. They will also continue to be a key revenue source for local authorities and this is something that should be applauded, not begrudged.
Local authorities have a moral and legal obligation to manage their finances responsibly so as to provide the services that their communities need. Local authority owned car parks are valuable public assets and so councils should be responsible for making sure that those assets are used efficiently. So, if there is excess parking, those car park assets are not being used efficiently. As a result, parking becomes an oversupplied commodity; wasteful, costly to maintain and devalued.
In order to maintain parking revenues, local authorities must find ways to ensure that there is just enough capacity to meet demand and that car park utilisation is as near optimal as possible.
That’s an easy thing to say. It is somewhat harder to achieve but it is certainly feasible.
In my previous blog, I pointed out that many local authorities already gather real-time data on car park utilisation. It isn’t a technologically difficult thing to do and not a costly thing to do either. According to Microsoft’s “2019 Manufacturing Trends” report, the average price of an Internet of Things (IoT) sensor has declined from $1.30 in 2004 to $0.44 in 2018, and will reduce further as the volume of connected devices grows.
Guildford Borough Council uses our system to monitor parking utilisation through a live dashboard. This also gives them the ability to analyse years of data so to accurately model demand and performance. In this way, they manage enforcement on a day to day basis but can also gather evidence to inform decision making and engage stakeholders.
But data capture is only the first part of the equation. The interesting part is what having that data enables.
If we were to use this data to determine the optimal capacity of a town’s car parks and simply close every space above that number, we would simply create chaos. Drivers would find themselves endlessly circling the streets in search of a space. In the best case scenario, this experience would drive them on to public transport for their next visit. Perhaps more realistically, the experience may lead them to drive to a different town and never return but before they do so, their cars will have added to the pall of GHGs and particulates that hangs over the streets. It’s also likely that their rising levels of frustration and inattention may have led to an accident.
Optimising parking utilisation must also include ways to engage the driver. They must be guided to the best space for them and in doing so, it’s possible not only to make that visitor experience a positive one but one that also provides a means to reduce the negative impacts of their drive.
In planning workshops around the country we often hear the term ‘carrots and sticks’ used when talking about the policies that need to be put in place to drive behavioural change but in most cases the options available to local authorities seem to relate only to ‘sticks’.
Local authorities are understandably wary of using punitive measures to drive behaviour change. Congestion zone charging has still not been adopted outside of London and although the legislation which allows local authorities to impose them has been in place since 2000, only Nottingham has a Workplace Parking Levy (WPL) in place. Bristol nearly implemented a WPL that would have raised £27 million, but abandoned it due to opposition from local businesses. Leicester, Reading, Edinburgh, Glasgow, Cambridge, Hounslow and Camden are all proposing WPLs but even under a climate emergency, it’s not a common proposal.
Given the level of data that can be collected, I would suggest that the time has come to look at a solution that could reduce the impact of cars in many city centres whilst protecting local authority revenues.
Technology has been the enabler for the sharing economy. Companies like Uber and Airbnb could not have become what they are without ubiquitous access but the other aspect of their business model that technology has enabled is the way that their services are priced. Both use a dynamic model which means that prices rise and fall in accordance to the level of demand.
For Uber, algorithms within the app manage revenue at peak times with surge pricing. Fares automatically increase as demand increases and outstrips the supply of available drivers in the area. This ensures a reliable service can be maintained for customers who are willing to pay a premium price. Those that are under no time pressure can choose to wait, and the app has the facility to alert these customers when the price begins to drop again.
Traditionally car park pricing has been determined by historical data that is often unorganised. This leaves car park operators without a reliable way to measure demand and adjust their prices in accordance with this but systems like those we have developed with Guildford Borough Council mean that live data and historic data can be used to create algorithms that will optimise pricing. Charging a premium for parking when demand is at its highest offsets potential losses during times of low demand for parking spaces. In addition, reduced fees during periods of low demand incentivise drivers to make use of vacant parking spaces.
If this is linked to smart signage and mobile apps, drivers can be made aware of nearby, under utilised parking that may be considerably cheaper if, that is, you have no objection to walking for a few extra minutes.
Drivers who are prepared to pay more are more likely to be able to find a space. Some drivers may change their mode of transport during peak parking times or spread the cost of peak time parking through car sharing. Others that do not use parking spaces during peak hours will find that their parking costs may reduce. In all cases, it means that they are paying car parking charges based on the actual value of the space rather than an arbitrary static charge.
As well as data from sensors, car park operators may also want to take into account data from parking enforcement officers and surveys, weather, street closures, holidays, local business activities, and local events.
Companies like Uber and Airbnb have proven that technology enabled dynamic pricing can be highly profitable, but local authorities also have an obligation to protect human, social and natural capital too. So, what if these algorithms were not just based on predicting supply and demand to change and direct behaviour? Just as sensor networks can be used to detect levels of occupancy, they can also detect air-quality levels and traffic levels. It would be possible to link smart signage and parking pricing systems that would direct drivers away from areas in which pollution levels are rising toward those less intensively affected at that time. Air pollution levels vary according to not just traffic intensity but also weather conditions but the type of connected real time technology that we have can easily be used to reflect real time conditions.
As always, data is key to success. Managing car park data is not a one-off task; we have seen during the COVID-19 pandemic just how quickly situations can change and with permanent consequences. Dynamic pricing is simply one of the things that it can enable. More strategic value will come from the ability to recognise and react to longer term trends.