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August 1, 2018
By Dimitrios Spiliopoulos
LONDON - More than half of the world’s population now live in cities — and the figure will rise to more than two thirds by 2050, according to a United Nations forecast.
Growing numbers of city residents put pressure on energy and water resources, transport networks, environment, national healthcare budgets as well as many more aspects of the city.
Thankfully, some of the most important problems faced by cities around the world can be solved or reduced by Internet of Things (IoT) and artificial intelligence (AI) enabled solutions.
Rapid urbanisation and a growing population is causing more and more problems for mobility in the city. Commuting has become a hassle; congestion in the EU is often located in and around urban areas and costs nearly EUR 100 billion annually, or 1 % of the EU’s GDP.
Mobility challenges are plentiful, and not just limited to traffic congestion. They are also about efficiently connecting (time, cost, effort) different neighbourhoods with public transit; helping citizens and professionals at the last mile journey; giving access to critical stations with multiple modes and from multiple regions; offering a variety of options to the people to move around (including bicycle; and more.
City officials must therefore understand how people move around the city to plan accordingly the location of stations, bike routes, and traffic lights, as well as to optimise the schedule of each city activity without disturbing others.
Today, thanks to the use of IoT and AI-enabled solutions, cities can improve and solve many of these key urban mobility issues:
Optimise availability of public parking using real time parking sensors. These can show to the drivers where the nearest parking is without going around blindly. Finding parking in less time can reduce both traffic jam and air pollution
Understand where, when, and which people are moving through the city. To achieve this, analyse the anonymous and aggregated mobile data from smartphones. If this is combined with other data generated by connected city furniture, then the insights are priceless. Smart city furniture could be connected lights, smart benches, and connected traffic lights, while other city assets could be connected bikes and buses, connected buses and rubbish bins. The analysis of all this combined data can generate insights and automations that we could never think otherwise.
Plan maintenance and improvements in transport networks efficiently based on data collected by the IoT enabled assets. For example, potholes can be identified by data generated from smart bikes/lights due to the shaking sensors. At the same time, the schedule of when to send the workers to provide essential maintenance can be planned based on the available data from the sensors around that street, to avoid traffic disruption.
Of course, there are even more IoT applications that can improve the mobility in the city. Having said this, improving mobility can improve also air quality. Based on European Commission statistics, urban mobility accounts for 40% of all CO2 emissions of road transport and up to 70% of other pollutants from transport.
Despite the efforts of some mayors to handle the level of air pollution in their cities, in most cases the quality of the air that we breathe in the cities is deteriorating due to a variety of reasons. Urbanisation has enormous environmental consequences.
Apart from the obvious damaging consequences to our health, there is also a significant negative impact on the economy. For example, last year alone the costs of air pollution to the National Health Service (NHS) and social care in England were estimated to be £157 million. The latest findings, published in a report from PHE, warn these costs could reach as much as £18.6 billion by 2035 unless action is taken.
With the power of IoT and AI, cities have the ability to understand at a real-time, granular level the biggest air pollution hotspots, the causes, and what it means for citizens. Using real-time insights, the city administrators can take informative decisions about how to tackle the problems and how to prioritise their investments.
Furthermore, if data from well-distributed, public air quality sensors is combined with anonymous mobile data from the network of mobile operators, such as of O2, then the insights can be really valuable. Cities can plan new pedestrian and cycling routes, electric vehicle chargers or parking spaces based on air quality levels. Thus, both the combined data from sensors and mobile phones is critical.
In other words, along with taking decisions based on historic data or expert advice, councils now have all the tools necessary to optimise their decisions based on real-time data or even to automate processes based on specific incidents. Cities are capable of customising their actions at a neighbourhood level.
The increase in life expectancy has coincided with a dramatic rise in the number of elderly people who are 'highly dependent'. Expenditure on the care of older people is increasing substantially and quickly; almost 190,000 more people in UK aged 65 years or older will require care by 2035, marking a rise of 86 per cent, according to the paper published in The Lancet.
These high costs are paid either by families or by city councils, and the way that these services are offered is very impractical and inefficient. For example, councils need to send home care employees to visit a sick or elder person three times per day. For obvious reasons, services like this cannot scale without the support of technology.
There are many IoT-enabled products in the market that can help local authorities remotely monitor the conditions of these people, get notified if and when they took their medicines, if they need any help, and more. Remote checks can take place and alerts can be sent in case of incidents. Professionals can meanwhile direct their time towards more added value services which can better impact the lives of vulnerable people. Some of my favourite examples of IoT and AI solutions are:
Smartwatches for elders that can monitor their location, health conditions, and medicine schedule, as well as help in collecting and sending medical data.
Passive devices tracking movement, temperature, humidity and noise. The device can learn the daily patterns of the person in the house using machine learning algorithms and send notifications to the person, or to the selected stakeholder (family members, city home care service or others) when a daily pattern is disrupted. You can even interact with these devices in order to remind something or to inform others that a task has been completed.
Smart meters that monitor water or electricity consumption - through machine learning, they are able to identify disruption in the daily patterns of the person. For example, if the user of the flat usually boils water at 10:00 and turns on the TV at 17:00, if one or both of these actions do not take place, it may mean that the person feel unwell or has left the house alone. Then, an alert can be sent to the predefined stakeholder in order to check closer what the issue is.
All of these home care solutions can work passively without the need for intervention from the user or someone else.
Dimitrios Spiliopoulos is an IoT strategist based in London.
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