Why it matters
SDG 11 strives to make cities and human settlements inclusive, safe, resilient and sustainable. Rapid urbanisation and population growth have forced millions of people into living in slums and informal settlements – the number of slum dwellers reached over 1 billion in 2018, representing one in four urban residents. The UN estimates that 3 billion people will still lack adequate and affordable housing by 2030. Furthermore, more than 90% of people worldwide live in areas exceeding the World Health Organization guideline for healthy air.
The industry contribution
Mobile technology contributes to SDG 11 by providing data analytics and edge computing in combination with fast connectivity to enable smart traffic and cities and to empower municipalities to provide safe and reliable public transport solutions while reducing air pollution. Additionally, operators provide emergency broadcast systems and facilitate emergency communication, which enables effective risk mitigation of disasters and environmental health.
Enabling safe, affordable and sustainable transport systems
Target 11.2: By 2030, provide access to safe, affordable, accessible and sustainable transport systems for all, improving road safety, notably by expanding public transport, with special attention to the needs of those in vulnerable situations, women, children, persons with disabilities and older persons.
Only 50% of the world’s urban population had access to safe and reliable public transport in 2019. Mobile technology can help transport systems to become more sustainable through use cases such as mobile ticketing, timetabling, smart ride and bike sharing, smart traffic light control, and air monitoring. Smart city IoT connections nearly tripled from 2015 to 2019, reaching over 272 million, while IoT vehicle connections have increased by more than 200 million over the past five years, reaching more than 775 million connections. When using smart mobile solutions, bus boarding times can be reduced by up to 75% and aggregated data can reveal which routes are over or underserved, resulting in more optimised timetable scheduling. Smart traffic control systems can also be deployed to reduce travel times, which also reduces CO2 emissions.
In Côte d’Ivoire, mobile-enabled smart cards have sped up boarding times and improved cash management and transparency for a bus operator. In Canada, a smart traffic management system in the city of Toronto enables traffic lights to self-learn and recognise patterns. A trial period proved that once implemented, the smart traffic solution can shorten travel time by up to 25% while reducing CO2 emissions by 13%.
Case Study
Malaysia: tackling traffic congestion in urban areas
Context
Rising traffic congestion in cities is curbing economic growth, increasing greenhouse gas (GHG) emissions and impacting citizens’ quality of life. This is no different in Malaysia, where there are on average two cars per resident, and it is a major problem both in terms of environmental degradation and the loss of time for commuters.
Solution
Launched in 2016, Stars is a smart traffic analytics and recognition system from Telekom Malaysia (TM ONE) that uses 3G and 4G connectivity. The system programs traffic lights to respond to real-time data controlled by connected cameras. Sensors utilising cloud computing and analytics automatically adjust traffic lights to optimise traffic flow. They also predict traffic patterns.
Impact
The solution is now monitoring more than 130 junctions across four municipalities, reducing traffic waiting time around main roads by 65%. TM ONE believes the system could ultimately be deployed at approximately 1,800 junctions across Malaysia. Further, with the rollout of 5G, TM ONE is now piloting a 5G-enabled smart traffic light solution in two locations in Malaysia. The 5G capabilities will enable new features e.g. detection of police cars and ambulances to automatically adjust traffic flow in emergencies. 5G will also facilitate edge computing for improved latency and response time.
Source: Leading the World of Innovation in Asia-Pacific, GSMA
Reducing the number of deaths and number of people affected by disasters
Target 11.5: By 2030, significantly reduce the number of deaths and the number of people affected and substantially decrease the direct economic losses relative to global gross domestic product caused by disasters, including water-related disasters, with a focus on protecting the poor and people in vulnerable situations.
By enabling communications and access to information, education, and financial and health services, the use of mobiles phones provides essential humanitarian assistance during emergency situations and disasters. The expansion of network coverage and mobile adoption means more people can use mobile in emergency situations. Operator investments have significantly increased network quality and resilience, which is crucial in maintaining communications services in disaster-stricken areas. For instance, in Turkey real-time mobile analytics helps to inform time-sensitive decisions for emergency disaster response. Meanwhile, in Pakistan, the use of innovative technology enables a variety of services such as fleet monitoring for humanitarian agencies to monitor the location of staff in high-risk areas.
Big data for sustainable and cleaner air
Target 11.3: By 2030, enhance inclusive and sustainable urbanisation and capacity for participatory, integrated and sustainable human settlement planning and management in all countries.
Target 11.6: By 2030, reduce the adverse per capita environmental impact of cities, including by paying special attention to air quality and municipal and other waste management.
Big data and IoT solutions can contribute to helping public administrations optimise urban development and management and evaluate the quality of the air in places that do not have any type of monitoring. These solutions save resources and enhance the decision-making processes of public authorities. In Croatia, IT company Smart Sense’s collaboration with Deutsche Telekom supports indoor and outdoor monitoring of multiple air quality parameters – including relative humidity, temperature and airborne particles – on room, street and city levels. In Brazil, the use of big data and forecasting algorithms enable pollution levels to be predicted in São Paulo 24 to 48 hours in advance, allowing local authorities to take preventative measures.
Case Study
South Korea: big data analytics to measure air quality and provide mapping
Context
The South Korean government has been tackling dust pollution for many years, having been ranked as one of the world’s most polluted countries in 2017 as it issued 85 ultrafine dust warnings, up from 41 in 2016, a more than 100% growth. The government introduced an emergency law in 2019 to address fine dust in the air.
Solution
South Korean Telecommunications firm KT began its Air Map Korea project in 2017 to collect data on dust and create an air map for Korea. The company pledged $9 million in initial investments. Using NB-IoT technology through 4G base stations and big data analytics, the operator provides real-time insights. It has deployed around 2,000 monitoring stations nationwide, with a further 500 planned, and 7,000 mobile sensors that enable a denser and more precise network of dust detection.
Impact
Its mobile application, which derives information from the government’s fine dust monitoring network based on the KT IoT platform, tracks fine dust levels and provides citizens instant updates on dust warnings and advisable information, such as whether it is suitable to do exercise outdoors.
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Maximising impact by 2030
Enablers that could help maximise the mobile industry’s impact on SDG 11 include the following:
- Scaling proof of concept projects for implementation, such as IoT sensors and air-quality measurement solutions. This will require continuous work with local governments.
- Leveraging network-derived mobility data to help monitor population movements, especially during disasters.
- Strengthening IoT technology standards specific to infrastructure use and increase network resilience.