AI could save lives by predicting risks and optimizing traffic flow

These road safety challenges create profound social instability. Families face trauma and financial hardship after losing loved ones. Pedestrians, who account for around 44 percent of annual road deaths, are disproportionately vulnerable due to inadequate walkways, poor lighting, and limited pedestrian-focused planning. Combined, these elements point to a transportation ecosystem struggling to protect its citizens or adapt to rapid urban transformation.

As major cities face relentless strain from urbanization, deteriorating infrastructure, unsafe driving behavior, and fragmented traffic management systems, a new academic analysis outlines how artificial intelligence could offer a decisive turning point in restoring order to the country’s increasingly overwhelmed transport network.

The study, titled “Leveraging AI in Mitigating Road Accidents and Alleviating Traffic Congestion: A South African Perspective” and published in Eng. Proc., focuses on South Africa, arguing that the country's current traffic ecosystem is structurally incapable of addressing the rapid deterioration in road safety and traffic flow, and proposes an AI-powered Integrated Technological Transportation Ecosystem (ITTE) designed to modernize mobility, minimize human error, and enable predictive, coordinated traffic management nationwide.

Escalating crashes and congestion signal a system under severe strain

The study describes a national mobility landscape buckling under population growth, rapid urban migration, and infrastructure decay. South Africa’s largest metros continue to absorb thousands of new residents each year, intensifying pressure on outdated road networks that were never designed for current traffic volumes. As congestion worsens, delays have become routine, commute times have lengthened, and uncertainty in daily travel has contributed to economic inefficiency and declining quality of life.

Aggravating these pressures is a steady rise in fatal traffic collisions. National road safety reports show that fatal crashes increased from 10,180 in 2023 to 10,339 in 2024. Fatalities also climbed from 11,883 to 12,172 in the same period. Road injury is now one of the leading causes of death among South Africans aged five to 29, reflecting a broader pattern seen across low- and middle-income countries where 90 percent of global road deaths occur.

The author identifies multiple structural failures behind these trends. Ageing road surfaces, inconsistent signage, insufficient lighting, and recurring power outages create dangerous conditions for motorists and pedestrians alike. These physical hazards are intensified by rampant unsafe driving behaviors, including speeding, drunk driving, fatigue, reckless maneuvers, and widespread non-compliance with traffic laws. Weak enforcement capacity further undermines deterrence, while authorities lack integrated monitoring tools capable of detecting risks or responding quickly to emerging hazards.

Apart from safety concerns, congestion alone has become a severe drag on national productivity. Traffic delays disrupt supply chains, hinder public service delivery, and inflate transport costs for households and businesses. The economic toll is staggering: the estimated cost of crashes in 2024 reached ZAR 217.53 billion, a sharp rise from the previous year. Over a ten-year period, South Africa’s average annual crash-related costs exceed ZAR 177 billion, underscoring the long-term fiscal consequences of a system under strain.

These road safety challenges create profound social instability. Families face trauma and financial hardship after losing loved ones. Pedestrians, who account for around 44 percent of annual road deaths, are disproportionately vulnerable due to inadequate walkways, poor lighting, and limited pedestrian-focused planning. Combined, these elements point to a transportation ecosystem struggling to protect its citizens or adapt to rapid urban transformation.

AI model proposed to reinforce traffic safety and optimize mobility

To address these entrenched challenges, the author proposes the Integrated Technological Transportation Ecosystem (ITTE), a conceptual AI-driven model designed to transform traffic control, surveillance, and public communication. The system integrates data from vehicles, public transport, road infrastructure, and smart devices into a centralized Transport Management System (TMS) capable of real-time analysis and predictive decision-making.

At the foundation of the model is a network of interconnected technologies: smart cameras, sensor-equipped vehicles, smart infrastructure, adaptive traffic lights, smartphones, and Wi-Fi–enabled public transport. These elements would continuously transmit situational data—such as vehicle proximity, speed patterns, pedestrian movement, environmental conditions, and road surface integrity—into the central TMS.

AI-powered algorithms would then process these inputs to detect irregularities, identify unsafe driving, recognize emerging congestion patterns, and predict collision risks before they escalate. Once a risk is observed, the system could automatically trigger interventions such as adjusting traffic signal timing, rerouting vehicles, activating automated braking systems, or issuing alerts to motorists, pedestrians, and authorities.

The model focuses on real-time situational awareness, a capability that traditional traffic systems in South Africa lack. In the current environment, traffic information flows are fragmented, manual reporting is inconsistent, and authorities respond reactively rather than proactively. ITTE reverses this structure by prioritizing early detection, predictive analytics, and automated prevention, forming the basis for a more intelligent, responsive, and coordinated mobility ecosystem.

The author notes that modern technologies such as edge AI, GPS systems, IoT networks, and deep learning already offer proven solutions in cities worldwide for identifying accident risks, optimizing signal cycles, and improving incident response times. The proposed South African adaptation integrates these global advancements into a tailored system addressing the country’s specific infrastructural weaknesses, behavioral risks, and enforcement gaps.

Under the ITTE model, public transportation would also benefit significantly. Smart synchronization between buses and traffic signals would reduce delays and support reliable service delivery, encouraging commuters to shift away from private vehicles. This modal shift is essential for addressing chronic congestion in metropolitan areas.

Additionally, adaptive road systems and digital signage could keep commuters informed about optimal routes, changing road conditions, and high-risk areas. A continuous feedback loop between the TMS and road users ensures that information flows at the speed required to avoid collisions and reduce bottlenecks.

Economic, social, and safety gains highlight the urgency of AI integration

The study outlines how adopting a centralized AI-enabled TMS could produce wide-ranging benefits for both mobility and public welfare. First, it could sharply reduce the frequency and severity of collisions by addressing unsafe driver behaviors and environmental risks in real time. The system’s predictive capability would make it possible to intervene before incidents occur instead of relying on reactive methods that often come too late to prevent injury or loss of life.

Second, congestion could decrease substantially as signal optimization smooths traffic flow, dynamic rerouting helps vehicles avoid gridlock, and real-time communication empowers drivers to make informed decisions. Reduced idling, fewer delays, and improved journey reliability would also support environmental sustainability by lowering emissions from congested corridors.

Third, the economic benefits could be significant. By minimizing accident-related costs and improving transport efficiency, South Africa could relieve pressure on public finances, streamline logistics, and strengthen the competitiveness of its urban centers. Enhanced reliability of transport services could also support economic mobility for low-income populations who depend heavily on public transit.

Fourth, an AI-driven transportation ecosystem could democratize access to safety information. With mobile alerts and publicly accessible updates, commuters would gain timely awareness of hazards, empowering them to protect themselves and others. Such transparency fosters a culture of collective responsibility and informed participation, key factors in reducing human error.

The integration of smart infrastructure and mobile platforms also encourages a collaborative relationship between authorities and road users. Commuters can contribute real-time data, report hazards, and participate in safety enhancements, reinforcing the feedback loop driving continuous improvement in the system.

Finally, the study positions the proposed model within the broader mission of the Sustainable Mobility and Transportation Symposium, emphasizing the global movement toward sustainable, intelligent, and inclusive mobility. The proposed AI intervention aligns with emerging international standards, including the African Union’s efforts to establish a regional strategy for trustworthy AI.