How AI can transform Autonomous Traffic Scheduling
In the wake of urbanization, the world is observing a continuous increase in the growth of population. Due to this factor, there is a growing need for transportation which increases vehicle traffic, including prospering businesses like cargo to meet the needs of the people. However, transportation faces acute challenges to address constant congestion and poor traffic control management with the legacy infrastructures. Artificial intelligence (AI) offers advanced and innovative solutions for these challenges with its autonomous capabilities. The integration of AI is already proving to be a boon for several industries as it has transformed these industries in the way they manage their operations. With AI, transportation departments can benefit tremendously in traffic scheduling as per the traffic patterns, thereby reducing accidents and better optimization of routes for a smoother traffic flow.
Current Traffic Scenario
Traditional traffic management systems often struggle to keep pace with the onslaught of vehicles flowing in rapidly at peak hours. The problem worsens with the modern generation of cars and motorbikes with high speeds to be monitored regularly. Traffic management systems are slower to react, leading to longer congestion that causes delays with slow-moving traffic. Weather conditions and lack of maintenance cause some of the traffic lights to malfunction, which causes a lack of synchronization and prevention of free-flowing traffic.
While autonomous cars are still a few decades away before they prove effective on our roads, it is better equipped with more robust technologies from now than later. City planners and traffic controllers have to cope with the ever-growing mix of human-driven cars and semi-autonomous and autonomous cars soon. Keeping the traffic moving freely is trickier as it requires instant monitoring, reactive capabilities, and adaptability in the traffic management systems. This is where AI is highly effective and can play a more prominent role in autonomous traffic scheduling systems.
Potential Contribution of AI in Traffic Scheduling
Enhancing Road Safety
Traffic scheduling is not limited to managing the congestion on the road but ensuring the overall safety, regulation compliance, and eliminating driving hazards to ensure safety on the roads for all users. Traffic lights can be better optimized with the waiting time on traffic points allowed for pedestrian crossings. With AI, human errors can be reduced, and autonomously traffic lights operate with the cognitive sensing capabilities of AI systems. These models can also monitor regulatory compliance by the drivers and monitor vehicle maintenance with sensors to update records on car emissions.
Efficient Planning
AI is capable of performing with a large amount of data generated from traffic signals across the city. The traffic control departments can use AI efficiently for operational tasks such as optimal route scheduling based on the user data generated by various traffic signals. It allows to minimizes the wait time. Additionally, AI can detect traffic in real-time, and the department can use this data to manage the routes effectively during the day to control slow-moving traffic due to heavy congestion.
Monitoring and Traffic Prediction
Predictive capabilities of AI provide real-time traffic congestion forecasting using traffic monitoring data, including historical and current data. The traffic forecasting data can facilitate better services by allocating extra time to traffic signals where congestion is unavoidable. The results on possible congestion and related road information can be forwarded to the citizens and logistics departments for better route planning. AI can also predict the need for scheduling maintenance for traffic systems to keep them running efficiently at all times.
AI can also monitor traffic safety, speed limit compliance, road conditions, and alert about possible maintenance required in a particular area. Traffic schedulers can be alerted in the event of a vehicle breakdown in odd hours to take an instant action to remove it and avoid unnecessary congestions. If there are accidents or ongoing maintenance, the traffic controllers and citizens can be alerted in real-time to avoid specific routes and plan for alternative routes beforehand possibly.
In Bengaluru, India, Siemens Mobility has developed an AI-based traffic monitoring system integrated into the traffic cameras. These cameras can detect vehicles in real-time, and the information is sent to the central traffic control, where the AI algorithms provide information on the density of traffic on the road at a given time. The information is further relayed to the traffic lights based on the results about the potential road congestion.
Alibaba’s City Brain focuses on minimizing road congestion based on the data acquired from the traffic lights, CCTV cameras, and video recognition technologies to control the traffic flow.
Connected Mobility Infrastructure
The idea of connected mobility is to have interconnected traffic signals interlinked with another to alert each traffic point about the possibility of incoming traffic based on traffic conditions in a nearby traffic intersection. The purpose is to reduce the transit time of vehicles and coordinate by providing critical information on driving hazards requiring rescue vehicles. In developed countries, AI mobility solutions are incorporated to enable green phases for cyclists and optimize well with the ongoing traffic conditions and better communication flow for electric buses to operate efficiently.
In the modern urban infrastructure, intelligent healthcare solutions are increasingly getting developed alongside the mechanisms for intelligent traffic management offerings. The connected mobility solutions aim for solutions where the traffic systems can communicate with nearing innovative healthcare systems in hospitals for emergency medical assistance situations.
Cities like Pittsburgh have already implemented AI intelligent traffic management solutions called Surtrac. The AI technology is designed to optimize traffic flow as multiple dominant flows are shifting throughout the day. Surtrac follows a decentralized way to control traffic within the road network. The AI system offers autonomous traffic control, emission information, idle traffic signal time, and travel time. Surtrac solutions can allocate green time autonomously based on actual incoming vehicle flows, which can also predict the outflow to be forwarded to the neighboring traffic intersection to increase more visibility of future incoming traffic. The future Surtrac innovations are expected to equip cars with talking capabilities with the traffic signals for more real-time information on the traffic conditions.
Similarly, a pilot project is ongoing in the Netherlands that offers traffic lights to be activated when the sensor detects more cyclists through body heat signals.
Selective Vehicle Traffic Management
The purpose of selective vehicle traffic management is to enable emergency vehicles to be detected as a priority to pass quicker and avoid traffic congestion. Siemens’s Unobtrusive above-ground detection is an AI-based solution with high quality and accurate detection rates for traffic control systems for local buses and emergency vehicle priority.
AI-Driven Traffic Scheduling is the Need for Current and Future Generations
As AI continues to rise, it has become more advanced with sophisticated innovations capable of providing solutions with pinpoint accuracy. Today, real-time planning and decision-making are of high importance and even more when it comes to traffic scheduling. The growing concerns of increasing vehicle traffic and pedestrians require hi-tech solutions capable of solving and managing traffic control faster and efficiently. With AI, there is no issue with weather conditions, and even malfunctioning of traffic systems can be predicted ahead of time to ensure no disruptions occur in traffic management. Manual interpretations can go wrong alongside fatigue and pollution playing significant roles in low-quality traffic management. The use of traditional systems causes additional problems with no scope of flexibility or adaptability. Thus, AI emerges to be the robust solution that is likely to play a significant role in managing and scheduling traffic with minimal or no manual interventions, thereby improving the traffic control efficiency and reduce the cost of operations. With other crucial additions in a traffic scheduling system, such as monitoring, maintenance, and regulatory safety compliance, AI has indeed turned out to be the most comprehensive traffic scheduling system that can keep roads safer and congestion-free.
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