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AI in Traffic Management

Artificial Intelligence (AI) has a wide scope of application and can be used to make measurable impact in the creation of smart traffic systems. In this article, we explore how AI can be used to enhance traffic efficiency, its many benefits, and how it set to expand in the future.

Published

August 2, 2023

The use of Artificial Intelligence (AI) in vehicular traffic

Introduction

The Importance of Traffic Management

Traffic Management refers to the organization, guidance, and control of a traffic system consisting of stationary and moving traffic, traffic lights and physical infrastructure, pedestrians and cyclists.

Traffic Management has undergone rapid evolution since the introduction of smart tech such as AI-based video analytics, and given way to Active Traffic Management (ATM). ATM allows traffic to be managed in a dynamic way, i.e, according to current or expected traffic conditions.

Intelligent Traffic Management
Intelligent Traffic Management is a key component of smart urban planning processes

The importance of this lies in enhanced safety and efficiency of traffic systems - a lowered rate of emissions, conservation of resources, quick identification of safety-critical events to minimize injuries and accidents, and reinforcement of traffic rules.

Role of AI in improving Traffic Efficiency

The use of AI in traffic management is relatively recent, and is based on a focus on the collection and consequent analysis of real-time data. AI technologies offer traffic planners access to tools that can be used to analyze, detect, and predict patterns in traffic and help the establishment of a traffic network where everything functions like clockwork.

In this article, we explore the many ways in which AI aids traffic management, its overall impact, benefits and challenges, and what to look forward to in the future.

How AI can transform Traffic Management

Real-time Traffic Monitoring and Analysis

This category deals with AI-based methods that can be used to monitor and analyze traffic flow in real time.

  • Traffic flow prediction - AI models can be designed to run analysis on historical and real-time traffic data. This is done in order to consolidate the data and use it to understand patterns and trends in traffic flow. Predictive analysis is used by traffic planners to forecast future conditions so that personnel are better able to deal with it effectively in terms of resource allocation, route optimization to minimize traffic congestion, and the adjustment of traffic signal times.
  • Incident detection and management - AI-powered systems can be used to identify and detect traffic incidents such as accidents, wrong-way driver detection, overspeeding, or road blockages. Once detected, this information can be used to immediately dispatch personnel to the site and ensure a speedy response. It can also be used to hasten supplementary actions such as rerouting traffic from the area.
  • Adaptive traffic signal control - Traditional traffic signal systems are programmed to operate on fixed schedules, proving inefficient in unanticipated scenarios and causing inefficient traffic flow. Adaptive traffic signals are deployed to align with shifts in demand. These signals can identify peak demand conditions and adjust their timings accordingly. This helps in optimizing traffic flow and reduces congestion by placing priority on high-traffic roads.

The video below shows how intelligent video analysis can be used together with regular security cameras to obtain real time information about traffic flow and congestion. The insights generated by such solution can be used to optimize the traffic control to changing traffic patterns.

Intelligent Transport Systems (ITS)

Intelligent Transport Systems are smart means of transportation, wherein traffic conditions such as demand, weather, and accidents are continuously monitored.

  • Vehicle-to-everything communication (V2X) - Vehicle-to-everything communication (V2X) refers to the communication between vehicles and any entity that impacts, or may be impacted by, the vehicle. This includes vehicle-to vehicle communication (V2V), vehicle-to-infrastructure communication (V2I), vehicle-to-pedestrians communication (V2P), and vehicle-to-network communication (V2N). These are part of a system that works to improve road safety and traffic conditions. A key enabler of ITS, V2X is linked to several co-existing benefits such as an improved exchange of safety-critical information, the ability for drivers to make more informed decisions, optimization of the performance of a transportation system, and reduced consumption of fuel.
  • Connected and autonomous vehicles - Connected and autonomous vehicles are part of V2V, and refer to vehicles that exchange information directly with nearby vehicles. The data shared contains information about speed, position, acceleration, and braking. This is done in order to enable collision and congestion warnings, route optimization, and other advanced applications.

AI-based Solutions for Traffic Optimization

Traffic Prediction Models

Traffic prediction models are models that forecast traffic conditions using techniques trained to extract relevant information from historical or real-time data about traffic flow, congestion levels, commute times, etc. and use it to generate accurate and reliable predictions. 

Predictive analysis in traffic
The use of predictive analysis is used to better anticipate traffic conditions in the future and prepare accordingly

AI finds wide application in this area, and can be used to create and better understand the phenomenon of traffic forecasting.

Machine Learning Models

Machine learning models or algorithms are trained using historical databases to learn and identify patterns between the variables that impact traffic flow. The algorithms are then used to automatically detect and adapt to these patterns, if experienced in real-time. 

One of the primary benefits of using a machine learning model is that it can be used to analyze data of increased complexity and non-linear relationships between variables to generate predictions of enhanced accuracy.

Data Sources used in Predictions

A process of predictive analysis is usually the consequence of several sources of data - historical records, weather data, GPS traces from vehicles and mobile devices, and existing information about road networks. This is also where real-time sources come in such as security camera-based video analytics and sensors, which can be used to ascertain the reliability of predictions.

Digital Twins

As the name may suggest, digital twins are an increasingly popular tool used to forecast the results of a particular project or function. They are virtual replicas of physical assets, used by data scientists or IT professionals to run simulations of the process under development.

They can be used to build virtual models of traffic-based structures such as bridges, highways, and road networks. The digital twin is then calibrated to different KPIs and conditions such as varying levels of congestion, maintenance events, shifts in traffic demand, etc. to evaluate the effect of these factors on traffic flow. Commonly known as 'device shadow', digital twins allow personnel to accurately gauge the efficiency and impact of a physical asset prior to its official establishment, and identify areas for improvement.

Traffic Signal Optimization

The optimization of traffic signal systems is one of the best ways to regulate traffic flow in a way that corresponds to demand requirements, congestion, and pedestrian data.

traffic signal optimization
Traffic Signal Optimization is used to better regulate traffic flow depending on traffic demand, weather conditions, speed limits, etc.

Coordinated Progressive Signaling

Deployed in urban areas typically expected to have busy traffic conditions, coordinated progressive signaling enables a network of intersection signals (signals deployed at busy cross-sections) are coordinated and can be leveraged to maintain a constant flow of traffic.

Coordinated Adaptive Signal

Adaptive traffic signals are deployed to align with shifts in demand. These signals can identify peak demand conditions and adjust their timings accordingly. This helps in optimizing traffic flow and reduces congestion by placing priority on high-traffic roads.

Route Optimization and Navigation

AI-based Navigation Applications

AI-powered navigation applications have transformed the way that people navigate and travel. There is more than just directions to the destination, these apps leverage real-time traffic data from sources such as GPS signals, traffic sensors, and user-generated data.

This data is, in turn, used for dynamic rerouting of vehicles based on conditions. The primary principle here is variable speed limit (VSL) that can be aligned to be congestion-responsive or weather-responsive, and slow or speed up traffic as required.

Integration with Connected Systems

One of the many useful results of an AI-oriented navigator is that it offers users integration with a system of interconnected devices and networks. This can be used to facilitate services that directly or indirectly impact traffic conditions in an area - such as ride-sharing apps, parking areas, and EV charging stations. 

Benefits and Impact of AI in Traffic

Improved Traffic Flow and Reduced Congestion

As mentioned multiple times in the article already, AI-based traffic technology is predominantly used to drive an improvement in traffic management and bring a reduction in congestion levels.

Smart traffic management is a delicately woven together network of interconnected processes, each with measurable influence on the functioning of the other. What AI does is allow easier and faster coordination in order to facilitate traffic personnel, drivers, and commuters to be able to better optimize their travel routes and time.

To state an example, during times of high traffic demand, a protocol called hard shoulder running (HSR) is often deployed. The dynamic use of hard shoulders as extra driving lanes to temporarily increase roadway capacity is carried out. The shift in traffic demand can be anticipated more reliably with AI-based techniques in place, thereby better preparing personnel to deal with the situation.

Safety and Accident Prevention

AI can be used to secure a traffic plan that heroes the objectives of safety for every vehicle, pedestrian and cyclist - in order to ensure that accidents and injuries are at a minimum.

AI can also be leveraged in the case of connected vehicles so as to further the cause of V2X, and allow emergency services to be better positioned to access accident sites in real time. An example of this can be understood by highlighting Emergency Vehicle Preemption (EVP), wherein the normal timing of traffic lights are interrupted to allow emergency vehicles to pass through intersections quickly and safely.

This can be done through various methods such as deployment of pedestrian counters and bicycle counters, traffic cameras that are configured to collect and analyze data in real time, smart street lights, and additional facilities for vulnerable road users (VRUs).

Environmental Benefits

The fight against climate change is ongoing, with the EU proposing to cut down on vehicle-based emissions by 55% from what it was in 1990, by the year 2030. One of the key players in the achievement of this object is AI, and how it can be leveraged for actions such as infrastructural changes that reduce travel time, optimized parking lots (did you know German drivers spend an average of 41 hours a year searching for a suitable parking spot?), and a general awareness of fuel consumption.

Economic Impact

The economic impact of AI in traffic promises to be massive and can be already felt in the ways that AI-based traffic systems impact daily processes.

An example of this can be found in the use and deployment of resources. With tools for predictive analysis and real-time traffic monitoring, it is now possible to deploy resources as per requirements. Excess expenditure can be avoided and remaining funds can be diverted towards welfare and development programs to further enhance the economy and livability of the area.

Challenges and Considerations

Data Privacy

While AI has the potential to offer countless benefits in the traffic domain, it is important that data privacy standards be stringently maintained. One of the ways in which to do this is by ensuring the anonymization of data streams, to protect private information such as identity, financial figures, etc. 

In the context of VSaaS for traffic, privacy masks are a good way to carry out the same. It is automatic and applicable to any detected object, and makes privacy absolutely airtight.

It is also necessary to ensure that all AI-powered applications be compliant with local and global privacy regulations (GDPR for the EU, CCPA in the US, etc.)

Security

Data security refers to the safeguarding of digital data in a database against unwanted access such as in a cyber attack or data breach. The primary objective is to protect the data against exposure, deletion, or corruption.

There are many ways to implement good data security. Some well-known options are multi-factor authentication, encryption of data, and using a firewall. When applied in conjunction, they can largely enhance how secure the data is.

Ethical Decision-Making

One of the key advantages of AI is that it allows the automation of most operational processes, thereby allowing for less error and reduced bias. However, the human component is crucial in making sure that ethical principles are upheld when making important decisions.

True success with AI can only be achieved when a judicious mixture of man and machine comes together with the aim of eliminating bias.

Public Acceptance and Trust

Just as with any other phenomenon, the use of AI in traffic management can be fully implemented only when it is accepted by all stakeholders in the process - personnel, planners and architects, drivers and commuters, law enforcement, and the general public.

Awareness is imperative, as is positive reinforcement through consequence-based decisions to help cement the public’s trust in AI.

Future Trends and Innovations

The future of AI in traffic is alive with possibilities, each one more impressive than the last.

One of the most important future trends is smart cities, and how intelligent traffic management can be juxtaposed with urban planning hierarchies in order to build smarter, greener cities. 

The core concept of smart cities relies heavily on data-oriented decision making. A city is typically composed of many simultaneously occurring systems - road traffic, rail and water-based transportation, infrastructure, parking facilities, etc. In order to effectively coordinate every individual cog, it is essential to have access to consistent, reliable data and be able to perform speedy analysis on it.

With the help of AI and machine learning, large volumes of data can be analyzed and used to anticipate future events.

More about Isarsoft

With Isarsoft Perception, your camera systems become part of your business intelligence. Whether the goal is to increase efficiency, customer satisfaction or safety, Isarsoft Perception provides the insights needed for informed decisions.

The Isarsoft Perception Dashboard
The Isarsoft Perception Dashboard

Contact us, to learn more about how to turn security cameras into intelligent sensors.

References

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