Image Source: IEEE — Smart Town Traffic Management System Using LoRa and Machine Learning Mechanism

Artificial intelligence and Machine Learning (ML) techniques have gained traction in transportation system management as a key part of Smart City and Intelligent Transportation Systems (ITS) projects and initiatives - topics Antaira explored in previous white papers and blogs. Some of the most significant and recent advancements in AI-based transportation networks happened with the integration of sophisticated ML algorithms into ITS technologies. ML algorithms "learn" from studying traffic patterns, pedestrian behaviors, and other experiences so they are constantly improving their models and by extension, improving the safety of roadways.

Traffic management is highly complex. The only possibility of dealing with its tsunami of data is to abandon traditional traffic management approaches and turn control over to AI. Artificial intelligence will analyze, summarize, and finally relay back to an administrator actionable insight that can reduce congestion, and possibly save lives. Sequenced follow-up actions can range from dispatching emergency services to adjusting traffic signal timing system controls. In most cases, responses are automated without human interaction.

AI’s use in ITS has been heralded as a new era of mobility, one characterized by improved driver safety and comfort, reduced traffic congestion, lowered carbon emissions, and greater speed and efficiency in supply chain management tasks. That’s not to say AI is without challenges: Infrastructure expense, managing competing priorities among city leaders, and coordinating the multiple parties and technologies involved in projects have slowed adoption, as have public concerns over privacy and security related to sensing. Despite these challenges, combined with ongoing advancements in technology and collaborative efforts from key industry stakeholders, the future of AI in transportation holds immense potential for startups and private companies to pitch their solutions to authorities.

Practical Uses of AI in Transportation

Artificial intelligence integrated with ITS creates a context-aware solution that merges real-time data from connected road infrastructure with predictive analytics to effectively coordinate traffic across key city arteries. Today, we are on the cusp of a proliferation of traffic applications, technologies and services where AI integration takes center stage, namely:

· Autonomous Vehicles:

Artificial intelligence is revolutionizing the development of autonomous vehicles. Advanced AI algorithms and machine learning methods, such as deep learning, enable vehicles to perceive their surroundings, make real-time decisions, and navigate safely. As AI and machine learning technology continues to advance, we can expect increased adoption of self-driving cars, trucks, and even drones, which have the potential to improve road safety, reduce traffic congestion, and enhance overall transportation efficiency. Despite concerns around the technology and its ability to safeguard passengers from harm, KPMG has predicted the adoption of self-driving vehicle technology could reduce the frequency of accidents by approximately 90%.

· Smart Traffic Management:

Artificial intelligence optimizes traffic flow by analyzing real-time data from various sources, including sensors, cameras, and connected vehicles. By using AI algorithms, smart transportation systems can dynamically adjust traffic signals, manage and predict traffic patterns, and help drivers find parking spots to improve the overall performance and efficiency of road networks. Besides helping traffic planners, insights from AI assist commuters with key details on traffic predictions, accidents or road blockages and provide suggestions on the shortest routes to their destinations.

· Road Enforcement:

When a car violates a speed limit or other law, a traffic enforcement camera system, which consists of a camera and a vehicle-monitoring device, detects and identifies the offending vehicle and immediately tickets the driver based on the license plate number. This is referred to as mobile license plate recognition. Tickets for moving violations are mailed. Cameras also identify red light violations, illegal railroad crossings, HOV occupancy offenders, and cars traveling in lanes reserved for buses.

· Predictive Maintenance:

AI can help transportation companies monitor the health of their vehicles, trains, and aircraft by analyzing sensor data and detecting anomalies, just as it does in Smart Factories. Predictive maintenance empowers operators to identify potential failures before they occur, reducing downtime and improving safety. Likewise, predictive maintenance is being applied to road maintenance. By detecting potholes and cracks in roads before they can get worse, AI helps to minimize closure times and reduce the cost of larger repairs. Well-kept road infrastructures also benefit drivers by improving traffic safety and preventing accidents.

· Supply Chain Optimization:

AI can optimize supply chain operations by analyzing vast amounts of data, including historical demand, weather patterns, and traffic conditions. AI algorithms and artificial intelligence can optimize route planning operations, warehouse management techniques, security and operations, and inventory control methods, leading to more efficient logistics and reduced costs.

· Customer Experience and Personalization:

AI-powered systems can personalize the travel experience for passengers. Chatbots, virtual assistants, artificial intelligence, and voice recognition systems and technologies can be developed to provide real-time travel information, answer inquiries, communicate, and assist passengers in various tasks and aspects of their journey, enhancing customer satisfaction.

Hardware Requirements for Artificial Intelligence Transportation Systems

When we think of the capabilities and speed of future AI hardware systems, next generation GPUs systems are likely to come to mind. These GPUs perform highly parallel operations and simultaneous computations at breakneck speed when used as accelerators combined.

Connecting road infrastructure takes much more than GPUs. Gathering instantaneous traffic and lane-related data requires layers of IoT devices capable of autonomously monitoring and broadcasting critical lane and roadway data. Devices include an assortment of different types of sensors, GPS, inductive loops, RFID, pass readers, radar detectors, and IP surveillance video cameras, along with integration with connected traffic and systems, lights and smart toll gates.

Below is a partial list of different types of data collected from these devices:
· Real-time traffic conditions
· Car counting
· Accident detection
· Weather and traffic conditions including air quality
· Public events scheduling
· Congestion at traffic light intersections
· Toll booth monitoring
· Ticket cameras that flag unsafe drivers
· Measuring distance between cars
· Presence of bicycles and pedestrians at crosswalks
· Automated license plate reading
· Outside news feeds, social network media mentions and connected vehicles.

Dispatching the collected network data from a location at point A to point B is achieved by high-speed industrial switches, wireless routers, PoE injectors, media converters, wireless access points, and gateways. All of this industrial networking equipment is hidden away in roadside cabinets and on top towers. Whether network data is transmitted wirelessly, by cellular, or via Ethernet cables, the network data must make its way to a traffic center where analysis is performed by AI/ML software. Anomalies are detected, patterns uncovered, and traffic behavior predicted using algorithms built upon past events.

Artificial intelligence gives meaning to data. Better informing decisions means problems can be solved instantaneously by such actions as altering bus and subway schedules, dynamically using weather conditions, adjusting roadway speed limits and traffic light timing, re-routing emergency response vehicles, or applying smart pricing systems on highways, bridges, and HOV lanes. Perhaps the simplest example would be video cameras detecting an increase in cars nearing a busy intersection. Automatically extending the green light at the intersection will prevent a traffic jam by dissipating the number of cars at the location of the stop. Still another example would be highway speed limits being lowered in response to sensors detecting a dangerous presence of snow and ice on roads.

Antaira's Roadmap for Traffic Management Systems

Behind the scenes making all this automation possible are industrial hardware devices seamlessly performing their duties. Antaira is uniquely qualified to provide the automation for your traffic and control application with these resilient, high quality full-performance system solutions.

Designing a reliable AI-powered intelligent transportation system starts with hardened roadway networking equipment. Virtually every roadside device is subjected to challenges that can knock connections offline. For this reason, each roadway networking device -- from the edge to the control center -- must be environmentally hardened, have a reliable power source, and feature robust connectivity and redundancy to transmit data 24/7/365 without interruption.

At Antaira, “industrial-grade” means that our devices are hardened to comply with international performance standards, such as NECA, ASTM, IEC, UL, CE, ISO, IEEE and EIA/TIA. Commercial-grade components are avoided since these parts are not designed to handle wide temperature swings, heavy vibration, clogging dust, and power surges. Antaira networking equipment is protected by sturdy metal housings rated IP-30 or higher. Decades of engineering rugged networking equipment for petrochemical, industrial automation and agricultural markets have taught Antaira how to manufacture robust connectivity solutions for transportation management systems.

Despite their strength, Antaira devices remain compact enough to easily fit inside heavy-duty NEMA enclosures attached to traffic signal poles or concrete slabs. Antaira offers a complete selection of products specially engineered for tight spaces that feature DIN rail mounting, compact footprints, DC voltage inputs, high EFT/ESD protection capabilities, managed control and unmanaged capabilities, and extended low and high temperature tolerances.

Another key attribute to a successful AI-powered intelligent transportation system is speed. Antaira industrial Ethernet switches support 10G data transmission for bandwidth heavy collection, transmission, and application of real-time traffic data. Ideally suited for backbone and edge device applications, Antaira 10G industrial Ethernet switches manage large and massive amounts of data with smooth and seamless transmission, even in high-density scenarios, therefore reducing the chance of delays or data packet loss. Plus, if you are planning on installing a mix of 10G copper and fiber, Antaira has you covered. Along with multiple 10G Ethernet ports for high density connectivity, many of our 10G industrial switches feature 1G/10G dual rate SFP+ fiber ports. ITS infrastructures are rapidly displacing copper and coax with fiber for both data and video transmission, so this is a feature that you’ll want in your industrial Ethernet switches.

As for redundancy, Antaira takes an open protocol approach. We put interoperation ahead of proprietary protocols so our products are easily interoperable with those of other manufacturers. Redundancy protocols are often proprietary. While this may not pose a problem in small networks, in traffic applications that require frequent expansion, proprietary protocols are both time-consuming and expensive. It compels the network administrator to keep building out using proprietary solutions from a single manufacturer, even if those solutions no longer meet their changing needs. Antaira open protocols help you deliver continuous uptime without limiting you to inflexible proprietary solutions. By using best-of-breed open-source solutions, Antaira ensures that no matter where, how, and when you connect, you've always got options to ensure your data layer delivers. With Antaira, you can use all popular network redundancy protocols including STP, MST, RSTP and ERPS.

What about security? Unfortunately, transportation data is susceptible to cyberattacks that can expose private information or let hackers alter traffic operations with disastrous consequences. As a trusted leader in network security, Antaira engineers its industrial switches, media converters, and wireless routers to streamline the cybersecurity process by incorporating robust, DoD-compliant layer 2 and layer 3 security technologies. Antaira security controls give administrators the tools to build on existing security policies and standards. For example, our Authentication, Authorization, and Accounting mechanism can track users' activities while limiting essential security controls to employees who require them. In addition, Access Control Lists (ACLs) can further further security and filter accessibility by limiting network traffic to only trusted sources, restricting access to designated networks, and allowing user access only to authorized devices.

Finally, all those devices mounted in hard-to-reach places like inside pavement, on weather stations, on top bridges, inside tunnels and on other outdoor infrastructure need power. Simply running a wire to the nearest electrical outlet isn’t the answer. That’s why Antaira 10/100/1000TX industrial switches offer high-port count Power over Ethernet (PoE) technology. Any IEEE 802.3af, 802.3at or 802.3bt (90W) compliant power devices can be safely powered by Antaira industrial Ethernet switches without the need for any additional wiring. Antaira industrial Ethernet switches boast a variety of PoE features such as scheduled power cycles, power budgeting, and port settings, as well as Light Layer 3 network management.

Privacy Issues

AI’s skeptics fret that if we run the transportation playbook forward, there is the real possibility that data safety and privacy will be compromised. To prevent this from occurring, it is important that each development in AI offers steps forward in transparency, stronger governance, adherence to applicable laws, and regulatory compliance to manage potential risks in bias, discrimination, and privacy violations. An ecosystem of tools around safety, compliance, and privacy is emerging as AI gains further traction in transportation applications. Finding balance will require new anti-privacy rules to be constructed in a way that allows innovation to flourish and supports a level playing field.

Antaira has the Industrial Networking Answers

Traffic can be chaos. Designing an intelligent traffic system shouldn’t be.

The Antaira technical support team is here to help you find the best-fit connectivity solutions for your intelligent traffic applications. If needed, you can talk directly to our engineers to gain a better understanding to prevent you from hitting any bumps in the road. Contact Antaira today via email at ITS@antaira.com or call us at (714) 671-9000.