Traditional traffic control systems, primarily reliant on fixed traffic lights and manual surveillance, face efficiency and adaptability challenges. Their preset operating schedules do not account for real-time traffic conditions, leading to congestion, increased emission levels, and accidents.
Manual traffic monitoring is also labor-intensive and prone to human error. It leads to suboptimal traffic flow and safety concerns.
Integrating modern technologies like Artificial Intelligence (AI) and Machine Learning (ML) into traffic control systems presents a modern solution to these age-old problems.
These systems leverage AI, real-time data analysis, ML, and predictive algorithms to dynamically manage traffic flow based on current conditions. This adaptive approach enhances traffic efficiency, reduces congestion, minimizes emissions, and improves road safety.
Modern systems can process vast amounts of data from sources, including cameras and sensors, to make instant decisions that traditional systems simply cannot match.
This article highlights five growth-stage innovative traffic control startups implementing AI in transport systems. These startups have the potential to grow rapidly, are in a good market position, or can introduce game-changing traffic control tech to the market in the next 2-3 years.
This makes them a great option to partner, collaborate, or acquire.
1. Machine Can See Traffic Patterns with Pseudo-LiDAR Technology
| Founding Year | 2019 |
| Headquarters | New York, US |
| Total Funding Amount | $126K |
| Last Funding Round/Amount | Non-Equity Assistance |
| Website | https://www.machinecansee.com/ |
Traditional 2D imaging solutions fail to understand complex scenes and movements in urban environments accurately. Traffic cameras often struggle with depth perception, object tracking across occlusions, and real-time data processing. These factors are crucial for effective traffic management and autonomous vehicle navigation.
To solve these issues, “Machine Can See” has developed a technology known as Pseudo−LiDAR, which leverages AI to transform monocular cameras into advanced 3D sensing systems.
This technology uses standard cameras to capture depth information and then process it to simulate LiDAR output, allowing for precise 3D mapping and real-time object tracking.
The system includes features like 3D motion prediction, physics simulation for enhanced scene understanding, and the ability to handle camera movement and predict movements despite temporary occlusions.
This technology delivers LiDAR-like capabilities without the high costs associated with actual LiDAR sensors. Pseudo−LiDAR provides a cost-effective, scalable, and efficient solution that significantly reduces data transmission needs by processing data on edge computers.
The system does not require extensive cloud computing resources and allows immediate data deletion post-analysis. Its vector-based output ensures that only essential information is extracted to maintain privacy.
Co-founder and CEO Vladan Damjanovic has been leading Machine Can See with his extensive experience. He has been a co-founder and CEO of multiple startups like Printing Cute, AlteaCasa LLC, KAZA.rs, and MyBabycards.ie.
Vladan has experience in technical roles like website developer and UI/UX designer, as well as operational roles (CloudParc).
The startup raised its latest non-equity assistance funding on Apr 30, 2021.
2. Intelligent Traffic Control (ITC) Using ML And Computer Vision Algorithms to Mitigate Traffic Congestion
| Founding Year | 2019 |
| Headquarters | Tel Aviv, Israel |
| Total Funding Amount | $10 Million |
| Last Funding Round/Amount | Series A/$5 Million |
| Website | https://www.itc.city/ |
Every year, drivers spend countless hours stuck in traffic, affecting individual productivity and having broader economic and environmental impacts. Traffic congestion leads to significant economic losses due to wasted fuel and lost labor, and it is responsible for a considerable portion of urban greenhouse gas emissions. The existing traffic management systems are often unable to adapt dynamically to changing traffic conditions, leading to inefficiencies and prolonged congestion.
ITC has developed a sophisticated AI-powered traffic management system to tackle these challenges. This system utilizes state-of-the-art computer vision algorithms and machine learning techniques to measure, predict, and mitigate traffic congestion.
By leveraging off-the-shelf cameras installed at various intersections, ITC’s technology captures and analyzes traffic data with a 99% accuracy rate. The system identifies road users, including vehicles, buses, cyclists, and pedestrians, and gathers detailed behavioral data.
ITC’s technology predictive capability and dynamic response sets it apart from other technologies. The system can forecast traffic patterns hours in advance, allowing for preemptive adjustments to traffic light sequences to smooth traffic flow before congestion can form. This proactive approach helps to prevent traffic jams and gridlocks, significantly enhancing the efficiency of road networks.
Aharon Brauner is the co-founder and CEO behind this traffic control startup. He developed the idea of ITC in conjunction with Ben Gurion University within the Google accelerator.
Brauner holds a BS in Computer and Electrical Engineering from Ben-Gurion University. His specializations include RF and Electromagnetic wave distribution, VLSI, Semiconductors, and nanotechnology. He worked as an R&D Engineer for medical startup keepMED Ltd.
ITC raised its latest Series A funding on Apr 18, 2023, for $5 Million.
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3. Asura Technologies’ AI-driven Vehicle Recognition Unit Monitor Roads, Tolls, And Parking
| Founding Year | 2017 |
| Headquarters | Budapest, Hungary |
| Total Funding Amount | $19.8 Million |
| Last Funding Round/Amount | Convertible Note/$7 Million |
| Website | https://www.asuratechnologies.com |
Traditional traffic control solutions rely heavily on static hardware such as radars, laser scanners, and RFID scanners, which can be costly, inflexible, and often inefficient. Due to the need for multiple devices, these systems typically have limitations in data integration and high operational costs. Their poor adaptability to varying traffic conditions also leads to vehicle detection and data analysis inaccuracies.
To address these challenges, Asura Technologies has developed a suite of AI-driven traffic monitoring tools, including the Asura Vehicle Recognition Unit. This platform utilizes advanced AI and machine learning algorithms to enhance traffic monitoring and management.
By leveraging continuous video streaming, Asura’s technology enables precise vehicle detection, biometrical access control, emission zone monitoring, congestion charging enforcement, and public transport priority without additional triggering devices.
The system is fully customizable and integrates seamlessly with existing infrastructures.
Apart from traffic control, the startup offers solutions for tolling and parking challenges with its AI-integrated systems.
Asura’s products rely on AI-driven video analytics instead of hardware sensors. This approach eliminates the need for cross-calibration of multiple devices, significantly reducing maintenance costs and improving system reliability.
Mate Kiss has been leading this startup as CEO and co-founder since its inception. He is also a board member of Parking Revenue Recovery Services, Inc. (US). His extensive experience includes roles of Director of Business Development, Business Development Manager, and Project Development Manager.
Asura Technologies raised its latest funding of $7 Million from a Convertible Note round on Sep 1, 2022.
Related Read: Smart tire companies making transportation safe and sustainable!
4. ThruGreen Integrating Traffic Control Systems with Its Advanced Traffic Management Platforms
| Founding Year | 2017 |
| Headquarters | Virginia, US |
| Total Funding Amount | $40K |
| Last Funding Round/Amount | Convertible Note/$40K |
| Website | https://thrugreen.com/ |
Traditional traffic control systems often rely on decentralized and manual monitoring methods that can be time-consuming and error-prone. These systems struggle to adapt to real-time changes, leading to inefficient traffic flow, increased congestion, and higher risks of accidents. Moreover, the maintenance and management of these systems require significant manpower, as technicians must physically visit each traffic control site to adjust settings or perform routine checks, which is costly and inefficient.
To streamline these processes, ThruGreen has developed an advanced traffic management platform that centralizes and automates traffic infrastructure monitoring, management, and integration. Its technology suite includes real-time monitoring and management tools that can be accessed remotely from a laptop or phone.
The system incorporates data traffic controllers, backup battery systems, MMUs, LiDARs, detectors, signs, RSUs, CCTVs, school flashers, cellular routers, GPS transponders, and vehicle location APIs to a single interface. This system enables real-time alerts and notifications via email and text.
ThruGreen’s technology allows for immediate remote adjustments to traffic settings. This reduces the need for on-site visits and enhances the responsiveness to traffic incidents and maintenance needs.
David Nguyen has been the founder and CEO of this startup since its inception. He also owns Volta Grand Prix, LLC, a startup that develops futuristic EV racing karts.
His industry experience includes roles such as Technical Advisor, Automotive Engineering Manager, Senior Engineer/Scientist, and Automotive Engineer.
Nguyen worked with companies like AAA National, American Trucking Associations, Oblon, and Schafer Corporation.
ThruGreen secured $40k by providing a convertible note in its latest funding round on Aug 31, 2017.
5. Valerann Lanternn System Enhancing Road Operators’ Capacity
| Founding Year | 2016 |
| Headquarters | Tel Aviv, Israel |
| Total Funding Amount | $22.8 Million |
| Last Funding Round/Amount | Series A/$17 Million |
| Website | https://www.valerann.com/ |
Current traffic control relies on systems that can be static and unresponsive to real-time conditions. This often leads to congestion, inefficient roadway use, and increased accident rates. These systems cannot dynamically adapt to varying traffic patterns, leading to suboptimal traffic flow and increased pollution.
To address these challenges, Valerann has developed “Lanternn by Valerann™,” a sophisticated AI-driven software solution integrating various data sources to offer real-time, comprehensive road condition insights. This platform utilizes AI to fuse data from navigation apps, IoT sensors, cameras, social media, and connected vehicles, providing actionable intelligence for traffic management and control centers.
Lanternn by Valerann™ enhances the capacity of road network operators to respond promptly to changes in traffic conditions, thereby improving road safety and reducing congestion.
Co-founder and CEO Gabriel Jacobson has been leading this startup with his law and economics education background. He holds an MBA from London Business School. Before founding Valerann, he was a Commercial lawyer for S. Friedman & Co.
Valerann raised $17 Million from its latest Series A funding round on Feb 22, 2022. Out of three investors, HG Ventures participated as the lead investor in this round.
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Authored By: Naveen Kumar, Market Research
