Xtelligent

Xtelligent is an artificial intelligence and algorithm-based company tackling urban traffic congestion. Founded in 2018 by Michael Lim, the Los Angeles-based startup is commercializing technology developed with funding from U.S. governmental bodies, including the Department of Transportation and the National Science Foundation. Lim's background in infrastructure, including work for the United Nations in post-conflict zones, inspired him to apply his data and infrastructure skills to optimize U.S. infrastructure projects. The company's core research has been recognized with multiple awards, including the Donald P. Eckman Award in 2017.

The firm's main line of business is upgrading outdated traffic signal networks, 98% of which currently operate on 1970s-era timer technology. Xtelligent targets municipalities and cities as its primary clients, offering a capital-light, scalable solution to enhance traffic flow for vehicles, public transit, and pedestrians. The business model centers on deploying its advanced traffic control system, which integrates with existing and new sensor technologies to create a decentralized and responsive network. This approach is designed to be more resilient to disruptions like accidents and cyber-attacks.

Xtelligent's flagship product is an Adaptive Traffic Control System (ATCS) that employs AI, machine learning, and advanced control algorithms. Unlike opaque "black box" algorithms, the company's technology is designed to be understandable to traffic engineers. It mimics the way the internet manages data flow, allowing for real-time, decentralized adjustments to traffic signal timing based on current conditions. This system has demonstrated significant benefits, with modeling from the University of Southern California showing potential for a 50% improvement in throughput performance and road reliability. Real-world applications have shown a 29% improvement in network travel times. The technology also contributes to environmental goals by reducing congestion and vehicle idling, which can lower greenhouse gas emissions by an estimated 25%.

Keywords: traffic management, adaptive traffic control, smart city, intelligent transportation systems, AI in transportation, traffic signal control, urban mobility, congestion reduction, transportation technology, Michael Lim, decentralized network control, traffic flow optimization, sustainable transportation, smart intersections, road network reliability, GHG emissions reduction, urban infrastructure, transportation data analytics, V2X communication, connected vehicles