TrubAI: Understanding Human Behavior to Build Transportation Ecosystems Better

A smart transportation revolution is underway, not in the rate and scale of new infrastructure development, but in how predictive analytics and intelligent data-driven solutions can be used to understand human behavior, further driving decisions and solutions for greater efficiency. Leading that charge is TrubAI, an up-and-coming company harnessing the power of artificial intelligence and smart analytics to tackle the growing challenges of urban logistics and infrastructure. With an intelligent and adaptable approach, TrubAI is shaping the future of traffic management, optimizing mobility, and making cities more efficient and sustainable in the process. Urban areas face a host of transportation issues, from unpredictable congestion and slow response times to accidents and inefficient use of infrastructure. Traditional traffic management solutions, reliant on static sensors and historical data, often fall short in responding to the dynamic nature of urban mobility.

Adero Miwo, Founder, TrubAI and fellow congestion-hater, articulates the problem succinctly: “Transportation networks today operate in a reactive state rather than a proactive one.” Congestion is not conducive to modern day life. It is a byproduct of it, to be sure, but it certainly does not have to be a “guaranteed” factor. Miwo describes the inception point of TrubAI: “I was just in traffic one day and thought to myself, ‘why am I slowed down for 40 minutes and there’s nothing that happened?’  I would get frustrated in the car. It was an extreme irritation and then I realized I had managed to pass it down to my kid, which is not good. I just thought, ‘let’s figure this out.’” Transportation issues and increased congestion, when there is no good reason for it, is simply a waste of time, and time is precious. “We all have to get from point A to point B every day. Our mood is movement, and we need to figure out ways to make it more efficient and more enjoyable,” Miwo says.

Helping Transpose Predictions into Solutions

The aspect of TrubAI that is the most important to understand is the company’s focus on human behavior and other culture-driven scenarios that help transpose predictions into solutions. As Miwo says, “We were able to predict which mode of local transportation with about 80% accuracy a user would choose all the way down to the ride share company like Uber versus taxi versus Waze–we were able to predict with greater accuracy and think about solutions in those contexts.” Urban planning solutions also rely on making spaces sustainable and usable. When considering where to place EV chargers, urban planners look for empty space, like at a gas station for instance. “But the process takes 45 minutes. It’s not like filling up your tank for 30 seconds. No one likes to hang out at the gas station so if you’re putting a charger in a place where a person can’t even really socialize, there’s nobody there. Instead, putting them by a cafe, for instance, will allow people to kill time there, so more people will use that set of EV chargers,” Miwo explains.

Another example is improving bus efficiency or bus app efficiency. TrubAI’s systems would start to contextualize the data about who really lives there in the local context to understand those traffic patterns. “We really look at who lives in that area, which is extremely dynamic. In this particular area, you have a lot of long-term homeowners. You have a lot of people over 75, so your bus efficiency is probably going to slow down, so you might want to redistribute that timing,” Miwo says. Using a combination of machine learning algorithms, IoT-enabled sensors, and predictive analytics, TrubAI provides real-time insights that help cities and urban planners make data-driven decisions.

A Solution with Adaptability and Scalability

The platform collects and processes vast amounts of quantitative traffic data from multiple sources, including surveillance cameras, GPS tracking, road sensors, and historical congestion patterns. The model is also built on qualitative research that details human behavior patterns, mostly driven by academic research. Through advanced AI modeling driven by Anthropoid, TrubAI can take queries from end users and scan through the data to provide a detailed PDF. TrubAI’s hub of proprietary data is also used in tandem with any specific datasets that the end user uploads. “The PDF report is easy to read and detailed, providing the solution and giving more explanation behind why the problem is occurring. The system also checks back in with the end user if prompted to provide another response,” Miwo explains. Continual feedback to the system, provided by end users, can help train the model too. The model determines the best, most cost-efficient solution for the end user but also seeks to explain what the problem is occurring in the first place.

While other transportation solutions rely on static rule-based systems, TrubAI’s key differentiator lies in its adaptability and scalability. The company’s AI-driven approach allows it to provide real-time recommendations that are tailored to specific environments, whether in large metropolitan areas or industrial logistics hubs. For instance, TrubAI is also partnering with Autodesk to create digital twins (virtual replicas) of site environments, so even a real estate development company could input data into TrubAI and prompt the system to determine whether a 57-car garage, for instance, is reasonable to place in a specific spot.  If it isn’t, the digital twin can be used as a visual reference point to determine other practical areas for implementation.

These types of insights, driven by TrubAI’s platform, help ground solutions in the real-world. This is also true for scenarios where CO2 emissions need to be curbed and TrubAI can provide the analysis for why the problem is occurring and how to remedy it. “Essentially, we take the human and quantify it qualitatively,” Miwo says.

Building Success Stories

A compelling case study underscores the impact of TrubAI’s technology. A major metropolitan city faced escalating congestion issues, with peak-hour traffic delays increasing annually. By deploying TrubAI’s AI-driven analytics, the city achieved a 23% reduction in congestion, a 15% improvement in travel times, and a significant decrease in carbon emissions from idling vehicles. Beyond municipal applications, TrubAI has helped logistics companies reduce fleet travel times by 18%, lowering fuel costs and increasing delivery efficiency. These tangible benefits illustrate the transformative potential of how AI can be used to provide insight into adoptable, affordable solutions.

TrubAI’s success is driven by a team of experts at the intersection of artificial intelligence, urban planning, and transportation engineering. The leadership team includes machine learning pioneers, former city planners, and experienced technologists who collectively bring a deep understanding of transportation challenges. By combining expertise in data science with practical industry knowledge, TrubAI has built a solution that is both innovative and highly effective.

Looking ahead, TrubAI is poised to expand its footprint globally. The company is actively working on next-generation AI models that leverage even more sophisticated deep learning techniques to enhance predictive capabilities. Additionally, TrubAI is exploring partnerships with autonomous vehicle manufacturers and urban infrastructure developers to create fully integrated smart transportation ecosystems. TrubAI’s mission is to build a world where transportation is seamless, efficient, and intelligent. By realizing the potential of human behavior to predict known patterns that guide solutions implementation, not only are the solutions themselves sustainable and more cost efficient, but they restore quality to our movement–as individuals, as commuters, and as a collective.