With decades of experience navigating the complex intersection of logistics, technology, and government policy, Rohit Laila offers a sharp perspective on the regulations that shape the American trucking industry. His insights are crucial as federal agencies turn to data-driven methods to craft safety rules. We sat down with him to discuss a new federal inquiry into driver schedules and crash risk, exploring the potential pitfalls of biased data, the limitations of current metrics, and the real-world impact these studies could have on the men and women behind the wheel.
A federal plan to link driver schedules to crash risks will rely heavily on telematics data. How might this approach create a biased sample by excluding certain carriers, and what specific steps could researchers take to ensure owner-operators and smaller fleets are properly represented?
This reliance on telematics is a huge red flag because it creates an inherent bias right from the start. You’re immediately filtering out a massive segment of the industry—the owner-operators and small fleets who might not use these specific, prescribed systems. It’s like trying to understand a city’s traffic patterns by only polling people who drive one brand of car. To fix this, the FMCSA needs to get its hands dirty with some real outreach. They can’t just put out a notice and hope for the best. They need to actively target and recruit these smaller carriers, perhaps offering incentives or alternative data submission methods that don’t require expensive, specific technology. The goal must be to build a sample that mirrors the actual makeup of the industry, not just the most technologically convenient slice of it.
Hours-of-service logs are often used to measure compliance, but not necessarily a driver’s actual fatigue level. Could you explain why HOS data alone is an insufficient measure of alertness and describe what alternative metrics or control groups would create a more scientifically valid study?
It’s a fundamental misunderstanding of the job. An HOS log is just a clock; it tells you how long someone has been “on-duty,” but it reveals absolutely nothing about their physical or mental state. A driver could be perfectly alert after eight hours or dangerously drowsy after just three, depending on sleep quality, stress, or even the time of day. We know crash rates can spike during certain hours for reasons that have no connection to a trucker’s logbook, like increased commuter traffic. To make this study scientifically sound, you must incorporate control groups. For example, you could compare drivers on different schedules in similar traffic conditions or analyze incident rates across the general motoring public during the same timeframes. Without that baseline for comparison, you’re just drawing conclusions in a vacuum.
The stated goals of the federal inquiry include assessing hours-of-service provisions, which could lead to new regulations. Based on the current study design, what are the potential real-world consequences for a driver’s daily schedule and pay if flawed data is used to change these rules?
The consequences are enormous and could be devastating. Imagine regulations being rewritten based on data that only represents large carriers with predictable, dedicated routes. The new rules might look sensible on paper but could be completely unworkable for an owner-operator who needs flexibility to adapt to changing loads and shipping delays. You could see a tightening of rules that unnecessarily restricts their earning potential, forcing them into inefficient schedules or making it impossible to find safe parking during mandated breaks. It’s not just about a few minutes here or there; it’s about their livelihood. If the data is flawed, the rules will be flawed, and it’s the driver on the highway who will pay the price, both in their wallet and potentially in their safety.
The proposal involves selecting just 60 carriers to represent the entire trucking industry. How does such a small sample size risk producing skewed results, and what would a more statistically sound and diverse selection process look like in practice?
Using only 60 carriers to represent an industry with hundreds of thousands of companies is statistically questionable, to say the least. You simply cannot capture the immense diversity of trucking—from a single-truck owner-operator hauling produce to a mega-fleet with thousands of vehicles on dedicated routes—with such a tiny group. The risk of getting skewed, unrepresentative results is incredibly high. A sounder process would start by segmenting the industry by fleet size, type of haul, and geographic region. Then, you’d need to randomly select a statistically significant number of carriers from each of those segments, not just 60 total. It’s a more complex and labor-intensive approach, but it’s the only way to ensure the final data is a true reflection of the industry and not just an arbitrary snapshot.
What is your forecast for the future of hours-of-service regulations and data-driven safety enforcement?
I believe we’re at a crossroads. The push toward data-driven regulation is inevitable and, if done right, can be a good thing. However, the current approach shows a dangerous lack of understanding of the industry’s complexity. My forecast is that we’ll see continued conflict between regulators and the industry, particularly smaller operators, until the agencies embrace a more collaborative and scientifically rigorous method of data collection. If they continue down this path of using biased, narrow datasets, they will create regulations that are not only ineffective but potentially harmful. The future of effective safety policy depends on whether they are willing to listen to a full range of voices and gather data that reflects the real world, not just the one that’s easiest to measure.
