Waymo robotaxis operate empty roughly 42% of the time, according to California Public Utilities Commission data, but this mirrors traditional taxi services and reflects the fundamental economics of shared transportation, not a design flaw. As cities grapple with robotaxi expansion, concerns about deadheading, curb hogging, and charging station use have surfaced. However, a closer look at the numbers and logistics reveals that many of these complaints misunderstand how taxi services, autonomous or human-driven, must operate to serve riders efficiently.

How Do Robotaxis Compare to Traditional Taxis on Empty Miles?

The 42% empty-mile rate for Waymo vehicles is actually competitive with traditional taxi services. New York City taxis, for example, operate with no passengers aboard for 40% to 60% of their miles, depending on the neighborhood and traffic patterns. Manhattan's dense core sees better utilization than outer boroughs, but the overall range is similar to what Waymo is achieving. This suggests that robotaxis are not inherently worse at passenger matching than human drivers; they're simply subject to the same economic constraints.

The empty miles come from two main sources: traveling to pick up passengers and, when no immediate fare is assigned, moving toward zones where demand is expected. This predictive positioning allows robotaxis to offer shorter wait times, which is essential for competitive service. Additional empty miles occur during trips to depots, charging stations, and maintenance facilities, costs that human-driven taxis also incur, though drivers typically handle vehicle cleaning at home rather than traveling to a service location.

Why Is Deadheading Unavoidable for Any Taxi Service?

Deadheading, or traveling with no passengers, is not a flaw but an inherent feature of shared transportation. The alternative is private car ownership, where vehicles sit parked 90% to 95% of the day, consuming vast amounts of urban land. A taxi, whether robot or human-driven, is in use roughly 50% of the day, a significant improvement in land efficiency. However, this efficiency comes with the tradeoff of empty miles between rides.

Reducing deadheading requires a larger fleet, so that the distance between consecutive rides shrinks. But a larger fleet means more vehicles sitting idle during off-peak hours, particularly at night. This is another unavoidable cost of shared transportation. Some of this inefficiency could be offset through ride-pooling, where vehicles with four or five seats carry multiple passengers during peak hours and operate solo rides during slower periods. This approach could narrow the gap between peak and off-peak demand, though it requires passenger cooperation.

Steps to Understand Robotaxi Economics and Urban Planning Trade-offs

• Understand the parking cost equation: Circling to avoid parking would cost robotaxi operators roughly $20 per hour for gasoline vehicles or less for electric models, compared to parking fees of $5 per hour today and potentially under $1 per hour in the future. The math strongly favors parking, making deliberate circling economically irrational.
• Recognize the curb allocation challenge: Cities must decide whether to treat robotaxis differently from private cars, taxis, and ride-hails when allocating curb space for pick-up and drop-off. Restricting robotaxi access to curbs increases deadheading, creating a policy trade-off between curb availability and empty miles.
• Consider enforcement advantages of autonomous vehicles: Robotaxis can be programmed to obey traffic and parking rules, and corporations operating them face significant penalties for violations. This makes robotaxis potentially the most rule-compliant vehicles on the road, unlike private drivers or human-operated ride-hails.

One persistent concern is that robotaxis might circle endlessly to avoid parking costs. However, this scenario is unlikely given the economics. Operating costs for robotaxis are substantial, and parking is far cheaper than continuous driving. Additionally, Waymo has publicly stated it will not engage in this practice. If any company were to circle deliberately, cities have powerful enforcement tools; catching a robotaxi breaking rules is far easier than policing human drivers, and the penalties for corporate violations are severe.

Advertising-driven circling presents a different concern. Some human-driven vehicles already circle in certain areas to display ads, a practice that consumes road capacity without serving passengers. This behavior could reasonably be banned in congested zones, but it is distinct from the operational deadheading necessary for taxi service.

The curb allocation debate reflects a broader principle: should robotaxis be treated the same as other vehicles? If private cars can use curb space for parking or standing, and taxis can use it for pick-up and drop-off, then robotaxis should have similar access. Restricting robotaxis while allowing other vehicles creates an uneven playing field. One strong argument for equal treatment is that robotaxis, over time, are likely to become the most compliant actors on the road. Enforcing rules on private drivers or even human Uber drivers is difficult; enforcing rules on corporations operating autonomous fleets is straightforward.

Waymo's empty-mile performance has improved over time and is expected to continue improving as fleet size grows and ride volume increases. Larger fleets mean shorter distances between consecutive passengers, reducing the proportion of empty miles. This improvement trajectory suggests that current concerns about deadheading may diminish as the robotaxi market matures.

Cities and regulators should recognize that robotaxis face the same fundamental constraints as traditional taxis. The choice is not between robotaxis with some empty miles and robotaxis with none; it is between shared transportation with inherent deadheading and private car ownership with massive parking demands. Understanding this trade-off is essential for making informed policy decisions about curb access, charging infrastructure, and robotaxi operations in urban environments.