From Curb Hierarchies to Priority Rules: A Queueing Decision Screen for Bus, Freight, and Ride-Hailing Access

City streets are no longer just the edges where cars drive; they have become crowded marketplaces where buses, delivery trucks, and ride-hailing cars all compete for the same few feet of pavement. In the past, a curb was often just a place to park, but today it is a scarce resource that must serve public transit, commercial deliveries, and private passenger pickups simultaneously. When a bus stops to let passengers on, a delivery truck cannot drop off a package, and a ride-hailing vehicle cannot pick up a rider. This creates a bottleneck where every vehicle waits for the one before it to finish. The core problem for city planners is deciding who gets to go first. Should the bus always jump the line because it carries many people? Should the delivery truck go first because it is unloading goods? Or should the ride-hailing car go first because it is just picking up a single passenger? For years, the answer has often been a guess based on tradition or political pressure, rather than a clear calculation of what actually saves the most time for everyone.

A researcher at Hanyang University has developed a new way to answer this question, turning a complex traffic problem into a simple decision screen. Instead of asking which type of vehicle is most important in the abstract, the study asks a different question: how much social value is lost for every minute a specific vehicle waits, compared to how long that vehicle actually occupies the curb? The researcher modeled the curb as a single service point where vehicles arrive at a steady rate and stay for a random amount of time. Some vehicles, like buses, might have a very high cost if they are delayed because they carry hundreds of passengers, but they also might take a long time to load. Other vehicles, like ride-hailing cars, might have a lower cost per vehicle but stop for a very short time. The study found that the best rule for deciding who goes first is not to look at the vehicle type, but to divide the cost of waiting by the time the vehicle spends at the curb.

This approach reveals that a vehicle with a high waiting cost does not automatically deserve priority if it also takes a long time to serve. For example, a delivery truck might have a high cost because a failed delivery hurts a business, but if it takes twenty minutes to unload, letting it go first might block the curb for everyone else for too long. Conversely, a ride-hailing car that costs little to wait for but only needs thirty seconds to pick up a passenger might be a better candidate for priority if it is stuck behind a long-stopping truck. The study calculates a specific index for each vehicle type: the social cost of one minute of waiting divided by the average time that vehicle occupies the curb. The vehicle with the highest number on this list should go first. In a test case using realistic numbers, the study showed that following this rule—putting buses first, then freight, then ride-hailing—reduced the total time lost by all vehicles combined.

However, the study also makes a crucial point that often gets overlooked: just because a priority rule saves time does not mean it is worth the cost to enforce it. Changing how a curb works requires money. It might involve painting new lines, hiring enforcement officers, installing cameras, or building a digital reservation system. The researcher created a second part of the decision screen to answer this question. By calculating exactly how much time and money is saved by reordering the vehicles, the study determines the maximum amount a city can spend on enforcement before the system stops making sense. If the cost of the signs and officers is higher than the value of the time saved, the city is better off doing nothing and letting vehicles arrive in the order they show up. In the test case, the savings from prioritizing buses, freight, and ride-hailing in the correct order were enough to justify an operating cost of about 13.3 units of value per minute. If the city's enforcement costs were higher than that, the priority system would actually make things worse overall.

The study explicitly argues against the idea that there is a single, universal rule for all cities, such as "buses always go first" or "freight always goes first." The right order depends entirely on the specific numbers for that street at that time. In some situations, a delivery truck might need to go before a bus if the truck's loading time is very short and the cost of a failed delivery is extremely high. In other situations, a ride-hailing car might deserve priority if the pickup window is tiny and passenger waiting costs are high. The study also rules out the idea that simply having a high cost of waiting is enough to justify priority; the physical space the vehicle takes up matters just as much. The model assumes that vehicles arrive at a steady rate and that their stopping times do not change based on the rules, which is a simplification of reality, but it provides a clear, low-data starting point for cities.

The practical result is a workflow that city managers can use before they invest in expensive technology or complex simulations. They can measure how often different vehicles arrive, how long they stay, and how much it costs society when they wait. They then calculate the priority index for each group. If the index suggests a specific order, they calculate the total time saved. Finally, they compare that saving to the cost of the signs, staff, or software needed to enforce the rule. If the savings are larger than the costs, the priority system is worth implementing. If not, the city should stick with the current system. This approach separates the question of "who should go first" from the question of "is it worth the trouble," ensuring that cities do not spend money on rules that do not actually improve the flow of traffic.

In a specific example used in the study, the researchers plugged in numbers for a typical busy street. They found that under a standard "first-come, first-served" system, the total time lost by all vehicles was about 45.7 units per minute. By switching to the calculated priority order of bus, then freight, then ride-hailing, the total time lost dropped to about 32.4 units per minute. This difference of 13.3 units represents the maximum amount the city could spend on enforcement and still come out ahead. The study showed that other possible orders, such as putting freight first or ride-hailing first, would either save less money or actually make the total delay worse. This demonstrates that the intuitive idea of "transit first" is not always the best answer; sometimes, the math shows that a different order is better, or that no order is better than doing nothing.

The study concludes that the best way to manage a curb is to treat it as a calculation rather than a hierarchy. It is not about which vehicle is more important, but about which vehicle creates the most value per minute of curb space it uses. By focusing on the ratio of waiting cost to stopping time, cities can make decisions that are transparent and based on data rather than guesswork. This method allows planners to compare different strategies, from simple painted signs to complex digital reservation systems, on a level playing field. It ensures that the decision to prioritize one group over another is driven by the actual benefits to the community and the real costs of making it happen, rather than by tradition or assumption. The result is a tool that helps cities decide not just who gets the curb, but whether the fight for the curb is even worth the cost of the battle.