School Bus Delays

Table of Contents

This page is meant to be used, not necessarily read in one sitting.
You can scroll through the full story or use the questions below to jump straight to the parts that matter most to you.

What are the main takeaways?
What is the broader context for school bus delays in NYC and the Council’s role in addressing them?
What data does the City have on school bus delays, and what are its limitations?
How often are school buses late in NYC, and is it getting better or worse?
When buses are late, how long are students waiting?
Why are buses late — what’s unavoidable and what can be fixed?
Which students are most affected by delays, particularly students with disabilities?
How can the City improve school bus service and reduce delays?

Executive Summary

New York City’s school bus system serves about 150,000 students each day. When buses run late, students lose instructional time, families miss work, and schools are forced into daily contingency planning. This page uses the City’s required reporting under the Council’s Student Transportation Oversight Package (STOP) to examine how often delays occur, how long they last, who is most affected, and where accountability is limited by the data itself.

Key Takeaways

Data transparency is still a problem. The City relies on two public sources: NYC OpenData incident records and the DOE’s semi-annual Student Transportation Reports—and they have not consistently told the same story over time. Even when totals align, key details (like geography and route type in the STR “Late Arrival” tables) are missing, limiting the Council’s ability to identify where problems are concentrated and for whom.

Delays have become less common, but more disruptive. While the 2024–2025 delay rate (49 per 1,000 students) is the lowest in three years, average wait times have reached a seven-year high (37 minutes). This suggests that while fewer buses are late, the system is struggling more than ever to resolve disruptions once they occur.

Students with disabilities are disproportionately impacted by transit service gaps. While students with disabilities make up only about 40% of routed riders, they account for roughly 75% of all recorded delays. This inequity is even more severe for the longest disruptions: routes serving students with disabilities account for a staggering 95% of delays lasting over an hour in recent years.

Some of the longest delays point to operational issues the City can act on.Though Heavy Traffic is the most frequent reason for delays, “Problem Runs” keep students waiting the longest. Averaging 58 minutes, these delays reflect systemic issues in route planning and recurring scheduling breakdowns that the City can directly address to improve service reliability.

Staffing trends are a warning sign—especially for high-need students. Overall staffing per student has declined from the recent peak, and the sharpest concern is the collapse in medical-only staffing, which drops dramatically after 2022–2023 and remains extremely low in the most recent period with only 3 total in 2024-2025.

How can the city improve?

The Council can track meaningful improvement in some areas, but the data still limits accountability—especially for understanding where delays happen, which routes repeatedly fail, and whether student impact is being undercounted. The results suggest that reducing the most harmful delays will require:

Clearer, more complete reporting
Targeted operational attention to recurring Problem Runs
Staffing stabilization for the workforce that serves students with the greatest needs

What is the broader context for school bus delays in NYC and the Council’s role in addressing them?

New York City’s school buses move tens of thousands of students every day, including many of the city’s most vulnerable young people. When those buses run late, break down, or never show up, students miss instructional time, families scramble to rearrange work and care, and schools are left to manage the fallout.

In 2019, the Council passed the Student Transportation Oversight Package (“STOP”). The STOP legislation requires the Department of Education’s Office of Pupil Transportation (OPT) to report more information on bus route times, bus delay frequency, complaints about routes and employees, and other aspects of school bus service.

This page uses those public reporting requirements to examine patterns in bus delays, highlight where service is improving or worsening, and identify gaps in the City’s data that make it difficult to fully understand students’ experiences.

Throughout the analysis, we summarize what the available data can tell us, where transparency breaks down, and how those limitations affect efforts to improve school bus service.

What data does the City have on school bus delays, and what are its limitations?

For years, families, advocates, and elected officials have raised alarms not only about chronic school bus delays, but also about the quality of the data kept by the Department of Education’s Office of Pupil Transportation (OPT). Parents have documented students stuck on buses for well over an hour only to find that those trips are either missing from official records or not flagged as “late arrivals” at all.

Independent reviews, including reporting by Gothamist , have found that many delays families experience never show up in the City’s published datasets, and that the numbers used in public reporting can understate both the frequency and the severity of disruptions.

Before we draw conclusions, it is important to understand what the available data can — and cannot — reliably tell us.

There are currently two main public data sources on school bus delays in New York City.

Because both the Council and the public rely on these records to judge performance, it matters a great deal which data source we use and how consistent those sources are with each other.

A major limitation of both sources is their reliance on self-reporting by drivers and vendors, which can lead to missing delays and inconsistent reporting of delay severity.

Note on the data: Throughout this page, we apply the same filtering (removing weekends, school-closure days, and COVID-closure periods) to both datasets so that comparisons reflect delays on actual instructional days.

1. Bus Breakdown and Delays
A public dataset on NYC OpenData .

Updated daily
Lists every delay that bus companies report
Includes the route, reason, and length of each delay
Does not include broader context information such as student counts, vendor staffing, and fleet details

This is the most detailed source on individual incidents, but it depends heavily on what vendors choose to report and how they classify each delay.

2. Student Transportation Reports
Semi-annual reports from OPT:
Student Transportation Reports .

Updated bi-annually
Include student counts, vendor staffing, and fleet details
Include a “Late Arrival” table that OPT describes as a more carefully reviewed record of delays, though with limited detail on individual incidents

These reports function as the more “official” delay counts and provide substantially more system context. The tradeoff is timeliness and accessibility: they are released only twice a year and are not published on NYC OpenData, which makes them harder for the public to access, analyze, and reuse.

How different are these two sources?

When we look at the number of delays reported by each dataset over time, we see that they do not tell the same story.

For the first several years after the STOP package passed, the two datasets reported very different total counts of delays. In School Year 2021–22, the Student Transportation Report shows about 82,000 more delays than appear in the OpenData feed A staggering 54% discrepancy.

By School Year 2024–25, this gap has virtually vanished, shrinking to just 224 delays (a 0.3% difference).

Even once the total counts begin to line up, the amount of detail we have on each incident is very different.

The Student Transportation Report tables, in particular, offer far less transparency. That data leaves out key details — most notably the borough where the delay occurred and whether the route was general education, for students with disabilities (special education), or Pre-K — which makes it difficult to see who is affected and where.

Furthermore, even for the data source that OPT describes as more accurate, the “Late Arrival” table in the Student Transportation Reports, there are important quality concerns.

In the next section, we compare the number of reported late arrivals with the number of delays that lasted more than an hour. The mismatch between those two figures highlights how much student impact may be hidden or undercounted, even in the more carefully vetted dataset.

When we compare the number of reported late arrivals to the number of delays lasting over an hour, there is a striking discrepancy.

The number of over-an-hour delays reported by OPT averages about 8,133 per year.

Yet, over the last three school years, less than 1% of these severe delays were officially recorded as resulting in a late arrival.

In School Year 2024–2025, while families dealt with over 15,000 severe delays, the City officially logged only 51 delays as late arrivals.

Assuming (for illustration) that 80% of delays exceeding an hour actually result in a late arrival, we can calculate an “expected” late-arrival count. While this is a rough estimate rather than a verified rate, it highlights the vast discrepancy between official records and the likely daily experience of families.

This translates to a massive reporting gap: in the most recent year for every 1 late arrival officially recorded, an estimated 238 others likely occurred but remained invisible in the data. At this rate, official records are capturing less than 0.5% of the estimated 12,180 late arrivals that students likely experienced.

In other words, many students are facing waits of an hour or more that never actually show up as ‘Late Arrivals’ in the City’s official records. This suggests a significant gap in the tracking system: while the City is now better at counting delays, it is missing the actual impact those delays have on the school day. This accountability gap means that for most families, the official data doesn’t reflect the daily reality of students missing valuable classroom time.

To provide the most accurate picture possible, this report uses a hybrid approach to handle the City’s data.

For all subsequent sections, we use the OpenData records to determine the volume, duration, and specific causes of bus delays. To make these numbers meaningful across different years and boroughs, we use the Student Transportation Reports to normalize the data, calculating rates based on the official number of routed students and school sites.

This allows us to track whether service is actually improving, regardless of changes in total student enrollment or the number of active bus routes.

How often are school buses late in NYC, and is it getting better or worse?

When buses run late, it isn’t just an inconvenience, it reshapes students’ mornings, families’ workdays, and schools’ ability to start on time. The number of recorded delays gives a sense of how often the system breaks down for the students who rely on it every day. In this section, we look at how many delays were reported in 2024–2025, how that compares with recent years, and when during the school year those disruptions are most likely to occur.

Looking at the most recent school year, 2024–2025, we can track delays per 1,000 students across each month.

In the 2024–2025 school year, there were about 49 delays per 1,000 students.

This adds up to 72,778 total delays for the year, with an average of 7,278 student delays reported every single month.

When we compare 2024–2025 to the prior two school years, the overall delay rate was lower, an encouraging sign.

The year before, 2023–2024, had an average of about 58 delays per 1,000 students and the year before that, 2022–2023, had an average of about 66 delays per 1,000 students.

Looking back to 2018–2019, the same seasonal pattern shows up again and again: delays typically spike in early fall as routes restart and schedules shift, then taper later in the year (though not evenly).

One pattern is especially consistent: a sharp decline in June across nearly every year, likely reflecting fewer instructional days and changing schedules.

Notably, the 2024–2025 school year generally saw a lower rate of delays than most previous years.

The takeaway is clear: while transit challenges at the start of the school year persist, 2024–2025 demonstrated progress by maintaining generally lower delay rates than previous years.

How much do delays vary by borough?

Delay patterns aren’t the same everywhere in the city. The public data doesn’t show delays by neighborhood or council district, but it does let us compare patterns across the five boroughs.

Since some boroughs have more routes and stops than others, raw delay counts can be misleading. To make comparisons fair, we look at the number of delays per 1,000 unique bus sites/stops in each borough.

A site is any distinct address where a bus picks up or drops off students, as well as distinct school addresses. This approach allows us to compare boroughs on a common scale.

Across boroughs, there are clear and persistent differences.

Manhattan consistently experiences the highest number of delays per 1,000 sites across most school years. On average, it sees about 353 delays per 1,000 sites each school year.

The Bronx is next, averaging about 192 delays per 1,000 sites—well below Manhattan, but higher than most other boroughs.

Brooklyn sits in the middle, at about 151 delays per 1,000 sites.

Queens is lower, averaging about 98 delays per 1,000 sites.

Staten Island is typically lowest, at about 84 delays per 1,000 sites, with one notable exception in 2022–2023 when its rate jumped.

Taken together, these patterns suggest that bus delays are shaped not just by citywide conditions, but by local factors such as traffic density, route design, and day-to-day operations. This means that reducing delays effectively may require solutions tailored to specific boroughs, rather than a one-size-fits-all approach.

When buses are late, how long are students waiting?

The impact of a delay isn’t just about whether it happens, it’s about how long students are left waiting. A five-minute delay is very different from a bus that arrives an hour late or not at all. Long delays mean lost instructional time, missed therapies, and families scrambling to rearrange work and care at the last minute.

In this section, we look at how long bus delays lasted during the 2024–2025 school year, and how those wait times compare with prior years. The goal is to understand whether delays are usually brief, or whether long, disruptive waits are becoming more common.

Note on the data: Because delay durations are enterred as ranges (e.g., “30–45 minutes” or “1-1.5 hours”), the times shown here are calculated averages based on these reported buckets. While not exact to the second, they provide a consistent year-over-year look at whether wait times are lengthening.

For the 2024–2025 school year, we can track the average length of a bus delay each month.

On average, a bus delay in 2024–2025 lasted about 37 minutes.

Compared with the prior two years: Average delay times in 2024–2025 are higher.

In 2023–2024, delays averaged about 33 minutes—roughly 4 minutes shorter than the latest year. Similarly, in 2022–2023, each delay lasted about 34 minutes.

Looking back to 2018–2019, 2024–2025 stands out as having the highest average delay duration in the entire period.

Interestingly, the length of a typical delay stays about the same all year long, even though the total number of delays tends to rise and fall with the seasons.

The bottom line: Delays have become less frequent but more severe. Because average wait times are increasing, each late bus now carries a higher cost for families in lost learning time and logistical stress.

How much do waiting times vary by borough?

Just as delay counts vary across the city, so do the wait times families experience when a bus runs late.

Manhattan has the longest delays on average, about 40 minutes per delay — meaning that when buses are late there, they tend to stay late longer.

Queens also shows relatively long delays with an average of about 38 minutes per delay, and unlike some other boroughs, average wait times have steadily increased in recent years, reaching 47 minutes on average by 2024–2025.

Brooklyn falls in the middle, but with a clear upward trend over time. Average delays have grown from the low 30-minute range earlier in the period to around 45 minutes in the most recent year.

Staten Island is also mid-range, averaging about 34 minutes per delay across all years.

The Bronx consistently has the shortest delays, typically under 30 minutes for much of the period, though average wait times have risen to about 35 minutes in the most recent year.

Taken together, these patterns show that delays are not only more frequent in some boroughs, but also longer in others. That suggests improving bus service will likely require borough-specific approaches, rather than relying on a single citywide fix.

Why are buses late — what’s unavoidable and what can be fixed?

Not every bus delay has the same root cause, or the same solution. Some delays are driven by factors outside OPT’s direct control, like weather or citywide traffic. Others point more clearly to operational decisions: how routes are designed, how vehicles are maintained, and how quickly recurring problem routes are corrected.

In this section, we analyze school year (2024–2025) data (as reported by drivers and vendors) to answer two questions: what causes delays most often, and which causes lead to the longest wait times?

Here are the five most common reasons for bus delays this year.

In School Year 2024–2025, the most common cause of delays was Heavy Traffic, a largely external factor that accounted for over 50,000 individual delays.

The next-highest category was “Other”, a broad catch-all for various issues that accounted for nearly 13,000 delays. The size of this category suggests that a significant portion of transit disruptions remains difficult to categorize with the city’s current reporting tools.

Third was “Problem Run”, a term used for recurring challenges with specific routes—often stemming from flawed scheduling, poor route design, or chronic operational breakdowns. In 2024–2025, these systemic issues led to 2,691 delays.

Rounding out the top five were Mechanical Problems, which resulted in roughly 2,400 delays.

Finally, Late Returns from Field Trips was the fifth-most common reason, contributing to nearly 1,000 disruptions over the course of the year.

But when we look at which reasons keep students waiting the longest, the story changes.

Problem Runs rise to the top. These delays reflect structural issues in route design and scheduling, factors that the OPT and vendors have more direct control over than traffic or weather. On average, Problem Run delays last 58 minutes.

Next are Accidents, with delays averaging about 46 minutes.

They are followed closely by “Other”, which averages 45 minutes. As a broad catch-all category, it likely bundles multiple issues.

Heavy Traffic also remains a factor in delay severity, with an average wait of 42 minutes.

Finally, Weather Conditions rounds out the top five, with delays averaging 39 minutes.

Taken together, these findings suggest that while external factors account for the highest volume of delays, Problem Runs are tied to the longest waits. Because these are rooted in route design and scheduling, they represent a critical opportunity for operational reform and improved oversight.

For policy making, it can be helpful to step back and group delay causes into two broad buckets: external factors and internal ones.

Some delays, such as those caused by weather or accidents, are largely unavoidable and outside the control of OPT or the vendors themselves. Others, however, point more directly to how the system is run. Mechanical problems, flat tires, and recurring route issues are areas where better planning, maintenance, or oversight could reduce delays.

Looking across the full period from 2017–2025, this is how delay causes break down overall.

External causes, such as heavy traffic, weather conditions, and accidents, are the most frequent source of delays year after year, accounting for about two out of every three delays.

The “Other” category, which also includes “late return from field trip” and “delayed by school,” stands out for a different reason. It is poorly defined, yet over the full period it makes up about 19% of all delays, a substantial share that obscures what is actually going wrong and limits accountability.

Internal issues, such as mechanical problems, buses that won’t start, flat tires, and other vendor-controlled conditions, represent the next largest portion of delays at about 11%.

Problem Runs make up the smallest share, roughly 5% of all delays. However, because these are tied directly to route design and scheduling, they represent the most “preventable” category. These are not random events like traffic or weather; they point to systemic flaws that OPT and vendors could reduce through better planning, monitoring, and proactive follow-up.

Stepping back, the message is clear: while many delays are driven by things no one can control, like traffic and weather, a large share of delays still falls into categories that can be improved. One of the biggest points of concern is the size of the “Other” category: when nearly one in five delays is logged under a vague label, it becomes hard for policymakers and families to know what’s actually going wrong. At the same time, “Problem Runs” point to recurring route breakdowns — a more actionable place for OPT and vendors to intervene through better routing, scheduling, and follow-up when the same runs keep failing.

Focusing on the last three school years, we can see how these categories change across the school calendar.

External causes are consistently the number one source of delays the last three years. They peak in the fall and winter months and taper off as the weather warms, reflecting broader conditions outside OPT’s direct control.

“Other” is consistently the second-largest category. Because it is a catch-all label, it is hard to tell from this data what is driving these delays or what actions would prevent them.

Internal issues are comparatively steady from month to month. In the most recent school year 2024-2025, they appear lower than in the two prior years, which may reflect improvement, differences in reporting, or both.

Problem Run delays also show up consistently across all three years, with a notable spike in the 2023–2024 school year.

Overall, the pattern is consistent: many delays are tied to citywide conditions, but a meaningful share comes from categories where targeted operational fixes and clearer reporting could make a real difference.

Which students are most affected by delays?

School bus delays affect all students, but they do not affect all students equally.

In this report, Special Education (or students with disabilities) refers specifically to students whose Individualized Education Programs (IEPs) mandate specialized transportation services. These students are legally entitled to curb-to-school service, picking them up directly at home. When these buses fail, students miss the specialized instruction and therapies they are legally guaranteed to receive.

By contrast, general education students receive stop-to-school service, walking to a shared bus stop for pickup. While delays are disruptive for all families, curb-to-school disruptions are uniquely impactful for students who rely on door-to-door transportation to access their education.

We start by looking at all students who are routed for bus service, whether or not they experienced a delay across all years.

Most routed students in NYC are General Education students (stop-to-school), at about 56%.

About 41% of routed students receive Special Education accommodations (curb-to-school).

A smaller group, about 4%, are in Pre-K and Early Intervention programs.

By themselves, these shares reflect the mix of students who rely on bus services. The inequities emerge when we compare this to who is actually being delayed.

Putting these together shows a clear pattern: students with disabilities bear a disproportionate share of delays in the current system.

The bar chart below shows both parts of the story side by side: who relies on bus service and who experiences delays.

Looking first at who rides, the pattern is what you would expect. Most bused students are in general education, followed by special education, with a smaller share in Pre-K and early intervention programs.

But when we isolate who gets delayed, the pattern shifts. Special education students make up a smaller share of riders, but account for the largest share of delays.

This gap between who rides and who gets delayed is one of the clearest signs of disproportionate impact in the OPT data.

The inequities are not only about how often buses are late, but also about how long students are left waiting. (Note: Reported durations are averages based on driver estimates).

Special Education students are not only delayed more often, they also face longer delays. Historically, these delays have averaged about 38 minutes.

But that figure has surged to nearly 44 minutes in the most recent school year.

General Education routes have the shortest delays overall. Their delays are about 10 minutes shorter on average than routes serving students with disabilities.

They also stay relatively steady from year to year with an average of 30 minutes this recent school year.

Pre-K and Early Intervention routes initially saw the longest wait times in our records, but those durations have steadily improved. While they once sat at the top of the list, they now fall in the middle, typically longer than General Education but shorter than Special Education.

Most recently, these delays averaged 36 minutes each.

These differences become even more pronounced when we focus only on delays lasting over an hour.

Special Education students account for the overwhelming majority of over-an-hour delays in recent school years, reaching roughly 95% of all delays lasting over an hour in the most recent year.

This is especially troubling because students with disabilities are often the ones most harmed by extreme delays. For some students, an IEP includes a medically necessary limit on how long they can be on the bus. When a delay stretches past an hour, it is not just inconvenient. It can directly undermine the transportation accommodations the student is entitled to receive.

Pre-K and Early Intervention students account for a much larger share of over-an-hour delays earlier in the period starting at 55% percent in 2018-2019 school year, but their share declines in later years.

By the most recent school years, they represent a relatively small portion of these longest delays with an average of roughly 5% the last three years.

General Education students consistently make up the smallest share of over-an-hour delays, far below their share of riders at just 0.8% in the most recent school year.

Taken together, this shows that the longest and most disruptive delays are increasingly concentrated among students with disabilities, who often have the least flexibility to absorb extended waits. This disparity reaches its extreme with delays lasting over an hour, where routes serving students with disabilities now account for roughly 95% of all such incidents. For these students, these aren’t just inconveniences; they are direct disruptions to medically necessary travel limits and mandated school-day services.

Finally, we look at what is causing these long delays for different student groups.

For General Education students, the top causes of over-an-hour delays are “Other” at 57%, Heavy Traffic at 27%, and Weather Conditions at 6%.

This mix includes clearly external factors like traffic and weather, along with a large and poorly defined “Other” category.

When “Other” is this dominant, it becomes difficult for families and policymakers to understand what is truly driving the longest delays or what changes would actually prevent them.

When we shift to Special Education students on the right, we see an important difference.

While Heavy Traffic (73%) and “Other” (15%) remain the top two factors, a unique third category emerges for these routes.

Problem Runs now comprise 10% of over-an-hour delays for Special Education students.

In the 2024–2025 school year alone, Problem Runs accounted for 2,047 extreme delays for these students.

Unlike traffic or weather, Problem Run points to recurring breakdowns in how routes are planned and managed. Because these long delays are concentrated among students with disabilities, this is an especially urgent area for targeted fixes in routing, scheduling, and follow-up when the same runs repeatedly fail.

How have staffing levels changed over time?

Staffing remains a persistent challenge across the school bus system, and it is especially consequential for students with disabilities who rely on consistent drivers, attendants, and medical staff.

When staffing is thin, problems compound quickly. Driver shortages can disrupt routes, missing attendants can make it harder to serve students with complex needs, and limited medical staffing can restrict which students can be safely transported. All of this makes it harder for the system to recover from disruptions and prioritize students who depend most on reliable service.

In this section, we look at how staffing levels have changed over time and how many employees are available relative to the number of students served.

To make year-to-year comparisons fair, we focus on employees per 1,000 students. OPT reports monthly counts for both students and employees.

In the 2024–2025 school year, OPT reports about 102 employees per 1,000 students on average each month (15,074 total).

That is roughly 11 percent lower than the 2022–2023 peak, when OPT reported about 115 employees per 1,000 students on average.

The same decline shows up in the raw counts. 2023–2024 had about 14,892 employees each month, down from the average count of 16,174 in 2022–2023.

Overall, staffing appears to be trending down, both in the rate and in the overall counts.

The chart below shows total staffing over time, broken out by role.

Drivers only make up a large part of the workforce and are essential for operating daily routes.

Driver staffing remained fairly steady for several years, but the most recent data shows a sharp decline. In 2024–2025, the system averaged about 52 drivers per 1,000 students—a 15% drop from the 61 drivers per 1,000 students reported in 2022–2023.

Attendants only play a critical role for students with disabilities and younger students who require additional supervision and support during transport.

Attendant staffing is also relatively stable across years, averaging about 48 attendants per 1,000 students.

Employees serving as both driver and attendant represent a much smaller share of the workforce and fill dual roles where needed.

This group remains consistently small at about 1 to 2 employees per 1,000 students.

Overall, the mix of roles is fairly consistent year to year, but the recent dip in drivers suggests the system may have less staffing capacity to keep routes running reliably.

Finally, we isolate medical-only staff, a very small but essential part of the workforce for students with significant medical needs. This category includes highly trained professionals, such as dedicated Paramedics and EMTs, as well as drivers who are dually certified to provide emergency medical care.

This is where the staffing picture is most alarming. Between 2018–2020, the City maintained roughly 4 medical staff per 10,000 students. By 2024–2025, that rate has plummeted to just 0.2—representing a 95% collapse in specialized medical coverage since before the pandemic.

The most recent decline was sudden and severe. After reaching a small recovery peak in 2022–2023, medical staffing fell by 85% in a single year heading into 2023–2024.

The workforce remains at this historic low today, with an average of just 3 medical staff total citywide. This means students who require a paramedic or medical professional to travel safely are now being served by a system with almost no specialized medical capacity left in the official reporting.

When the smallest but most specialized staffing group declines this dramatically, it raises serious questions about whether the system can reliably meet the needs of students who require medical support and have the least flexibility when service breaks down.

How can the City improve school bus service and reduce delays?

“Problem Runs” are delays caused by how routes are designed, timed, and sequenced, rather than by traffic, weather, or accidents. Because these delays stem from planning and scheduling decisions, they represent one of the clearest opportunities for improvement.

To show what is at stake, we run a simple “what-if” check using the historical data. We start with what actually happened each month, then recalculate the total after removing delays labeled “Problem Run.” The figure that follows shows the percent reduction from the real monthly total under that scenario. This is not a prediction. It is a way to quantify how much overall delay volume is associated with routes that repeatedly break down and, in principle, could be improved through better routing, scheduling, and follow-up.

First, we look at how often buses were late after removing “Problem Run” delays from the record.

Each point represents one month. A value of -10% means that, compared with what actually happened, the total number of delays that month would have been about 10 percent lower if “Problem Run” delays had not occurred.

In some months, the difference is small. In others, the potential impact is large. November 2021 shows the biggest drop, around -37 percent.

There are also notable reductions around January 2024 to March 2024, at roughly -17 percent.

Across the full period, removing “Problem Run” delays is associated with an average reduction of about 5% in total delays. That is about about 406 fewer delays in an average month.

These are exactly the kinds of delays that routing and scheduling reforms are most likely to reduce.

Turning to how long students were left waiting, we repeat the same exercise for total delay duration.

Across the full period, removing “Problem Run” delays is associated with an average reduction of about 5% in total delay time. That equals about 275 fewer hours of total delay time in an average month.

The biggest impact appears in November 2021, when total delay time would have been roughly 37% lower without “Problem Run” delays. We see another stretch of sizable reductions in 2024, with several months showing drops in total delay time in the low-to-mid teens.

Overall, the message is consistent: when “Problem Runs” rise, both the number of delays and the time students spend waiting rise with them. That makes “Problem Runs” one of the clearest and most operationally actionable levers for reducing both the frequency and the severity of delays.

The data also show that meaningful improvement is possible when operations, oversight, and incentives are aligned.

One concrete example is the transition from Reliant Transportation to NYCSBUS. After the takeover, delay rates dropped sharply. That shift suggests that better day-to-day management and stronger accountability can translate into real, measurable improvements for families.

The figure below compares delays per 1,000 students for Reliant and NYCSBUS over time, with the vertical dashed line marking the transition point.

Before the transition, routes associated with Reliant averaged about 48 delays per 1,000 students.

After the transition, NYCSBUS averaged about 25 delays per 1,000 students. In other words, the delay rate was roughly cut in half after operations changed. This improvement is statistically significant.

Delay times show a similarly significant improvement.

Before the transition, routes associated with Reliant averaged about 36 minutes per delay.

After the transition, NYCSBUS averaged about 30 minutes per delay. This statistically significant 18% reduction in wait times suggests that disruptions are not only happening less often but are being resolved more quickly under the new management.

These comparisons do not prove that the takeover was the only reason delay count and durations fell, but it does show that large reductions are possible. It also reinforces why consistent, linkable data matter. Without it, the City cannot reliably spot what is working, replicate it, and hold the system accountable.

Legislative Solutions

Clearer, more complete reporting
Targeted operational attention to recurring Problem Runs
Staffing stabilization for the workforce that serves students with the greatest needs