Behind the scenes with MBTA data.

As an essential measure of the performance of the MBTA, we report our best estimates of ridership each month both on the MBTA Back on Track Dashboard and to the National Transit Database. As we have discussed on the blog, the source data for ridership comes from different systems and is measured in different ways. There are also many riders and trips that we are unable to measure from our equipment, and whose travel we need to estimate. This post will discuss the methods we use to count riders and trips, and to estimate those we can’t directly count. We will also discuss some of our future plans for improving these estimates and our reporting.

Recap: The Sources

We use different systems to collect the raw data depending on the technology available. The two main sources are Automated Passenger Counters (APCs), which are currently installed on most of the bus fleet, and the Automated Fare Collection system that counts CharlieCard taps and other payment methods on rail and bus services. APCs are also being installed on the Commuter Rail coaches and are being installed on the MBTA’s new Green, Red and Orange Line vehicles which are expected to come into service over the next few years.

For services where we do not have significant APC coverage, we use estimates based on data from the AFC system. The AFC system counts every interaction with a piece of fare equipment (for ridership purposes, these are faregates and fareboxes). We also conduct manual counts at various times and places to check against our automatically collected data, or in cases like Commuter Rail where we have limited automatic data.

Recap: The Measure

We report ridership as Unlinked Passenger Trips (UPT), which counts each boarding of each vehicle as one “unlinked” trip, even if it was part of a longer journey. While this gives additional credit to transfer trips, it is the industry standard and is required by the NTD, so we currently report ridership in this manner. We are investigating other measures of ridership and hope to be able to provide them along with UPT in the future.

How we estimate ridership from raw data

Bus: For our bus network, with a few small exceptions, we have enough APCs installed that we can use them to estimate ridership with minimal scaling and uncertainty. For each day type and route, we compare the boardings counted by APCs on trips with buses equipped with them to the total number of trips scheduled and scale the ridership up. We then scale the ridership back down to account for scheduled service that did not run. 

Rapid Transit: We currently have very limited coverage of APCs on the Rapid Transit system and need to use the AFC data to estimate ridership. We start with the raw validations (taps, ticket insertions, or cash payments) at each AFC location. From here we apply three different factors in order to estimate total ridership from the validations. These factors are explained below:

  • Non-Interaction: Non-Interaction factors account for people who entered the MBTA system without interacting with fare equipment. These are most often children, employees, people actively evading the fare or people who entered when the fare equipment was not functioning. These factors are calculated based on a sample of manual observations of people entering faregates, conducted each year.
  • Station Splits: We usually assume that every validation at a faregate at a station leads to a person boarding the line that serves that station. At stations that serve multiple lines, we do not directly know which line someone who validated there then boarded. For example, someone validating at Government Center could then board either a Green Line or Blue Line vehicle without any further interaction with fare equipment. To estimate these data, we apply a factor called a “station split” to “split” the boardings at such stations between the lines that serve each station. These factors are currently based on past surveys of passengers, but at the conclusion of this fiscal year we will update them using ODX.
  • Behind-the-Gate: As noted above, we report ridership as unlinked passenger trips – every boarding of each vehicle. This means that for trips where passengers transferred lines without passing through a faregate or an APC, we cannot directly measure their second trip and we therefore need to estimate it with a factor. Currently, we do this using the answers from surveys of passengers. We ask them as they are waiting for a train where they are going, and determine how many additional unlinked trips we can estimate for each boarding based on which line they boarded. For example, if our survey showed that there were 121 unlinked trips for 100 passengers surveyed, the “behind the gate” factor for that line would be .21, and we would multiply the count of boardings (after the other factors were applied) by 1.21 to estimate total unlinked trips. We are also updating this factor at the end of the fiscal year using the ODX algorithm.

Putting it all together

The following chart shows an example of how we calculate final ridership from raw faregate interactions, with all three factors applied. These numbers are rounded to the nearest thousand.

A chart depicting average Red Line weekday ridership, with examples of how non-interaction, station splits, and behind-the-gate activity affects our ridership estimates.

First, we sum all the interactions at all faregates at stations with Red Line service. This will over-count the riders at stations that serve multiple lines. Then, we apply the “split factors” to the total interactions at stations that serve multiple lines (there is a different factor for each station-line combination) and apply those interactions to the other lines. This is represented by the -27 in the second column on the chart above. We then have a subtotal of 194,000 interactions that can be attributed to the Red Line.

Third, we apply the non-interaction factor to scale these taps to account for people who entered without interacting with the faregate. This brings our running total to 206,000.

Finally, we apply the additional trips from the other lines that could have behind-the-gate transfers to the Red Line (Green and Orange). These are counted in a similar calculation that is conducted on the interactions recorded at gates on those lines. This adds an additional 36,000 unlinked trips to our total, giving us our final ridership estimate of 242,000 average weekday UPT on the Red Line.

Green Line Surface

The Green Line is the most extensive and complex light rail system in the country, and this complexity presents myriad data challenges, as we have detailed on the blog. For ridership reporting, the surface-running portion of the Green Line presents some unique issues that we must account for. First, there is a high level of non-interaction on the Green Line due to the operational practice of allowing passengers with passes to board at the back door. While we believe the revenue loss from this is relatively low, it does mean we have a large non-interaction factor that we use for Green Line. We continually monitor and improve this factor, and as the new Type 9 cars, equipped with APCs, come into service, we will be able to use these to better estimate non-interaction.

Second, the Green Line fareboxes are not hard-wired to the AFC central database. This means they must be manually “probed” to download their transaction data (cash payments into the fareboxes are collected through a different process). Since the AFC system was installed nearly 15 years ago, this is a much more difficult process than it might seem; data can only be probed in certain places in the train yard, and vehicles do not always come into these places in the yard for any operational reason (by contrast, fareboxes on buses are probed much more regularly since it is part of the nightly re-fueling process). In fact, a large portion of the data from surface AFC interactions are not downloaded to our database until weeks or sometimes months after the transaction occurred.

In order to account for this probing lag, we have developed a process to impute taps for which we do not have data yet, based on the amount of service we see that each vehicle has provided (measured by stations visited from our AVL system) and the number of taps per vehicle-stop visit that we have recorded in each month in the past.

This process consists of four steps: first, we evaluate how much AFC data is missing and likely to come in through a future probing. We conservatively estimate AFC data to be missing if a vehicle is seen to be in service during a particular date but did not record any AFC records. Next, we estimate what the missing data is likely to be based on the same month of the prior year (to account for seasonal ridership trends), in terms of taps per vehicle-stop visit we tend to see in that month. We then look at the number of stop visits that occur on the vehicles with currently missing AFC, and scale them up by this estimate. Finally, every month, as more probed data comes in, we replace the estimates with real data. 

Ridership on the Dashboard

We put all of the above together into our ridership update six weeks after the end of each month. This is the earliest date we feel confident that we have enough Green Line surface data to estimate its ridership. After QA/QC, we combine the above calculated ridership with the ridership reporting we get from Commuter Rail, Ferry and the RIDE to display our average weekday ridership for each month. 

We are working on more detailed and granular ridership tools which will allow users of the Dashboard to explore our ridership data in different ways as data quality and availability improves. Look for these in a future update to the Dashboard.


In the last five years, the MBTA and other large transit agencies across the country have seen drops in their ridership, especially on buses and during off-peak times. This is counter to historical trends; given increased population and economic growth in Boston, we would typically expect ridership to increase. The changes are also not uniform; ridership on the Commuter Rail system, for example, seems to be growing significantly.

Analysts at the Office of Performance Management and Innovation (OPMI) decided to investigate possible causes of this decline in ridership. We have posted some of this work on the blog here and here, and we are excited to post the full report below. The report linked below explores bus ridership, examining what factors are causing the decline in bus ridership specifically, and how these factors differ depending on the neighborhood.

The paper includes two significant analyses: a longitudinal regression looking at bus ridership at the transit-system level across the United States and a geographically weighted regression (GWR) focusing on local differences within the MBTA area. 

Read the full report: “Location, Location, Location: A Neighborhood-Level Analysis of Changes in MBTA Bus Ridership”

Suggested Citation


Thistle, I., & Zimmer, A. (2019). Location, location, location: A neighborhood-level analysis of changes in MBTA ridership (unpublished). MBTA – Office of Performance Management and Innovation, Boston. Retrieved from: https://massdot.box.com/v/busridershipreport


If you have any further questions or concerns related to this report, please reach out to us at This email address is being protected from spambots. You need JavaScript enabled to view it.

In this post, we investigate the occurrence of people traveling in groups using a single CharlieCard or CharlieTicket.

Occasionally, when people navigate transit in larger groups made up of family, friends, colleagues, etc., one rider will use their individual fare card to tap in and pay for the other people that they are traveling with. Both CharlieCards and CharlieTickets can be used by multiple different people to pay for fares at the same faregate or farebox.

Imagine a CharlieCard to be a wallet with two compartments: the first compartment holds a pass and the second compartment holds stored cash value. When a card with both a pass and stored value is tapped against a fare box or a fare gate, the first tap is processed by the pass compartment and the subsequent taps from other group members are processed by the stored value compartment. While both a CharlieCard and CharlieTicket can be used for pass-back, one advantage of using a CharlieCard is that up to four pass-back taps are tracked to provide transfer discounts. This feature is not available with CharlieTickets.

Pass-back Overview

Group travel occurs for various different reasons: e.g., leisure trips, school field trips, during sports events, or general tourist travel. By studying pass-back situations, we can identify group travel patterns and bus routes and Rapid Transit stations at which group travel occurs. We performed this analysis in order to identify where people travel in groups and how many people relied on this functionality. The results will inform how we can best serve these trips when this functionality is eliminated as part of our new fare collection system (AFC 2.0). As part of AFC 2.0 we plan to allow all-door boarding and move to a proof of payment fare verification system. This will require everyone to have their own fare card or other method of payment (smartphone, etc.). 


In the following sections, we will provide some context and data about pass-back use on first a yearly, and then monthly, basis. We first took a broad look at 2017 pass-back use before diving more specifically into October 2017. We’ll explain how the findings of our analysis allowed us to ascertain answers to the following questions:

  • When and where is pass-back used the most?
  • Who uses pass-back?
  • How many people relied on the one fare card when traveling in groups?

To conduct this analysis, we queried our ODX database to select all transactions during October 2017, for all users except employees and contractors. Multiple taps at the same location on the same card or ticket within 10 minutes of each other (excluding the initial tap) were identified and tagged as pass-back taps. We then calculated pass-back rate separately for bus routes and Rapid Transit stations. This analysis helped us identify the share of all taps used for pass-back and the share of all CharlieCards/Tickets used for pass-back.

But first, let’s look at 2017…

Pass-back in the Year 2017

In the year 2017, 1.48% of all taps were pass-back taps. Ninety-three percent of these taps were on Cards/Tickets with stored value and 7% were on Cards/Tickets with a pass. The second and the third quarter accounted for 59% of the total pass-back rate for the year. The below chart shows the month-to-month use of pass-back across bus and Rapid Transit.

We observed that July was the month with the highest pass-back rate of 2.12% and that pass-back was more likely to occur during the warmer months compared to colder ones. This is probably due to higher numbers of tourists using the MBTA during the spring and summer for events like baseball games and festivals. 

The above chart shows the month-to-month use of stored value and pass-back on stored value across bus and Rapid Transit. You can see that while more stored value is used during the summer, there is also a higher rate of pass-back use.

This observation helped us identify the months during which group travel occurs most frequently. With the confirmation that the spring and summer have higher likelihoods of people navigating our system together in groups, we can improve our operations and customer service geared towards group travel.

In the following sections, we’ll perform a deeper dive into the data gleaned from our October 2017 pass-back analysis. 

When and Where is Pass-back Used the Most?


When looking at specific days of the week, we divided the system’s service time into five categories, as listed below. 

Category Time Time Period
1 03:00 - 06:59 Sunrise and Early AM
2 07:00 - 08:59 AM Peak
3 09:00 - 15:59 Midday Base and Midday School
4 16:00 - 18:39 PM Peak
5 18:30 - 02:59 Evening, Late Evening, and Night

By splitting service hours into these five categories, we were able to identify which specific times of day had the highest number of pass-backs. The bar graph below shows the pass-back rate per day and per time category. 

From the above chart, we can see that on weekdays the pass-back rate was higher between 9 AM and 03:59 PM (time period 3) in comparison to other times of day. Additionally, the pass-back rate was higher during weekends than on weekdays. 

It is apparent that people are more likely to travel in groups on the weekends than weekdays, perhaps for social and/or leisure outings. 


In order to identify specific locations where higher rates of pass-back is occurring in the system, we examined buses at the route level and Rapid Transit at the station level. The following two charts show the top routes and stations ranked by the pass-back rate. 

Route 435 (Liberty Tree Mall - Central Square, Lynn or Neptune Towers via Peabody Square) had the highest pass-back rate of 2.78%.

Riverside station had the highest pass-back rate of 4.87%. The top three Rapid Transit stations with the highest pass-back rates are Riverside, Museum of Fine Arts, and Science Park. 

From the above analysis, we noticed that for bus routes, the highest pass-back rates were in the North Shore (Lynn and Salem) and at shopping centers. For rail stations, the highest pass-back rates were at locations known for attracting tourists, short-term visitors, and schools. 

The high usage on bus routes in Lynn and Salem might indicate a need for more CharlieCard distribution in these areas. We are currently working to establish community partnerships to give people more access to blank CharlieCards. 

Who Used Pass-back?

There were about 0.7 million Charlie cards used by Adult users in the month of October 2017 and 12.57% of them were used for pass-back. This was high compared to other fare media (for Adult users). 

We then looked at the number of rides the adult users took in the month of October 2017 and the percentage of trips that used pass-back. The below chart suggests that infrequent adult riders or people who take one to five rides in a month are the ones who most often used pass-back.

People who traveled on the rapid transit or bus network two times in a month, used pass-back on 10.75% of their trips. This led us to identify the percentage of cards using pass-back for each adult rider category. 

Type of Riders Adult User Type Category (Rides) Percentage of Cards Using Pass-back
Infrequent 1-5 rides per month 12.91%
Occasional 6-20 rides per month 16.32%
Frequent 20+ rides per month 9.31%

We categorized adult user ridership based on the number of rides they took during the month of October 2017 and found that 12.91% of infrequent rider CharlieCards were used for pass-back while more than 9% of frequent rider CharlieCards were used for pass-back. 

The above chart suggests that the percentage of trips taken using pass-back was high for infrequent riders, but the above table suggests the occasional and frequent rider cards were also used for pass-back.

The analysis in the above section will help us prepare for AFC 2.0. Infrequent-use cards are likely using pass-back more often because the people they are traveling with do not have cards. In AFC 2.0, we will make cards more available by having them available at vending machines, but also will provide the capability to pay with a device such as a smartphone, which should lessen the need for multiple people to share one type of payment media.

When Someone Uses Pass-back, How Many Other People Do They Tap In?

(How many people relied on the one fare card when traveling in groups?)

We analyzed the number of times people performed a pass-back and how many other people were tapped in with one card. We found that in the 87% of trips where pass-back occurred, one other person was tapped in.

We concluded that people are more likely to travel with one other person than with a whole group. This pattern will inform the way we think about possible group-specific fare products in the future; we should keep in mind that “groups” are often no larger than two riders. 


Our comprehensive analysis of the pass-back data had many interesting takeaways. The study revealed that the pass-back rate is generally higher during the warmer months and at rapid transit stations used by tourists and people attending sporting events. The high usage of pass-back on bus routes on the North Shore indicates that we need to improve CharlieCard distribution in these areas now. 

Approximately 13% of Adult riders with CharlieCards performed a pass-back. And pass-back is used more often by infrequent riders i.e., the percentage of trips taken by infrequent riders using pass-back is higher compared to frequent riders. In advance of AFC 2.0, this analysis is helping us prioritize locations for fare vending machines and consider fare products for infrequent riders.  

This type of analysis of existing AFC system data is very useful as we consider the changes that will occur with AFC 2.0. It is informing the development of the policies that will support the new fare collection system and ensure that it serves the needs of all types of riders.