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How to View Potential Transit Ridership Data in Replica
How to View Potential Transit Ridership Data in Replica

This article walks through how to understand potential ridership for new routes or service areas.

Lauren Massey avatar
Written by Lauren Massey
Updated over a week ago


With Replica, you have access to public transit travel data alongside comprehensive data about other mobility activities in order to put this travel into context โ€” including private auto and active transportation data, freight activity, employment status, car ownership status, household income, age, race/ethnicity, and more. One of the benefits of Replica data is that you can also see potential transit ridership for new routes or proposed route extensions.

Follow the steps below to see these types in insights in Replica.

Understanding Potential Ridership Along a Proposed Route

In this example, we use Replica's seasonal (Places) data to better understand who is traveling to and around the areas along the proposed NYS Route 110 Bus Service in Long Island, NY.

We leverage Places Studies to do this analysis. Click here to view the example study shown below.

Step 1: Create your study

  • Access the "Studies" page in Replica's platform

  • Click "New Study" in the upper right-hand corner of the page

  • Name your study, select your megaregion, season, and day of the week (choose between seeing patterns for a typical weekday or weekend day) for which you'd like to see transit data for

Step 2: Select your study area (the proposed route)

In this example, we want to learn more about the trip-takers who are traveling to the parts of an area that will make up a future transit route. We use the proposed NYS Route 110, which will include a Bus Rapid Transit (BRT) service in Long Island, NY.

In Studies, you can select an area to set as your trip origin point, trip destination point, trip pass through point, or trip intersecting point (which means a trip started, ended, or passed through). You can set your geography breakdown as census-based geographies (e.g., census tracts), or you could create a custom geography that includes the area of the proposed route (see more info on custom geo's here). In this example study, we'll look at the trips intersecting the area that makes up the proposed route in Long Island. To do this, we'll follow the below steps:

  • Click the "+" icon at the top of your study to launch the set of study filters

  • Scroll through the alphabetized list of filters to find the "Trips Passing Through" filter, which allows you to see all trips occurring in a given area. Click the "+" icon next to the filter.

  • Next, select your geography from the interactive map. By default, you'll study will show census tracts. Customize your selection using the "Geo Breakdown" dropdown in the left-hand corner. You can choose from census-based geographies: 2010 or 2020, depending on what's most relevant for the season you've selected, or, choose from a list of custom geographies that you or other members of your organization have uploaded. If you'd like to create a custom geography of the service area for this study, access this page to do so. In this example, we'll use 2020 census block groups (BGs) and select the BGs that make up the future service area. We can use the pointer tool to individually select the BGs off the map, or, we can use the polygonal lasso tool to batch-select the BGs off the map. Once we select the BGs off the map, we click "Save".

  • Now, your study will update to show the trips and trip-takers who travel in this area on a given weekday in the current season.

Step 3: Learn more about these trips and trip-takers using map layers and the Summary Panel.

Below are an example of some of the insights you can see in your study:

  • Map Layers: Available in the left-hand side of your study.

    • Turn the "Trips by Origin" map layer on to understand where within the city these trips are most commonly originating

    • Turn the "Trips by Destination" map layer on to understand where within the city these trips are most commonly ending

    • Turn the "By Home Location" or "By Work Location" to learn where the trip-takers live or work who are traveling through the service area.

  • Summary Panel: Click the "Show Summaries" button at the top right corner of your study to expand the summary panel. This contains summary metrics about the trips and the trip-takers who meet your study's filter criteria.

    • Trip tab:

      • Primary Mode module: Understand what modes are most commonly used to travel through this area. Trip: You can further filter your study by clicking on one or some of the modes to learn more about the transit trip-takers who are traveling through this area, for example.

      • Trip Purpose module: Understand why people are traveling to or from this area.

      • Start time module: Understand at what time of day people are traveling to and from this area.

    • People tab: Understand who this route can service by looking through people characteristics.

      • Household income: Understand the distribution of household income of the trip-takers in the area.

      • Race & Ethnicity module: This shows a breakdown of the race and ethnicity characteristics of your trip-takers.

      • Employment status: This module shows the employment status of the trip-takers, including whether or not they are in the labor force, and if so, whether or not they are employed.

Step 4 (Optional): Download your dataset

Click the "Dataset" tab near the top left corner of your Study to download detailed records of the trips and travelers that meet your filter criteria. In this example, we've filtered down to ~524K trips and ~256K trip takers. The trip table will contain a detailed record of each one of these trips (via a unique activity_id), including origin and destination points, trip purpose, and more. The population table will contain an individual record of each trip taker (via a unique person_id), including detailed individual and household characteristics, like income, race and ethnicity, home and work location, and more.

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