Hopthru Analyze

Introduction

Hopthru Analyze provides a fast and seamless suite of tools for analyzing ridership data. Analyze can be used in planning efforts including but not limited to:

• Understanding ridership data at the system, route, trip and stop level
• Comparing ridership data over multiple date ranges
• Filtering ridership data by date, day of week and time of day
• Identifying and diagnosing service inefficiencies when it comes to ridership
• Identifying which stops are good candidates for amenity enhancements
• Identifying which routes have crush loads

Accessing Hopthru Analyze

Swiftly customers with a subscription to the Hopthru Analyze platform can access the tools at platform.hopthru.com. If you are having issues accessing the tool, please email [e-mail].

Segment ridership by system, route, and stop

Consider how deeply you want to visualize your ridership data by toggling between views in your schedule and filtering by date.

By default, the Hopthru dashboard will show the full system.

Schedule views

• Full system: by default, Hopthru aggregates data for all routes.

• Routes: breaks down insights by routes.

• Stops: dive into any stop for detailed analysis

Metrics: Ridership insights for your agency

Hopthru Analyze calculates ridership insights by ingesting and processing a static GTFS file, Vehicle Assignment data, and raw APC data. 

Using these data inputs, the platform can create valuable metrics to analyze ridership, such as:

• Boardings: how many riders boarded the vehicle by total number or by average daily
• Alightings: how many riders alighted the vehicle by total number or average daily
• Activity: boarding and alighting totals or averages
• Boardings per revenue hours: the average number of boardings per hour of scheduled revenue service
• Load: average number of passengers on a given vehicle or the max number of passengers on a given vehicle
• Passenger miles traveled: the average or daily miles traveled by passengers

By default, clicking through the full-system view on the dashboard shows the average daily boardings for your entire agency over a four-week period, broken down by several helpful views.

For example, below, on the left you will see your average daily boardings total tallied for you in chart view. This is helpful for a high-level understanding of daily ridership. The view will also show you boardings by date and and day of the week to highlight meaningful trends across dates. 

Last, a map view on the right with color-coded average daily boardings will show you which routes have higher saturations of average daily boardings, allowing you to analyze average daily boardings by routes.

Use Filter and Compare for further analysis

Dive deeper into your metrics by filtering and comparing your data.

Date filter

Further drill into your ridership metrics by filtering through various date filters.

• Date range: filter by specific dates or date ranges
• Days of the week: filter by select days of the week

Compare

The compare button allows you to compare several service dates side-by-side on chart view. Use this to spot trends for specific date periods, like comparing your end-of-quarter ridership data against your beginning-of-quarter ridership data.

Just need a report? Try Insights

Do you just need a report of your ridership insights? Hopthru Analyze has you covered. Insights, display metrics through data tables using the Insights view. 

The insights tool contains functionality similar to the visual charts and map views but instead breaks the data down into a report using tables. These reports can be exported or scheduled for delivery to your email using subscriptions.

Insights can be found on the last tab of the top navigation bar.

Create a report

Create a report by clicking on the  “Create new” button. The button opens a modal that allows you to create reports by insight type (system, routes, stops, trips, layers).

Exports

Users can export their Insights data by navigating to their Report > Table Actions > Export Results to CSV

Subscription

Use the subscription button under “Subscriptions” to schedule a daily, weekly, or monthly delivery of your data.

Use cases

Identify and diagnose ridership service inefficiencies

Have you ever wondered whether there are areas where riders do not board service? You can use the “Boardings through revenue hour” metric to evaluate “dead zones,” or areas where there are no boardings on a route.

Instructions

1. Select the routes tab on the top navigation
2. Sort by the least efficient routes on chart view.
3. Navigate to the route or stop in the map view.
4. Click through the route

The yellow circled routes, Route 301 and 406, are candidates for poor route efficiency since they have lower boardings per revenue hour. You can share this data with your planning team to help optimize service on these routes.

5. On the route-specific view, use the colors to identify dead zones. Areas with heavier colors approaching the route/stop are a good sign. Lighter colors should influence you to consider whether the planned route is effective.

The green-circled areas are effective, while the stop areas in gray do not have any boardings. You can share this data with your planning team to help optimize service on these routes.

Identify which routes have crush loads

Provision adequate service by quickly identifying trips that have crush loads.

Instructions

1. Using the Insights tab, create a create a report using trips.
2. Select “filter” > “add filter” > “ridership” > “average daily max load”
3. Use a value that is just below your vehicle’s max capacity. For example 20, for a bus that fits 21 passengers
4. Run filter and adjust date ranges/filter as needed.
   1. You can customize the order of the columns under “table actions” as needed.

The outbound Route 401 trip at 07:20 and the inbound trip at 15:00 seem to be experiencing crush loads. You can share this data with your planning team to help optimize service on these routes.

Identify hardware issues with your APCs 

Monitor the health of your vehicle’s APC devices by using the Vehicle Issues feature. 

Instructions

1. Navigate to Settings > Vehicle issues. This view summarizes vehicle reports by time period. 
2. Identify problematic vehicles by reviewing Events. 
   1. No raw vehicle APC data: Raw APC data is missing for a vehicle in service
      1. Vehicles with ”Pending” have seen an improvement in reporting
      2. Vehicles with “Resolved” have seen a resolution in raw vehicle APC data reporting due to several days of consistent reporting
   2. Partial APC records: Very little APC data exists for a vehicle in service
   3. Raw APC activity imbalance: Delta between boardings and alightings exists for a vehicle in service
   4. Unscheduled Raw vehicle APC data: Vehicle reported data but was not scheduled for service

Based on the report of the vehicle events above, there are a number of vehicle IDs that do not have raw APC data reporting. You can share these insights with your IT or hardware maintenance team to have these devices inspected for issues.

Identify which stops are good candidates for amenity enhancements

Improve the rider experience by identifying stops that are great candidates for amenity enhancements, such as benches, shelters, and more. 

Analyze allows you to pinpoint stops with high average daily boardings, which agencies can use as an indicator for amenity improvements.

Instructions

1. Navigate to the stops view
2. Filter for high-frequency stops by:
   1. Select average daily boardings as your metric
   2. On your filter, set the value to > 50 (you can switch the value depending on your definition of high-frequency). 
   3. If you have imported tags labeling existing stops, add additional filter criteria for Tag = “Benches” or the name of your amenity = False

Because these stops have high average daily boardings and no benches installed at the stop, they may be great potential candidates for benches. Share these insights with the teams responsible for amenity enhancements at your agency to help improve the rider experience.

FAQs

Is there a certain period that I must wait to query Hopthru data in our System?

Data in Hopthru is not final until 14 days have passed since the service date.

There are two reasons for this:

1) Data can continue to trickle in from vehicles that were unable to previously upload data

2) Hopthru's expansion algorithm uses data up to 14 days past the service date. Expansion is a process where we use historical averages to provide ridership data for trips that ran, but where APC data is not available. 

Does HopThru support APIs that agencies can use to pull the HopThru data out of the system?

No APIs. There are self-serve data exports in the Insights panel and in the Exports panel in Settings. Everything available in the Exports panel in Settings should also be available in Insights.

Can I customize the color scale shown in the map?

No. The scale is auto-populated into 8 categories, evenly distributing all results into those categories ranging from yellow to dark purple.

Can I customize the times of day in the date range filter?

Please contact [e-mail] if you would like to update the times of day filter.

What is the difference between max load vs. avg. max load?

Hi, the max load metric is the highest load observed over a given date range. Whereas, the avg daily max load is what i think you expect. It's the max load from each day in that date range which is then averaged.

For example, if you have a time range of 5 days and the max loads are:

1. 10
2. 3
3. 6
4. 11
5. 7

The max load would be 11. The avg daily max load would be: (10 + 3 + 6 + 11 + 7) / 5 == 7.4

max load is not a super useful metric across broad time ranges. avg daily is much more useful.