Using Mobility Data to Keep Public Transit in Check and Make It Better

Public transit agencies have loads of data ready to go — from GPS on buses to fare card taps. This mobility data helps them check how things are going and make services better.

Lately, “data-driven” has been the buzzword in transit, helping agencies get better on-time performance, attract more riders, and smooth out operations. Here’s a breakdown of how they use mobility data to keep an eye on and improve public transit.

Learn how Urban SDK’s Mobility Data Platform helps agencies track performance and optimize service reliability.

Tracking On-Time Performance

One big deal for any transit system is on-time performance or OTP — basically how many buses or trains stick to their schedules. In the past, agencies would rely on random checks or drivers to report if they were on time.

These days, with real-time GPS on every vehicle, agencies can keep track of punctuality nonstop. Every stop can log if a bus or train shows up early, on time, or late. This info usually feeds into a dashboard where everyone can see how things are doing.

For instance, the Maryland Transit Administration (MTA) has a performance dashboard that shows OTP by bus, rail, and even by specific route. By keeping a close watch on OTP, agencies can spot trouble spots — like a bus route that’s always late in the afternoon. Then they can dig into what’s causing the delays, whether it’s traffic issues or not enough scheduled time, and take steps to fix it.

Example: Before MTA Baltimore redesigned its network in 2017, bus OTP was only 59.5%. After reworking routes and adding bus lanes and transit signal priority, they used data to improve schedules — boosting OTP to nearly 80%.

Better Planning and Scheduling

Mobility data is key for smarter scheduling. Agencies look at patterns in ridership (using fare data or automated passenger counts) and running times (from GPS logs) to figure out the best timetables and how often buses should run.

If data shows a bus is regularly stuck in traffic for an extra 5 minutes, the schedule can get adjusted to minimize delays. Using years of accurate performance data instead of guesses helps agencies create timetables riders can actually rely on.

Ridership data by time of day is also super useful for spreading buses across routes — adding more during rush hours or cutting back when ridership is low. Route performance analysis, combining ridership and on-time stats, can help decide whether to split, speed up, or simplify routes.

Many cities have used data to add express or limited-stop routes where demand is high, making trips faster and drawing in more passengers.

Analyzing Ridership and Crowd Levels

Data from fare collection (like ticket taps) or passenger count systems gives agencies near real-time insight into how many people are riding and where they’re headed.

If a route sees a steady drop in ridership, it’s a red flag to investigate — maybe there’s an issue with reliability or changing travel needs. On the flip side, if buses are constantly packed, it’s a cue to boost service.

Some agencies use crowding data from onboard sensors to deploy extra buses dynamically. During the pandemic recovery, many closely tracked ridership trends to see which routes rebounded fastest and redirected resources accordingly.

Getting a Handle on Delays

Mobility data works like a detective when figuring out why delays happen.

By layering GPS data with traffic info, agencies can pinpoint chronic problem areas. For example, if delays always occur at a certain intersection at 5 PM, that’s strong evidence for adding Transit Signal Priority (TSP) or rerouting.

Real-time data also supports smarter signal coordination — holding a green light for a delayed bus, for instance. Agencies can then measure impacts right away, like faster travel times and improved OTP.

Dwell time analysis — studying how long buses stay at stops — reveals bottlenecks, such as overcrowded stops that might benefit from off-board payments or all-door boarding.

Check out Urban SDK’s Traffic Management Solutions to see how delay data improves network flow.

Integrating Customer Experience and Feedback

Mobility data isn’t just numbers; it also includes passenger feedback.

Some agencies have apps for riders to report issues like crowding or delays in real time. Others analyze social media for transit complaints. When agencies overlay this feedback with operational data, they get a fuller picture.

For instance, even if the data shows a route is “on time,” riders might still feel frustrated if intervals between buses are uneven. That mismatch can lead to schedule adjustments.

Open data also empowers the public to monitor performance, fostering accountability and continuous improvement.

Changing Routes and Networks

Mobility data drives network redesigns too. Many cities have rebuilt their bus systems based on how people actually travel.

By combining smart card and cell phone mobility data, planners can detect shifts like more suburb-to-suburb travel rather than just downtown commutes.

Houston’s 2015 bus network redesign is a classic case — they used ridership and demographic data to create a more efficient grid system that boosted both frequency and ridership.

Keeping Things Reliable

Mobility data also covers vehicle health and maintenance.

Modern buses come with sensors that flag mechanical issues. Tracking metrics like miles before breakdowns helps prevent missed trips.

Predictive maintenance — identifying vehicles at risk before failure — improves reliability and cuts downtime.

Measuring Improvements

Whenever agencies roll out changes like bus lanes or more vehicles, mobility data helps them evaluate impact.

For example, Baltimore saw OTP rise from about 60% to nearly 79% after adding dedicated lanes and signal priority. Having that kind of measurable proof helps agencies justify continued investment and show tangible progress to city leaders and the public.

Growing Ridership and Revenue

Ultimately, better data leads to better service — and that draws riders back.

When agencies correlate ridership with performance, they often see a clear link: when OTP exceeds 90%, ridership rises. Data can also expose underused periods like weekends, helping optimize service to grow both ridership and revenue.

Examples of Data-Backed Improvements

• Maryland MTA (Baltimore): Used GPS data to measure the effects of bus lanes and signal timing, improving OTP by nearly 20 points.
• New York City Transit: Built a daily dashboard tracking line-level OTP and delays; launched the Fast Forward plan using this data.
• Los Angeles Metro: Used cell phone mobility data to redesign bus routes, boosting ridership.
• Via (Microtransit): Leveraged ridership data from on-demand shuttles to refine fixed-route planning.

Looking ahead, predictive analytics will help anticipate issues like bus bunching before they happen. Real-time ridership data can also inform crowd management, such as showing which trains have open seats.

Conclusion: Data as the Backbone of Modern Transit

All in all, mobility data is the backbone of how public transit operates today.

It creates a continuous loop — monitor performance, spot problems, implement fixes, and measure results. Riders get better, more reliable service, and agencies can use data to justify funding and guide investments.

The days of hunch-based transit planning are over; data now powers every decision, from timetables to technology — making transit smarter, faster, and more responsive to what riders actually need.

FAQ: Using Mobility Data to Enhance Public Transit

Q1: What types of mobility data help transit agencies track performance?
Ans: Data from GPS vehicle tracking, fare card taps, automatic passenger counts, and onboard sensors give real-time and historic views of bus/train locations, ridership, on-time arrivals, delays, and crowding.​

Q2: Why is on-time performance (OTP) important and how is it measured?
Ans: OTP indicates how often vehicles arrive as scheduled. Continuous GPS tracking combined with stop arrival logs allows agencies to identify late or early arrivals, recognize chronic delay points, and measure improvements over time.​

Q3: How does ridership data improve transit planning?
Ans: Real-time ridership info from ticketing or sensors helps allocate resources dynamically - adding more vehicles during peak demand, identifying declining routes for review, and supporting service adjustments to better match travel patterns.​

Q4: How do agencies use mobility data to analyze and reduce delays?
Ans: Layering GPS with traffic and stop dwell time data reveals where bottlenecks happen. Agencies deploy interventions like Transit Signal Priority or route changes informed by data to speed up service and measure impacts immediately.​

Q5: Can passenger feedback be integrated with mobility data?
Ans: Yes. Rider reports from apps and social media complement operational data, giving agencies a fuller picture of issues like crowding or uneven service frequency, enabling more targeted service improvements.​

Q6: How is mobility data changing network design?
Ans: Mobility and demographic data help redesign routes to better reflect current travel patterns, such as growing suburb-to-suburb trips, leading to more efficient and attractive transit options.​

Q7: What role does mobility data play in vehicle health and maintenance?
Ans: Sensors track bus mechanical metrics, enabling predictive maintenance that prevents breakdowns and reduces missed trips, boosting overall reliability.​

Q8: How is data used to measure transit system improvements and support investment?
Ans: Post-implementation data verifies impacts of improvements like bus lanes or signal priority by tracking OTP and ridership change, helping agencies justify funding and communicate successes to stakeholders.

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