CTtransit improves the rider experience and service reliability with Swiftly
CTtransit, the transit agency serving Hartford, New Haven, and Stamford, Connecticut, set out to solve two core challenges: giving riders accurate, real-time information about their buses, and identifying schedule improvements to boost on-time performance. Partnering with Swiftly, a transit data platform, helped the agency make measurable progress on both fronts.
CTtransit at a Glance
- Service area: Hartford, New Haven, and Stamford, CT
- Fleet size: 573 buses
- Ridership: 18.5 million annual passenger trips
Goal 1: Deliver High-Quality, Real-Time Predictions to Riders
CTtransit wanted to give passengers more reliable, real-time information about when their bus would actually arrive. Using Swiftly’s Real-Time Passenger Predictions product, the agency achieved:
- 24% more accurate ETAs compared to its previous system
- 67% reduction in predictions that would have caused passengers to miss their bus
As CTtransit’s Planning and Marketing team put it:
“We’re thrilled to be working with Swiftly to improve the information our passengers are using every day. We know that reliable service and information is the key to bringing riders back. Our customer complaints and call volume have already dropped.”
Goal 2: Improve Scheduling and On-Time Performance
The second goal was to identify schedule improvements that would shorten run-times, improve on-time performance, and give planners a fast way to measure network performance in real time and historically. Using Swiftly’s On-Time Performance and Run-Times products, CTtransit saw:
- 5% improvement in on-time performance
- 19% reduction in late departures
- Validated the need for more recovery time on two routes to reduce operator fatigue—changes that were implemented with the fall 2022 schedule pick
Justin Cayless, AGM of Transit Services at CTtransit, shared:
“We’ve been focusing on enhancing overall service delivery and are having a lot of success with using Swiftly Run-Times to adjust our schedules with significant improvements to our On-Time Performance. We’re able to use Swiftly data to validate what operators are telling us to adjust recovery time and make schedule changes accordingly.”
The Takeaway
By combining real-time passenger predictions with data-driven schedule adjustments, CTtransit was able to rebuild rider trust through more accurate arrival information while also tightening up operational performance across its network—a reminder that better data can move the needle on both the customer-facing and operational sides of transit service at the same time.
