Use big data and machine learning to increase network performance


The bus data engine

Visualise and report key metrics, accurately predict journey times and optimise your timetables

CitySwift is a cloud-native, specialist data engine for modern bus networks.

Operational information is combined with a vast array of big data sources to develop rich datasets that are unique to each bus network and location. Machine learning models are trained on this data to deliver highly accurate predictions.

The CitySwift bus data engine currently powers two products: SwiftMetrics delivers bus network analysis at scale and SwiftSchedule rapidly generates optimised bus timetables.

It also enables the development of custom applications such as When2Travel, which uses AI to make accurate bus capacity predictions on a stop-by-stop basis, helping passengers make the right journey choices. 



Bus network analysis at scale

Fully understand and improve the performance of your bus network. SwiftMetrics seamlessly integrates with your existing technology systems to instantly visualise and automatically report detailed reliability, efficiency and demand metrics – enabling informed decision-making for network development and growth.

  • Instantly visualise detailed reliability, efficiency and demand metrics
  • Track and report network performance and KPIs
  • Review historical data with deep-dive insights
  • Analyse mobility patterns to predict future trends and opportunities
  • Create, customise and share visually-rich dashboards
  • Export information in a variety of formats
  • Augment and integrate with your existing technology infrastructure


Rapid bus timetable optimisation

Create enhanced timetables that balance vehicle supply with passenger demand. SwiftSchedule seamlessly integrates with your existing scheduling systems to accurately predict journey times based on operating conditions and passenger demand, significantly reducing network review timescales and increasing network efficiency.

  • Accurately predict journey times and passenger demand
  • Rapidly generate new, optimised timetables
  • Analyse and improve the performance of existing timetables
  • Customise schedules with route and stop constraints
  • Model multiple future scenarios
  • Share timeband information with scheduling software
  • Integrate with your scheduling tools


Bus Network Development Strategy for the ‘New Normal’
'Bus has a very bright future indeed'
Meet the graduates
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