Objectives of the service
Transport operators managing bus and shuttle services face significant operational challenges: delays that cascade through schedules, underutilised capacity on certain routes, and reactive decision-making without early warning systems. These issues result in service degradation, reduced efficiency, and customer dissatisfaction.
This feasibility study evaluates whether predictive analytics can meaningfully improve operational decision-making for transport operators. The solution leverages Global Navigation Satellite System (GNSS) positioning data from the RideTandem driver application, combined with booking, schedule, and historical operational data, to generate actionable insights through RideTandem's Hub platform.
The study assesses the technical feasibility of using real-time and historical operational data to generate predictive analytics for delay patterns, stop utilisation, and operational risk. It evaluates data source suitability and quality, and determines whether insights can be surfaced early enough to support proactive intervention within operator workflows. Critical assumptions are validated through proof of concept testing with internal operations teams and partner transport operators. These findings will inform decisions on potential progression beyond the feasibility phase based on demonstrated technical viability, operational relevance, and efficiency improvements.
Users and their needs
The users currently targeted in this feasibility study are small to medium-sized bus, coach and shuttle service operators delivering scheduled or contracted transport services. These users are involved in internal validation activities during the feasibility phase. The primary geographic focus is on operators based in the United Kingdom, with relevance to similar operators across Europe.
These operators typically rely on basic tracking or scheduling tools that provide limited insight into service performance or emerging operational risks. As a result, operational decision-making is often reactive and manual.
Key user needs and challenges include:
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Earlier visibility of delay risk and service disruption.
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Improved understanding of route performance and underused capacity.
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Better use of historical operational data to inform planning and intervention.
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Decision-ready insights that integrate into existing workflows without adding operational overhead.
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Tools that are simple to adopt and do not require in-house technical expertise.
The feasibility study explores whether analytics-driven insights can address these needs using available operational data, and whether such insights are operationally relevant for the targeted user group.
Service/ system concept
The analytics platform delivers three core capabilities to transport operators through RideTandem's Hub interface:
Performance Insights: Historical reports showing route-level and schedule-level service metrics including delays, stop utilisation patterns, and capacity issues. Operators use these reports to identify systemic issues and plan route improvements.
Risk Indicators: Route health scores that flag operational issues by analysing past performance data. This serves as an early warning system, highlighting routes likely to experience problems and opportunities for optimisation.
Predictive Alerts: Real-time trip risk scoring that identifies journeys likely to encounter issues before they occur and provides contextual insights, enabling operators to intervene proactively.
How the System Works:
The platform ingests live location data transmitted by driver smartphones via GNSS satellites, alongside route features, boarding data, timetables, and vehicle and driver information. These inputs are processed through Extract–Transform–Load (ETL) pipelines that clean and structure the data, resolve timing inconsistencies, validate geolocation accuracy, and generate derived operational features.
Analytics and modelling services use the processed data to compute performance metrics, route health indicators, and trip-level risk scores. Analytics and model outputs are regularly refreshed as new data becomes available and are surfaced through the RideTandem Hub as operational dashboards, route reports, and predictive alerts, supporting both strategic planning and live decision-making.
Space Added Value
Global Navigation Satellite System (GNSS) technology plays a crucial role in RideTandem's platform by providing accurate location data from vehicles in real-time, serving multiple critical functions.
Real-time service monitoring uses location data gathered from the driver's mobile application, enabling accurate tracking of trips against scheduled times. This data is shared with passengers and operations teams via the Hub application. The application implements data buffering and synchronisation processes to store location updates locally and transmit them when connectivity is restored, ensuring continuity of tracking information even when drivers pass through areas with limited network coverage in the small towns and semi-rural areas served.
Location data is an essential input for the predictive analytics model, enabling pattern recognition across different routes and conditions. Archived location data enables detailed analysis of route performance, providing the foundation for recommendations and alerts. The historical dataset includes five years of operational data from services operated across the UK, containing location data matched against scheduled routes and times, along with vehicle types and capacity information which can be used to identify patterns and forecast service performance under various conditions.
Current Status
Completed: Full infrastructure deployed. ETL pipeline processes data from operational sources nightly. Analytics database and API operational in production.
A Testing platform was launched in November for internal testing of Route and schedule reports and metrics. Feedback via 1:1 sessions, a workshop with account managers and team shadowing sessions has led to iterative improvements.
In Progress: Resolving upstream data quality issues—driver app location/timing gaps identified, correction scripts have been deployed and application fixes are ongoing. Iterating route and schedule reports and route health score based on feedback sessions.
Next: Integration of validated reports into the RideTandem Hub. Operational alerts development using route health scores with contextual information (schedule issues, driver changes). External operator validation planned in March.
Prime Contractor(s)
