Turning vehicle data into practical guidance for drivers and fleet managers.
NAVARCHOS 2 set out to transform the original NAVARCHOS monitoring and reporting system into an intelligent, cloud-based fleet management platform. The project brings together applied research in eco-driving, fleet analytics and route planning to help transport and logistics businesses improve productivity and reduce operating costs.
The research focuses on moving beyond vehicle tracking and manual data analysis to recommendations and metrics that support more efficient, environmentally responsible fleet operations.
The project proposes machine learning algorithms that combine live and historical OBD II and GPS data with weather, traffic information from fleet vehicles and established eco-driving rules. The aim is to provide timely, driver-centric notifications and recommendations that encourage more economical driving.
Intelligent metrics are designed to assess how drivers follow eco-driving recommendations and support driver competence classification. The research also examines how fuel consumption relates to driver behavior and vehicle condition, giving managers a richer basis for coaching and operational decisions.
NAVARCHOS 2 proposes a move from a single-server system to a cloud architecture capable of collecting, processing and analyzing transport data at scale.
A big-data-ready architecture is designed for high availability, performance and multi-tenancy. Flexible resource allocation and reduced failure risks aim to support reliable fleet services as customer demand and data volumes grow.
A dedicated collection gateway is planned to receive vehicle tracking data and bring in available external sources, including weather, traffic and public transport information. This would reduce reliance on retrieving vehicle data through tracking hardware providers' platforms.
A complex event processing pipeline is designed to recognize meaningful situations across incoming data streams. Parallel processing and coordinated event engines aim to address latency, scalability and the complexity of detecting events in real time.
The project plans to adapt open-source routing and scheduling engines to improve vehicle and driver allocation. The goal is to support timely deliveries, reduce unnecessary travel and fuel use, and improve coordination across logistics operations.
Historical tracking data provides a foundation for assessing fleet utilization, understanding driving patterns and identifying opportunities for improvement.
Planned analytics include automatic detection of regions of interest, comparisons between planned and actual routes, and analysis of driver schedules. These insights aim to reveal inefficient journeys and help identify potentially unauthorized vehicle use.
Fleet and driver comparisons draw on fuel usage, idling, harsh acceleration and braking, route efficiency and vehicle utilization. These measures are intended to make performance easier to understand and support targeted improvements.
NAVARCHOS 2 aims to strengthen a fleet management system developed in Cyprus through research and technological innovation. Its ambition is to deliver an easy-to-use, competitive platform with opportunities in both local and international markets.
The evolution continues with NAVARCHOS 3, which focuses on data-driven predictive maintenance and broader compatibility with fleet IoT devices. Explore NAVARCHOS 3 →
NAVARCHOS 2 is funded by the Research and Innovation Foundation through the RESTART 2016–2020 Programmes, under agreement ENTERPRISES/0916/0072.
