Mobility is a central aspect of our everyday life. Providing real time information that enhances mobility in urban areas not only can significantly help the protection of the environment, but it can also greatly improve our quality of life as well. Furthermore traffic congestion is one of the most common problems in large cities and has grave consequences to health, financial and overall quality of life of all urban residents. Public transport operators increasingly use Automatic Vehicle Location (AVL) systems to monitor and manage their fleet of vehicles. These AVL systems produce enormous amounts of real time data concerning daily movements in urban areas. Up to now, these data are mainly used internally for fleet management and existing operations improvement and optimization. A new estimation model has been devised and implemented to uncover, predict and distribute real time general traffic information in urban areas. Based on bus AVL data, provided by the existing telematics system of Urban Public Transport Organisation of Thessaloniki (OASTH), Greece, the model dynamically produces graphic animated general traffic information over proper GIS background (e.g. Google maps), thus eliminating the need to use costly traditional methods of collecting general traffic data, such as inductive pavement loops, vehicle sensors etc.

The Speed-O system efficiently utilizes advanced processing algorithms and their implementations to recover average speed information in real time from existing telemetry infrastructure of a fleet of vehicles. The mathematical algorithms for the nested adaptive refinement we used, are efficient, cost-effective and scalable to handle the traffic network of any metropolis. Our system builds a hierarchical model of the major urban roads used by buses, and utilizes successive telemetry data points to form and solve large and sparse linear systems of equations that estimate the traffic conditions.

Speed-O provides accurate and fine-detailed, real-time, city-wide traffic information data and enables the observation, analysis and prediction of traffic flow and opens new opportunities for online route planning services. Furthermore, as the model gets automated input, it only needs minor calibration tests to adapt to other urban areas with different traffic characteristics. Therefore, it is easy to incorporate it into existing (classic and mobile) web sites.

“SPEED-O: Circulatory Model of Urban Group” is a collaboration of 3 carriers, the Organization of Urban Transportation of Thessaloniki (O.A.S.TH.), the Department of Computer and Electrical Engineering (HMMY) of Aristotle University of Thessaloniki and Link Technologies LLC (LINK) and was funded by the ministry of Ministry of Education and Religious Affairs of General Secretariat for Research and Technology of Greece.