Highly-dynamic, Pervasive Monitoring of Traffic Congestion Levels

The characteristic requirement that Intelligent Transportation Systems have for information on the transport network can often be satisfied by the wealth of available data gathered by statically deployed sensors, such as induction loops. Data derived from dynamically deployed sensors, for example, GPS sensors mounted on probe vehicles travelling on the network, can capture additional information aspects, especially in areas where no static sensors are deployed, and may complement such static sensor data. In this paper we present an intelligent transportation service for highly-dynamic and pervasive monitoring of traffic congestion levels based on information derived from dynamically deployed sensors. We outline how context information of probe vehicles can be statistically analyzed to extract information on the state of traffic within the transport network at any given time. Our service uses scheduled public transport vehicles as probe vehicles to monitor the traffic congestion levels within the national road network. The service can provide detailed, upto-date traffic congestion information in a pervasive manner and even in areas that are not sufficiently augmented with static sensors. This approach enhances the accuracy and reliability of the provided congestion information and is expected to improve the service quality form a user’s perspective. Ultimately, we expect to integrate this service with other user services, for example, for real-time passenger information or for multimodal travel planning.