Context Aware Traffic Congestion Estimation to Compensate Intermittently Available Mobile Sensors

Estimating the degree of traffic congestion at run time is crucially important for intelligent transportation systems (ITS) especially when selecting travel routes. One of the challenges when using a mobile sensor (e.g. mobile phone, GPS) as source of traffic data stems from its mobility. This paper proposes a novel approach to fusing mobile data and an algorithm for traffic congestion estimation to compensate intermittent available mobile sensors. Furthermore, our proposed model is also tolerant to incomplete data and it is open to any type of sensors. This approach differs from other traffic congestion estimation techniques in that it utilizes discoverable context even on sensorless road segments in real time instead of entirely relying on a sensor-based data of observed road segment. Implementation and validation through extensive experimentation with real data confirms feasibility of the proposed approach.