Detecting traffic events using the sensor network infrastructure is an important service in urban environments that enables the authorities to handle traffic incidents. However, irregular measurements in such settings can derive either from faulty sensors or from unpredictable events. In this paper, we propose an efficient solution to resolve in real-time the source of such irregular readings by examining the correlation and the consistency among neighbor sensors and exploiting the wisdom of the crowd. Our experimental evaluation illustrates the efficiency and practicality of our approach.
|Number of pages||10|
|Journal||CEUR Workshop Proceedings|
|Publication status||Published - 1 Jan 2015|
|Event||2nd International Workshop on Mining Urban Data, MUD 2015 - co-located with 32nd International Conference on Machine Learning, ICML 2015 - Lille, France|
Duration: 11 Jul 2015 → …