In recent years, urban mobility demand has become highly variable over time challenging the sustainability of transportation networks of major cities. At the same time, various types of incidents such as accidents, construction zone closures and weather hazards exacerbate the already congested transportation network. Timely detection of such events can offer an unprecedented opportunity to mitigate the consequences. In this paper, a partially observable Markov decision process (POMDP) framework is proposed for continuous active collision detection in a road segment equipped with spatially distributed speed sensors of variable accuracy. To this end, measurement selection strategies are designed that can quickly estimate the existence of a collision by appropriately selecting which sensors to query and when. A Kalman-like filter is used for estimation purposes. The efficacy of the proposed framework is shown on real data collected on the 405 freeway in the Los Angeles County.