Secondary crashes are some of the most critical incidents occurring on highways. Such crashes can induce extra traffic delays and affect highway safety performance. Transportation agencies are interested in understanding the mechanism of the occurrence of secondary crashes and implementing appropriate counter measures. However, no well- established procedure identifies secondary crashes; this deficiency in turn impedes the possibility of investigating the underlying mechanism of their occurrence. The intent of this study was to develop an online scalable approach for helping to identify secondary crashes for the large number of highways with insufficient traffic surveillance units to collect the continuous traffic data required to classify such crashes accurately. The developed approach consisted of two major components: (a) acquisition of open source traffic data and (b) identification of secondary crashes through the use of these data. Unlike existing approaches based on static thresholds, queuing models, or infrastructure-based sensor data, the developed approach took advantage of various open-source data to identify traffic conditions in the presence of incidents. This study proposed to develop virtual sensors collecting traffic data from private traffic information providers such as Bing Maps, Google Maps, and MapQuest. The availability of such data greatly expands the ability of transportation agencies to cover more highways without installing infrastructure sensors. The virtual-sensor output provides the basic input to run the developed automatic identification algorithm for identifying secondary crashes. The algorithm is described step by step to provide a readily deployable approach for transportation agencies interested in identifying secondary crashes on their highway networks.