“Vision Zero” puts traffic safety at the forefront of NYC’s agenda with the goal of ending traffic deaths and injuries on City’s streets[1]. Traffic safety has also been and continues to be one of the main focus areas of USDOT for more than several decades. This research project is one of the several on-going research efforts undertaken by Professor Ozbay’s research group at the NYU Polytechnic School of Engineering & CUSP.

Secondary crashes are one of the most critical types of traffic incidents that frequently affect the performance of highways. They can induce extra traffic delays and affect highway safety. Transportation agencies are interested in understanding the mechanism of the secondary crash occurrence and implementing appropriate countermeasures. However, there is no well-established procedure to identify secondary crashes, which in turn impedes the possibility of investigating their underlying occurrence mechanism.

This research aims to develop an on-line scalable approach to help identify secondary crashes for a large number of highways that have few or no traffic surveillance units collecting continuous traffic data needed to classify secondary crash accurately. The developed approach consists of two major components: (a) acquisition and processing of a large amount of open source traffic data and (b) identification of secondary crashes through the use of these data. This research introduces the idea of developing virtual sensors for collecting traffic data from traffic information providers such as Bing Maps, Google Maps and MapQuest. The availability of such data greatly expands our ability to cover more highways without having to install infrastructure based sensors. Massive data collected through virtual sensors provide the basic input to run the developed automatic identification algorithm for identifying secondary crashes. The algorithm provides a readily deployable approach for transportation agencies interested in assessing the degradation in system performance as a result of secondary crashes.







pic Depiction of the proposed open source data based approach to accurately identify secondary crashes