Strategies to Identify and Mitigate Secondary Crashes in Real-time

Traffic incidents are the primary source of non-recurring congestion. In addition to affecting roadway operations, traffic congestion resulting from an incident exposes other vehicles to the risk of being involved in additional incidents, typically referred to as secondary crashes. Secondary crashes adversely affect traffic operations and impose risk on the safety of both road users and incident responders. Transportation agencies have been looking for ways to mitigate secondary crashes. However, secondary crash mitigation has several challenges. The length of the queue caused by an initial incident and the amount of time this queue lasts on the road varies, depending on the characteristics of the respective incident. Since identifying potential secondary crashes is difficult, investigating the factors that may influence these crashes becomes even more challenging. Moreover, the limited knowledge of what constitutes a secondary crash and its contributing factors largely impede mitigation strategies. The goal of this research was to investigate approaches to mitigate secondary crashes on freeways. To achieve this goal, a readily implementable data-driven approach to identify secondary crashes in real-time was developed. This approach is more accurate in identifying secondary crashes since it better reflects the changes in traffic characteristics caused by the primary incident. Following the identification of secondary crashes, the next step involved developing a secondary crash likelihood model. This model established the relationship between the likelihood of secondary crashes and influential factors, i.e., incident characteristics, traffic flow attributes, temporal attributes, presence of work zone, and other geometric attributes. The model results indicate that the presence of work zones significantly influenced the occurrence of secondary crashes. Overall, as expected, roadway geometric, temporal, traffic flow, incident, and weather attributes were found to influence secondary crashes. The probabilistic relationship between factors that influence the risk of cascading crashes was also explored. Crashes are termed as “cascading” when the subsequent secondary crashes occur within the impact area of the prior secondary crashes and the primary incident. Cascading crashes were found to be most likely to occur when traffic is in the transition state, i.e., when there is a platoon of vehicles traveling at high differential speeds. Once an incident has occurred, traffic conditions upstream of the incident change with time, and so does the likelihood of secondary crashes. The likelihood model was implemented to dynamically predict the risk of a secondary crash in real-time. The proposed model accounts for the temporal variation of prevailing conditions that influence the likelihood of secondary crashes. This model could be used to develop an Advanced Traffic Management System (ATMS) to proactively prevent secondary crashes. Through this system, first responders will be more vigilant and better prepared in case secondary crashes occur. In addition, motorists upstream of the primary incident could be warned about the potential for secondary crashes