Evaluating Traffic Operation Conditions during Wildfire Evacuation Using Connected Vehicles Data

Abstract

With climate change and the resulting rise in temperatures, wildfire risk is increasing all over the world, particularly in the Western United States. Communities in wildland–urban interface (WUI) areas are at the greatest risk of fire. Such fires cause mass evacuations and can result in traffic congestion, endangering the lives of both citizens and first responders. While existing wildfire evacuation research focuses on social science surveys and fire spread modeling, they lack data on traffic operations during such incidents. Additionally, traditional traffic data collection methods are unable to gather large sets of data on historical wildfire events. However, the recent availability of connected vehicle (CV) data containing lane-level precision historical vehicle movement data has enabled researchers to assess traffic operational performance at the region and timeframe of interest. To address this gap, this study utilized a CV dataset to analyze traffic operations during a short-notice evacuation event caused by a wildfire, demonstrating that the CV dataset is an effective tool for accurately assessing traffic delays and overall traffic operation conditions during the selected fire incident. The findings also showed that the selected CV dataset provides high temporal coverage and similar travel time estimates as compared to an alternate method of travel time estimation. The study thus emphasized the importance of utilizing advanced technologies, such as CV data, to develop effective evacuation strategies and improve emergency management.

Methodology for Monitoring Border Crossing Delays with Connected Vehicle Data: United States and Mexico Land Crossings Case Study

“…Recently, emerging, anonymized connected vehicle (CV) trajectory data have emerged as an attractive data source due to the granular waypoint-level information, near real-time availability, and lack of requirement for deploying fixed ITS sensor infrastructure. Such data have already shown great promise and applicability in a variety of domains [43], monitoring work zone operations [44,45], evaluating work zone safety solutions [46], assessing traffic signal performance [47], evaluating the impact of inclement weather on roadway mobility [48,49], monitoring volume trends [50], and monitoring detour activity. The fundamental benefit of CV data is the waypoint-level fidelity which allows for a CV journey to be tracked continuously as it passes through a study location.…”

Analysis of Connected Vehicle Data to Quantify National Mobility Impacts of Winter Storms for Decision Makers and Media Reports

“…Emerging near real-time CV trajectory-level data provides far greater detail on individual passenger vehicle journey waypoints, and thus alleviates any over or underrepresentation concerns. Researchers have already demonstrated local or state-level examples of the versatility and sufficient representativeness of this CV trajectory data for use in assessing data coverage and filling gaps in traffic counts [29][30][31][32], monitoring mobility and safety through construction work zones [33][34][35][36][37], movement-level detection and performance monitoring at signalized intersections [38][39][40][41], and observing human mobility dynamics [42][43][44]. A pair of recent reports have built upon these methodologies and evaluated the usability of nationally available CV data sets at representative penetration rates towards analyzing the safety and mobility impacts of summer work zone construction as well as winter storm events on interstate travel in the United States [45].…”