Effect of Trip Generation Time on Evacuation Time Estimates. Issue 11 (November 2019)

• Record Type: Journal Article
• Authors: Herrera, Nelida; Smith, Todd; Parr, Scott A.; Wolshon, Brian
• Is Part Of: Transportation research record. Volume 2673:Issue 11(2019)
• Page Start: 101
• Page End: 113
• Publication Date: 2019-11
• Subjects: Transportation -- Periodicals; Roads; Transport -- Périodiques; Routes -- Périodiques; Routes -- Conception et construction -- Périodiques; Roads; Transportation; 388.05
• DOI: 10.1177/0361198119850793
• Languages: English
• ISSNs: 0361-1981

Abstract

The entry of evacuation traffic into a road network plays an important role in how efficiently a threatened area will clear prior to the onset of hazardous conditions. Trip generation times are also instrumental in forecasting evacuation times when using traffic simulation. Given the dependency on loading rates, there is a need to understand the sensitivity of evacuation time estimates (ETEs) to network loading behavior. The contribution of this work is to quantify the impact of faster and slower loading of the population over a range of population sizes and network topologies during a nuclear power plant emergency. Examining the impact of trip generation time on ETEs, the results of this research show consistency with both prior research and observation. Specifically, in smaller, more confined areas, clearance times generally follow the loading curves that precipitated them. In larger population areas, however, it is likely that longer loading times tend to meter departures thereby limiting the formation of significant congestion despite significantly higher demand. These results suggest that clearance times generally follow loading patterns, unless there are other demand or capacity conditions (localized or global) which lead to the formation of congestion and propagate delay thereby increasing travel times. Although this research was focused on evacuations associated with nuclear power plant emergencies, it is assumed that these findings could be directly applicable to other emergencies and non-emergency scenarios where unbalanced surges in demand occur.