4.6 Article

Assessing operational performance of New Zealand's South Island road network after the 2016 Kaikoura Earthquake

出版社

ELSEVIER
DOI: 10.1016/j.ijdrr.2020.101553

关键词

Operational Performance; Corridor Analysis; Trip Analysis; Kaikoura Earthquake; Traffic Simulation; Road Network; Validation

资金

  1. National Science Challenge (NSC) - Resilience to Nature's Challenges (RNC)
  2. QuakeCoRE (a New Zealand Tertiary Education Commission-funded Centre)
  3. University of Auckland

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The Kaikoura earthquake, a 7.8 (Mw) magnitude event, occurred 2 min after midnight on 14th November 2016 NZDT, around 60 km south-west of Kaikoura at a depth of 15 km. The earthquake caused numerous landslides, many of them significant, resulting in widespread disruption and closure of sections of State Highway 1 (SH1), which is a critical corridor in the South Island of New Zealand. Post-disaster operational performance of the road network, in terms of average travel time, density, and count data, has not been assessed to date. A mesoscopic traffic simulation model of the South Island's road network was, therefore, validated against 7-day Average Daily Traffic (ADT) data starting from Day 8 after the Kaikoura earthquake. Corridor analysis and trip analysis were conducted to assess post-disaster operational performance of the road network. Corridor analysis results indicate a significant increase in traffic count and density, and a minor decrease in average travel speeds on four main corridors namely: SH65, SH63, SH6 (between SH63 and SH65), and SH7 (between SH65 and SH1), serving as the main alternative mutes after the earthquake. Trip analysis results show a significant increase in the average travel time from Marlborough to other affected traffic zones (typically 20-50% and as much as 90% in the worst case) due to the increased travel distances on the alternate routes. Such information can now be used to calculate the economic impact of the Kaikoura earthquake, in terms of increased vehicle operating costs and travel time costs, by comparing the pre- and post-disaster scenarios.

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