Crash analysis of mountainous freeways with high bridge and tunnel ratios using road scenario-based discretization

Mountainous freeways with high bridge and tunnel ratios are a new type of road that rarely contain many special road sections formed by various structures. The crash characteristics of the road are still unclear, but it also provides conditions for studying how various road environments affect traffic. In view of the various structures and differences in the driving environments, a scenario-based discretization method for such a road was established. The traffic-influence areas of elementary and composite structures were proposed and defined. Actual data were analyzed to investigate the crash patterns in an entire freeway and in each special section through statistical and comparative research. The results demonstrate the applicability and validity of this method. The crash rates were found to be the highest in interchange and service areas, lower in ordinary sections, and the lowest in tunnels, being mostly attributed to collisions with fixtures. The crash severity on bridges and bridge groups was significantly higher than that on the other types of road sections, being mostly attributed to single-vehicle crashes. The annual average daily traffic and driving adaptability were found to be related to crashes. The findings shed some light on the road design and traffic management implications for strengthening the traffic safety of mountainous freeways.

Medienart:

E-Artikel

Erscheinungsjahr:

2020

Erschienen:

2020

Enthalten in:

Zur Gesamtaufnahme - volume:15

Enthalten in:

PloS one - 15(2020), 8 vom: 10., Seite e0237408

Sprache:

Englisch

Beteiligte Personen:

Sun, Zongyuan [VerfasserIn]
Liu, Shuo [VerfasserIn]
Li, Dongxue [VerfasserIn]
Tang, Boming [VerfasserIn]
Fang, Shouen [VerfasserIn]

Links:

Volltext

Themen:

Journal Article
Research Support, Non-U.S. Gov't

Anmerkungen:

Date Completed 13.10.2020

Date Revised 03.11.2023

published: Electronic-eCollection

Citation Status MEDLINE

doi:

10.1371/journal.pone.0237408

funding:

Förderinstitution / Projekttitel:

PPN (Katalog-ID):

NLM313518920