Developing safety risk index for truck preferred arterial corridor.

Details

• Creators:
  Qin, Xiao ; Sultana, Most Afia ; Chitturi, Madhav V. ; Noyce, David A.
• Corporate Creators:
  South Dakota State University. Department of Civil and Environmental Engineering ; University of Wisconsin--Madison. Traffic Operations and Safety Laboratory
• Corporate Contributors:
  United States. Department of Transportation. Research and Innovative Technology Administration
• Subject/TRT Terms:
  • Trucking safety
  • Arterial highways
  • Truck crashes
  • Crash rates
  • Crash data
  • Crash severity
  • Crash causes
  • Crash risk forecasting
  • Highway corridors
• Publication/ Report Number:
  CFIRE 04-15
• Resource Type:
  Tech Report
• Geographical Coverage:
  United States
• Corporate Publisher:
  National Center for Freight and Infrastructure Research and Education (U.S.)
• Abstract:
  Truck safety has been of great interest to transportation officials, engineers and researchers for many years because ; of the amount of freight transported by trucks, the safety impact of trucks in traffic, and trucks’ invaluable ; contribution to the country’s economic growth. Connecting between traffic generators, arterial streets are key links ; for door-to-door deliveries. It is imperative to study and evaluate truck safety impact on arterial streets in response ; to the continued strong growth of truck traffic. This project provided a comprehensive analysis of truck-related ; crashes that occurred on arterial streets. ; By collecting extensive roadway geometries, pavement conditions, traffic data on selected arterial corridors heavily ; traveled by trucks, truck crash frequency and injury severity contributing factors have been identified. Statistical ; models have been tested with different combinations of datasets, with and without access parameters. Without the ; access related variables, truck miles traveled, annual average daily traffic (AADT), signal density, shoulder width, ; pavement service index (PSI) and its standard deviation are statistically significant factors for predicting the crash ; frequency. After incorporating access information, commercial driveway design related variables exhibit statistical ; significance while the previously significant variables such as AADT, PSI and its standard deviation are no longer ; statistically significant. This noticeable change of the statistical models warns that a spurious relationship may be ; formed if a causal relationship cannot be sufficiently supported via the data collected. For crash severity prediction, ; twelve contributing factors such as posted speed limit, lane width, number of lanes, pavement condition index, and ; undivided roadway portion were identified. Subsequently, the corridors safety performance measured by a truck ; crash severity index (CSI) as a function of crash frequency and injury severity has been established. It is ; anticipated that the findings in the study will not only benefit state and local agencies in planning, designing, and ; managing a safe arterial corridor for trucks and other motorists, but also help motor carriers to optimize their routes ; from a safety perspective.
  More +
• Format:
  PDF
• Funding:
  DTRT06-G-0020
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/27721/dot_27721_DS1.pdf
• File Type:
  [PDF - 1.93 MB]
• Collection(s):
  University Transportation Centers ; US Transportation Collection
• Main Document Checksum:
  urn:sha-512:1b957f2c03b87b2fc173223e7686c7572f9b6d904d4825f5c49f218952a85ffd7e0431cf9cfe26609c395d43da715bec1693af099792fa2cf8e741dd4794deb1