Enhancing non-motorized safety by simulating non-motorized exposure using a transportation planning approach : final report.

Details

• Creators:
  Eluru, Naveen ; Yasmin, Shamsunnahar ; Bhowmick, Tanmoy ; Rahman, Moshiur
• Corporate Creators:
  University of Central Florida. Department of Civil, Environmental and Construction Engineering
• Corporate Contributors:
  Safety Research Using Simulation (SAFER-SIM) University Transportation Center ; United States. Department of Transportation. University Transportation Centers (UTC) Program ; Florida. Department of Transportation
• Subject/TRT Terms:
  Accident Exposure Crash Risk Forecasting Crash Severity Cyclists Injury Severity Nonmotorized Transportation Pedestrians Pedestrian Safety Policy Analysis Safety Improvement Simulation Transportation Planning Bicycle Safety

  More +
• Resource Type:
  Tech Report
• Geographical Coverage:
  United States;Florida
• Edition:
  Final report
• Corporate Publisher:
  Safety Research Using Simulation (SAFER-SIM) University Transportation Center
• Abstract:
  Safety researchers and analysists have employed land use and urban form variables as surrogates for
  
  traffic exposure information (pedestrian and bicyclist volumes and vehicular traffic). The quality of these
  
  crash prediction models is affected by the lack of “true” non-motorized exposure data. The current
  
  research effort is focused on developing a transportation planning simulation framework to generate
  
  exposure information for crash prediction models. Specifically, the research effort is focused on evaluating
  
  non-motorist exposure measures in terms of demand at a planning level. The evaluated exposure
  
  measures are incorporated in examining non-motorist safety, which would allow us to devise more
  
  evidence-based policy implications for improving overall safety and activities related to non-motorized
  
  modes of travel. The proposed research approach recognizes that non-motorized safety is affected by
  
  vehicular volumes and non-motorized activity at a macro-level in the urban region. The vehicular and nonmotorized
  
  exposure measures are generated to enhance the vulnerable road user crash prediction
  
  models. In identifying non-motorist exposure measures, we develop aggregate-level demand models to
  
  identify critical factors contributing to non-motorist generators and attractors at a zonal level. In
  
  evaluating non-motorist safety, we estimate four different aggregate level models: (1) zonal-level crash
  
  count model for examining pedestrian-motor vehicle crash occurrences, (2) zonal-level crash count model
  
  for examining bicycle-motor vehicle crash occurrences, (3) zonal-level crash severity model for examining
  
  pedestrian crash injury severity by proportions, and (4) zonal-level crash severity model for examining
  
  bicycle crash injury severity by proportions. These models are estimated as a function of zonal level
  
  sociodemographic characteristics, roadway/traffic attributes, built environment, land-use characteristics,
  
  and exposure measures identified from demand models. The formulated demand models are estimated
  
  by using 2009 National Household Travel Survey data and the crash models are estimated by using the
  
  Signal Four Analytics crash database for the year 2010 for the Central Florida region. Model estimation
  
  results are further augmented by a validation exercise. To demonstrate the implication of the estimated
  
  models, we also perform policy analysis for ten different scenarios, including changes in traffic volume
  
  within the vicinity of central business district, reduction in zonal-level speed limit, increasing walking
  
  facilities, and restrictions on the number of traffic lanes. From the policy scenario analysis, we identify
  
  beneficial changes to existing infrastructure and traffic operation for improving non-motorized road user
  
  safety at a planning level. The research methodology as proposed in our study recognizes that zonal-level
  
  attributes are likely to influence non-motorist exposure. At the same time, non-motorist exposure along
  
  with the zonal-level attributes are critical factors in developing non-motorist safety models.
  
  
• Format:
  PDF
• Funding:
  DTRT13-G-UTC53
• Collection(s):
  University Transportation Centers US Transportation Collection
• Main Document Checksum:
  urn:sha-512:8f6d43b33ab3f61d090f1789995c4b35ee3c7abc56f61e431e93ab286f1181e1fa90e1eb372ad3edbe1f299a2372af19a3f92cb85880b79e477b5853e12187c6
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/32653/dot_32653_DS1.pdf
• File Type:
  [PDF - 3.30 MB ]