Feasibility of Using Traffic Data for Winter Road Maintenance Performance Measurement

Details

• Creators:
  Cao, Luchao (Johnny) ; Fu, Liping
• Corporate Creators:
  University of Waterloo. Innovative Transportation System Solutions (iTSS) Lab
• Corporate Contributors:
  Ontario. Ministry of Transportation
• Subject/TRT Terms:
  Condition Surveys Data Collection Decision Support Systems Snow Removal Traffic Data Winter Maintenance Wireless Communication Systems
• Resource Type:
  Tech Report
• Geographical Coverage:
  United States ; Iowa
• Corporate Publisher:
  Ontario. Ministry of Transportation
• Abstract:
  The research presented in this report is motivated by the need to develop an outcome based WRM performance measurement system with a specific focus on investigating the feasibility of inferring WRM performance from a traffic state. The research studied the impact of winter weather and road surface conditions (RSC) on the average traffic speed of rural highways with the intention of examining the feasibility of using traffic speeds from traffic sensors as an indicator of WRM performance. Detailed data on weather, RSC, and traffic over three winter seasons from 2008 to 2011 on rural highway sites in Iowa, US are used in this investigation. Three modelling techniques are applied and compared to model the relationship between traffic speed and various road weather and surface condition factors, including multivariate linear regression, artificial neural networks (ANN), and time series analysis. Multivariate linear regression models are compared by temporal aggregation (15 minutes vs. 60 minutes), types of highways (two-lane vs. four-lane), and model types (separated vs. combined). The research then examined the feasibility of estimating/classifying RSC based on traffic speed and winter weather factors using multi-layer logistic regression classification trees. The modelling results have confirmed the expected effects of weather variables including precipitation, temperature, and wind speed; it verified the statistically strong relationship between traffic speed and RSC, suggesting that speed could potentially be used as an indicator of bare pavement conditions and thus the performance of WRM operations. It is also confirmed that a time series model could be a valuable tool for predicting real-time traffic conditions based on weather forecast and planned maintenance operations, and that a multi-layer logistic regression classification tree model could be applied for estimating RSC on highways based on average traffic speed and weather conditions.
• Format:
  PDF
• Collection(s):
  US Transportation Collection
• Main Document Checksum:
  urn:sha-512:a93129001ca13437f2eaeffb27dc37ca30852439b7ae093c610941c0ec9a3105a0a6536cd6867a532cc4b8d1a116dbd6d215c4df6e46b0d8e555fd92472293bc
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/88152/dot_88152_DS1.pdf
• File Type:
  [PDF - 2.64 MB ]