Government Documents (VTTI)

Permanent URI for this collection

https://hdl.handle.net/10919/55013

Browse

Browsing Government Documents (VTTI) by Author "Arafeh, Mohamadreza"

Now showing 1 - 2 of 2
  • Thumbnail Image
    Linear Regression Crash Prediction Models : Issues and Proposed Solutions
    Rakha, Hesham A.; Arafeh, Mohamadreza; Abdel-Salam, Abdel-Salam Gomaa; Guo, Feng; Flintsch, Alejandra Medina (Virginia Tech. Virginia Tech Transportation Institute, 2010-05)
    The paper develops a linear regression model approach that can be applied to crash data to predict vehicle crashes. The proposed approach involves novice data aggregation to satisfy linear regression assumptions namely error structure normality and homoscedasticity. The proposed approach is tested and validated using data from 186 access road sections in the state of Virginia. The approach is demonstrated to produce crash predictions consistent with traditional negative binomial and zero inflated negative binomial general linear models. It should be noted however that further testing of the approach on other crash datasets is required to further validate the approach.
  • Thumbnail Image
    Microscopic Analysis of Traffic Flow in Inclement Weather
    Rakha, Hesham A.; Krechmer, Daniel; Cordahi, Gustave; Zohdy, Ismail H.; Sadek, Shereef; Arafeh, Mohamadreza (United States. Federal Highway Administration, 2009-11)
    Weather causes a variety of impacts on the transportation system. An Oak Ridge National Laboratory study estimated the delay experienced by American drivers due to snow, ice, and fog in 1999 at 46 million hours. While severe winter storms, hurricanes, or flooding can result in major stoppages or evacuations of transportation systems and cost millions of dollars, the day-to-day weather events such as rain, fog, snow, and freezing rain can have a serious impact on the mobility and safety of the transportation system users. Despite the documented impacts of adverse weather on transportation, the linkages between inclement weather conditions and traffic flow in existing analysis tools remain tenuous. This is primarily a result of limitations on the data used in research activities. The scope of this research included use of empirical data, where available, to estimate weather impacts on three categories of sub models related to driver behavior, longitudinal vehicle motion models (acceleration, deceleration and car-following models), lane-changing models and gap acceptance models. Empirical data were used to estimate impacts of adverse weather on longitudinal and gap acceptance models but no suitable datasets were identified for lane changing models. Existing commercial microsimulation software packages were then reviewed to identify whether and how weather-related factors could be utilized in these models. The various sub models used in these packages to estimate longitudinal motion, lane-changing and gap acceptance models were evaluated. The research found that for the most part, weather-related factors could be incorporated into these models, although the techniques vary by package and by type of model. Additional empirical research is needed to provide confidence in weather-related adjustment factors, particularly as relates to ice and snow. This report concludes with some recommendations of future research related to weather and traffic flow. Additional work is proposed related to human factors and microscopic traffic modeling.