Project Administrator I/Research Scientist, Roadside Safety and Physical Security
Project Title: Using Vehicle Dynamics Simulation and Surrogate Models to Understand Factors and Mechanisms Affecting Rollover
This project investigates the feasibility of developing surrogate model for rollover crashes using vehicle dynamics simulations. Expected project tasks and outcomes are as follows:
Project Administrator I/Associate Research Scientist, Roadway Safety
Project Title: Developing CMFs from Probability Analyses
Since the advent of the HSM, crash frequency is the basis for safety evaluations of countermeasures where ample crash data are available. One of the goals for safety analyses is estimating CMFs. The HSM defines a CMF as “the ratio between the number of crashes per unit of time expected after a modification or measure is implemented and the number of crashes per unit of time estimated if the change does not take place”. In other words, the change in expected crash frequency due to a countermeasure. The success of applying methods such as the cross-sectional, and before-after analyses to estimate CMFs is highly dependent on the size of the data available.
Significant challenges are present when evaluating the safety performance of improvements with limited crash data. It is generally accepted that a representative sample of locations is needed for a robust crash frequency safety analysis, but when crash frequency is significantly low, most sites in such a representative sample have zero crashes. Fortunately, probability-based analysis is applicable as long as the dataset contains an appropriate number of sites with and without crashes. Probability-based analysis (otherwise known as risk analysis) is therefore a promising alternative to overcome the issue of limited crash data for safety evaluations. However, it is not completely clear how a safety effect estimated from probability analysis relates to the crash-frequency- based definition of CMF in the HSM. Therefore, it is desirable to determine if reliable CMFs for countermeasures can be estimated using risk analysis in cases where crash data is significantly scarce, and thus non-representative samples are used. This research is important because it will determine if developing reliable CMFs using probability-based analysis is a viable option in situations when crash frequency is very low.
Project Title: Estimating Effects of Driver Age and Distraction on Freeway Operations and Safety Using NDS Data
This project will develop and calibrate a model to examine the relationships between driver age and distraction on vehicle speeds, following distances, driver reaction times, and crash risks. Expected project tasks and outcomes are as follows (tentative dates also shown):
Data to be used: The research team will use the Freeway data obtained for the strategic project led by Avelar/Hammond last fiscal year. This dataset consists of 847 events from the NDS (including 82 near-crash events and 10 crashes). There are 105 potentially useful variables and up to 300 speed readings per event. Gap and headway data are available for multiple events as well.
Advancement to the state-of-the-practice: This project will develop a quantitative framework characterizing the impact and interactions of driver age and distraction on operations and crash risk. The results are expected to: (1) provide formulations for realistic vehicle-following behavior, with potential for microsimulation implementation; (2) provide an estimation of crash risk given key operational conditions; (3) inform future research on older driver interactions with vehicle technologies; and (4) identify elements for improved strategies to driver outreach and education.
Project Adminstrator I, Crash Analysis
Project Title: Evaluating Curve Speed Behaviour Using SHRP 2 Data
The first objective is to assess the transferability of the speed prediction models that were developed using Texas data. The second objective is to find whether driver familiarity has any relationship with the speeds on horizontal curves. The following tasks will be performed to accomplish these objectives.
The research team plans to use speed profiles from the existing data as the NDS vehicles traversed through approach tangents and curves. It is envisioned to collect the data stream that start at about 15 seconds’ travel time upstream of the curve beginning and end after the vehicle passes the curve ending. The team would also use the RID geometry and crash data as needed. The project team has access to all the data needed for accomplishing the study objectives.
Importance of Project
This research effort would strengthen the understanding of curve traffic operations and the relationship between curve speed choice and safety. Specifically, this effort will assess the transferability of both curve speed models and the methods to assess curve severity, which in turn can be used to diagnose and prioritize curve sites for safety treatments.