University of Idaho

Safety Data Management and Analysis: Assessing the Continuing Education Needs for the Pacific Northwest, Analysis of Options – Phase 2


PI: Kevin Chang (UAF), kchang@uidaho.edu
Dates: 12/16/2015 – 12/15/2016

Recent advancements in data collection capabilities have allowed transportation-related agencies to collect mountains of safety data. There is an immediate need to find out what types of safety data are being collected, what types of safety analysis can be done with the collected data, and what (other) types of safety data and analysis approaches are required to meet the
safety objectives. Read More

Safety Data Management and Analysis: Addressing the Continuing Education Needs for the Pacific Northwest


PI: Kevin Chang (UI), kchang@uidaho.edu
Co-Investigators: Cynthia Chen (UW), Robert Perkins (UAF), Ali Hajbabaie (WSU), Shane Brown (OSU)
Dates: 01/16/2015 – 06/15/2016
Led by: University of Idaho (UI) Professor Kevin Chang, this project is the PacTrans Multi-Institution Education Project for 2015-2016.

Safety data collection, management, integration, improvement, and analysis activities are integral to developing a robust data program that leads to more Informed Decision making, better targeted safety investments, and overall improved safety outcomes. Safety data includes crash, roadway, traffic, licensing, and vehicle data. With the increased complexity of the safety data management and analysis activities, and with the limited resources most transportation agencies have, there is a critical need to provide the transportation workforce in the Pacific Northwest with the resources needed to effectively manage and analyze safety data. Read More

Modeling Passing Behavior on Two-Lane Rural Highways: Evaluating Crash Risk under Different Geometric Conditions


PI: Kevin Chang (UI), kchang@uidaho.edu
Co-Investigators: Ahmed Abdel-Rahim (UI), Brian Dyre (UI)
Dates: 01/16/2015 – 06/15/2016

The primary goal of this project is to provide a better understanding of a driver’s passing behavior and model their decision-making on two-lane rural highways under different geometric configurations.  This project will specifically examine passing behavior on horizontal curves on two-lane rural highways and explore how the different degrees of curvature influence driver behavior.  The outcome of the project will provide state DOTs with guidelines that allow them to improve the safety and efficiency of traffic operations along this particular type of highway setting. Read More

Enhancing the Resilience of Idaho’s Transportation Network to Natural Hazards and Climate Change


PI: Tim Frazier (UI), tfrazier@uidaho.edu
Dates: 07/01/2013 – 7/31/2015

The goals of this research are to determine both the process (i.e., methodology) and the technology (i.e., models) through which the vulnerability science community may provide value on critical and pervasive hazard risk-related issues to state and regional decision makers in Idaho for the purpose of transportation infrastructure resilience enhancement. To achieve the research goals, this study will conduct a probabilistic risk and vulnerability assessment of the state’s transportation network to current and future hazards with a special focus on increased flooding and landslide hazards associated with climate variability and change. Read More

Modeling Passing Behavior on Two-Lane Rural Highways: Evaluating Crash Risk under Different Geometric Condition


PI: Michael Dixon (UI)
Dates: 07/01/2013 – 7/31/2015

Passing maneuvers on rural two-lane highways are a complex task with a significant effect on safety, capacity, and service quality. This maneuver, which involves driving in the lane of the opposing traffic, is associated with simultaneously increasing crash risk and increasing the driver’s speed. Understanding drivers’ passing behavior and their decision-making on two-lane rural highways can significantly contribute to accurately predicting risk and service quality. Only limited research has been conducted to capture and document drivers’ perception of when they need to pass and passing decision-making. This is partly because it is difficult to collect detailed data on driver perceptions and passing behavior in the real-world environment. Read More

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