Date Available

12-8-2016

Year of Publication

2016

Document Type

Master's Thesis

Degree Name

Master of Science in Civil Engineering (MSCE)

College

Engineering

Department/School/Program

Civil Engineering

Advisor

Dr. Mei Chen

Abstract

The growth of data has begun to transform the transportation research and policy, and open a new window for analyzing the impact of crashes. Currently for the crash impact analysis, researchers tend to rely on reported incident duration, which may not always be accurate. Further, impact of the crashes could linger a much longer time at upstream, even if the records are correct for the crash spot and it is a challenge to quantify the impact of a crash from the complex dynamics of the recurrent and non-recurrent congested condition. Therefore, a difference-in-speed approach is developed in this research to estimate the true crash impact duration using stationary sensor data and incident logs. The proposed method used the Kalman filter algorithm to establish traveler’s anticipated travel speed under incident-free condition and then employ the difference-in-speed approach to quantify the temporal and spatial extent of the crash. Moreover, potential applications such as statistical models for predicting the impact duration and total delay were developed in this research. Later, an analysis on distribution of travel rate was performed to describe and numerically show to what extent crashes influenced travel rates compared with the normal conditions at different periods of the day and by the crash types. This study can help to shape incident management policies for different types of crashes at different periods and illustrates the usages of data to improve the understanding of crashes, their impact, and their distribution in a spatial-temporal domain.

Digital Object Identifier (DOI)

https://doi.org/10.13023/ETD.2016.516

Download

Share

COinS