Year of Publication

2012

Degree Name

Doctor of Philosophy (PhD)

Document Type

Doctoral Dissertation

College

Engineering

Department

Civil Engineering

First Advisor

Dr. Nikiforos Stamatiadis

Abstract

Traffic safety is one of the most essential aspects of transportation engineering. However, most crash prediction models are statistically-based prediction methods, which require significant efforts in crash data collection and may not be applied in particular traffic environments due to the limitation of data sources. Traditional traffic conflict studies are mostly field-based studies depending on manual counting, which is also labor-intensive and oftentimes inaccurate. Nowadays, simulation tools are widely utilized in traffic conflict studies. However, there is not a surrogate indicator that is widely accepted in conflict studies.

The primary objective of this research is to develop such a reliable surrogate measure for simulation-based conflict studies. An indicator named Aggregated Crash Propensity Index (ACPI) is proposed to address this void. A Probabilistic model named Crash Propensity Model (CPM) is developed to determine the crash probability of simulated conflicts by introducing probability density functions of reaction time and maximum braking rates. The CPM is able to generate the ACPI for three different conflict types: crossing, rear-end and lane change. A series of comparative and field-based analysis efforts are undertaken to evaluate the accuracy of the proposed metric. Intersections are simulated with the VISSIM micro simulation and the output is processed through SSAM to extract useful conflict data to be used as the entry into CPM model. In the comparative analysis, three studies are conducted to evaluate the safety effect of specific changes in intersection geometry and operations. The comparisons utilize the existing Highway Safety Manual (HSM) processes to determine whether ACPI can identify the same trends as those observed in the HSM. The ACPI outperforms time-to-collision-based indicators and tracks the values suggested by the HSM in terms of identifying the relative safety among various scenarios. In field-based analysis, the Spearman’s rank tests indicate that ACPI is able to identify the relative safety among traffic facilities/treatments. Moreover, ACPI-based prediction models are well fitted, suggesting its potential to be directly link to real crash. All efforts indicate that ACPI is a promising surrogate measure of safety for simulation-based studies.

Share

COinS