Investigating the relation between instantaneous driving decisions and safety critical events in naturalistic driving environment.

The availability of large-scale naturalistic driving data provides enormous opportunities for studying relationships between instantaneous driving decisions prior to involvement in safety critical events (SCEs). This study investigates the role of driving instability prior to involvement in SCEs. While past research has studied crash types and their contributing factors, the role of pre-crash behavior in such events has not been explored as extensively.

Impact of cyberattacks on safety and stability of connected and automated vehicle platoons under lane changes.

Connected and automated vehicles (CAVs) offer a huge potential to improve the operations and safety of transportation systems. However, the use of smart devices and communications in CAVs introduce new risks. CAVs would leverage vehicle to vehicle (V2V) and vehicle to infrastructure (V2I) communication, thus providing additional system access points compared to traditional systems.

A Bayesian modeling framework for crash severity effects of active traffic management systems.

Transportation agencies utilize Active traffic management (ATM) systems to dynamically manage recurrent and non-recurrent congestion based on real-time conditions. While these systems have been shown to have some safety benefits, their impact on injury severity outcomes is currently uncertain. This paper used full Bayesian mixed logit models to quantify the impact that ATM deployment had on crash severities.

Left turn crash risk analysis: Development of a microsimulation modeling approach.

The use of traffic simulation to analyze complex transportation issues has become common practice in transportation engineering. The further application of microsimulation to the analysis of traffic safety in a systematic, rigorous, and controlled fashion is becoming increasingly viable as simulation models improve and tools for quantifying surrogate safety measures become readily accessible. Using a calibrated traffic microsimulation model and surrogate safety assessment model analysis, this paper examined how the risk for left-turn crashes varied as traffic conditions changed at a signalized intersection.

Improving freeway segment crash prediction models by including disaggregate speed data from different sources.

Traditional traffic safety analyses use highly aggregated data, typically annual average daily traffic (AADT) and annual crash counts. This approach neglects the time-varying nature of critical factors such as traffic speed, volume, and density, and their effects on traffic safety. This paper evaluated the relationship between crashes and quality of flow at different levels of temporal aggregation using continuous count station data and probe data from 4 lane rural freeway and 6 lane urban freeway segments in Virginia.

Crash severity effects of adaptive signal control technology: An empirical assessment with insights from Pennsylvania and Virginia.

Adaptive signal control technology (ASCT) is an intelligent transportation systems (ITS) technology that optimizes signal timings in real time to improve corridor flow. While a few past studies have examined the impact of ASCT on crash frequency, little is known about its effect on injury severity outcomes. Similarly, the impact of different types of ASCTs deployed across different states is also uncertain.

Estimating safety effects of adaptive signal control technology using the Empirical Bayes method.

Introduction: Adaptive signal control technology (ASCT) has long been investigated for its operational benefits, but the safety impacts of this technology are still unclear. The main purpose of this study was to determine the safety effect of ASCT at urban/suburban intersections by assessing two different systems.

Method: Crash data for 41 intersections from the Pennsylvania Department of Transportation (PennDOT), along with crash frequencies computed through Safety Performance Functions (SPFs), were used to perform the Empirical Bayes (E-B) method to develop crash modification factors (CMF) for ASCT.

A Cyber-ITS framework for massive traffic data analysis using cyber infrastructure.

Traffic data is commonly collected from widely deployed sensors in urban areas. This brings up a new research topic, data-driven intelligent transportation systems (ITSs), which means to integrate heterogeneous traffic data from different kinds of sensors and apply it for ITS applications. This research, taking into consideration the significant increase in the amount of traffic data and the complexity of data analysis, focuses mainly on the challenge of solving data-intensive and computation-intensive problems.

Understanding and managing disaster evacuation on a transportation network.

Uncertain population behaviors in a regional emergency could potentially harm the performance of the region's transportation system and subsequent evacuation effort. The integration of behavioral survey data with travel demand modeling enables an assessment of transportation system performance and the identification of operational and public health countermeasures. This paper analyzes transportation system demand and system performance for emergency management in three disaster scenarios.