In the previous reporting period, we conducted a targeted safety research needs assessment in our region to generate the MRI component of our program. This was completed with the assistance of TDOT and the SASHTO research subcommittee. To recap, seven DOTs of the 12 SASHTO states responded and reported their priority safety research needs. We supplemented these data with input from our university partners in the states from which no survey responses were received. Based on this input, we selected and designed projects to address common and high-priority needs within Region 4. We also coordinated our MR Initiatives with specific research topics identified by the USDOT modal administrators. This process generated four MR Initiatives, which are strategically focused research efforts with multiple sources of matching funds, and collaborations with other UTCs and/or research entities. These initiatives are continually coordinated and progress is reviewed quarterly. With the completion of the first program year each initiative area is reexamined and further focused to ensure future progress and project success.
MRI 1. Crash Modification Factors and the Highway Safety Manual
Raghavan Srinivasan, Highway Safety Research Center, Coordinator
With the publication of the Highway Safety Manual (HSM), there is a now a formal document that can be used to link roadway design with safety consequences. Part C of the HSM provides prediction models that can be used for project level analysis to assess the safety impacts of alternative designs. Crash modification factors (CMFs), which provide an estimate of the safety effectiveness of specific treatments, are available for selected treatments from Part D of the HSM. However, there are many treatments for which reliable CMFs are not available. One of the principal thrusts of MRI 1 is developing CMFs for high priority engineering treatments (stop to signal conversion and two-way to multiway stop conversion). This thrust complements NCHRP Project 17-63 (also being conducted by STC team members) that is developing guidelines for the development of crash modification functions. In addition, research is focusing on work zone procedures in the HSM, verification of previously developed Safety Performance Functions, and performance comparison of four calibration methodologies for SPFs focused on two-lane rural roads.
MRI 2. Integrated Simulation and Safety
Essam Radwan, University of Central Florida & Nikiforos Stamatiadis, University of Kentucky; Co-Coordinators
Simulation has evolved into a productive tool for predicting and evaluating safety on roadways and street networks. Simulation aptly defines human actions, addresses the effectiveness of roadway design and traffic operations on transportation safety, and helps to develop surrogate safety measures. Judicious and creative implementation of simulation tools holds great promise for enhancing HSM methodologies and approaches. Projects within this initiative evaluate the use of simulation in assessing and possibly predicting safety levels for roadway environments for pedestrian and bicycle conflicts with vehicular traffic; review of commonly used simulation tools and their capability to model incidents, accidents, and traffic operation under large-scale incidents requiring evacuations; and are developing vehicle-to-vehicle crash prediction models for intersections. Projects have resulted in an interim draft report being completed, papers and abstracts have been submitted and student theses have been initiated and one completed.
MRI 3. Exploring Socio-Demographic Characteristics and Culture Factors in Differential Safety Performance across Geography
Shashi Nambisan, University of Tennessee & Steve Polzin, University of South Florida; Co-Coordinators
The southeastern US has the highest roadway incident and injury rates in the country. While this disparity in roadway safety has been explored numerous times, these studies most often investigate the physical design characteristics of the transportation infrastructure. Some studies focus on the weather, government policies (e.g., speed limits, seat belt law), and the role of human factors in designing the infrastructure or vehicles. When socio-demographic characteristics are considered, they are typically limited to gender, age, and race or ethnicity. The results have not provided a comprehensive picture or convincing explanation for regional safety performance differences. The research effort underway with this initiative expands this limited set of characteristics to include socio-demographic characteristics, risk-taking and health characteristics, land use patterns, and other measures that consider the culture and values of the population as potential explanatory factors. Specific projects now underway include the determination of the extent to which population characteristics might explain differential safety performance (dataset development and comprehensive analysis). Research work includes a special focus on motorcycles and heavy vehicle safety. Multiple papers are in various stages of submission and review that includes publication in a major peer reviewed journal.
MRI 4. Big Data for Safety Monitoring, Assessment, and Improvement
Asad Khattak, University of Tennessee, Coordinator
Emerging sensor and communication technologies have made traffic, mobility, safety, and other information available ubiquitously and in real-time with appreciable temporal resolution and spatial accuracy. Some of the real time and dynamic data come from infrastructural investments by government agencies for traditional traffic monitoring (e.g., in-road loops and RTMS sensors), some come from private enterprises for logistical operations (e.g., RFID), and still other data come from crowd-sourced personal electronics (e.g., smartphone and Bluetooth equipped units). All these and other increasingly available data can be collected, fused, and mined to help monitor, assess, and improve transportation safety in real-time as well as after the fact. Along with the potential benefits of big data come issues related to large data volumes, high data velocities, varied data types and formats, and the veracity of the data’s accuracy and reliability, thus making big data and the proactive applications that rely upon it vulnerable to interruption, overload, and misuse. Projects within this initiative are: assessing/ investigating data sources and scenarios for how different data types can contribute to improving safety; macroscopic safety analysis and real-time crash risk analysis (including pedestrians); analyzed real-time travel time data quality collected from license plate readers, Bluetooth readers, probe vehicles, INRIX database, NAVTEQ, Remote Traffic Microwave Sensors (RTMS). Projects have already resulted in several research papers and presentations at the Transportation Research Board Annual meeting in Washington, D.C.