DRAFT Vision Statement—Safety
Lead: Srinivas Pulugurtha
The vision of the focus group on “safety” is to research and promote application of spatial data and information science technologies to improve safety on transportation networks. To achieve this goal, it is imperative that spatial data and information science technologies are explored and tools developed to improve data accuracy, evaluation, integration, and analysis in a wide range of areas such as engineering, emergency preparedness, enforcement, as well as education. Automation, integration, and the use of advanced technologies play a vital role in achieving the goals. Thus, the group emphasizes focusing on these aspects to address pressing safety needs.
Topics for Research and NCHRP Problem Statements:
- Automate collection of quality crash data using advanced technologies (with Global Positioning Systems capabilities) by officers and investigators. This reduces subjectivity and other errors while improving data quality. However, there is a need for consistent national / international standards for the collection of crash data and tools and systems based on these standards. It has to be noted that spatial accuracy is relative to conditions at the time of the crash.
- Identify precise location of crashes, incidents and violations and tie it to causation – example: location of back (upstream end) of the queue is time dependent (work zone, urban congestion at intersections) – need to establish if crashes are related to intersections and space is not enough to know that.
- Integrate Traffic Information Systems with automatic crash data collection and identify location of crashes in real time. Studies have shown that crashes and incidents contribute to a significant portion of congestion costs. Dissemination of information about location of crashes, their severity and estimated clearance time to the system users using advanced technologies can help them better plan their trips (congestion mitigation strategies) and thus reduce congestion and its impacts.
- Conduct research and develop automated tools to identify high risk locations (could comprise sites, linear zones, or other areal extents). Such tools will help better plan and focus deployments to enhance safety at high risk locations and maximize derived benefits.
- Automate tracking of vehicular action in time and space that avoids violation of privacy; integrating black box information (last “n” seconds of vehicular dynamics) to highly accurate readings of space and time. In combination with similar information from all involved vehicles (and perhaps nearby vehicles), this information could be used to better understand the circumstances leading to the event for proper adjudication and mitigation. Spatial tracking of violations leads to more effective enforcement.
- Deployment of real time provision of occupant and vehicle information to emergency rooms and emergency response dispatch for appropriate response and preparedness (see recent NHI Johns Hopkins work). Information is post-processed for vehicle engineering use. Providing good emergency response within the “Golden hour,” especially in rural America, will help significantly improve survivability of crash victims. Mapping of patterns for planning of emergency response capability and possible pre-deployment would be invaluable in this regard.
- Application of advanced spatial statistics to crash patterns (e.g., spatial Bayes), sensitivity analysis of network topology and segmentation issues. The effect of spatial and temporal data quality issues are the better understood and fed back into resource allocation process.
- Development and application of tools to address travel needs and concerns of the elderly population. One needs to look at locations of various resources (such as quick care centers, hospitals, senior centers, mental and emotional health services, hospitals, social security administration offices, and so on) elderly need to access, potential routes to travel to these location from major elderly trip generating areas, and methodologies to identify high crash locations and possible solutions to improve safety along these routes. Such efforts would also serve as a building block to develop strategies to address the driver anxiety & limitations, safety concerns and records, and licensing implications for this population group.
- Collect curve and grade information for safety research using remote sensing technology, and spatial and image processing techniques (in lieu of driving or surveying roadways).
Members of the Safety Focus Group:
Group Lead: Srinivas Pulugurtha