More research on safety management from a comprehensive, systems approach is desirable. This is a hybrid session. Each author of seven selected papers addressing a specific aspect of safety management will present a poster depicting the research in a rapid-fire, five-minute pitch followed by one-on-one discussions with the authors in the session room.
Safety Management: From Start to Finish
Observations on the Use of CMF-corrected Crash Prediction Models to Identify Sites with Promise
Mark Poppe, J2 Engineering and Environmental DesignShow Abstract
The Highway Safety Manual (HSM) provides guidance on the application of crash prediction models for: network screening, evaluation of alternative designs, and evaluation of implemented safety improvements. A variety of models are available. They may be classified as network screening level (or simple models), project level (or CMF-corrected models), and evaluation level models.
Identifying sites with promise using crash prediction models is often based on assessing the difference between the expected number of crashes (Ne) at a site and the predicted number of crashes (Np) for similar sites within the population. A large difference between Ne and Np may denote a safety problem and be used to identify and rank sites with promise.
The HSM indicates that CMF-corrected models may be used for network screening purposes. However, issues arise in the analysis regarding the definition of “similar sites.” When using CMF-corrected models, the definition of “similar sites” changes with changes in the CMF adjustments particular to each site. Use of (N e – Np) for identifying and ranking sites with promise does not work well when using a CMF-corrected estimate of Np . A large composite CMF will increase Np and thereby decrease the value (Ne – N p ). But a large composite CMF may point to a site with promise.
This paper examines this phenomenon in detail, reviews a case study, and suggests that simple models may be preferable for identifying sites with promise.
Multiobjective Evaluation in Countermeasure Selection at Two-Way Stop-Controlled Intersections Considering Traffic Operation, Safety, and Environment
Zhao Yang, Nanjing UniversityShow Abstract
Yuanyuan Zhang, University of Southern Mississippi
Renwei Zhu, China Academy of Urban Planning & Design
Yin Zhang, Nanjing University
This study aims to develop a procedure to conduct multi-objective evaluation in traffic countermeasure (CM) selection process at two-way stop-controlled (TWSC) intersections. To illustrate the procedure, the economic benefits of three vehicle safety related CMs were calculated considering not only the safety impacts but also the operational and environmental impacts. First, for each countermeasure, VISSIM simulation models were developed to obtain the average delay, vehicle emission and fuel consumption for the intersection both before and after the treatment. The traffic operational impacts were calculated as the change in delay costs. The environmental impacts were calculated as the change in vehicle emission and fuel consumption costs. Next, the safety impacts were calculated as the crash reduction benefits for different CMs using safety performance functions (SPFs) and crash modification factors (CMFs). Finally, the life cycle cost (LCC) method was used to combine the different components in the total benefit. The Monte Carlo (MC) simulation method was used to conduct uncertainty analysis by using random sampling from probability descriptions of uncertain input variables to generate a probabilistic description of results. The findings showed first, that the operational and environmental impacts accounted for a large proportion of the total impacts, which can significantly affect the selection of CMs. Second, the rankings of the CMs differ depending on whether the safety impacts alone are considered, or whether the safety, operational and environmental impacts are considered together. The study illustrates the detailed process of evaluating projects considering multiple objectives. This process offers policy and decision makers a solid and practical reference of how to conduct multi-objective evaluation. The findings also explain how different objectives can countervail with each other in improving motorist safety at TWSC intersections.
Improving the Self-explaining Performance of Czech National Roads
Jiri Ambros, Transport Research Centre (CDV), Czech RepublicShow Abstract
Veronika Valentova, CDV - Transport Research Centre
Ondrej Gogolin, Transport Research Centre (CDV), Czech Republic
Richard Andrasik, Transport Research Centre (CDV), Czech Republic
Jan Kubecek, Transport Research Centre (CDV), Czech Republic
Michal Bíl, Transport Research Centre (CDV), Czech Republic
Improving the road network according to principles of self-explaining roads is a promising way of increasing level of safety; however there are no universal guidelines on how to measure and improve the self-explaining performance of existing roads. In order to apply this approach on Czech national roads, a presented study was conducted, consisting of five steps: (1) automated segmentation into tangents and horizontal curves; (2) collection of floating car data and calculation of speed; (3) development of multivariate speed models for estimation of speed also on segments not covered by floating car data; (4) network-wide application of the models and evaluation of speed consistency, i.e. differences of speed on tangents and following curves; (5) identification of substandard curves, categorization and proposal of optimization in terms of consistent placement of traffic control devices or reconstructions. The paper describes all the steps, as well as several checks conducted along the way, such as comparison of profile speed and floating car speed, interpretation of regression models or validation of predicted speed consistency against long-term average of crash frequency. The methodology was certified for practical use and will be applied by Czech national road agency.
Developing a Web-Based Tool to Track Highway Safety Planning Progress in California
Katherine Chen, University of California, BerkeleyShow Abstract
Sang Hyouk Oum, University of California, Berkeley
Jill Cooper, Safe Transportation Research and Education Center
A Strategic Highway Safety Plan (SHSP) is a comprehensive, statewide, data-driven safety plan that coordinates activities across agencies to reduce traffic fatalities and serious injuries on all public roads. In 2015, California updated its SHSP with the input of hundreds of stakeholders. The challenge with implementing a multi-year effort across many primary actors is tracking decisions and progress in an efficient manner, as well as, having a state’s safety program be accountable and transparent to its stakeholders.
The Safe Transportation Research and Education Center at UC Berkeley developed a tracking tool for California’s updated SHSP. The steering committee and other key stakeholders involved in the SHSP implementation phase provided substantial input. The SHSP Tracking Tool is a user-friendly, low-cost, easily maintained resource that multiple stakeholders update. Written in PHP on a single-page website, the SHSP Tracking Tool is a mechanism that allows users to contact leaders, track progress, run reports, and review performance measures on all SHSP projects. Moving forward, the tool will also serve as a primary repository of SHSP internal documents and a community forum to evaluate progress and advance the efforts of California’s SHSP safety stakeholders. Further, it reflects federal and state transportation calls to ensure safety efforts are data- and performance-driven. It can be adapted for any state’s SHSP.
The Safest Path: Analyzing the Effects of Crash Costs on Route Choice and Accessibility
Mengying Cui, The University of SydneyShow Abstract
David Levinson, University of Sydney
The "safest path" is proposed to optimize the on-road safety of individuals and minimize the cost of crashes. In this study, the framework of a link-based crash cost analysis is built and applied to assess the crash cost of each link segment on the road network of the Minneapolis - St. Paul area based on Safety Performance Functions from the perspective of travelers. The safest path is then found for all OD pairs to compare flow patterns and accessibility distributions with those based on the traditional shortest travel time path. While, the safest path does not coincide with the shortest path, the accessibility distributions have similar patterns.
Probability of Secondary Crash Occurrence on Freeways Using Private-Sector Speed Data
Noah Goodall, Virginia Department of TransportationShow Abstract
A percentage of crashes on freeways are suspected to be caused in part by the congestion or distraction from earlier incidents. Identifying and preventing these secondary crashes are major goals of transportation agencies, yet the characteristics of secondary crashes—in particular the probability of their occurrence—are poorly understood. Many secondary crashes occur when a vehicle encounters non-recurring congestion, yet previous efforts to identify incident queues and their secondary crashes have relied either on deterministic queuing theory, or on data from uniformly-spaced, dense loop detectors. This study is the first analysis of secondary crash occurrence integrating incident timelines and traffic volumes with widely-available (and legally obtained) private sector speed data. Analysis found that 9.2% of all vehicle crashes were secondary to another incident, and that 6.2% of these crashes were tertiary to another primary incident. Secondary crashes occurred on average once every 10 crashes and 54 disabled vehicles. The findings support a fast incident response, as the probability of secondary crash occurrence increases approximately one percentage point for every additional 2-3 minutes spent on-scene in high volume scenarios.
GIS-Based Spatial and Statistical Analysis of Severe Crash Hotspot Accessibility to Hospitals
Mehmet Ulak, Stony Brook UniversityShow Abstract
Ayberk Kocatepe, Connetics Transportation Group
Eren Ozguven, FAMU-FSU College of Engineering
Mark Horner, Florida State University
Lisa Spainhour, Florida State University
Previous studies have examined the hospital accessibility problem, and exhaustively investigated several aspects of roadway crashes such as their severity, frequency, influential factors, and clustering behavior. However, even though studies have looked at crashes and hospital accessibility separately, the relationship between them, in terms of accessibility of severe crash hotspots to hospitals with emergency services, still remains unclear. In this study, we investigate this accessibility using a geographic information systems (GIS)- and statistics-based analysis to detect high risk locations. We also examine several environment-, traffic-, and human-related factors to identify the determinants of the crashes that constitute the hotspots via a hierarchical multinomial logistic regression analysis. Results show that several roadway segments portend an elevated threat of injury and fatalities on drivers and passengers not only due to a higher probability of being severely injured, but also because of their low accessibility with respect to hospitals having emergency service. Regression analysis, on the other hand, illustrates and verifies that particular spatial, traffic-, and roadway related factors such as intersection presence or speed limits imperil traffic safety substantially. The knowledge gained from this study can help agencies and officials pinpoint and investigate high risk locations to enhance the safety of roadway users.