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1 S U M M A R Y Application of Crash Modification Factors for Access Management Background The Highway Safety Manual (1st Edition) (AASHTO 2010) has revolutionized the transportation engineering practice by providing crash modification factors and functions (CMFs), along with methods that use safety performance functions (SPFs) for assessing and quantifying the safety consequences of planning, designing, and operating highway facilities. It recognizes that access management is an effective component in the operation and safety of roadways but includes only a few opportunities to quantify the safety effects of access management strategies. The validity of applying access management CMFs to existing SPFs is also in question since most of the SPFs in the Highway Safety Manual (1st Edition) were established without consideration of base conditions for access management features. The question remains as to whether or not these SPFs, along with the corresponding CMFs, adequately capture the differences in access management features. Objectives The objective of NCHRP Project 17-74 was to develop and refine CMFs and SPFs for access management features and develop guidance to assist transportation agencies in quantifying the safety impacts of their decisions related to access management. Specifically, this project: ï· Verified the reliability of using existing SPFs from the Highway Safety Manual (1st Edition) to quantify the safety performance of urban and suburban arterials, ï· Quantified the safety performance of access management features by developing and refining CMFs and SPFs for various levels of analysis (site, segment/intersection, and corridor), ï· Provided guidance for the use of access management related CMFs from the Highway Safety Manual (1st Edition) and the CMF Clearinghouse, and ï· Identified opportunities for future research. Approach The first phase of the project focused on information gathering and refinement of the work plan. This included a literature review, survey of practitioners, data reconnaissance, and gap analysis. The literature review summarized current information on the safety effects of access management strategies at three levels: site, intersection, and corridor. This formed the basis for the remainder of the project as it helped to identify existing high-quality CMFs and SPFs for access management strategies (which were relatively limited). The literature review also informed the gap analysis, helping to identify areas where high-quality CMFs and SPFs are not available. The survey of practitioners sought to identify transportation agency policies, practices, needs, and challenges related to quantifying the safety effects of access management strategies. The survey results identified priority strategies, the extent to which agencies are quantifying the safety effects of access
2 management strategies, and the level of analysis (i.e., site, intersection, or corridor) that would be most helpful for future efforts to quantify safety effects. The survey results indicated the following regarding the quantification and tracking of safety effects of access management strategies and the existence of policy or procedures for assessing these effects: ï· Most respondents do not quantify the safety effects of access management strategies to support the decision-making process, ï· More than 80 percent indicated their agencies do not have a policy or procedure for assessing the safety effects of access management strategies, ï· Most indicated a priority need for estimating the effects of combination strategies, while less than half indicated a priority need for focusing on individual strategies, and ï· Corridor level analysis was the highest priority as opposed to segment/intersection- or site-level analysis. As part of the survey, and through a parallel effort, the project team identified and reviewed existing data sources to determine the availability and quality of data to support this research. The data reconnaissance helped to determine the feasibility of evaluating priority strategies and strategy combinations. It also helped to identify potential data and methodological issues to consider during the data collection and analysis, thus informing the study design and data collection plan. In the review, answers to the following specific questions were sought: ï· What data are readily available in existing databases? ï· How many miles of road and counts of intersections (by facility type) are available? ï· What is the availability of suitable reference locations? ï· What is the quality of existing data? ï· Which agencies provided the data? ï· Who is the point of contact for acquiring the data? Following the literature review, survey, and data reconnaissance, the project team summarized current knowledge and identified gaps to establish research priorities and appropriate study design(s). The project team then met with the panel to discuss these priorities and determine the focus of the research, considering the schedule, budget, and data availability. The primary research question was: How does the existing Highway Safety Manual (1st Edition) Predictive Method (i.e., combination of SPFs and CMFs) perform for sites with similar geometry, but different access management features (e.g., access density and spacing, corner clearance, and turning restrictions) not captured in the crash prediction model? While the research focused on chapter 12: Urban and Suburban Arterials from the Highway Safety Manual (1st Edition), it also considered the urban and suburban arterial SPFs developed for NCHRP Project 17-62, Improved Prediction Models for Crash Types and Crash Severities, that predict crashes by crash type and severity and will presumably support the Highway Safety Manual (2nd Edition). The following is a list of access management strategies that were a focus of the research: ï· Manage location, spacing, and design of median openings and crossovers, ï· Manage location and spacing of unsignalized access, ï· Manage the spacing of signalized and unsignalized access on crossroads in the vicinity of freeway interchanges, ï· Establish functional area and corner clearance criteria, ï· Manage spacing and density of traffic signals, and ï· Right-turn treatment. The second phase of the project implemented the work plan, including data collection, analysis, and documentation. The data collection leveraged existing datasets to minimize duplication of efforts and maximize the amount of data for analysis in this study. Specifically, the project team used existing crash, roadway, and traffic characteristic data from NCHRP Project 17-62 and supplemented these datasets with
3 additional access management variables. NCHRP Project 17-62 data includes segments from Ohio and Minnesota and intersections from Ohio and North Carolina. The project team performed manual data collection for over 3,500 intersections, both signalized and stop-controlled, and nearly 4,000 segments throughout Ohio, over 240 intersections from North Carolina, and nearly 500 segments from Minnesota. The data analysis focused on assessing, validating, and enhancing the Part C Predictive Method from the Highway Safety Manual (1st Edition) to investigate the effect of access management variables on crash predictions. The following is an overview of the general procedure to assess the Part C Predictive Method from the Highway Safety Manual (1st Edition): 1. Evaluate if the Part C Predictive Method shows prediction bias for access management features (i.e., does it over- or under-predict for site characteristics related to access management?), 2. If substantial prediction bias exists, determine if there is a discernable and explainable pattern, 3. Attempt to create CMFs to remove the prediction bias for the access management features. In doing so, evaluate if the relationships make sense (i.e., is the direction and magnitude of effect reasonable?), and 4. Using a validation dataset, determine if the findings are consistent among datasets. If no significant bias was found, then it was concluded that the Part C Predictive Method performs well at predicting crashes and the addition of CMFs for those variables is not likely to improve the prediction performance. If significant prediction bias was observed, the project team employed two alternate but related methodologies to estimate CMFs for each access management strategy. The first approach was based on the calibration of SPFs and the second was based on estimating a new regression model. To validate the results, the project team used a second stateâs data to test the same crash prediction models and look for consistency in the findings of goodness-of-fit and any CMFs developed. Based on the results of the analysis, the project team developed a Practitioner Guide is to assist transportation planners, designers, and traffic engineers in quantifying the safety impacts of access management strategies and making more informed access-related decisions on urban and suburban arterials. The guide presents methods to quantify the safety performance of individual locations (i.e., intersections or segments) as well as corridors that represent multiple adjacent intersections and segments. The safety performance can then be used in the decision process to compare against other quantitative measures (e.g., costs, operational efficiency, environmental impacts) or perceptions (e.g., fairness, convenience, competitiveness related to property access and businesses). By quantifying safety performance and considering safety alongside other factors, agencies will better understand the comprehensive costs and benefits of projects, which will lead to more informed decisions and more impactful investments. The project team vetted the guide with a focus group of 40 practitioners representing diverse perspectives from highway safety and access management. The practitioners also represented a diversity of agencies and organizations from state and local transportation agencies to consultants and academia. Results The Part C Predictive Method from the Highway Safety Manual (1st Edition) provides separate methods for estimating the safety performance of segments and intersections and both include some access management features. For segment-level predictions, the Part C Predictive Method accounts for the number and type of driveways along the segment. For intersection-level predictions, the Part C Predictive Method accounts for the presence of left- and right-turn lanes, left-turn signal phasing (at signalized intersections), and right-turn-on-red restrictions (at signalized intersections). The research from NCHRP Project 17-74 confirmed the Part C Predictive Method performs relatively well across a range of several other access management features not accounted for in the existing method. Specifically, the Part C Predictive Method (i.e., combination of SPFs and CMFs) performs well for sites with similar geometry, but different access management features such as median opening spacing, number of median openings by type, and corner clearance along a segment. There are, however, a few scenarios where the existing models do not perform
4 well across sites with different access management features. Notable scenarios include locations with channelized right-turn lanes and locations with nearby ramp terminals. As such, this research developed adjustment factors to account for differences in the predictions at locations with channelized right-turn lanes and locations with nearby ramp terminals. The following is a brief summary of those results: ï· For distance to ramp terminal, bias was not observed in the predictions for signalized intersections. The cumulative residual (CURE) plots did not show substantial bias for stop-controlled intersections although the CURE plot does indicate that for distances under 1,500 feet, there may be an underprediction of crashes. The recommended CMF (adjustment factor) for three- and four-legged stop- controlled intersections is 2.12 if there is a ramp terminal within 1,500 feet. The CMF is intuitive in that more crashes are predicted as the distance to ramp terminal is smaller. ï· For channelizing right-turn lanes, the results were inconsistent and generally not statistically significant at the 95-percent confidence level for three- and four-legged signalized intersections and four-legged stop-controlled intersections. For three-legged stop-controlled intersections, the recommended CMF (adjustment factor) is 0.72 for the presence of a channelized right-turn lane, compared to the base condition of a right-turn lane with no channelization. Another significant finding of this research is that the Part C Predictive Method should not be used to estimate the safety effect of variables related to access spacing and density. The Part C Predictive Method is only applicable to estimating the safety performance of individual segments and intersections, assuming independence among each unit of analysis. While the results can be aggregated from multiple segments and intersections to estimate the safety performance of a corridor, as suggested in the Highway Safety Manual (1st Edition), this method does not consider the potential interactions among adjacent or nearby sites (e.g., access spacing and density). The existing Part C Predictive Method may even produce counterintuitive results (e.g., fewer estimated segment crashes with an increase in the number of intersections along a corridor). The structure of the data for the Highway Safety Manual (1st Edition) Part C Predictive Method is such that roadways are segmented by intersection and midblock areas and crashes are assigned to one of the two. One of the important findings of this research found that as the number of intersections in a segment increased, the expected number of segment crashes often decreased. This is contrary to expectations since the presence of more intersections should lead to more conflicts, and certainly not fewer, even away from the intersections. However, the structure of the data may contribute to this counterintuitive finding. Specifically, if more intersections are present, then it is more likely that a crash will be assigned to an intersection and not a segment. Given this finding, the data are not conducive to segment-level analysis of some variables (e.g., intersection density, spacing, etc.). Instead, these variables are better assessed at the corridor level. A corridor-level predictive method is presented in the Practitioner Guide, which is more appropriate for considering interactions among access management features and estimating the safety effect of variables related to access spacing and density.