Systemic Pedestrian Safety Analysis (2018)

Below is the uncorrected machine-read text of this chapter, intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text of each book. Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.

62 Background and Motivation The Oregon Department of Transportation (Oregon DOT) created a systemic safety method to inform their Statewide Bicycle and Pedestrian Safety Implementation Plan with a goal to identify and prioritize candidate project corridors through a data-driven process to reduce fatal and severe-injury pedestrian and bicycle crashes on all public roads (regardless of jurisdiction) throughout Oregon. At the time of this research, Oregon DOT was collecting additional data hypothesized to influence pedestrian and bicycle crash risk, and it is anticipated that they will revise their list of pedestrian and bicycle crash risk factors. Oregon DOT’s All Roads Transportation Safety program splits its available funding (primar- ily highway safety improvement program funds) evenly between two project prioritization types: hot spot and systemic analyses. Funding for systemic analyses is further disseminated into three emphasis areas identified by Oregon’s Strategic Highway Safety Plan, which include roadway departure improvement projects, intersection improvement projects, and pedestrian–bicycle safety improvement projects. Together, these three emphasis areas account for approximately 90% of the fatal and injury crashes in Oregon. While projects targeting roadway departure and intersection crashes can readily be priori- tized based on traditional benefit–cost analyses, applying these methodologies to pedestrian safety is more challenging because of general low frequencies of vehicle–pedestrian crashes at specific locations. Improvement projects would likely result in the exclusion of many sites with- out reported crashes, but potential for crashes, from funding consideration. To help alleviate this concern, Oregon DOT applied the predictive methods of the HSM (AASHTO 2010) and C H A P T E R 1 0 Case Example 2: Oregon Department of Transportation Key Takeaways • Complemented a hot spot analysis approach with a systemic analysis approach. • Used a set of risk factors established by an expert panel and applied weights to produce a high risk score. • Used sophisticated GIS-based techniques to create a high risk score for individual roadway segments and corridors along the entire state highway system. • Prioritized projects using a cost-effectiveness index.

Case Example 2: Oregon Department of Transportation 63 cost-effectiveness analysis to identify or help prioritize pedestrian and bicycle safety improve- ment projects without relying only on existing crash data. The cost-effectiveness method also does not rely on monetizing pedestrian and bicycle crashes to prioritize projects. Step 1: Define Study Scope Oregon DOT initially planned to focus on both state and local roads, but due to a lack of consistent roadway inventory data for local roads, risk factors were identified for state high- ways only. Step 2: Compile Data Oregon DOT had a history of using network screening for motor vehicle crashes and had compiled relevant roadway inventory for the state-owned road system. Pedestrian risk fac- tors identified in the next section were available or added to the inventory. Crash counts were also added. Step 3: Determine Risk Factors Oregon DOT formed an expert panel to help identify key risk factors (see Table 20) to use in subsequent steps in the analysis. Each risk factor was assigned a point value between 1 and 4 depending on certain conditions and a weight value relative to other risk factors. Pedestrian Risk Factor Relative Weight Risk Factor Score Proximity to signal 1 1 point if at least one signal is located on the segment or within 100 feet of the segment Proximity to transit stop 2 1 point for segments with one transit stop located on the segment or within 100 feet of the segment; 2 points for two or more transit stops Pedestrian-activated beacons or flashers 2 1 point subtracted (rewarded) for the presence of an enhanced midblock crossing Posted speed limit 3 2 points for posted speed limit of 35 or 40 mph 4 points for posted speed limits above 40 mph Undivided 4-lane segment characteristic 3 2 points if segment is an undivided 4-lane segment Number of non-severe injuries and pedestrian involved but not injured in crashes 4 2 points if a non-severe injury or pedestrian-involved crash was reported within 100 feet; 1 additional point for each additional injury or pedestrian involved AADT 4 2 points for AADT between 12,000 and 18,000 4 points awarded for AADT above 18,000 Number of severe injuries resulting from pedestrian-involved crashes 5 4 points if a severe injury was reported; 2 additional points awarded for each additional severe injury Number of fatalities resulting from pedestrian-involved crashes 5 4 points if a fatality was reported Table 20. Oregon DOT–identified pedestrian risk factors.

64 Systemic Pedestrian Safety Analysis Step 4: Identify Potential Treatment Sites Oregon DOT staff screened the network to identify potential treatment sites by applying a two- pronged approach: a crash-based approach and a risk-based approach. Under the crash-based approach, priority corridors were identified for both pedestrian and bicycle improvements by using crash frequency and severity over 5 years. Under the risk-based approach, a similar list of corridors was developed by identifying the presence of the risk factors described in Table 20. Due to lack of local road data, the risk-based approach was only applied to state-maintained roadways. The statewide network was divided into 0.10-mile segments to identify locations where risk factors are present. The scores in Table 20 were applied to 9,490 0.10-mile segments on the state network. Rural segments (as defined by the Census Bureau’s rural-urban classification), freeways and interstates, and connectors and frontage roads (based on the highway name) were excluded because the risk factors did not apply to those facilities. A segment’s score represented the sum of points awarded for all risk factors present. The higher the score, the greater the risk of a pedestrian or bicycle crash. The prioritized individual segments were grouped into longer candidate project corridors where one or more countermeasures could be applied to reduce crash frequency and severity; this allowed for efficiency for project development and construction. To establish candidate project corridor boundaries around the highest scoring individual segments, the segments were screened again to establish an additional score for each segment that accounts for the scores of upstream and down- stream segments. This resulted in a unique corridor score for each 0.10-mile segment, calculated as the average score for segments on the same roadway within one-half mile in each direction. The analysis was conducted using a spatial analysis model developed with ArcGIS 10.1 Model Builder. The spatial analysis selects and aggregates the scores of each segment of the network. The corridor aggregate score was divided by the number of segments, resulting in a score for each segment that reflects the average risk per mile long corridor. Step 5: Select Potential Countermeasures A variety of countermeasures were identified to address the crash patterns and risk factors identified through crash analysis. The countermeasures were evaluated to identify documented effectiveness, ease of implementation, and relative construction costs. A list of priority counter- measures was then identified, and a countermeasure toolbox was developed to assist in selecting the appropriate set of countermeasures for each project corridor, based on the effectiveness indicated by quantitative CMFs developed by empirical studies. Most of these identified coun- termeasures carried over into the HSIP’s approved crash reduction factor list. Step 6: Refine and Implement Treatment Plan After the potential project corridors were identified for improvements through the risk-based network screening process (previously described in Step 4), combined vehicle–pedestrian and vehicle–bicycle crashes were predicted based on the available safety performance functions using the predictive method in Part C of the HSM. For prioritization, the number of predicted (HSM SPF-derived) crashes were compared with the number of observed (historical crash data) crashes, and the higher value was used in the analyses. Projects were prioritized using a CEI, which compares the reduction in number of crashes due to the implementation of the countermeasure to the project cost, as shown below: CEI project cost expected reduction in pedestrian or bicycle crashes =

Case Example 2: Oregon Department of Transportation 65 The expected reduction in pedestrian crashes would be determined by the difference in the expected crashes without treatment and the expected crashes with treatment. This ratio esti- mates the cost to reduce one vehicle–pedestrian crash. The projects with the lower CEIs were selected for implementation. A CEI is the inverse of benefit–cost analysis (where the higher benefit/cost is selected for implementation); therefore, a lower value indicates a better per- forming alternative. By applying this cost-effectiveness approach rather than the traditional benefit–cost analysis approach, Oregon DOT was able to select 28 pedestrian improvement projects using a systemic and data-driven process. Other Steps and Lessons Learned to Date Since this study, Oregon DOT has continued these efforts and undertaken additional analyses to identify risk factors and develop SPFs to apply in their systemic process. See Monsere et al. (2017) and Siddique et al. (2017) for more information.