Further Development of the Safety and Congestion Relationship for Urban Freeways (2014)

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3Background SHRP 2 Reliability Project L07 has focused specifically on the identification and evaluation of design treatments that can be used to reduce delays due to nonrecurrent congestion and improve travel time reliability (1). The objectives of Project L07 were to (1) identify the full range of possible design treat- ments used by transportation agencies to improve travel time reliability and reduce delays due to key causes of nonrecurrent congestion, (2) assess their costs and operational and safety effectiveness, and (3) provide recommendations for their use and eventual incorporation into appropriate design guides. Three separate analyses of the design treatments were con- ducted in Phase 2 of Project L07: operational, safety, and benefit–cost. The traffic operational analysis methodology developed in Phase 2 built on work completed in SHRP 2 Project L03. As part of the traffic operational analysis, a spreadsheet-based Analysis Tool was developed to allow highway agencies to analyze and compare the effects of a range of design strategies on a given highway segment using the ana- lytical procedures developed in Phase 2 of Project L07. High- way agencies can input data about a highway (e.g., geometrics, volumes, crash totals), and the Analysis Tool computes delay and reliability indicators resulting from various design treat- ments, further translating those results into life-cycle costs and benefits. In addition to the traffic operational benefits of reducing congestion, the potential safety benefits were explored as well. The reduction of congestion through application of design treatments or intelligent transportation system (ITS) improve- ments has been widely thought to have a positive effect on safety, but this relationship had not been well quantified in previous research. Congestion may result in stalled or slowed traffic, and the situation in which high-speed vehicles approach the rear of an unexpected traffic queue clearly presents a sub- stantial risk of collision. The potential for collision within queues of stop-and-go traffic is also clear. Thus, on the one hand, the frequency of both of these conditions can be ame- liorated by treatments to reduce nonrecurrent congestion. On the other hand, collision severity is clearly a function of speed, so the lower speeds on roadways during congested periods may reduce overall collision severity. This trade-off between crash frequency and severity in congested versus uncongested conditions has never been satisfactorily quanti- fied. Previous research on this issue for freeway facilities has been conducted by Zhou and Sisiopiku (2) and by Hall and Pendleton (3). In particular, Zhou and Sisiopiku suggest that different crash types respond in different ways to volume-to- capacity (v/c) ratios based on hourly volumes. The research results presented below illustrate why a difference between crash types appears reasonable. Relationships between safety and congestion were developed in Phase 2 of Project L07 for application in the spreadsheet- based Analysis Tool (1, 4). The safety-congestion relationship developed in Phase 2, shown in Figure 1.1, is used to quantify the safety benefits associated with the reduction in congestion resulting from implementation of specific design treatments. Figure 1.1 suggests that a reduction in congestion within the range of traffic operational conditions from LOS C to LOS F should result in a corresponding reduction in crashes. The safety-congestion relationship in Figure 1.1 was devel- oped from analyses of traffic operational and crash data for the freeway systems of two metropolitan areas: Seattle and Minneapolis–St. Paul. Figures 1.2 and 1.3 show the safety-versus-congestion data for freeways in Seattle and in Minneapolis–St. Paul, respectively. The plot for the Seattle data in Figure 1.2 generally shows a U-shaped relationship, with the lowest crash rates in the middle of the traffic density range at about LOS C. Crash rates at lower densities (i.e., better LOS) are slightly higher than the minimum crash rate, due primarily to single-vehicle crashes. Crash rates at higher densities (i.e., poorer LOS) are substantially higher than the minimum crash rate, due to multiple-vehicle crashes. C h a p t e r 1 Introduction

4increase to the point that rear-end or sideswipe (e.g., lane changing) crashes become more frequent. Data confirm that single-vehicle crashes predominate at lower traffic densities and multiple-vehicle crashes predominate at higher traffic densities. Since the relationship between congestion and safety was based on only two metropolitan areas, SHRP 2 added a new task to Project L07—designated as Task IV-5—to further explore the relationship between safety and congestion using data from other metropolitan areas. The research in Task IV-5 was conducted to determine whether a similar U-shaped rela- tionship between safety and congestion exists for the freeway systems of other metropolitan areas and how that relation- ship can best be generalized for broader application in the analysis of design treatments. The research also investigated whether the relationship applies to a full range of nonrecur- rent congestion scenarios. Objective The objective of Task IV-5 was to further develop the relation- ship between safety and congestion that was initially devel- oped in Phase 2 of the research and to test the relationship for various nonrecurrent congestion scenarios. Task IV-5 was managed in six subtasks as follows: • Subtask 5A. Identify additional areas for data collection. • Subtask 5B. Obtain data for selected additional areas. The relationship implied by Figure 1.2 appears promising to evaluate the safety effects of design treatments intended to reduce nonrecurrent congestion. For example, if a particular treatment shortens the duration of several incidents and results in 5 h per year with traffic operations in LOS C rather than LOS F, the safety-congestion relationships will provide a basis for quantifying that safety benefit as a specific number of crashes reduced. Figure 1.3 shows a plot of crash rate and traffic density data for the Minneapolis–St. Paul area analogous to that shown for the Seattle area in Figure 1.2. The Minneapolis–St. Paul data show a relationship similar to Seattle, but the U-shaped curve is not as pronounced and is complicated by highly variable data (a secondary peak) in the traffic density range from 30 to 40 passenger cars per mile per lane (pc/mi/ln)—that is, LOS D through E+. However, regression modeling has still confirmed the U-shaped nature of the crash rate–traffic density relation- ship. There is no obvious explanation for this secondary peak, which is not present in the Seattle data and may be a quirk of the data for Minneapolis–St. Paul. The U-shaped relationship between crash rate and traffic density has a clear interpretation. At low traffic densities, there are few vehicle-vehicle interactions; and inattentive, fatigued, or impaired drivers are likely to depart from their lane or leave the roadway. As traffic volumes increase, drivers (including even inattentive, fatigued, or impaired drivers) are more likely to collide with another vehicle than run off the road. Further- more, at high traffic densities, vehicle-vehicle interactions Crash type FI observed FI predicted PDO observed PDO predicted Total observed Total predicted Cr as he s pe r M VM T 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5 6.0 6.5 Traffic density (pc/mi/ln) 0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 Figure 1.1. Observed and predicted total, FI, and PDO crash rates versus traffic density for Seattle and Minneapolis–St. Paul metropolitan areas combined (1, 4). FI  fatal and injury, PDO  property damage only, and pc/mi/ln  passenger cars per mile per lane.

5 To ta l C ra s he s pe r M VM T 1 2 3 4 5 6 7 8 9 10 11 Traffic density (pc/mi/ln) 0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 FI Cr a s he s pe r M VM T 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 Traffic density (pc/mi/ln) 0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 PD O C ra s he s pe r M VM T 0 1 2 3 4 5 6 7 Traffic density (pc/mi/ln) 0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 (a) (b) (c) Figure 1.2. Observed (a) total, (b) FI, and (c) PDO crash rates versus traffic density for freeways in the Seattle area.

6To ta l C ra s he s pe r M VM T 0.5 0.6 0.7 0.8 0.9 1.0 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 Traffic density (pc/mi/ln) 0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 FI C ra s he s pe r M VM T 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45 0.50 0.55 0.60 Traffic density (pc/mi/ln) 0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 PD O C ra s he s p er M VM T 0.4 0.5 0.6 0.7 0.8 0.9 1.0 1.1 1.2 Traffic density (pc/mi/ln) 0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 (a) (b) (c) Figure 1.3. Observed (a) total, (b) FI, and (c) PDO crash rates versus traffic density for freeways in the Minneapolis–St. Paul area.

7 • Subtask 5C. Develop safety-congestion relationships for each selected area. • Subtask 5D. Compare and combine the safety-congestion relationships. • Subtask 5E. Test the safety-congestion relationships for specific nonrecurrent congestion scenarios. • Subtask 5F. Revise the Project L07 Analysis Tool to the extent needed to implement the Task 5 results. The background for this work and the research plan for each subtask are presented in Chapter 2. Organization of the report This report presents the results of the research to further develop a safety-congestion relationship for urban freeways. The remainder of this report is organized as follows. Chapter 2 describes the technical approach to the research and presents a summary of the database and results by state. Chapter 3 com- pares the safety-congestion relationships developed in each metropolitan area, presents a combined safety-congestion rela- tionship, and explores the application of this relationship to recurrent and nonrecurrent congestion. Chapter 4 presents the conclusions and recommendations of the research.