Gaps and Emerging Technologies in the Application of Solid-State Roadway Lighting (2024)
Chapter: 3 CMFs for Solid-State Roadway Lighting Applications
CHAPTER 3
CMFs for Solid-State Roadway Lighting Applications
Lighting can affect safety in multiple ways. For example, well designed lighting extends the range of drivers’ visual detection and provides additional time for drivers to prepare for potential upcoming hazards. Lighting may also help reduce driving workload and therefore alleviate driver fatigue. On the other hand, roadway lighting may cause glare and introduce increased fixed-object crash risks. The development of high-quality CMFs for lighting therefore needs to take into careful consideration a number of factors, such as study design, data quality and availability, sample size, and control of potential bias. In general, different CMFs can be developed for different crash types, crash severity levels, site conditions, and environmental conditions.
The CMF Clearinghouse currently uses several criteria to assess the quality of a CMF, including the study design, sample size, standard error, potential bias, and data source. Although most of these criteria can be relatively objectively controlled, the evaluation process is subject to opinions (CMFClearinghouse.org). Currently, NCHRP 17-72 is developing a new CMF rating procedure that revises the 5-star rating scale to a 150-point score system, where CMFs scored 100 and above may be considered high-quality CMFs worthy of inclusion in the second edition of the Highway Safety Manual (HSM). The new scoring system proposed consists of three broad quality evaluation areas, including data (55 points), statistical analysis design (75 points), and statistical significance (20 points).
For before-after studies, the new scoring system requires studies to properly address issues such as regression to the mean bias, changes in traffic volume over time, suitability of reference/comparison groups, and quality of safety performance functions (SPFs). For cross-sectional studies, the new system emphasizes aspects such as the similarity of sites with and without treatment, proper consideration of correlation between variables, and evaluation of spatial and temporal correlations. Although on a different score scale, the new CMF scoring system is considered mostly consistent with the current star rating system when being applied to exiting CMFs in the current CMF Clearinghouse (CMFClearinghouse.org). Note that neither scoring system takes into consideration geographic regions where data is collected, therefore affecting transferability of the results. For this reason, the rating/score of a particular CMF may not directly indicate its applicability for a specific location. For example, a 5-star fatal crash CMF developed based on European data may poorly reflect U.S. experiences.
This portion of the project provides guidelines on how to develop high-quality CMFs for roadway and street lighting measures based on this research, relevant literature, and the research team’s experience. While the results of this project are not immediately implementable, the results highlight data and informational needs to develop high-quality CMFs for local roads and residential areas.
General Methods
A number of methods have been commonly used for CMF development: (Carter, Srinivasan, Gross, and Council; Carter, Srinivasan, Gross, Himes, et al.; Gross and Donnell). Details of these methods can be found in the full report.
Before-After Comparison Group Approach: This method considers crash and traffic changes irrelevant to the subject treatment by including an untreated control group for comparison. An important aspect of this method is the selection of a suitable comparison group.
Empirical Bayes Before-After Approach: This method is similar to the before-after comparison group method in that both draw information on the expected crash frequencies from a group of untreated sites. However, the empirical Bayes before-after method utilizes SPFs to account for crash changes due to traffic and potential roadway changes over the study period.
Full Bayes Study: This method is another approach to derive CMF information based on before-and-after treatment data. Compared to the empirical Bayes method, the full Bayes method allows the development of complex models that are more flexible and accommodating for smaller sample sizes.
Cross-Sectional Approach: Cross-sectional studies are based on comparisons between safety data from sites with and without the subject treatment. This approach is particularly suitable for studies where before-treatment data is not available.
Case-Control Approach: The case-control method is similar to the cross-sectional method in that it also uses data with and without the treatment. Case-control studies rarely develop CMFs that compare crash frequencies.
Cohort Studies: Cohort studies compare risks of a certain outcome between two well-controlled groups with and without the subject treatment. This approach is less common in safety studies since it is difficult to conduct such controlled experiments on real roadways to enable sufficient crash data.
Lighting Specific Considerations
Compared to many other safety treatments, roadway and street lighting systems are somewhat unique in nature, as it is difficult to identify locations with both before-and-after data. In state and local jurisdictions that have a lighting policy, roadway and street lighting systems are typically installed during roadway construction or at a time historically beyond current institutional records, especially in urban areas. In states and localities that do not have a lighting policy, lighting is rarely used as a safety treatment either at the project level or systemically. For this reason, a cross-sectional approach is frequently more suitable for the development of CMFs based on systemic lighting-related treatments (Gross and Donnell).
One exception, however, is rural intersections. States and localities seldom use lighting systemically on rural roadways, yet lighting systems are sometimes used at the project level as a safety treatment (e.g., during the highway safety improvement process, or HSIP). In this case, it is possible to identify multiple locations where both before-and-after crash data is available for the development of lighting-related CMFs based on such locations. Because of roadway lighting’s use as a safety treatment at such locations, it is highly likely a safety analysis has proved the effectiveness of lighting beforehand. CMFs developed using these locations may not be applicable for other locations with distinctive characteristics.
With recent development of LED technology, some states have been replacing existing high-pressure sodium (HPS) systems with LED systems and/or installing new LED lighting systems during safety and new construction projects. This provides an opportunity for developing CMFs for the use of LED lighting systems using a before-after approach. For these scenarios, CMFs may be developed for either with-without LED lighting systems or HPS-LED safety comparisons.
Location Selection
The proper selection of study locations for lighting-related studies can be particularly challenging due to the different lighting practices at different states and local jurisdictions. To select sufficient and appropriate
study locations, it is necessary to adequately address the following factors based on the type of CMFs to be developed:
Jurisdictional Policy Differences: When using study sites from different states or regions, researchers must carefully address the jurisdictional differences in lighting policies, driver populations, and traffic control and roadway design practices.
Within Jurisdiction Use Where Considered Beneficial to Safety: Within some jurisdictions, lighting systems are frequently used where engineers believe there is a safety benefit. The use of lighting is rarely random and requires awareness to properly account for these factors in study designs, particularly for cross-sectional analyses.
Continuous Versus Isolated Lighting: One characteristic of lighting unique from other safety treatments is the difference in visual reactions that drivers have to continuous lighting and isolated lighting. Driving behaviors during the transition period are therefore different from those in either unlighted or lighted environments.
Levels Designed to Meet Criteria: Lighting levels are a continuous variable designed to meet a set of criteria in the sense that illuminance and uniformity levels can be theoretically any value within a range between no artificial lighting and an over-lit system. Therefore, during site selection, it is common to have a dumbbell-shaped sample distribution over lighting variables (e.g., in terms of average illuminance, large proportions of the sites fall into either the 0–3 lux range that is known to be associated with non-designed light or the 8 lux and above range).
Analysis Area
Safety analyses of lighting systems frequently need to clearly define analysis areas to identify crashes associated with the studied lighting systems. Lighting studies frequently focus on site types, such as segments of freeways and non-freeways, intersections, mainline ramp locations, crosswalks (midblock or intersections), and parking lots/rest areas. For each type of site, analysis areas should be strategically defined, taking into consideration factors such as drivers’ visual behaviors, lighting design areas, and data sample size.
Isolating Safety Impacts
Isolating the safety impacts of lighting during safety studies can be particularly challenging because, as previously stated, lighting is frequently installed at locations where it is most likely to benefit safety, and it can be difficult to identify unlighted sites with similar conditions in the same region. Many agencies conduct warranting analyses when determining whether lighting should be installed at certain locations. Most such analyses take into consideration nighttime crash data as a primary factor. Without properly addressing the location selection issue, this practice results in an endogeneity issue (due to simultaneity of higher nighttime crash frequencies and use of lighting) for safety researchers analyzing lighting effects (Banks et al., 2014; Kim and Washington, 2006). If the lighting system cannot reduce nighttime crash frequencies to the level below that at locations without a nighttime safety challenge, improper cross-sectional analyses may lead to the problematic conclusion that lighting correlates with higher nighttime crash frequencies.
Statistical modeling techniques have been developed to address endogeneity issues. In the case of nighttime crash analysis, it is a fairly common practice for researchers to use daytime crash frequencies as a controlling method for evaluating safety treatment effectiveness during nighttime (Gross et al.). Such an approach assumes that lighting systems do not have any safety effects during daytime, and therefore, separate daytime models involving the lighting variable would enable an estimate of the crash risks at subject sites in cross-section studies.
To isolate the safety impacts attributable to lighting, studies should include variables describing the complexity of the study site as it pertains to crash risks. Examples of such variables include number of lanes, geometric alignment, presence of hazardous roadway features (e.g., turning lanes, merging lanes, and raised channelization), pavement marking conditions, and potential visual distractions in the background. In addition, studies should carefully consider key factors that help isolate lighting safety impacts, such as annual average daily traffic (AADT) and hourly volume, crash types, weather conditions, and traffic conditions.
Data Sampling and Collection
Sample Size Requirements
Sufficient sample sizes are an important requirement for CMF studies. In general, a rule of thumb is that the larger the sample size, the more reliable the CMFs developed for most, if not all, methods. While some statistical methods allow the determination of sample sizes quantitatively, such methods require assumptions of crash distribution that are sometimes not necessarily accurate. To increase sample sizes for lighting-related safety studies, an obvious method is to identify more study sites. However, this approach may not be practical in many cases due to site availability and the fact that crashes are relatively rare events. Researchers routinely use multiple years of crash data to increase sample sizes. When using multiple years of crash data, it is also important to ensure that traffic patterns, roadway configurations, and traffic control at the study sites remained unchanged during the entire analysis period (AASHTO, 2014).
Lighting Performance Factors
It is also important to know that lighting performance may change over time. For example, the lighting levels of luminaires, including LED systems, deteriorate over time due to luminaire performance, dirt accumulation, and the maintenance condition of the system. Therefore, designers create higher initial illuminance values for such systems and expect them to meet illuminance requirements after a certain length of time passes. Seasonal changes in roadside vegetation may also affect lighting performance over time. Such factors need to be considered when necessary if multiple years of crash data are used.
Field Measurement
Note that while current lighting systems, when designed to standard (some roadway/street lighting applications at certain agencies do not necessarily go through the design process), mostly follow the same recommendations outlined in Illuminating Engineering Society (IES) RP-8 (various years) or the AASHTO Roadway Lighting Design Guide. The implemented lighting, however, can be significantly different from the computer-generated designs, and agencies rarely inspect lighting after the systems are installed. To accurately analyze lighting effects, researchers may need to conduct field lighting measurements to clearly understand lighting levels at the study sites. While luminance is a more useful measure from a design standpoint, this study targeted illuminance because it is a more reliable measure with which to conduct analysis.
CMF Types
Different types of CMFs may be developed for lighting based on various factors, as described in the following subsections.
Crash Types
Crash type examples include separate CMFs for single-vehicle crashes and multi-vehicle crashes.
Location
Location examples include CMFs for intersections, freeway ramp locations, and freeway segments. For intersections, researchers may develop separate CMFs for approaches and intersection boxes.
Lighting Variables
Many previous safety studies analyzed lighting as a binary variable (i.e., present versus absent). While this type of CMFs provides valuable information on the safety effects of lighting, it may not accurately quantify lighting effects due to the different safety performance of lighting systems at different illuminance, luminance, veiling luminance, surround ratio, and uniformity levels. As such, it may be of best interest to practitioners in some cases to use detailed lighting variables, such as minimum illuminance levels, average illuminance levels, and lighting uniformity levels. The increased implementation of LED systems (versus legacy HPS systems) currently also provides the safety community an opportunity to study the effect of correlated color temperature (CCT) of lighting systems on safety.
Examples
Several example analyses were provided in the report, including consideration of the conversion of traditional technologies to LED and of changes in lighting level.
Discussion
Based on this study, the project team would also like to provide the discussion points presented in the following subsections.
CMF Method and Data Considerations
This study demonstrates that, for a before-after comparison analysis to perform well, analysts need data that can relatively accurately depict the temporal changes of key factors, such as traffic, roadway, and driving population. For this reason, a traffic safety analysis that draws conclusions based on more years of data may not be as robust as one based on more study sites but fewer years of data. For cross-sectional studies, therefore, the use of more study sites is helpful if site selection is carefully performed to control factors that affect lighting performance. Introducing before data as a control in cross-sectional analysis of lighting will also require considering the potential temporal changes that may affect safety over time.
This is particularly true for lighting studies, as roadway/street lighting in many cases works as a secondary safety measure, and the safety effects can be impacted by other factors. For example, factors such as the condition and availability of visual aids on roadways (e.g., lane markings and delineation or channelization devices), the roadway configuration, presence of roadway/roadside objects, and vegetation can all affect the safety effectiveness of lighting. Lighting at sites with different lane marking conditions, for example, can have significantly different safety effects. The frequently different conditions in such factors across various areas and/or jurisdictions can therefore make site selection particularly challenging.
Interpretation of Lighting Safety Effects
Compared to many safety treatments that have clearly attributable safety effects (e.g., fixed-object crashes and safety barriers), understanding and quantifying lighting safety effects can be difficult in some cases. Accordingly, findings purely based on quantitative data analyses may sometimes be misleading for users with limited expertise in lighting. Lighting affects traffic safety primarily by affecting drivers’ visual performance (acknowledging the potential of introducing lighting infrastructure to increase fixed-object crash risks). Luminance levels, lighting uniformity, and color temperature of a lighting system can all affect a driver’s visual performance. Therefore, lighting systems of the same design may not necessarily be beneficial at all times and in all locations. The safety performance of lighting systems is potentially affected by factors such as luminaire and installation conditions, transition between locations of different illuminance levels, weather, and glare. Potential data issues not addressed in the analysis also further complicate the subject.
Use of Measured Lighting Data
Many previous lighting safety analyses used lighting presence data as a binary variable (i.e., present, or not present). This method has significant limitations since it does not consider the different characteristics of different lighting systems. Measured lighting data are in many cases a better option for a more thorough understanding of lighting effects on safety and driver behaviors. While using measured lighting data, however, analysts should understand field lighting levels are a result of lighting systems designed to a recommended practice (most commonly the IES RP-8 standard or AASHTO Lighting Standard). Thus, the lighting levels in the measurement data are generally not randomly distributed within the full range but instead tend to center around the recommended levels for designed lighting and a much lower level for non-designed lighting. In between these levels, values are limited and are generally due to reduced levels from distantly adjacent luminaires on the roadway or ambient light outside of the right of way.
Conclusions
The analysis indicates that:
- More sites considered over fewer years of data collection may provide more reliable results, as factors such as traffic volume and composition are less likely to change over a shorter timeframe.
- Use of properly measured lighting data is beneficial to the analysis, as the impacts of the overall lighting condition are assessed based on the installed lighting as well as the natural variation in lighting design. This is true whether the lighting is designed and installed or if it is light from off-road sources. This does pose an issue, as measured data is not typically available.
- Similar sites need to be considered, as the impact of one lighting design may not be the same under all application conditions.
- The use of hourly or at least ADT data that considers time of day, weekly, and seasonal variations is recommended to consider the specific roadway conditions more accurately at the time period used in the analysis and to ensure that nighttime traffic exposure data is accurate.
