Night Time Crashes at Rural,

¶ … Night Time Crashes at Rural, Unsignalized Intersections." This work was jointly funded by the Iowa DOT Office of Traffic and Safety, Iowa Highway Research Board (IHRB), and MUSCO Lighting. The research was conducted by a team of researchers from the Center for Transportation Research and Education, Iowa State University. The principal researcher for the team was Neal Hawkins who is the Associate Director of Traffic Operations. The researchers identified the need to reduce the number of night time accidents in rural areas. This position was supported with statistical data from the Iowa DOT crash database, for accidents for the period 2001 to 2005. The research employed a quantitative methodology utilizing a cross-sectional research design. The researchers also engaged multiple data collection methods, descriptive statistics and Bayesian statistical analysis to achieve their objectives.

The research problem

The research problem engaged by this team was an assessment of the relationship between street lighting and the reduction of night time crashes at rural intersections, in the state of Iowa. In concert with this problem the researchers identified two research objectives firstly "to determine whether street lighting and other low-cost measures, such as advance stop sign rumble strips, were effective in reducing nighttime crashes" (Neal et al., 2008, p. xii). The second objective as identified by the team was "to collect a large sample of rural intersections both with and without lighting" (Neal et al., 2008, p. xii).

Description of research process

Using data from the DOT Iowa, the researchers summarized and categorized the most common type of night time crashes at rural Iowa intersections. This was done to identify the most effective approach to reducing the number of crashes. The underlying assumption was that the effectiveness of the strategy to reduce crashes will be dependent on the type of crashes at the location. Following this, a city lighting census was conducted. The survey was distributed to all 99 counties but only 27 responded. This survey when combined with the warranty summary identified intervening variables for the study. Data from rural intersections with and without lighting were then collected. The assessment data for the intersections were collected between the periods October 2005 to September 2006. Intersections were selected based on the following criteria; intersections had at least three paved approaches, the intersection must be at least 0.5 miles from an urban area and the intersection was not unusual. Data were collected for several categories of variables, general information, information by approach and lighting information. Each heading had several sub-headings for data collection. Data were collected for 274 intersections in 33 counties. The final data set however only included 223 intersections, as the others were removed because of the failure to follow selection criteria, the absence of the intersection in the GIMS street data base, and the close proximity of the intersection to railroad tracks. The study then compiled crash data for the period 2003 to 2005 for the selected intersections; crashes which took place within 150 feet of the intersection were selected for analysis. The crash time was defined as day or night using sunrise and sunset times from the U.S. Naval Observatory website for Ames, Iowa.

Flaws in the procedural design

The researchers chose to utilize a cross-sectional design because it was difficult to "generate before and after data" for the subject under consideration. The major issue with this design is that it will not identify a causal relationship, which the researchers suggested that they were interested in. The researchers were inclined to admit that "it is difficult to establish reduction with a cross-sectional model" (Neal et al., 2008, p.37). The cross-sectional design ran counter to the before and after comparison, which is the dominant design for this type of assessment (Green, Kenneth, Barrett, & Pigman, 2003, Walker & Roberts, 1976). The design employed failed to control for intervening and confounding variables, which the researchers noted may have contributed negatively to the work. Additionally little attempt was made to identify and eliminate human factors that influenced the accident. Street lighting is associated with human factors; it was however treated as disconnected from the drivers, and their ability to relate to a lighting challenge. It may have been useful to determine if human type factors provided better explication of accidents under those conditions.

The researchers did not state that type of sample design employed. The sampling in this work is problematic as observed in the survey stage as the majority of the population did not return the questionnaire. While it was not stated the interview process produced high levels of non-response, and hence non-response error may introduce systematic errors, that may impact on later choices. For the data collection from the intersections again the sample design employed was not mentioned. The reader is informed that 274 intersections are chosen from 33 counties, there is no information on the population size or how the sample is procured. So that a proper critique of the sample designs appropriateness is difficult. However based on the researchers comments the sample size appeared inadequate for the research. The researchers noted that they were unable to assess the impact of type and location of lighting and this may have been because of inadequate sample size (Neal et al., 2008, p.39). Another weakness centered on critical assumptions that were made for which the researchers provided no empirical evidence. They decided that 150 feet was the cut off point for accidents no rationale was provided for that determination. Secondly even though they noted that for many of the night time accidents they were uncertain as to the cause but assumed that lighting was involved. It would seem prudent to provide greater empirical support for critical decisions in the study.

Data Analysis