Street lighting and nighttime crashes at rural unsignalized intersections

¶ … 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).

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

Bayesian statistical analysis was employed to meet the research objectives. Several models were developed based on the variables of concern and the models tested using a hierarchical Bayesian modeling utilizing a Poisson distribution. The initial testing of the two models, one for day and the other for night, did not produce statistically significant results. After an adjustment of the models by the removal of some variables, and the reclassification of lighting as present or absent, they were able to determine that intersections with no lighting are 2.01 times more likely to have accidents than those with lighting. The researchers were unable to successfully assess the impact of other low cost measures. This is not surprising because as Gelman (2008) points out that "Bayesian statistics is about making probability statements, frequentist statistics is about evaluating probability statements" (p.2). While the decision to use Bayesian analysis appears to have been a reasonable choice it limited the tools available to the researchers. The researchers may have found greater fecundity using a frequentist analysis of the data. It is possible that ANOVA and multiple regression could have been employed to great success in this study (Wortman et. al., 1972).

Limited and justifiable conclusions

The researcher should be commended for their fidelity to the data in this study. The conclusions were congruent with the results of the data analysis. They concluded that the use of lighting may be effective in high crash areas. They were unable to make any conclusive statement on the use of other low cost non-lighting measures. Thus the study did not allow the researcher to provide practical guidance to the state on the removal or installation of lighting.