Safety modeling via segmentation of transportation networks

This dissertation proposes a methodology to address a long-standing question in traffic safety relating to the evaluation of safety risk and the benefits associated with safety interventions. The problem of evaluating safety interventions is compounded by the challenge of modeling the effect of heterogeneity simultaneously alongside the modeling of the marginal effect of an intervention. This dissertation attempts to provide some perspective to this problem via multiple scales, by proposing a joint model of heterogeneity and selection bias using a discrete-count approach, and using this framework to address the following research questions: a) What is the impact of selection bias on safety intervention due to scale? In other words, if safety interventions are applied at locations where accident patterns are severe and frequent, how does one account for the lack of intervention at less problematic locations? And how does a statistical methodology derived for selection bias provide inference across scales, as segments are scaled up from very small lengths to lengths of the order of corridors? b) How does one represent insights into the policy implications of selection bias in a manner that integrates context (i.e., roadway location and characteristics) and scale?;I use freeway roadway lighting as an example safety intervention to make these evaluations in this dissertation. Roadway lighting is installed in order to improve traffic flow, thereby also contributing to improved roadway safety. Roadway lighting is installed in various forms -- as in median-side lighting, versus right-side lighting, versus tunnel lighting, versus ramp-mainline merge points, versus, installations on both sides of the traveled way. This dissertation involved data collection on all 1,528 centerline miles of interstate freeway in Washington State and analyzed the correlation of accident frequencies with roadway lighting installation, after accounting for roadway geometrics and traffic flow levels. It was determined that certain installations are more effective than others, when selection bias is taken into account. For example, right-side lighting installation is found to be effective in reducing accident frequencies compared to other types of lighting installation, indicating a 30% reduction in accident frequencies compared to segments where there is no roadway lighting at the lighting segmentation scale. Such a result appears to justify the installation of right-side lighting at critical locations such as ramp merge points or departure points. The key phrase is "appears to justify". This dissertation explores the extent to which scale affects inferences such as the above. With different scales of segmentation, such as interchange and noninterchange segments, one mile uniform length segments, or accident-cluster length segments, right-side installation has a smaller reduction of accident frequencies compared to accident reduction at the lighting segmentation scale. In the case of accident-cluster level segmentation, right-side lighting installation is associated with an increase in accident frequency. This example result demonstrates that the scale of data plays a very important role in safety inferences, especially when heterogeneity and selectivity bias are accounted for.;While roadway lighting is used as an example for application of this dissertation's analytical framework, it is expected that the full-purpose self-contained computational framework for analyzing safety outcomes will be of substantial interest to the safety community at large. One can use this framework for the analysis of any safety intervention at any scale. The framework incorporates the typical geometric design decisions used in practice, and therefore, analysts can use this framework to address selectivity bias arising from roadway improvement projects involving all geometric types. In particular, the framework developed in this dissertation can also aid decision makers to conduct scenario testing. One example of scenario testing would be to examine the impact of energy-conservation efforts on traffic safety patterns on urban and rural freeways. Another would be to explore the design contexts associated with high levels of unobserved heterogeneity, where the discussion on the measurement of factors that do not currently exist in highway databases can be motivated. Example factors relating to heterogeneity could involve measures of segment-level kinematics such as speed, speed dispersion, and headway following distances. Or, they could involve microclimatic measurements such as pavement temperatures, determination of icing likelihoods, wind gust speeds and sun angles. (Abstract shortened by UMI.).