Search

Over the past several years, roadway safety management has evolved into data-driven or evidence-based science. The corner stone of a data-driven roadway safety management is the knowledge about useful patterns in the complex crash data. Crash data is often difficult to model with several confounding factors and discrete target variables such as crash counts or crash severity. The major goal of this dissertation was to contribute to the methodological realm of roadway safety management. The research objectives are in two folds: 1) to develop state-of-the-art model specifications for modeling crash data, and 2) to develop a probabilistic model-based site ranking framework. This research addresses methodological issues in crash frequency modeling such as unobserved heterogeneity, spatial correlation, and temporal patterns. Two novel specifications were developed to address these methodological issues: 1) negative binomial spatial with random parameters (NBSRP) modeled as multi-variate normal finite mixture distribution; 2) negative binomial spatial model with dynamic parameters (NBSDP). The NBSRP with finite-mixture specification allows for identifying the underlying sub-groups of road segments, and for skewness and multi-modality in the underlying random parameter distribution. The NBSDP specification employs dynamic linear model (DLM) formulation of the discrete negative binomial count model by exploiting recently developed polya-gamma data-augmentation techniques. NBSDP model facilitates to investigate the evolution of the model parameters over the time and to make safety predictions for a future year. Both NBSRP and NBSDP models simultaneously accounts for potential spatial correlation of crash counts from neighboring road segments. Bayesian methods have been widely used for model building and recently gaining further popularity due to the availability of efficient algorithmic techniques for the parameter estimation. Computationally efficient Bayesian estimation frameworks that leverage recent advances in data augmentation techniques were developed in this research to estimate the proposed count specifications. Bayesian estimation methods also facilitate statistical inference on site ranks, thereby allowing for probabilistic ranking. A computationally efficient site ranking framework was developed incorporating the recent probabilistic ranking techniques towards the end of this dissertation. Overall, this dissertation demonstrates the feasibility of designing Bayesian modeling frameworks for probabilistic roadway safety management, which facilitate online learning. The research ideas presented in this dissertation may be extended to bigger networks to test the feasibility of developing a safety management framework that automatically learns from the latest crash data sources over the time.

The main goal of this research is to propose a specification to model the unobserved heterogeneity in count outcomes. A negative binomial likelihood is utilized for modeling count data. Unobserved heterogeneity is modeled using random model parameters with finite multi-variate normal mixture prior structure. The model simultaneously accounts for potential spatial correlation of crash counts from neighboring units. The model extracts the inherent groups of road segments with crash counts that are equally sensitive to the road attributes on an average; the heterogeneity within these groups is also allowed in the proposed framework. This research employs a computationally efficient Bayesian estimation framework to perform statistical inference of the proposed model. A Markov Chain Monte Carlo (MCMC) sampling strategy is proposed that leverages recent theoretical developments in data-augmentation algorithms, and elegantly sidesteps many of the computational difficulties usually associated with Bayesian inference of count models.

Crack Attenuating Mix (CAM) is relatively a new asphalt mixture type used by the Texas Department of Transportation (TxDOT). These mixtures are fine graded, designed using relatively small stone sized aggregates and screenings with a high asphalt content, specifically for retarding the reflective cracking in thin asphalt overlays without sacrificing rutting resistance. The main goal of this study is to promote the use of local aggregates in CAM design. In order to achieve the goal, the development of a mix design procedure and aggregate quality guidelines for CAM design with local aggregates are essential. Several aggregate sources were identified and characterized in terms of their frictional, strength, and durability properties. A refined mix design procedure is recommended using a Superpave gyratory compactor with the capability to measure shear stress during compaction. The emphasis was on preventing rutting and flushing resulting from overfilling of binder in these mixtures, which was observed during compaction by consistent shear failures. The Hamburg Wheel Tracking Device (HWTD) was used for performance evaluation of rutting, whereas the Overlay Tester (OT) was used for cracking evaluation. It is hypothesized that current performance criteria in terms of HWTD and OT are too severe for being applicable to CAM mixes with local aggregates. A modification in HWTD failure criterion is recommended and is used in developing new aggregate guidelines. A correlation analysis was conducted relating aggregate properties and CAM performance in rutting and cracking. Finally, aggregate quality guidelines were developed for CAM using local aggregates based on the correlation study. The mix design recommendations as well as aggregate guidelines and quality criteria provided in the report are based on laboratory investigations and field validation of these is strongly recommended before implementation.