S. Ilgin Guler

Crashes involving vulnerable roadway users (VRUs), such as pedestrians and cyclists, have increased in recent years. Understanding relationships between these crashes and explanatory factors is critical to reversing this trend. However, most statistical models of VRU-related crashes rely primarily on vehicular exposure alone. VRU exposure is difficult to capture because of a lack of available data. To address this concern, this study incorporates several exposure metrics that capture nonmotorized and public transportation use at the census-tract level. A macroscopic-level crash-prediction model for the Manhattan area of New York City is developed that considers roadway and demographic variables, as well as bike-share trip information, subway flows, taxi movements, and person trips to various points of interest (POIs) as measures of travel exposure. The models are developed using geographically weighted negative binomial regression and various functional forms are considered for three different types of crash frequency. The results show that the number of shared-bike trips and POI visits are positively correlated with increases in pedestrian and cyclist crash frequencies; however, these features are less descriptive of motorist crash frequency. In addition, the explanatory power of POI information can be improved by considering only a subset of POI categories that represent “essential” trips. The spatial variation between motorist-related crash frequency and the exposure metric is observed to be more significant than the pedestrian- and cyclist-related crash frequency are to their exposure, and the shared-bike trips’ influence on the pedestrian- and cyclist-related crash frequency appears to be homogeneous across the Manhattan area.

Impact echo (IE) is capable of locating subsurface defects in concrete slabs from the vibrational response of the slab to a mechanical impact. For an intact slab (“good” condition), the frequency spectrum of the IE is dominated by a single peak corresponding to the slab’s “thickness resonance frequency,” whereas the presence of subsurface defects (“fair” or “poor” conditions) could manifest in various ways such as multiple distinct peaks at frequencies higher, or lower, than the thickness resonance. In previous research, the authors have proposed a frequency partitioning of the spectrum for IE signal classification. Firstly, the thickness resonance frequency band is identified using a data-driven approach and then the IE signals are represented by their energy distribution in three bands—frequencies less than, within, and greater than the thickness resonance. Following this feature extraction, an unsupervised clustering approach is used to identify the centroids for each signal class—good, fair, and poor—which are further used to classify any test signal into one of the three aforementioned classes. The classification is developed by training on unlabeled IE signals from real bridge deck data (the Federal Highway Administration’s [FHWA’s] InfoBridge dataset) without making use of any labeled data. This study aims to validate the proposed methodology on a labeled dataset of eight reinforced concrete specimens constructed at the FHWA Advanced Sensing Technology Nondestructive Evaluation laboratory having known artificial defects. Our findings indicate that the physics-based feature definition and the method developed on real bridge data are robust and can classify IE signals in the labeled data with moderate accuracy.