A Bayesian Multivariate Hierarchical Spatial Joint Model For Predicting Crash Counts By Crash Type At Intersections And Segments Along Corridors

The safety and operational improvements of corridors have been the focus of many studies since they carry most traffic on the road network. Estimating a crash prediction model for total crash counts identifies the crash risk factors that are associated with crash counts at a specific type of road entity. However, this may not reveal useful information to detect the road problems and implement effective countermeasures. Therefore, investigating the contributing factors for crash counts by different types is of great importance. This study aims to provide a good understanding of the contributing factors to crash counts by different types at intersections and roadway segments along corridors. Data from 255 signalized intersections and 220 roadway segments along 20 corridors have been used for this study. The investigated crash types include same direction, angle and turning, opposite direction, non-motorized, single vehicle, and other multi-vehicle crashes. Two models have been estimated, which are multivariate hierarchical Poisson-lognormal (HPLN) spatial joint model and univariate HPLN spatial joint model. The significant variables include exposure measures and some geometric design variables at intersection, roadway segment, and corridor levels. The results revealed that the multivariate HPLN spatial joint model outperforms the univariate HPLN spatial joint model. Also, the correlations among crash counts of most types exist at individual road entity and between adjacent entities. Additionally, the significant explanatory variables are different across crash types, and the magnitude of the parameter estimates for the same independent variable is different across crash types. The results emphasize the need for estimating crash counts by type in a multivariate form to better detect the problems and provide appropriate countermeasures

Exploring The Effect Of Different Neighboring Structures On Spatial Hierarchical Joint Crash Frequency Models

Corridor safety analysis is a primary interest of many road safety studies. Corridors typically contain intersections and roadway segments. Having both components while analyzing corridors in addition to corridor-level variables in a hierarchical joint model would provide a comprehensive understanding of the existing corridor safety problems. There will probably be spatial correlation among road entities along a corridor, especially if the distance between the road entities is not large. Therefore, it is crucial to consider spatial effects in the model. However, this data structure is relatively new, and the best spatial weight matrix for this hierarchical spatial joint model has yet to be investigated. Therefore, this study estimates a hierarchical Poisson-lognormal (HPLN) joint model with spatial effects and explores the effect of different neighboring structures. A total of thirteen HPLN joint models are estimated: one HPLN joint model with corridor random effect and twelve HPLN joint models with spatial effects. Four types of conceptualization of spatial relationships were considered: (a) adjacency-based, (b) adjacency-route, (c) distance-order, and (d) distance-based spatial weight features. The results show the importance of incorporating spatial effects in the model. It was found that having a joint model is important since one of the best models is the adjacency-based first-order model, where the feeding road entities in addition to the directly adjacent road entity of the same type as the road entity of interest are considered. The results confirm the importance of spatial autocorrelation between road entities along the same corridor

Crash Modeling For Intersections And Segments Along Corridors: A Bayesian Multilevel Joint Model With Random Parameters

Previous highway safety studies have focused on either intersections or roadway segments while some researchers have analyzed safety at the corridor-level. The corridor-level analysis, which aggregates intersections and roadway segments, may allow us to understand the safety problems in the wider perspective. However, it would result in losing some of the specific characteristics of intersections or roadway segments. Therefore, we proposed a multilevel joint model that explores traffic safety at the segment/intersection level, with the consideration of corridor-level variables. In addition, the variations in the roadway characteristics and/or traffic volumes across corridors have been considered using random parameters model. Nevertheless, sometimes corridors are excessively long and, thus, it is uncommon to find corridor-level variables that have fixed values for the entire length of corridors. Therefore, current corridors were divided into sub-corridors, which have similar traffic volumes and roadway characteristics, and constructed another multilevel structure based on the sub-corridor. As a result, four Bayesian models have been estimated, and these models are multilevel Poison-lognormal (MPLN) joint models with spatial corridor and sub-corridor random effects terms and MPLN joint models with random parameters, which vary across corridors and sub-corridors. Based on a 3-years crash data from 247 signalized intersections and 208 roadway segments along 20 corridors in two counties, results showed that four-roadway segment, five-intersection, and three-corridor/sub-corridor variables were significant, and they include exposure measures and some geometric design variables. With respect to model performance, it was found that the MPLN joint model with random sub-corridor parameters provides the best fit for the data. Lastly, it is suggested to consider the proposed multilevel structure for corridor safety studies

Interlayer Defect Detection in Intra-Ply Hybrid Composite Material (GF/CF) Using a Capacitance-Based Sensor

Combining two types of reinforcement fiber in a common matrix may lead to different failure modes such as micro-cracks between the layers when the structure is subjected to lower stress levels. Real-time damage detection should be integrated into the hybrid composite structure to provide structural integrity and mitigate this problem. This paper outlines the working mechanisms and the fabrication of an integrated capacitive sensor in an intra-ply hybrid composite (2 × 2 twill weave). Uniaxial tensile and flexural tests were conducted to characterize the proposed sensor and provide self-sensing functionality (smart structure). The sensitivity and repeatability of the capacitive sensor were measured to be around 1.3 and 185 µΔC/Co, respectively. The results illustrate that onset of damage between layers can be detected by in situ monitoring. It can be seen that the initial damage was detected at the turning point where the relative change in capacitance begins to reduce while the load increases. Finite element modeling was also constructed to analyze the test results and explain the reasons behind the turning point. It was shown that the carbon yarns experienced high transverse shear stress (τxz) in the crimp region, leading to inter-fiber cracks

Interlayer Defect Detection in Intra-Ply Hybrid Composite Material (GF/CF) Using a Capacitance-Based Sensor

Combining two types of reinforcement fiber in a common matrix may lead to different failure modes such as micro-cracks between the layers when the structure is subjected to lower stress levels. Real-time damage detection should be integrated into the hybrid composite structure to provide structural integrity and mitigate this problem. This paper outlines the working mechanisms and the fabrication of an integrated capacitive sensor in an intra-ply hybrid composite (2 × 2 twill weave). Uniaxial tensile and flexural tests were conducted to characterize the proposed sensor and provide self-sensing functionality (smart structure). The sensitivity and repeatability of the capacitive sensor were measured to be around 1.3 and 185 µΔC/Co, respectively. The results illustrate that onset of damage between layers can be detected by in situ monitoring. It can be seen that the initial damage was detected at the turning point where the relative change in capacitance begins to reduce while the load increases. Finite element modeling was also constructed to analyze the test results and explain the reasons behind the turning point. It was shown that the carbon yarns experienced high transverse shear stress (τxz) in the crimp region, leading to inter-fiber cracks

Exploring the Effect of Different Neighboring Structures on Spatial Hierarchical Joint Crash Frequency Models

Corridor safety analysis is a primary interest of many road safety studies. Corridors typically contain intersections and roadway segments. Having both components while analyzing corridors in addition to corridor-level variables in a hierarchical joint model would provide a comprehensive understanding of the existing corridor safety problems. There will probably be spatial correlation among road entities along a corridor, especially if the distance between the road entities is not large. Therefore, it is crucial to consider spatial effects in the model. However, this data structure is relatively new, and the best spatial weight matrix for this hierarchical spatial joint model has yet to be investigated. Therefore, this study estimates a hierarchical Poisson-lognormal (HPLN) joint model with spatial effects and explores the effect of different neighboring structures. A total of thirteen HPLN joint models are estimated: one HPLN joint model with corridor random effect and twelve HPLN joint models with spatial effects. Four types of conceptualization of spatial relationships were considered: (a) adjacency-based, (b) adjacency-route, (c) distance-order, and (d) distance-based spatial weight features. The results show the importance of incorporating spatial effects in the model. It was found that having a joint model is important since one of the best models is the adjacency-based first-order model, where the feeding road entities in addition to the directly adjacent road entity of the same type as the road entity of interest are considered. The results confirm the importance of spatial autocorrelation between road entities along the same corridor