A GIS approach to the development of a segment-level derailment prediction model.

Train related accidents, particularly derailments, can lead to severe consequences especially when they involve injuries or fatalities or when they involve hazardous materials that might result in environmental impacts. Whereas numerous road safety studies have suggested appropriate approaches to predicting vehicle-to-vehicle collisions, very few railway safety studies have considered predicting the number of derailments on rail tracks in North America. In addition, the existing few rail safety assessment and derailment prediction models have often been constrained by aggregated data limiting the safety assessments by, for example, failing to consider segment-level characteristics. This paper focused on the development of an integrated database for the development of a segment-level derailment prediction model for Canada's rail network. The primary objective of this paper is to report how challenges in the data integration process were overcome and also to develop a network screening tool to identify segments with high derailment risk in Canada's rail network. Negative binomial regression and the Empirical Bayes technique were used to estimate the predicted number of derailments on Canada's rail network at the segment level. A network screening process was then successfully applied to identify key segments of safety concern: the top ten segments of concern accounted for approximately 1% of the rail network allowing decision makers to focus their derailment mitigation efforts on a manageable part of Canada's vast rail network. The data processing approach and analysis in this study have strong implications for advancing research on rail safety in North America.

PleThora: Pleural effusion and thoracic cavity segmentations in diseased lungs for benchmarking chest CT processing pipelines.

This manuscript describes a dataset of thoracic cavity segmentations and discrete pleural effusion segmentations we have annotated on 402 computed tomography (CT) scans acquired from patients with non-small cell lung cancer. The segmentation of these anatomic regions precedes fundamental tasks in image analysis pipelines such as lung structure segmentation, lesion detection, and radiomics feature extraction. Bilateral thoracic cavity volumes and pleural effusion volumes were manually segmented on CT scans acquired from The Cancer Imaging Archive "NSCLC Radiomics" data collection. Four hundred and two thoracic segmentations were first generated automatically by a U-Net based algorithm trained on chest CTs without cancer, manually corrected by a medical student to include the complete thoracic cavity (normal, pathologic, and atelectatic lung parenchyma, lung hilum, pleural effusion, fibrosis, nodules, tumor, and other anatomic anomalies), and revised by a radiation oncologist or a radiologist. Seventy-eight pleural effusions were manually segmented by a medical student and revised by a radiologist or radiation oncologist. Interobserver agreement between the radiation oncologist and radiologist corrections was acceptable. All expert-vetted segmentations are publicly available in NIfTI format through The Cancer Imaging Archive at https://doi.org/10.7937/tcia.2020.6c7y-gq39. Tabular data detailing clinical and technical metadata linked to segmentation cases are also available. Thoracic cavity segmentations will be valuable for developing image analysis pipelines on pathologic lungs - where current automated algorithms struggle most. In conjunction with gross tumor volume segmentations already available from "NSCLC Radiomics," pleural effusion segmentations may be valuable for investigating radiomics profile differences between effusion and primary tumor or training algorithms to discriminate between them.

Multiple membership multilevel model to estimate intersection crashes.

Numerous studies have developed intersection crash prediction models to identify crash hotspots and evaluate safety countermeasures. These studies largely considered only micro-level crash contributing factors such as traffic volume, traffic signals, etc. Some recent studies, however, have attempted to include macro-level crash contributing factors, such as population per zone, to predict the number of crashes at intersections. As many intersections are located between multiple zones and thus affected by factors from the multiple zones, the inclusion of macro-level factors requires boundary problems to be resolved. In this study, we introduce an advanced multilevel model, the multiple membership multilevel model (MMMM), for intersection crash analysis. Our objective was to reduce heterogeneity issues between zones in crash prediction model while avoiding misspecification of the model structure. We used five years of intersection crash data (2009-2013) for the City of Regina, Saskatchewan, Canada and identified micro- and macro-level factors that most affected intersection crashes. We compared the fitting performance of the MMMM with that of two existing models, a traditional single model (SM) and a conventional multilevel model (CMM). The MMMM outperformed the SM and CMM in terms of fitting capability. We found that the MMMM avoided both the underestimation of macro-level variance and the type I statistical error that tend to occur when the crash data are analyzed using a SM or CMM. Statistically significant micro-level and macro-level crash contributing factors in Regina included major roadway AADT, four legs, traffic signals, speed, young drivers, and different types of land use.

Cross-classified multilevel models for severity of commercial motor vehicle crashes considering heterogeneity among companies and regions.

This study analyzes 86,622 commercial motor vehicle (CMV) crashes (large truck, bus and taxi crashes) in South Korea from 2010 to 2014. The analysis recognizes the hierarchical structure of the factors affecting CMV crashes by examining eight factors related to individual crashes and six additional upper level factors organized in two non-nested groups (company level and regional level factors). The study considers four different crash severities (fatal, major, minor, and no injury). The company level factors reflect selected characteristics of 1,875 CMV companies, and the regional level factors reflect selected characteristics of 230 municipalities. The study develops a single-level ordinary ordered logit model, two conventional multilevel ordered logit models, and a cross-classified multilevel ordered logit model (CCMM). As the study develops each of these four models for large trucks, buses and taxis, 12 different statistical models are analyzed. The CCMM outperforms the other models in two important ways: 1) the CCMM avoids the type I statistical errors that tend to occur when analyzing hierarchical data with single-level models; and 2) the CCMM can analyze two non-nested groups simultaneously. Statistically significant factors include taxi company's type of vehicle ownership and municipality's level of transportation infrastructure budget. An improved understanding of CMV related crashes should contribute to the development of safety countermeasures to reduce the number and severity of CMV related crashes.

Signet-Ring Cell Carcinoma of the Colon: A Case Report and Review of the Literature.

BACKGROUND: Colorectal adenocarcinoma (CRC) is the third leading cause of death in the United States. One of the histologic subtypes of CRC is signet-ring cell carcinoma (SRCC), which has a distinct molecular and tumor biology from that of adenocarcinoma. Primary SRCC diagnosed at an early stage is very rare as most cases are detected at an advanced stage. Therefore, overall prognosis of SRCC is poor. CASE PRESENTATION: A 36-year-old female presented to her primary care physician with new-onset progressive right lower quadrant pain without any significant past medical or family history. Computed tomography scan of the abdomen and pelvis with contrast showed a 4.9 × 3.5 × 3.1 cm, lobulated, septated cystic mass arising from the cecum. The mass demonstrated wall enhancement and contained focal areas of coarse calcification. There was nodal involvement either locally or distally. The patient underwent right hemicolectomy, and pathology revealed a high-grade mucinous carcinoma with signet-ring cell variant invading through the muscularis propria and into the subserosal adipose tissue. The margins were negative for tumor, and no lymphovascular or perineural invasion was noted. None of the 14 resected pericolonic lymph nodes was positive for malignancy. Hence, she was staged as pT3, pN0, pMx-stage IIA. The appendix was not involved. Microsatellite instability testing showed the preservation of MLH1, PMS2, MSH2 and MSH6 proteins by IHC and PCR. Carcinoembryonic antigen level was within normal limits. Due to the patient's young age, aggressive histology and microsatellite-stable status, adjuvant fluropyrimidine (5-FU)-based therapy with the single agent capecitabine was initiated. The patient completed 6 months of adjuvant therapy and has been disease free for approximately 18 months. CONCLUSION: Primary SRCC of the cecum is a rare disease. Given the poor prognosis of these patients, early-stage disease with microsatellite-stable patients should be considered for adjuvant 5-FU-based therapy in an attempt to prevent recurrence.

Safety diagnosis: are we doing a good job?

Collision diagnosis is the second step in the six-step road safety management process described in the AASHTO Highway Safety Manual (HSM). Diagnosis is designed to identify a dominant or abnormally high proportion of particular collision configurations (e.g., rear end, right angle, etc.) at a target location. The primary diagnosis method suggested in the HSM is descriptive data analysis. This type of analysis relies on, for example, pie charts, histograms, and/or collision diagrams. Using location specific collision data (e.g., collision frequency per collision configuration for a target location), safety engineers identify (the most) frequent collision configurations. Safety countermeasures are then likely to concentrate on preventing the selected collision configurations. Although its real-world application in engineering practice is limited, an additional collision diagnosis method, known as the beta-binomial (BB) test, is also presented as the secondary diagnosis tool in the HSM. The BB test compares the proportion of a particular collision configuration observed at one location with the proportion of the same collision configuration found at other reference locations which are similar to the target location in terms of selected traffic and roadway characteristics (e.g., traffic volume, traffic control, and number of lanes). This study compared the outcomes obtained from descriptive data analysis and the BB test, and investigates two questions: (1) Do descriptive data analysis and the BB tests produce the same results (i.e., do they select the same collision configurations at the same locations)? and (2) If the tests produce different results, which result should be adopted in engineering practice? This study's analysis was based on a sample of the most recent five years (2005-2009) of collision and roadway configuration data for 143 signalized intersections in the City of Saskatoon, Saskatchewan. The study results show that the BB test's role in diagnosing safety concerns in road safety engineering projects such as safety review projects for existing roadways may be just as important as the descriptive data analysis method.

Safety network screening for municipalities with incomplete traffic volume data.

Safety network screening is used to identify road locations, such as intersections and roadway segments that exhibit an unusually high number of expected collisions or an abnormally high proportion of a certain configuration of collisions. Current state-of-the-art network screening methods rely on safety performance functions (SPFs) that require traffic volume as an input, but many cities in North America, including the city of Saskatoon, do not collect traffic volume for every intersection and segment within the city limits. Lack of traffic volume data severely restricts the applicability of a SPF-based network screening method. The binomial and beta-binomial tests, however, are formal collision diagnosis tests that can be used as a supplementary tool for screening roadway networks that include roadway segments for which traffic volume data are not available. However, previous studies have applied these two collision diagnosis tests without explicitly defining the particular circumstances that indicate which test is preferable. This study introduces a formal statistical test known as the "C(α) test" to determine when there is a need to apply the beta-binomial test instead of the binomial test to screen a roadway network. The study targeted major arterial uncontrolled access segments in Saskatoon using five years (2005-2009) of collision data for the two most frequent collision configurations: (1) rear end, and (2) side swipe same direction collisions. ArcGIS was used to develop collision maps that visually display the screening results. The collision maps are designed to facilitate the governing agencies' decision-making processes when selecting appropriate safety countermeasures to reduce target collision configurations at screened locations.

Investigation of a supplementary tool to assist in the prioritization of emphasis areas in North American strategic highway safety plans.

An important potential benefit of a jurisdiction developing an upper-level traffic safety policy statement, such as a strategic highway safety plan (SHSP) or a traffic safety action plan, is the creation of a manageable number of focus areas, known as emphasis areas. The responsible agencies in the jurisdiction can then direct their finite resources in a systematic and strategic way designed to maximize the effort to reduce the number and severity of roadway collisions. In the United States, the federal government through AASHTO has suggested 22 potential emphasis areas. In Canada, CCMTA's 10 potential emphasis areas have been listed for consideration. This study reviewed the SHSP and traffic safety action plan of 53 jurisdictions in North America, and conducted descriptive data analyses to clarify the issues that currently affect the selection and prioritization process of jurisdiction-specific emphasis areas. We found that the current process relies heavily on high-level collision data analysis and communication among the SHSP stakeholders, but may not be the most efficient and effective way of selecting and prioritizing the emphasis areas and allocating safety improvement resources. This study then formulated a formal collision diagnosis test, known as the beta-binomial test, to clarify and illuminate the selection and the prioritization of jurisdiction-specific emphasis areas. We developed numerical examples to demonstrate how engineers can apply the proposed diagnosis test to improve the selection and prioritization of individual jurisdictions' emphasis areas.

A stochastic catastrophe model using two-fluid model parameters to investigate traffic safety on urban arterials.

During the last few decades, the two-fluid model and its two parameters have been widely used in transportation engineering to represent the quality of operational traffic service on urban arterials. Catastrophe models have also often been used to describe traffic flow on freeway sections. This paper demonstrates the possibility of developing a pro-active network screening tool that estimates the crash rate using a stochastic cusp catastrophe model with the two-fluid model's parameters as inputs. The paper investigates the analogy in logic behind the two-fluid model and the catastrophe model using straightforward graphical illustrations. The paper then demonstrates the application of two-fluid model parameters to a stochastic catastrophe model designed to estimate the level of safety on urban arterials. Current road safety management, including network safety screening, is post-active rather than pro-active in the sense that an existing hotspot must be identified before a safety improvement program can be implemented. This paper suggests that a stochastic catastrophe model can help us to become more pro-active by helping us to identify urban arterials that currently show an acceptable level of safety, but which are vulnerable to turning into crash hotspots. We would then be able to implement remedial actions before hotspots develop.