Multi-Level Crash Prediction Models Considering Influence of Adjacent Zonal Attributes

This study investigates factors affecting accidents across transport facilities and modes, using micro and macro levels variables simultaneously while accounting for the influence of adjacent zones on the accidents occurrence in a zone. To this end, 15968 accidents in 96 traffic analysis zones of Tehran were analyzed. Adverting to the multi-level structure of accidents data, the present study adopts a multilevel model for its modeling processes. The effects of the adjacent zones on the accidents which have occurred in one zone were assessed using the independent variables obtained from the zones adjacent to that specific zone. A Negative Binomial (NB) model was also developed, and results show that the multilevel model that considers the effect of adjacent zones shows a better performance compared to the multilevel model that does not consider the adjacent zones’ effect and NB model. Moreover, the final models show that at intersections and road segments, the significant independent variables are different for each mode of transport. Adopting a comprehensive approach to incorporate a multi-level, multi-resolution (micro/macro) model accounting for adjacent zones’ influence on multi-mode, multi-segment accidents is the contribution of this paper to accident studies

Development of safety improvement method in city zones based on road network characteristics

Background and Objective: Extensive studies have so far been carried out on developing safety models. Despite the extensive efforts made in identifying independent variables and methods for developing models, little research has been carried out in providing amendatory solutions for enhancing the level of safety. Thus, the present study first developed separate accident frequency prediction models by transportation modes, and then in the second phase, a development of safety improvement method (DSIM) was proposed. Materials and Methods: To this end, the data related to 14,903 accidents in 96 traffic analysis zones in Tehran, Iran, were collected. To evaluate the effect of intra-zone correlation, a multilevel model and a negative binomial (NB) model were developed based on both micro- and macro-level independent variables. Next, the DSIM was provided, aiming at causing the least change in the area under study and with attention to the defined constraints and ideal gas molecular movement algorithm. Results: Based on a comparison of the goodness-of-fit measures for the multilevel model with those of the NB model, the multilevel models showed a better performance in explaining the factors affecting accidents. This is due to considering the multilevel structure of the data in such models. The final results were obtained after 200 iterations of the optimization algorithm. Thus, to decrease accidents by 30 and cause the least change in the area under study, the independent variable of vehicle kilometer traveled per road segment underwent a considerable change, while little change was observed for the other variables. Conclusions: The final results of the DSIM showed that the ultimate solutions derived from this method can be different from the final solutions derived from the analysis of the results from the safety models. Hence, it is necessary to develop new methods to propose solutions for increasing safety

A survey of incidental ocular trauma by pencil and pen

AIM: To determine characteristic features of ocular trauma resulted from self-trauma by writing instruments among pediatric population. METHODS: Thirty-six children who suffered from self-inflicted ocular trauma with a writing instrument were included in this prospective cross-sectional study. RESULTS: The mean age was 5.6±2.7y with male: female ratio of 1.77. The right eye was involved two times more than the left eye. The superomedial (55.5%) and inferomedial (30.6%) quadrants were the most common sites of injury. The leading culprit was colored pencils (44.4%). During surgical exploration, no foreign body (FB) was found in 25 (69.4%) patients while an FB was found in 11 (30.5%) patients. Brain injury was present in two patients (5.6%) and only in superomedial quadrant injuries. Zone 1 was the most common site for ocular trauma associated with penetrating injury. The mean ocular trauma score (OTS) in penetrating injuries was 3.8±1.2. The best corrected visual acuity (BCVA) was 0.3±0.6 upon admittance and 0.08±0.21 after one year. The final BCVA was significantly correlated with the entrance site, better final BCVA was found in nasal entrance site (P<0.05). CONCLUSION: The ophthalmologists should keep a high index of suspicion to rule out penetrating eye injuries related to writing instruments in a young uncooperative child. Brain injury is a life-threatening event that should be ruled out by appropriate imaging. Medial canthal area as the most common site needs an especial attention in writing instrument injuries

Corneal aberrations in normal and keratoconic eyes using an OPD-Scan Ⅱ

AIM: To evaluate higher order aberrations(HOAs)of the corneal surface in normal and keratoconic eyes.METHODS: Using an OPD-Scan Ⅱ wavefront analyzer, aberrometric parameters of the corneal surface in one eye of 80 patients with keratoconus(KC)and 91 participants with normal eyes were evaluated. The Zernike coefficients from third- to sixth-order as well as root mean square(RMS)of primary coma, coma-like aberrations, and total HOA were calculated and compared between both groups.RESULTS: Statistically significant differences were found in all aberrometric parameters between the measurements of the KC and normal participants(PPCONCLUSION: Corneal wavefront measurements by means of OPD-Scan Ⅱ were significantly higher in keratoconic corneas than normal corneas

Defining Psychological Factors of Cycling in Tehran City

Studying active transportation (walking or cycling) is widespread in American and European research. Studies which include latent variables (LV) are growing to identify the exact results of determining the strategies to increase the utility of active transportation (AT). LVs help us conduct more accurate research. LVs are defined as psychological factors such as feeling safe while you ride at night, and thus they are not subjective and hard to understand, but very important to consider in order to increase the utility of using AT modes. In the present paper, most of the previous studies on cycling were reviewed. Different variables, including subjective and LVs, were included to maximize using the bicycle utility and introduced to have better sight for future researchers to deal with modeling AT mode choice. This study applied the latent class analysis to a sample of 345 survey respondents in Tehran, the capital city of Iran, exploring the variables affecting cycling behavior and a confirmatory factor analysis, and a structural equation modeling (SEM) was developed. Results show the importance of having a ‘will’ for using a bicycle, especially in difficult situations, and in view of cultural barriers that affect women cyclists

Histopathologic findings of keratoconus corneas underwent penetrating keratoplasty according to topographic measurements and keratoconus severity

AIM: To investigate the histopathologic and morphological changes of the corneas with keratoconus (KC) undergoing penetrating keratoplasty (PKP) according to topographic findings and severity of KC. METHODS: The corneal tissue of 35 samples with KC was retrospectively evaluated with conventional light microscopy. Topographic and pachymetric parameters of keratoconus corneas by means of Pentacam such as mean keratometry (K) and central corneal thickness (CCT) were recorded. Severity of KC was graded according to Amsler-Krumeich classification. RESULTS: Epithelial thinning and breaks in Bowman’s layer are the most common findings in keratoconus corneas (94.3% and 82.9% corneas, respectively). The results revealed statistically significant higher mean K value and lower CCT in the keratoconus corneas that were affected by epithelial thinning, breaks in the Bowman's layer, folds in the Descemet's membrane, epithelial scars, breaks in Descemet's membrane, and stromal scars than those corneas without these findings (P<0.05). Moreover, those corneas with epithelial thinning, breaks in the Bowman's layer, folds in Descemet's membrane, epithelial scars, and stromal scars had significantly more severe disease than those corneas without these findings (P<0.05). The presence of the stromal and epithelial scars were associated with the higher KC severity, in which, respectively, 87.5% and 80.0% of the corneas with stromal and epithelial scars had stage 4 of the KC severity. CONCLUSION: It seems that there are some specific patterns in histologic changes of the keratoconus corneas. The presence of pathologic findings was correlated with thinner and steeper corneas. Epithelial or stromal scars were associated with the highest disease severity. The description of histopathologic findings of KC may help in elucidating the pathogenesis of the disease and help pathologist in differentiating KC from other corneal diseases

A novel hybrid machine learning model for shopping trip estimation: A case study of Tehran, Iran

Online and offline shopping trips have a profound impact on various facets of urban life, including e-commerce, transportation systems, and sustainability. To assess the factors shaping consumers' decisions, we introduce a novel hybrid machine learning model that integrates the Gray Wolf Optimization (GWO) algorithm with a deep Convolutional Neural Network (CNN). This model is applied to predict shopping behavior based on a survey of 1000 active e-commerce users residing in areas 2 and 5 of Tehran. These individuals have made successful purchases through both online and offline services during the final 20 days of 2021. The GWO algorithm plays a pivotal role in selecting optimal features and hyperparameters for the deep Convolutional Neural Network, which is a powerful deep learning model for image recognition and classification. Notably, our model achieves an impressive accuracy of 97.81% while maintaining a MSE of 0.325, having identified seven out of ten key features as the most influential. To gage the effectiveness of our approach, we conduct a comparative analysis with alternative methods. The results unequivocally showcase the superiority of our proposed algorithm, which attains an accuracy of 97.81%. In contrast, other models such as CNN, LSTM, MLP, DT, and KNN yield accuracies of 95.63%, 94.04%, 90.12%, 86.49%, and 80.16%, respectively. This study offers valuable insights for transportation planners, e-commerce managers, and policymakers. Its primary objective is to assist them in formulating effective strategies aimed at reducing transportation costs, curbing pollutant emissions, mitigating urban traffic congestion, and enhancing user satisfaction all while fostering sustainable development

Macro-Level Modeling of Urban Transportation Safety: Case-Study of Mashhad (Iran)

Transportation safety can be aimed at the planning stage in order to adopt safety management and evaluate the long-time policies. The main objective of this research was to make use of crash prediction models in urban transportation planning process. As such, it was attempted to gather data on the results of transportation master plan as well as Mashhad urban crash database. Two modelling method, generalized linear model with negative binomial distribution and geographically weighted regression, were considered as the methods used in this research. Trip variables, including trip by car, trip by bus, trip by bus services and trip by school services, were significant at 95%. The results indicated that both finalized models were competent in predicting urban crashes in Mashhad. Regarding to results urban transportation safety will be improved by changing the modal share for example from private car to bus. The application of the process presented in this study can improve the urban transportation safety management processes and lead to more accurate prediction in terms of crashes across urban traffic areas

Evaluating the spatial effects of environmental influencing factors on the frequency of urban crashes using the spatial Bayes method based on Euclidean distance and contiguity

Built environmental factors are one of the most important causes of urban crashes. Studies have shown that in addition to crash data, which have spatial heterogeneity, factors influencing crashes also have a spatial correlation. The main goal of this study is to evaluate the spatial effects of environmental factors on the frequency of crashes in Shiraz, Iran, at the TAZ level. In the first step of the study, using component analysis models, important environmental factors affecting the crash were identified, and composite indicators were produced as independent variables. In the second step, to control the effect of correlation and heterogeneity of model variables, spatial statistical models based on Euclidean distance such as geographically weighted Poisson regression (GWPR), geographically weighted negative binomial distribution (GWNBR), as well as Poisson and distribution models Negative binomial based on neighbor distance is used in spatial Bayes method with INLA approach. The study's results showed that models based on distance and contiguity to evaluate the spatial effects of crash data and the factors affecting it at the TAZ level have higher accuracy than geographically weighted regression models, as well as indicators of land use diversity and access to the system. The public transport produced in the first step effectively increases the frequency of crashes, and in TAZs where this index is high, there is a higher probability of a crash. The results of this study can be important for city managers and planners to improve urban safety measures, development planning, and future city measures

Microbiologic Spectrum and Antibiotic Susceptibility Pattern among Patients with Urinary and Respiratory Tract Infection

Aim. To demonstrate the prevalence of isolated organisms in urinary/respiratory tract infections and their antibiotic susceptibilities in a tertiary care center. Methods and Material. Between January 2008 and January 2010, patients referring to the clinic of cardiology or those admitted to the cardiac wards were enrolled in this cross-sectional descriptive study. Urine and sputum sampling was done for all the patients and the specimens underwent microbiologic examination and, in case of isolation of microorganism, antibiotic disk diffusion test was performed. Results. Escherichia coli (E. coli) was the most prevalent isolated organism in-hospital and community-acquired UTIs and was highly resistant to cephalothin in all the samples followed by cotrimoxazole, and ceftriaxone. It revealed high sensitivity to imipenem, amikacin, and nitrofurantoin. Acinetobacter constituted the most prevalent organism isolated from respiratory secretions and represented the highest resistance to ceftriaxone and the greatest sensitivity to imipenem. Conclusions. E. coli and Acinetobacter remain the most common uropathogenic and respiratory organisms, respectively. However, their increasing resistance to wide-spectrum imipenem, meropenem, and vancomycin is a major concern