No abstract

The purpose of this study is to investigate and determine the factors affecting vehicle and pedestrian accidents taking place in the busiest suburban highway of Guilan Province located in the north of Iran and provide the most accurate prediction model. Therefore, the effective principal variables and the probability of occurrence of each category of crashes are analyzed and computed utilizing the factor analysis, logit, and Machine Learning approaches simultaneously. This method not only could contribute to achieving the most comprehensive and efficient model to specify the major contributing factor, but also it can provide officials with suggestions to take effective measures with higher precision to lessen accident impacts and improve road safety. Both the factor analysis and logit model show the significant roles of exceeding lawful speed, rainy weather and driver age (30–50) variables in the severity of vehicle accidents. On the other hand, the rainy weather and lighting condition variables as the most contributing factors in pedestrian accidents severity, underline the dominant role of environmental factors in the severity of all vehicle-pedestrian accidents. Moreover, considering both utilized methods, the machine-learning model has higher predictive power in all cases, especially in pedestrian accidents, with 41.6% increase in the predictive power of fatal accidents and 12.4% in whole accidents. Thus, the Artificial Neural Network model is chosen as the superior approach in predicting the number and severity of crashes. Besides, the good performance and validation of the machine learning is proved through performance and sensitivity analysis.

No abstract

In this study, the relationship between space mean speed ( SMS ), flow rate and density of pedestrians was investigated in different pedestrian facilities, including 1 walkway, 2 sidewalks, 2 signalized crosswalks and 2 mid-block crosswalks. First, statistical analysis was performed to investigate the normality of data and correlation of variables. Regression analysis was then applied to determine the relationship between SMS , flow rate, and density of pedestrians. Finally, two prediction models of density were obtained using genetic programming (GP) and group method of data handling (GMDH) models, and k-fold and holdout cross-validation methods were used to evaluate the models. By the use of regression analysis, the mathematical relationships between variables in all facilities were calculated and plotted, and the best relationships were observed in flow rate-density diagrams. Results also indicated that GP had a higher R 2 than GMDH in the prediction of pedestrian density in terms of flow rate and SMS , suggesting that GP was better able to model SMS and pedestrian density. Moreover, the application of k-fold cross-validation method in the models led to better performances compared to the holdout cross-validation method, which shows that the prediction models using k-fold were more reliable. Finally, density relationships in all facilities were obtained in terms of SMS and flow rate.

One of the most common damages in asphalt mixes is the destructive effect of moisture on the binder cohesion and binder–aggregate adhesion which is called moisture damage. There are various methods to improve adhesion and reduce moisture damage in asphalt mixes. The most common of them is using an appropriate additive for binder modification. Accordingly, the current research was conducted to investigate the effect of two nanomaterials (Nano CuO, and Nano SnO2) in 2 different percentages on 2 types of aggregates (granite and limestone) and a type of base binder. In order to investigate the effect of nanomaterials, indirect tensile cyclic loading (the same as resilient modulus test) in dry and wet conditions and surface free energy (SFE) method were used. The moisture sensitivity indicator which shows stripping percentage of aggregate surface in loading cycles using SFE results and indirect tensile cyclic loading, has been considered as the moisture sensitivity indicator in this research. Results of mechanical tests used in this research show that nanomaterials have significantly increased asphalt mixes strength in comparison to control specimens. Results obtained from SFE method show that nanomaterials increase the cohesion free energy; this change causes a reduction in the possibility of failure in binder membrane. Additionally, nanoparticles have increased and reduced basic component and acidic component of SFE, respectively. This leads to improvement of their adhesion with acidic aggregates, which is sensitive to moisture damage.

Pedestrians’ behavior plays an important role in designing pedestrian facilities, especially in urban areas. One of these behavioral characteristics is their moving speed on sidewalks, pedestrian streets, crosswalks at signalized intersections, and midblock crosswalks. The moving speed is strongly affected by personality traits such as age, weight, gender as well as environmental and geometric conditions. In this regard, the moving speed of 14,490 pedestrians was measured in urban area pedestrian-related facilities, including signalized intersections, midblock crosswalks, sidewalks, and pedestrian street in Rasht city, Iran. Then, using the t-test, the effect of gender characteristics, rainfall, and umbrella carrying on pedestrian moving speed was investigated. The results showed that with a 95% confidence level, the moving speed difference between male and female pedestrians was statistically significant for all the age and weight groups, except for the age group of adolescents. Also, hijab cover affects the moving speed of pedestrians, so that females with the hijab cover meet the lowest moving speed values compared with those with ordinary dress type (i.e., without hijab). However, for elderly females, weight and age are more effective parameters than the dress type. In other words, as the age and weight increase, the moving speed of females would reduce.

No abstract

No abstract