Road And Transportation Engineering

Spatial comparison of floodplain management alternatives in a raster GIS environment is conceptualized as a multi criteria decision making problem. A spatial MCDM technique is developed by combining the conventional Compromise Programming technique with GIS technology. This new technique is referred to herein as Spatial Compromise Programming (SCP). The main contribution of the proposed technique is its ability to address uneven spatial distribution of criteria values in the evaluation and ranking of alternatives. SCP is used to determine the best alternative for each geographic location within the region of interest. The analysis of floodplain management strategies for the Red River Valley region is chosen as a case study to illustrate application of the Spatial Compromise Programming technique.

This paper addresses the characterization of six different amorphous silica samples with respect to their application in cement paste. Different mixes are compared and analyzed using the mini spread-flow test. Also the granular properties, different void fraction states of packing and distribution moduli q are analyzed and compared using a mix design tool. A deformation coefficient is derived from the spread-flow test, which correlates with the value of specific surface area computed from the particle size distribution, and intrinsic density of the samples. Finally, the thickness of a constant water layer of 25 nm around the particles is computed at the onset of flowing. The granular analysis demonstrated that it is possible to decrease the water demand of the cement paste when nanoparticles are added and the resulting grading follows the modified Andreasen and Andersen curve (q = 0.5), and the concentration of nano silica is less than 5% bwoc.

Traffic accidents are occurred by a combination of factors that lead to casualties, injuries and property-damages. The four factors of human, vehicle, road and environment always play a key role in the occurrence of such accidents. The main purpose of this study was to investigate the effect of various factors on the severity of urban traffic accidents in Rasht metropolis in the years of March 2016 to March 2018 using frequency analysis of 7294 accident data. The results showed that various variables affected the severity of road accidents in Rasht metropolis, including accident season, accident day, accident time, road surface condition, driver gender, driver age, geometry of accident location, daylight condition and weather condition. The results of the frequency analysis of accidents for determining the most important factors indicated that a high percentage of accidents resulted in injuries and often occurred in the summer season, the start of the week, during the day, at the hours of 12 to 18, in dry road conditions, by male drivers and in the age group of 30 to 45 years, especially on straight roads and in clear weather.

In all trips, part of the journey is inevitable on foot. Part of the journey involves pedestrians crossing intersections and crossing and non-crossing sites. Therefore, any disruption to pedestrian crossings will reduce network capacity. So the most important measure is to identify the characteristics of pedestrians crossing with urban intersections. Understanding such things as speed, volume, and density of pedestrians is essential for controlling traffic flow and delay rate and can lead to better design of pedestrian-related facilities. The purpose of this study was to determine the relationship between speed, density and flow rate of pedestrians crossing lighted or lightless intersections. Therefore, by imaging two lighted intersections and two lightless intersections for 15 hours in the Rasht metropolitan, 8489 pedestrians crossings were collected. The results showed that there is a relationship between the flow rate and density with a correlation coefficient of 0.9828, but the relationship between the variables of velocity - flow rate and velocity - density have no significant relationship with the correlation coefficients of 0.08 and 0.1334, respectively. Also the results of ANFIS model showed that the correlation coefficient is about 0.99 which indicates the high efficiency of this model in predicting density values.