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Showing 5 results for Traffic Volume

Mahmoud Saffarzadeh, Maghsoud Pooryari,
Volume 3, Issue 2 (6-2005)
Abstract

This paper specifies the relationship among various factors contributing to road accidents including geometrical design characteristics, environmental and traffic specifications, by multiple regression analysis. The main objective of this paper is identification of problems associated with the safety issue of road networks by application of accident prediction models. Data from previous accidents were used to develop the models. Results of this study showed that the rate of road accidents is to a large extent dependent on the rate of traffic volume. Type of road and land-use are other important factors influencing the number and intensity of accidents. The mountainous roads in this respect require special attention regarding their safety factors. The quantitative rate of road safety upgrading has also been specified by adding traffic lanes in road networks.
Shahriar Afandizadeh, Jalil Kianfar,
Volume 7, Issue 1 (3-2009)
Abstract

This paper presents a hybrid approach to developing a short-term traffic flow prediction model. In this

approach a primary model is synthesized based on Neural Networks and then the model structure is optimized through

Genetic Algorithm. The proposed approach is applied to a rural highway, Ghazvin-Rasht Road in Iran. The obtained

results are acceptable and indicate that the proposed approach can improve model accuracy while reducing model

structure complexity. Minimum achieved prediction r2 is 0.73 and number of connection links at least reduced 20%

as a result of optimization.


F. Rezaie Moghaddam, Sh. Afandizadeh, M. Ziyadi,
Volume 9, Issue 1 (3-2011)
Abstract

In spite of significant advances in highways safety, a lot of crashes in high severities still occur in highways. Investigation of influential factors on crashes enables engineers to carry out calculations in order to reduce crash severity. Therefore, this paper deals with the models to illustrate the simultaneous influence of human factors, road, vehicle, weather conditions and traffic features including traffic volume and flow speed on the crash severity in urban highways. This study uses a series of artificial neural networks to model and estimate crash severity and to identify significant crash-related factors in urban highways. Applying artificial neural networks in engineering science has been proved in recent years. It is capable to predict and present desired results in spite of limited data sets, which is the remarkable feature of the artificial neural networks models. Obtained results illustrate that the variables such as highway width, head-on collision, type of vehicle at fault, ignoring lateral clearance, following distance, inability to control the vehicle, violating the permissible velocity and deviation to left by drivers are most significant factors that increase crash severity in urban highways.


A. Shariat Mohaymany, M. Babaei,
Volume 11, Issue 1 (3-2013)
Abstract

Since the 1990’s, network reliability has been considered as a new index for evaluating transportation networks under uncertainty. A large number of studies have been revealed in the literature in this field, which are mostly dedicated to developing relevant measures that can be utilized for the evaluation of vulnerable networks under different sources of uncertainty, such as daily traffic flow fluctuations, natural disasters, weather conditions, and so fourth. This paper addresses the resource allocation problem in vulnerable transportation networks, in which multiple performance reliability measures should be met at their desired levels, while the overall cost of upgrading links’ performances should be minimized simultaneously. For this purpose, a new approach has been considered to formulate the two well-known performance measures, connectivity and capacity reliability, along with their application in a bi-objective nonlinear mixed integer goal programming model. In order to take into account the uncertain conditions of supply, links’ capacities have been assumed to be random variables and follow normal distribution functions. A computationally efficient method has been developed that allows calculating the network-wise performance indices simply by means of a set of functions of links’ performance reliabilities. Using this approach, as the performance reliability of links are themselves functions of the random links’ capacities, they can be simply calculated through numerical integration. To achieve desirable levels for both connectivity reliability and capacity reliability (as network-wise performance reliability measures) two distinct objectives have been considered. One of the objectives seeks to maximize each of the measures regardless of what is happening to the other objective function which minimizes the budget. Since optimization models with two conflicting objectives cannot be solved directly, the well-known goal attainment multi-objective decision-making (MODM) approach has been adapted to formulate the model as a single objective model. Then the resultant single objective model has been solved through the generalized gradient method, which is a straightforward solution algorithm coded in existing commercial software such as MATLAB programming software. To show the applicability of the proposed model, numerical results are provided for a simple network. Also, to show the sensitiveness of the model to decision maker’s direction weights, the results of sensitivity analysis are presented..
Arash Sadrayi, Mahmoud Saffarzadeh, Amin Mirza Boroujerdian,
Volume 15, Issue 8 (12-2017)
Abstract

Pedestrians are among one of the most vulnerable road users. Speed of vehicles is considered as one of the major causes of danger for pedestrians crossing the street (making cross movements). Therefore, it is of utmost importance to devise suitable solutions for reducing speed of vehicles. One of these solutions is installation of Pedestrian Refuge Islands (PRI) in very wide midblocks. With regard to fluctuations in pedestrian and vehicle traffic volume in traffic hours, there are different variations in collisions between vehicle and pedestrian. In this article the effect of constructed PRI in Tehran on speed of vehicles and consequently their effects on probability fluctuations of fatal accidents are determined. Speed of vehicles in two phases of before and after arriving to the PRI is assessed. Additionally, speed of vehicles in non-observed volumes of vehicle and pedestrian are calculated using Aimsun.v6 simulation software. Paired T-test is applied to compare average speed of vehicles before and after the PRI. The results revealed that except for traffic volumes of 3000-4000 veh/h and 400-600 ped/h in other volumes reduction of average speed of vehicles as a result of PRI is significant. Also, the results show that in all volumes, these equipments reduce the probability of fatal accidents to under 10%. According to the results, it is recommended that PRI should be installed in midblocks where traffic volume of vehicles in each lane is less than 750 veh/h.



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