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Abdi Kordani, Ali
Salehan Institute of Higher Education
Civil Engineering, Building, Construction & Architecture

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Journal : Civil Engineering Journal

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Effect of Adverse Weather Conditions on Vehicle Braking Distance of Highways Abdi Kordani, Ali; Rahmani, Omid; Abdollahzadeh Nasiri, Amir Saman; Boroomandrad, Sid Mohammad
Civil Engineering Journal Vol 4, No 1 (2018): January
Publisher : Salehan Institute of Higher Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1122.454 KB) | DOI: 10.28991/cej-030967

Abstract

The effect of adverse weather conditions on the safety of vehicles moving on different types of roads and measuring its margin of safety have always been a major research issue of highways. Determining the exact value of friction coefficient between the wheels of the vehicle and the surface of the pavement (usually Asphalt Concrete) in different weather conditions is assumed as a major factor in design process. An appropriate method is analyzing the dynamic motion of the vehicle and its interactions with geometrical elements of road using dynamic simulation of vehicles. In this paper the effect of changes of friction coefficient caused by the weather conditions on the dynamic responses of three types of vehicles: including Sedan, Bus, and Truck based on the results of Adams/car Simulator are investigated. The studies conducted on this issue for different weather conditions suggest values ranging from 0.04 to 1.25. The results obtained from simulation based on Adams/car represent that the friction coefficient in values of 0.9, 0.8, 0.7, 0.6 do not effect on braking distance significantly and it is possible to attribute them all to dry weather condition. However, as it was anticipated the values of 0.5, 0.4, 0.28 and 0.18 have significant differences in braking distance. Hence, the values of 0.5, 0.4, 0.28 and 0.18 can be attributed to wet, rainy, snowy and icy conditions respectively.
Analyzing Microscopic Behavioral between Two Phases of Follower and Leader in Traffic Oscillation with Developing Artificial Neural Networks Mirbaha, Babak; Abdi Kordani, Ali; Salehikalam, Arsalan; Akbarinia, Farzad; Zarei, Mohammad
Civil Engineering Journal Vol 3, No 7 (2017): July
Publisher : Salehan Institute of Higher Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (980.535 KB)

Abstract

A Sudden speed drop in the leader vehicle of vehicle platoon results in propagating the deceleration wave from downstream towards the upstream flow. Points of wave propagation of the leader vehicle towards the follower vehicle identification are done based on Newell’s theory in trajectory data. Deceleration wave propagates based on two parameters, time and space, τ- δ. A follower driver performs different behavioural reactions that they result in deviating follower driver from Newell’s trajectory. In this paper, follower driver behaviour was identified based on two theories. The asymmetric microscopic driving behaviour theory and traffic hysteresis were used during the deceleration and acceleration phases, respectively. The data trajectories were classified into different traffic phases. Driver’s parameters were identified at the microscopic level. Since the follower driver had the nonlinear behaviour, artificial neural networks were developed. They were able to analysis and identify effective parameters of dependent variable between deceleration phases leading to congestion phase, based on the behavioural patterns. Analysis results present effective parameters based on any behavioural patterns. Spacing difference of two phases, deceleration and congestion phases, was the most effective parameter of both two behavioural patterns, under reaction – timid and over reaction – timid. Increasing the spacing difference of two phases results in decreasing (increasing) time based on under reaction – timid (over reaction – timid)..
Co-Authors Abdollahzadeh Nasiri, Amir Saman Akbarinia, Farzad Boroomandrad, Sid Mohammad Mirbaha, Babak Rahmani, Omid Salehikalam, Arsalan Zarei, Mohammad