Abstract. Traffic accidents involving heavy trucks have social and economic effects on society. However, little research has focused on the influence of heavy truck specifications such as weight. Apportioning the maximum permissible gross weight of trucks allows trucking companies/owners to consolidate loads, and therefore reduce the vehicle-kilometres required to collect and distribute a given amount of goods/material. While drivers/managers are responsible for ensuring that trucks are loaded appropriately and in compliance with regulations, some may take chances and overload vehicles. This increases the need for formal and documented inspections, in order to reduce traffic hazards on public roads due to overweight loading. According to a New South Wales Centre for Road Safety report in 2014, crashes involving heavy trucks often result in serious road trauma outcomes. When a heavy truck is involved in a crash, the vehicle mass raises the crash forces involved and hence increases the severity of the crash. Therefore, interventions should be established to mitigate or prevent these crashes from occurring. Currently, weight checks are required for trucks and truck drivers must drive to a weighbridge for a weight check. Since this is a random process, truck drivers may take the risk of driving an over-loaded truck on some occasions. This paper reviews existing studies concerning safe system interventions in relation to truck gross weight management and a framework is presented to effectively manage truck loading weight. The result may be a reduction of injuries and fatalities involving heavy trucks.

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Abstract. The increasing number of vehicles on Iran's highways and major roads has led to an increase in noise levels. As a result, traffic is now considered a main source of noise pollution. This paper reports on the modelling of traffic noise levels in Golestan National Park, Golestan using vehicle data and other environmental features. For the evaluation of noise and the recording of independent environmental variables, Sampling stations were selected using a systematic-random method at 76 points at various distances and between 0-250 meters from the road. At each sampling point, traffic flow (number and speed of vehicles, number of horn beeps) was measured for 15 minutes from 8 am to 8 pm. Simultaneously other environmental variables were assessed, including the geometry of the road surface and location conditions .The best multivariable regression based on the correlation coefficient (R) and the coefficient of determination (R2) was achieved. The R-square (73%) and the adjusted R-square (68%) of the regression equation were 73% and 68% respectively. The results of modelling show that the most important variables affecting noise pollution are distance from the road, roughness coefficient, speed of medium-weight vehicles, relative humidity, and height and number of light vehicles. There is a negative correlation with distance from the road and noise pollution.The accuracy of the model was found to be about ±5 dB. Therefore, the model is suggested for the prediction of traffic noise on the Asian Highway in Golestan National Park.