ramp metering

Taking advantage of short-term traffic forecasts and using it in traffic management will reduce possible blockages and by reducing the travel time and distance traveled by cars, it will reduce noise pollution, air pollution and also consumption costs. It will be fueled. By knowing the traffic in different hours and predicting it, you can have a better management and planning for the roads of the country, the characteristics of the traffic flow in a road is one of the most important factors in decision making and traffic policy in a region. In this study, using traffic evaluation protocols by means of neural network, a traffic evaluation prediction model was built. Also, the technical evaluation and its economic benefits are discussed. In this regard, by using the traffic count in urban highways and by using the neural network model, the traffic prediction has been done. In the neural network, 4 input variables were used, low or high density, The volume of the number of cars, the type of vehicle, the flow speed, the number of 10 neurons were used in the hidden layer, and finally the numerical model was displayed as a numerical matrix. To predict the current density with proper accuracy, which was done here with r=0.93. Also, to compare the linear regression model, the accuracy of the regression model is lower than the neural network model and r=0.88 was obtained at a significance level of less than 0.05.

jams and, as a result, long queues, increased travel time, increased chances of accidents, and environmental pollution. Several methods, such as ramp metering control, variable speed limit, lane change control, tolls, etc., have been studied and investigated to control highways. One of the approaches to improve the performance of these controls is their simultaneous and coordinated integration and implementation. In this paper, we will investigate the effect of the combination of three concurrent controls of ramp metering, variable speed limit, and highway tolls on the traffic conditions of the highway network. For this purpose, the reinforcement learning method has been used to manage the control values as variables. Traffic data is first predicted by predictive models based on neural networks and then given to the simulation of Sumo software. The controller model then selects the ramp rate, speed limit, and toll values based on the simulation outputs and applies them to the network. The results of implementing the proposed model show the significant impact of the model in improving traffic parameters . The model reduced the average travel time by 11.5% compared to the condition without controls, which is more than the effect of implementing single controls or a double combination of the mentioned methods. In addition, in the event of an accident in the network, the proposed management can significantly improve the network conditions compared to the situation without control.

The global population is booming, and Iran is no exception. This additional capacity is placing considerable strain on many aspects of human life; in particular on transport. Fossil fuels will continue to play an important part of the transport fuel mix, and therefore the limited and dwindling reserves need to be optimized for economic, social and environmental reasons. The goal of this research is to determine the gasoline transport capacity from ports of the country and local refineries to different destinations throughout Iran. It is the aim of the study to determine an optimization alternative for the gasoline transport capacity from and to each specified destination by applying the trans-shipment model. The goal is to minimize transport expenses due to sending gasoline from two sources to seventy seven destinations, by using forty seven intermediate points and 6,076 decision variables (transport direction). By achieving this goal, industry is confronted with demand and supply restrictions, simplicity of use and non-negative portable gasoline capacity. Referring to the results obtained, the expense of 73% of gasoline transport by pipe-line was 70%, and for road transport of 23%, it came to 25% of the total gasoline transport expenses. Share of transfer by ship for 3.4% yields an expense equal to 5%, and in addition the results show a 17% cost saving of the gasoline distribution expense in 1387.Urban highways are one of the most important infrastructure facilities that play a vital role in daily transportation. There are several approaches for the management of urban highways; one of which is the Intelligent Transportation System (ITS), which integrates advanced information and control technologies to improve traffic flow in transportation networks. Different methods have been developed to improve the performance of highway networks, among which control strategies such as ramp metering (RM), variable speed limits (VSL), route recommendation and variable message signs (VMS) are recognized as the most effective for relieving traffic congestion. This research paper discusses the optimal coordination of variable speed limits and ramp metering in a highway traffic network. The control problem is to determine the optimal combination of control variables that result in the best network performance. Efficiency of this problem was evaluated based on the total time that a vehicle spent in the network (TTS). The applied co-ordination approach incorporates the macroscopic traffic flow model METANET as a prediction model, and a genetic algorithm as an optimization algorithm to co-ordinate the RM and VSL in a highway network. The proposed model was implemented on a highway network with two ramps and two VSLs upstream of each one. The results showed that in cases where ramp metering is insufficient to prevent congestion, variable speed limits can prevent a traffic system breakdown and maintain a higher outflow. In other words, optimal co-ordination of ramp metering and VSLs can significantly reduce congestion and result in a lower total time in the network for vehicles. The results have been compared with a previous study in this field and have been shown to be more beneficial, due to the use of the GA in comparison to the search methods used previously in the literature.

Transportation management on both the supply and demand sides, due to ever increasing traffic problems is nowadays, considered as a crucial issue by transportation planners and engineers even in developing countries. Ramp metering as a transportation supply management measure with noticeable improvements in performance of the existing infrastructure and more effective use of the limited resources with comparatively little cost, is the focus of this paper. A mathematical programming model is designed to optimize the traffic volume on highways and their ramps via controlling the entrance volume of vehicles through different on-ramps, as decision variables. Flow conservation constraints are the major constraints in this model. A section of Sadr Highway is analyzed as the case for empirical study, for which traffic surveys including traffic counts is administered for the whole section under study. Results show that the flow of vehicles in the selected highway section can increase up to about 6 percent. The adversely high effect of Shariati on-ramp on the whole system under study indicates that its traffic flow can be reduced to 13 percent, with still positive effect on the whole system. Modarres on-ramp is the only ramp that is acting below its boundary limit and needs not any metering in the current circumstances.