Simulation of Processes for Optimizing the Delivery Routes of Goods on Urban Road Networks by a Synergetic Approach
01 Jan 2021-pp 175-196
TL;DR: In this article, a synergetic approach for simulation the processes of optimizing cargo delivery routes, taking into account the nonstationary dynamics of traffic flows on sections of the urban road network is proposed.
Abstract: A synergetic approach for simulation the processes of optimizing cargo delivery routes, taking into account the non-stationary dynamics of traffic flows on sections of the urban road network is proposed. Here, route optimization is carried out using a modified ant colony self-organization algorithm. At that, the analytical dependences of the change in the speed of the traffic flow on the characteristic time and load density on sections of the road network are determined in the framework of the synergetic Lorentz model. Simulation of the building of optimal routes is carried out using the proposed approach on the example of the Kiev city. The possibility of using this approach to solve the problems of efficient control of the process of routing freight traffic in conditions of the real dynamics of traffic flows is shown. Prospects for the use of the developed approach in intelligent transportation systems are discussed, for example, in solving problems of dynamic routing of vehicles using traffic prediction information.
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TL;DR: In this article , the authors proposed a synergetic approach for simulation the processes of optimization of routes for the delivery of goods taking into account the non-stationary dynamics of traffic flows on sections of the urban road network.
Abstract: The paper proposes synergetic approach for simulation the processes of optimization of routes for the delivery of goods taking into account the non-stationary dynamics of traffic flows on sections of the urban road network. Within the framework of synergetic Lorentz model, the analytical dependences of the change in the speed of the traffic flow on the characteristic time of the change in the dynamics of the traffic flow and the density of the traffic flow in the network sections are determined. Further, based on the obtained dependence of the traffic flow speed on its density by using a modified self-organization algorithm of the ant colony, the route on the network is optimized. Here the urban road network is represented as a bidirectional weighted graph. The main element of the modification is that it implements the possibility of asynchronous movement of the each ant colony agent at a certain speed. In addition, it is also possible to fix the results of optimization of a partially traveled path for calculating a further route when the edge weight (length) of the graph changes during the movement. This allows you to management the route optimization process, taking into account the dynamic state of the network, which depends on the speed of movement of vehicles on certain sections of the 171 network. These changes in speed can be caused by an increase in the congestion of network sections, the occurrence of traffic jams, emergency situations, etc. To test the proposed method, simulation of route optimization processes was carried out within the framework of the traveling salesman problem, taking into account the real dynamics of traffic flows on the example of the road network of Kyiv city. Here, when conducting research on some selected sections of the network, the average density of traffic flows at certain points in time during the day was experimentally determined. Based on the simulation results, a number of effects were identified. These effects are associated with the rebuilding of optimal routes when the average speeds of vehicles on the model sections of the network decrease to certain boundary values corresponding to certain modes of traffic flow. The paper discusses the possibility of using the proposed method in intelligent transportation systems. In particular, this method can be used to solve dynamic vehicle routing problems using information about traffic prediction. KEYWORDS: TRAFFIC FLOW, URBAN ROAD NETWORK, SYNERGETIC LORENTZ MODEL, SYNERGETIC APPROACH, ANT ALGORITHM
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TL;DR: An artificial ant colony capable of solving the travelling salesman problem (TSP) is described, an example of the successful use of a natural metaphor to design an optimization algorithm.
Abstract: We describe an artificial ant colony capable of solving the travelling salesman problem (TSP). Ants of the artificial colony are able to generate successively shorter feasible tours by using information accumulated in the form of a pheromone trail deposited on the edges of the TSP graph. Computer simulations demonstrate that the artificial ant colony is capable of generating good solutions to both symmetric and asymmetric instances of the TSP. The method is an example, like simulated annealing, neural networks and evolutionary computation, of the successful use of a natural metaphor to design an optimization algorithm.
1,908 citations
TL;DR: This paper describes how to construct a GA and the main strands of GA theory before speculatively identifying possible applications of GAs to the study of immunology.
729 citations
01 Jan 2009
TL;DR: Linking of Three-Phase Traffic Theory and Fundamental Diagram Approach to Traffic Flow Modeling and Conclusions and Outlook are linked.
392 citations
TL;DR: The theory of a jamming transition is proposed for the homogeneous car-following model within the framework of the Lorenz scheme and the stationary values of headway and velocity deviations and time of acceleration/braking are derived as functions of control parameter.
Abstract: The theory of a jamming transition is proposed for the homogeneous car-following model within the framework of the Lorenz scheme. We represent a jamming transition as a result of the spontaneous deviations of headway and velocity that is caused by the acceleration/braking rate to be higher than the critical value. The stationary values of headway and velocity deviations and time of acceleration/braking are derived as functions of control parameter (time needed for car to take the characteristic velocity).
85 citations