An Agent-Based Simulation to Explore Communication in a System to Control Urban Traffic with Smart Traffic Lights

Populational growth increases the number of cars and makes the transport infrastructure increasingly saturated. The control of traffic lights by intelligent software is a promising way to solve the problem caused by this situation. This article addresses this problem that occurs in urban traffic. An agent-based simulation of an urban traffic control system is proposed. The solution is offered as intelligent traffic lights as agents to alleviate traffic congestion at a given location. Each agent controls a crossing and maintains communication with agents from other corners. Thus, they can have greater control of a larger area and identify patterns that can help them to solve congestion problems. The results of our simulated experiments point to the improvement of the urban traffic when using the proposed Multiagent System, in comparison with an approach that uses crossing agents without communication and other that implements static traffic lights.

Bi-Level Optimization Model for Urban Traffic Control

The urban traffic control optimization is a complex problem because of the interconnections among the junctions and the dynamical behavior of the traffic flows. Optimization with one control variable in the literature is presented. In this research optimization model consisting of two control variables is developed. Hierarchical bi-level methodology is proposed for realization of integrated optimal control. The urban traffic management is implemented by simultaneously control of traffic light cycles and green light durations of the traffic lights of urban network of crossroads.

Real-time Queue Length Estimation Applying Shockwave Theory at Urban Signalized Intersections

Signal control is a basic need for urban traffic control; however, it is a very rough intervention in the free flow of traffic, which often results in queues in front of signal heads. The general goal is to reduce the delays caused, and to plan efficient traffic management on the network. For this, the exact knowledge of queue lengths on links is one of crucial importance. This article presents a link-based methodology for real-time queue length estimation in urban signalized road networks. The model uses a Kalman Filter-based recursive method and estimates the length of the queue in every cycle. The input of the filter, i.e. the dynamics of queue length is described by the traffic shockwave theory and the store and forward model. The method requires one loop-detector per link placed at the appropriate position, for which the article also provides suggestions.

The concept of drone operations in the urban areas

In this paper, the concept of drone operations that would be used for transportation of goods in the urban areas, in the lower part of the airspace above urban areas, is proposed. To make the most of the potential that this concept brings, it is necessary to harmonize the already existing operations with the future ones, to design the airspace for these operations, and to adopt regulations that will enable safe operations. Although it is necessary to invest large amounts of money to provide satisfactory conditions for the safe functioning of the system, the benefits that such a system brings will lead to multiple profitabilities. The concept of drone operations in urban areas has been described through three phases of evolution. The Phase 1 is the most favorable for the operator from the aspect of efficiency because it means moving from point A to point B by the shortest path. Phase 2 introduces the idea of a partial arrangement of the system. In the Phase 3, the concept of operations is described in a fully controlled and organized system, with the urban traffic control in charge of supervising and guiding unmanned aircraft. Depending on the criteria, the results show that the concepts of operations presented in phases 1, 2, and 3 can be more or less suitable for application and implementation in the lower airspace and can be used sequentialy.