Traffic congestion at a single intersection can propagate and thus affect adjacent intersections as well, potentially resulting in prolonged gridlock across an entire urban area. Despite numerous research efforts aimed at developing intelligent traffic signal control systems, urban areas continue to experience traffic congestion. This paper presents a novel agent-based fuzzy traffic control system for multiple road intersections. The proposed system is designed to operate in a decentralized manner, with each intersection having its own agent (fuzzy controller) functioning concurrently. The intelligent fuzzy controller of the system can recognize emergency vehicles, assess the queue length and waiting time of vehicles, measure the distance of vehicles from intersections, and consider the cumulated waiting times of short vehicle queues. Two distinct types of agent-based intelligent fuzzy traffic control systems were implemented for comparison: one involving collaboration between an agent and its immediate neighboring agent(s) (where one intersection exchanges traffic data with its immediate neighboring intersection(s)), and the other implementing a non-collaborative agent-based intelligent fuzzy traffic control system (where the individual intersection has no direct communication). Following the experimental simulations, the results were compared with those of existing intelligent fuzzy traffic control systems that lack any module to calculate the distance of the vehicles from the intersection. The results demonstrated that the proposed agent-based system of controllers exhibited superior performance compared with the existing fuzzy controllers in terms of indicators such as average waiting time, fuel consumption, and CO2 emissions. For instance, the proposed system reduced the average waiting time of vehicles at an intersection by 48.65% compared with the existing three-stage intelligent fuzzy traffic control system. In addition, a comparison was conducted between non-collaborating and collaborating agent-based intelligent fuzzy traffic control systems, where collaboration achieved better results than the non-collaborating system. In the simulation experiments, an interesting new feature emerged: despite any direct communication missing at multiple intersections, green waves evolved with time. This emergent feature suggests that fuzzy controllers have the potential to evolve and adapt to traffic complexity issues in urban environments when operating in an autonomous agent-based mode. This study demonstrates that agent-based fuzzy controllers can effectively communicate with one another to share traffic data and improve the overall system performance.

In this study, the concept of symmetry is employed to implement an intelligent fuzzy traffic signal control system for complex intersections. This approach suggests that the implementation of reduced fuzzy rules through the reduction method, without compromising the performance of the original fuzzy rule base, constitutes a symmetrical approach. In recent decades, urban and city traffic congestion has become a significant issue because of the time lost as a result of heavy traffic, which negatively affects economic productivity and efficiency and leads to energy loss, and also because of the heavy environmental pollution effect. In addition, traffic congestion prevents an immediate response by the ambulance, police, and fire brigades to urgent events. To mitigate these problems, a three-stage intelligent and flexible fuzzy traffic control system for complex intersections, using a novel hybrid reduction approach was proposed. The three-stage fuzzy traffic control system performs four primary functions. The first stage prioritizes emergency car(s) and identifies the degree of urgency of the traffic conditions in the red-light phase. The second stage guarantees a fair distribution of green-light durations even for periods of extremely unbalanced traffic with long vehicle queues in certain directions and, especially, when heavy traffic is loaded for an extended period in one direction and the short vehicle queues in the conflicting directions require passing in a reasonable time. The third stage adjusts the green-light time to the traffic conditions, to the appearance of one or more emergency car(s), and to the overall waiting times of the other vehicles by using a fuzzy inference engine. The original complete fuzzy rule base set up by listing all possible input combinations was reduced using a novel hybrid reduction algorithm for fuzzy rule bases, which resulted in a significant reduction of the original base, namely, by 72.1%. The proposed novel approach, including the model and the hybrid reduction algorithm, were implemented and simulated using Python 3.9 and SUMO (version 1.14.1). Subsequently, the obtained fuzzy rule system was compared in terms of running time and efficiency with a traffic control system using the original fuzzy rules. The results showed that the reduced fuzzy rule base had better results in terms of the average waiting time, calculated fuel consumption, and CO2 emission. Furthermore, the fuzzy traffic control system with reduced fuzzy rules performed better as it required less execution time and thus lower computational costs. Summarizing the above results, it may be stated that this new approach to intersection traffic light control is a practical solution for managing complex traffic conditions at lower computational costs.

Traffic congestion is a serious issue for cities and urban areas, owing to the increasing usage of vehicles. This phenomenon results in several negative consequences, such as high fuel consumption and loss of time. To address this problem, several countries have implemented optimal traffic signal control systems. However, these systems have some drawbacks, such as the need for expensive hardware and maintenance difficulties. Because the sensors are buried under the road surface, the system often cannot account for the full length of the vehicular queue. This, among several issues, inhibits the full potential of the technology’s effectiveness and sustainability. In addition, there is much uncertainty in traffic conditions, which points to the need for a model that includes vagueness in the control system. This study proposes a novel hierarchical structure for a three-stage fuzzy traffic control system. This new system assesses the vehicle queue, identifies heavy traffic, detects emergency cars, and adjusts the duration of traffic lights according to the traffic flow and waiting times of vehicles using fuzzy inference rules. This controller was evaluated and validated using a micro-simulation model of an isolated intersection. The obtained results revealed the increased adaptability and flexibility of the proposed system owing to its potential to differentiate a random number of traffic directions. It is also able to handle emergency vehicles and can decrease waiting times, stalling fewer cars, if there is a high traffic flow in the conflicting direction(s) and is a robust and scalable system with lower computational costs.

Over the past decades, there has been an ever-increasing saturation of traffic networks due to the growing number of road vehicles, and due to the available limited. To solve these problems, adaptive, (semi-) intelligent traffic control has been used widely for the last decades. These systems nevertheless, have some shortages, the most obvious one being that these systems use the presence of vehicles at the lanes immediately before reaching the intersections. The real queue size cannot be taken into consideration. In the present approach, the input values are supposed to come from cameras connected with image processing systems and directed microphones. We propose a new traffic signal control system with a hierarchical structure based on similarly Mamdani control, however, containing essentially novel elements and having more intelligent features. This new model and the connected algorithmic approach allow rather complex control strategies, but only a simple case study has been implemented. Compared with existing fuzzy traffic controls, the novel approach has more adaptability and flexibility, by having the potential to differentiate an arbitrary number of traffic directions and by increasing general safety by the additional emergency vehicle handling feature. In addition, the calculation with queues, and individual vehicles weighted with the waiting time makes the system more flexible than any existing intelligent model.

Speech is one of the natural ways of communication between humans, later extended as a means for human–computer interaction. It helps visually impaired people to read electronic texts and is used in information retrieval and language education. This paper proposed the development of a text-to-speech synthesizer for Afan Oromo (Oromo Language), using unit selection speech synthesizer approaches. Although several works have been conducted in the area of text-to-speech synthesis for technologically favored languages for many years, every language has its own unique features. So, speech synthesizer systems developed for one language cannot be used for another language, because the structures of one language are not presumably representative of others. It is clear that each program is based on the system corresponding to the phonetic rules of a certain language. Besides, the existing text-to-speech synthesizer for Afan Oromo was reviewed in this study and the result of developed prototype results are showing promising, however, still, their performance needs a lot of improvement in terms of intelligibility and naturalness using novel approaches and quality of corpus. Therefore, this research was initiated to develop the possibility of developing a prototype text-to-speech synthesizer to improve the performance of the text-to-speech synthesizer. In this study, Afan Oromo corpus was collected from genuine sources and prepared speech datasets both text and audio in collaboration with Afan Oromo experts. The performance of the synthesizer was tested by proper users for its intelligibility and naturalness using Mean Opinion Scale (MOS). The obtained result of naturalness of the prototype is 4.44 (very good) out of 5, which indicated that the result obtained is encouraging and better performance than the existing TTS of Afan Oromo in terms of intelligibility and naturalness. But the result scored in terms of intelligibility still needs further work. The main challenge is Afan Oromo has many dialects, so preparing a balanced text corpus from each dialect is very tough. Moreover, enhancement of the work is predicted to bring a reasonable level of intelligibility to the system.