Prediction of Vehicular Traffic Flow using Levenberg-Marquardt Artificial Neural Network Model: Italy Road Transportation System

In the last decades, the Italian road transport system has been characterized by severe and consistent traffic congestion and in particular Rome is one of the Italian cities most affected by this problem. In this study, a LevenbergMarquardt (LM) artificial neural network heuristic model was used to predict the traffic flow of non-autonomous vehicles. Traffic datasets were collected using both inductive loop detectors and video cameras as acquisition systems and selecting some parameters including vehicle speed, time of day, traffic volume and number of vehicles. The model showed a training, test and regression value (R2) of 0.99892, 0.99615 and 0.99714 respectively. The results of this research add to the growing body of literature on traffic flow modelling and help urban planners and traffic managers in terms of the traffic control and the provision of convenient travel routes for pedestrians and motorists.

The Lord of the Ring Road: A Review and Evaluation of Autonomous Control Policies for Traffic in a Ring Road

This study focuses on the comprehensive investigation of stop-and-go waves appearing in closed-circuit ring road traffic wherein we evaluate various longitudinal dynamical models for vehicles. It is known that the behavior of human-driven vehicles, with other traffic elements such as density held constant, could stimulate stop-and-go waves, which do not dissipate on the circuit ring road. Stop-and-go waves can be dissipated by adding automated vehicles (AVs) to the ring. Thorough investigations of the performance of AV longitudinal control algorithms were carried out in Flow, which is an integrated platform for reinforcement learning on traffic control. Ten AV algorithms presented in the literature are evaluated. For each AV algorithm, experiments are carried out by varying distributions and penetration rates of AVs. Two different distributions of AVs are studied. For the first distribution scenario, AVs are placed consecutively. Penetration rates are varied from 1 AV (5%) to all AVs (100%). For the second distribution scenario, AVs are placed with even distribution of human-driven vehicles in between any two AVs. In this scenario, penetration rates are varied from 2 AVs (10%) to 11 AVs (50%). Multiple runs (10 runs) are simulated to average out the randomness in the results. From more than 3,000 simulation experiments, we investigated how AV algorithms perform differently with varying distributions and penetration rates while all AV algorithms remained fixed under all distributions and penetration rates. Time to stabilize, maximum headway, vehicle miles traveled, and fuel economy are used to evaluate their performance. Using these metrics, we find that the traffic condition improvement is not necessarily dependent on the distribution for most of the AV controllers, particularly when no cooperation among AVs is considered. Traffic condition is generally improved with a higher AV penetration rate with only one of the AV algorithms showing a contrary trend. Among all AV algorithms in this study, the reinforcement learning controller shows the most consistent improvement under all distributions and penetration rates.

Pengenalan Pemanduan Lalu Lintas Penerbangan (Air Traffic Control) Di Indonesia

Not a few graduates from the Dirghantara Aviation Vocational School and Yappika Legok Vocational School who choose to continue their education at PPI Curug, but only a few graduates choose to major in Air Traffic Control. Due to the lack of interest in the graduates of the Dirghantara Aviation Vocational School and the Yappika Legok Vocational School, it is deemed necessary to have activities that can build interest in the Air Traffic Control major, namely through Community Service activities. The method used is face-to-face socialization at the PPI Curug Aviation Safety Building which is carried out in accordance with health protocols. The results of the service obtained are that it is hoped that students from the Vocational School know more about air traffic control and can spread knowledge to their friends so that students who have interest and curiosity able to understand more deeply the purpose of air traffic control. routinely either in the same location or in different locations with different target communities by other study programs. Socialization of professions in the field of aviation and study programs at PPI Curug to the public can be carried out with a wider scope nationally by collaborating with the Education Office online/video conference. Tidak sedikit lulusan dari SMK Penerbangan Dirghantara dan SMK Yappika Legok yang memilih untuk melanjutkan pendidikannya di PPI Curug, namun hanya sedikit yang memilih jurusan Pemanduan Lalu Lintas Udara. Dikarenakan kurangnya minat lulusan SMK Penerbangan Dirghantara dan SMK Yappika Legok tersebut, maka dirasa perlu adanya kegiatan yang dapat menumbuhkan minat terhadap jurusan Pemanduan Lalu Lintas Udara yaitu melalui kegiatan Pengabdian kepada Masyarakat. Metode yang digunakan adalah sosialisasi secara tatap muka di Gedung Keselamatan Penerbangan PPI Curug yang dilaksanakan sesuai dengan protokol kesehatan. Hasil pengabdian yang didapatkan adalah diharapkan pelajar dari SMK tersebut dapat lebih mengenal mengenai pemanduan lalu lintas udara dan dapat menyebarkan ilmu terhadap temannya sehingga muncullah pelajar yang memiliki minat dan keingintahuan untuk dapat memahami lebih dalam maksud dari pemanduan lalu lintas udara.kegiatan pengabdian seperti ini dapat dilakukan secara rutin baik di lokasi yang sama maupun di lokasi berbeda dengan sasaran masyarakat yang berbeda oleh program studi lainnya. Sosialisasi mengenai profesi di bidang penerbangan dan program studi di PPI Curug kepada masyarakat dapat dilakukan dengan cakupan yang lebih luas lagi secara nasional dengan bekerja sama dengan Dinas Pendidikan secara daring/video conference.

Job Analysis for Air Traffic Control

This article presents a review and categorization of job analyses on the role of air traffic controllers (ATCO). There are three parts – how the role has been conceptualized, why it was conceptualized in this manner, and what we can conclude from developments in ATCO job analysis. The article includes a history of job analysis in air traffic control and two tables summarizing task and worker analyses. A large amount of information is available on tasks and attributes and we conclude that ATCO job analyses have been carried out in a varied and disunited manner. While there is no universally accepted analysis for the role of ATCO, previous analyses could nonetheless be used as a foundation for future analytic work.

Intelligent Network Traffic Control Based on Deep Reinforcement Learning

This paper studies the problems of load balancing and flow control in data center network, and analyzes several common flow control schemes in data center intelligent network and their existing problems. On this basis, the network traffic control problem is modeled with the goal of deep reinforcement learning strategy optimization, and an intelligent network traffic control method based on deep reinforcement learning is proposed. At the same time, for the flow control order problem in deep reinforcement learning algorithm, a flow scheduling priority algorithm is proposed innovatively. According to the decision output, the corresponding flow control and control are carried out, so as to realize the load balance of the network. Finally, experiments show, the network traffic bandwidth loss rate of the proposed intelligent network traffic control method is low. Under the condition of random 60 traffic density, the average bisection bandwidth obtained by the proposed intelligent network traffic control method is 4.0mbps and the control error rate is 2.25%. The intelligent network traffic control method based on deep reinforcement learning has high practicability in the practical application process, and fully meets the research requirements.

Energy-Saving Methods for Urban Travel and Public Transportation in Smart Cities

In many countries, energy-saving and emissions mitigation for urban travel and public transportation are important for smart city developments. It is essential to understand the impact of smart transportation (ST) in public transportation in the context of energy savings in smart cities. The general strategy and significant ideas in developing ST for smart cities, focusing on deep learning technologies, simulation experiments, and simultaneous formulation, are in progress. This study hence presents simultaneous transportation monitoring and management frameworks (STMF ). STMF has the potential to be extended to the next generation of smart transportation infrastructure. The proposed framework consists of community signal and community traffic, ST platforms and applications, agent-based traffic control, and transportation expertise augmentation. Experimental outcomes exhibit better quality metrics of the proposed STMF technique in energy saving and emissions mitigation for urban travel and public transportation than other conventional approaches. The deployed system improves the accuracy, consistency, and F-1 measure by 27.50%, 28.81%, and 31.12%. It minimizes the error rate by 75.35%.

Busy and confused? High risk of missed alerts in the cockpit: an electrophysiological study

Of evolutionary importance, the ability to react to unexpected auditory stimuli remains critical today, especially in settings such as aircraft cockpits or air traffic control towers, characterized by high mental and auditory loads. Evidences show that both factors can negatively impact auditory attention and prevent appropriate reactions in hazardous situations. In the present study, sixty participants performed a simulated aviation task, varying in terms of mental load (no, low, high mental load), that was embedded with a concurrent tone detection paradigm, in which auditory load was manipulated by the number of different tones (1, 2 or 3). We measured both detection performance (miss, false alarm) and brain activity (event-related potentials) related to the target tone. Our results showed that both mental and auditory loads affected tone detection performance. Importantly, their combined effects had a massive impact on the percentage of missed target tones. While, in the no mental load condition, miss rate was very low with 1 (0.53%) and 2 tones (1.11%), it increased drastically with 3 tones (24.44%), and this effect was accentuated as mental load increased, yielding to the higher miss rate in the 3-tone paradigm under high mental load conditions (68.64%). Increased mental load, auditory load, and miss rate, were all associated with disrupted brain response to the target tone as showed by reductions of the P3b amplitude. In sum, our results highlight the importance of balancing mental and auditory loads to maintain or improve efficient reactions to alarms in complex environment.