In the last decade, substantial progress has been achieved in intelligent traffic control technologies to overcome consistent difficulties of congestion and its adverse effect on smart cities. Edge computing is one such advanced facilitating real-time data transmission among vehicles roadside units mitigate congestion. An edge computing-based deep reinforcement learning system demonstrated this study that appropriately designs a multiobjective reward function for optimizing different...

The number of vehicles is drastically increasing worldwide, especially in large cities. Thus there a need to model and enhance the traffic management help meet this rising requirement. primary purpose reduce congestion by optimizing signal, which currently one main concerns. Reinforcement Learning (RL) approaches Intelligent Transportation System (ITS) are infeasible for road networks. However, Deep (DRL) capable handling enlarged problem. In order manage flow network, strong coordination...

The traffic flow management is primarily done through signals, whose inefficient control causes numerous problems, such as long waiting time and huge waste of energy. To improve efficiency, obtaining real-time information an input dynamically adjusting the signal duration accordingly essential. Among existing methods, Deep Reinforcement Learning (DRL) has shown to be most effective solution. In this paper, a dynamic mechanism large scale road network proposed using policy gradient method. A...

Traffic congestion is one of the serious problems in recent times. Proper re-routing vehicles needs to be done for better management congestion. Emerging technologies like fog computing, Internet Things (IoT) Intelligent Transportation Systems (ITS) can make this process more productive deal with traffic An ITS enabled system designed here based on computing and IoT. The uses real time data its main aim re-route by executing a Next hop selection algorithm. It also considers which are...

In recent times technology is undergoing rapid changes and human life getting benefited from that. Modern transportation systems are of no exception. But, traffic congestion has turned out to be the most significant issues as late. A dynamic light control mechanism reduce road in Vehicular ad-hoc network proposed present work. The maximum waiting time vehicles red signal calculated dynamically by considering number stuck observing whether occurs lanes having green signal. random distribution...

The acceleration of urbanisation and the development pace industrialisation help to grow population metropolitan areas, thus increasing density traffic flow. sole way managing congestion is mitigate it through optimising signals at intersections a vast road network. synchronization amongst strongly needed in order alleviate allow vehicles travel smoothly along intersections. Reinforcement Learning (RL) techniques Intelligent transportation system (ITS) are not feasible for management large...

As globalisation has immensely intensified, the enormous expansion of vehicles in metropolitan areas significantly overloaded existing transportation networks. An appropriate traffic signal control strategy potential to have a favourable impact on urban transportation, both economically and environmentally. The main target this study is evaluate optimal pattern using fog computing based Deep Reinforcement Learning (DRL) method. effective implementation method Intelligent Transportation...

Vehicular ad hoc network performs crucial function in road safety, detection of traffic accidents and reduction congestion by disseminating messages among vehicles. An efficient broadcasting emergency vehicles vehicular is proposed this work. The main objective the algorithm to minimize broadcast storm at time dissemination message performance scheme studied on basis delay, overhead reachability it outperforms existing schemes.

Abstract In the current era, coordination of traffic flow is hindered by discrepancy between road infrastructure and number vehicles which leads to congestion. One widely used strategies mitigate congestion control signals with help deep reinforcement learning (DRL) in edge computing based intelligent transportation system. This article provides a comprehensive analysis most recent DRL algorithms, advantage actor‐critic proximal policy optimization multiple neural networks (DNNs), including...

The use of code-mixed languages (written in Roman character) on social media platforms is prevalent multilingual nations. Translation from to monolingual necessary for analysis, content filtering, and targeted advertising. Training translation models scratch difficult due the scarcity available resources extremely noisy nature real-time sentences. At moment, state-of-the-art language are routinely used applications. However, ineffective handling sentences as it usually written script but...

Deep Convolutional Neural Networks have led to series of breakthroughs in image classification. With increasing demand run DCNN based models on mobile platforms with minimal computing capabilities and lesser storage space, the challenge is optimizing those for computation smaller memory footprint. This paper presents a highly efficient modularized Network (DNN) model classification, which outperforms state art terms both speed accuracy. The proposed DNN constructed by repeating building...

Two conditions for reducing the number of learning iterations in backpropagation artificial neural networks are introduced. The first condition is to scale target output so that it falls within a small range (+or-0.1) point at which slope nonlinear activation function node maximum. This 0.5 sigmoid function. second learn input patterns selectively, not sequentially, until error reduced below desired limit. Introducing techniques does affect memory retention or generalization capabilities...

The growth of vehicles and inadequate road capacity in the urban area trigger traffic congestion raise frequency accident. Therefore need drastically reducing is a significant concern. Advancement technology like fog computing, Internet Things (IoT) Intelligent Transportation Systems (ITS) aid more constructive management congestion. Three IoT based Fog computing oriented models are designed present work for mitigating first two schemes vehicle dependent as they control depending upon number...