Communication Improvement and Traffic Control Based on V2I in Smart City Framework

2018 ◽  
Vol 2 (1) ◽  
pp. 18-33 ◽  
Author(s):  
Mamata Rath ◽  
Bibudhendu Pati
Keyword(s):  
Soft Computing ◽  
Traffic Control ◽  
Traffic Management ◽  
Smart Cities ◽  
Intelligent Vehicles ◽  
Traffic Information ◽  
Computing Technique ◽  
Smart Vehicles ◽  
Soft Computing Techniques ◽  
Intelligent Communication

This article describes how soft computing techniques are tolerant of imprecision, intended on approximation, focus on uncertainty and are based on partial truth. Current real-world problems pertaining to congested traffic is pervasively imprecise and therefore design of smart traffic control system is a challenging issue. Due to the increasing rate of vehicles at traffic points in smart cities, it creates unexpected delays during transit, chances of accidents are higher, unnecessary fuel consumption is an issue, and unhygienic environment due to pollution also degrades the health condition of general people in a normal city scenario. To avoid such problems many smart cities are currently implementing improved traffic control systems that work on the principle of traffic automation to prevent these issues. The basic challenge lies in the usage of real-time analytics performed with online traffic information and correctly applying it to some traffic flow. In this research article, an enhanced traffic management system called SCICS (Soft Computing based Intelligent Communication System) has been proposed which uses swarm intelligence as a soft computing technique with intelligent communication between smart vehicles and traffic points using the vehicle to infrastructure (V2I) concept of VANET. It uses an improved route diversion mechanism with implemented logic in nanorobots. Under a vehicular ad-hoc network (VANET) scenario, the communication between intelligent vehicles and infrastructure points takes place through nanorobots in a collaborative way. Simulation carried out using Ns2 simulator shows encouraging results in terms of better performance to control the traffic.

Communication Improvement and Traffic Control Based on V2I in Smart City Framework

2020 ◽  
pp. 1620-1636
Author(s):  
Mamata Rath ◽  
Bibudhendu Pati
Keyword(s):  
Soft Computing ◽  
Traffic Control ◽  
Traffic Management ◽  
Smart Cities ◽  
Intelligent Vehicles ◽  
Traffic Information ◽  
Congested Traffic ◽  
Intelligent Communication ◽  
Traffic Management System ◽  
Online Traffic

This article describes how soft computing techniques are tolerant of imprecision, intended on approximation, focus on uncertainty and are based on partial truth. Current real-world problems pertaining to congested traffic is pervasively imprecise and therefore design of smart traffic control system is a challenging issue. Due to the increasing rate of vehicles at traffic points in smart cities, it creates unexpected delays during transit, chances of accidents are higher, unnecessary fuel consumption is an issue, and unhygienic environment due to pollution also degrades the health condition of general people in a normal city scenario. To avoid such problems many smart cities are currently implementing improved traffic control systems that work on the principle of traffic automation to prevent these issues. The basic challenge lies in the usage of real-time analytics performed with online traffic information and correctly applying it to some traffic flow. In this research article, an enhanced traffic management system called SCICS (Soft Computing based Intelligent Communication System) has been proposed which uses swarm intelligence as a soft computing technique with intelligent communication between smart vehicles and traffic points using the vehicle to infrastructure (V2I) concept of VANET. It uses an improved route diversion mechanism with implemented logic in nanorobots. Under a vehicular ad-hoc network (VANET) scenario, the communication between intelligent vehicles and infrastructure points takes place through nanorobots in a collaborative way. Simulation carried out using Ns2 simulator shows encouraging results in terms of better performance to control the traffic.

scholarly journals Soft Computing Techniques Based Automatic Licence Plate Recognition Systems for Indian Vehicles

2019 ◽  
Vol 8 (12) ◽  
pp. 470-474
Keyword(s):  
Neural Network ◽  
Soft Computing ◽  
Traffic Management ◽  
Support Vector ◽  
Hybrid Technique ◽  
Management Problem ◽  
Computing Technique ◽  
Soft Computing Techniques ◽  
Recognition Systems ◽  
Licence Plate Recognition

Automobile industries are growing exponentially in last decade in India. Growth in the vehicle numbers results in much more road accidents and traffic management problem. Not only this, long queues at toll plazas and parking lot is also a major issue of concern. Problem of traffic management and long queues can be solved by automatic licence plate recognition systems. In this paper, an automatic Licence Plate Recognition Systems based on soft computing techniques are presented. Indian vehicle with licence plates were used for testing the implemented systems. Firstly the licence plate image is extracted from the vehicle image and the characters are segmented from the extracted licence plate image and then features are extracted from the segmented characters which are used for the recognition. Soft computing techniques random forest, neural network, support vector machine, and convolutional neural network are used for the implementation pusrpose. The results obtained for the applied soft computing technique are compared to the last. The future scope is the hybrid technique solution to the problem

scholarly journals Privacy-Preserving Smart Road-Pricing System with Trustworthiness Evaluation in VANETs

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3658
Author(s):  
Qingfeng Zhu ◽  
Sai Ji ◽  
Jian Shen ◽  
Yongjun Ren
Keyword(s):  
Traffic Control ◽  
Traffic Management ◽  
Location Privacy ◽  
Vehicular Ad Hoc Networks ◽  
Intelligent Transportation System ◽  
Smart Cities ◽  
Security Analysis ◽  
Geographical Location ◽  
Experimental Simulation ◽  
Control Center

With the advanced development of the intelligent transportation system, vehicular ad hoc networks have been observed as an excellent technology for the development of intelligent traffic management in smart cities. Recently, researchers and industries have paid great attention to the smart road-tolling system. However, it is still a challenging task to ensure geographical location privacy of vehicles and prevent improper behavior of drivers at the same time. In this paper, a reliable road-tolling system with trustworthiness evaluation is proposed, which guarantees that vehicle location privacy is secure and prevents malicious vehicles from tolling violations at the same time. Vehicle route privacy information is encrypted and uploaded to nearby roadside units, which then forward it to the traffic control center for tolling. The traffic control center can compare data collected by roadside units and video surveillance cameras to analyze whether malicious vehicles have behaved incorrectly. Moreover, a trustworthiness evaluation is applied to comprehensively evaluate the multiple attributes of the vehicle to prevent improper behavior. Finally, security analysis and experimental simulation results show that the proposed scheme has better robustness compared with existing approaches.

Utilization of Soft Computing Techniques in Sputtering Processes: A Review

2013 ◽  
Vol 832 ◽  
pp. 260-265
Author(s):  
Norlina M. Sabri ◽  
Mazidah Puteh ◽  
Mohamad Rusop Mahmood
Keyword(s):  
Soft Computing ◽  
Process Parameters ◽  
Optimization Techniques ◽  
Future Research ◽  
Film Properties ◽  
Reliable Technique ◽  
Computing Technique ◽  
Optimization Of Process Parameters ◽  
Soft Computing Techniques ◽  
Soft Computing Technique

This paper presents an overview of research works on the utilizing of soft computing in the optimization of process parameters and in the prediction of thin film properties in sputtering processes. The papers from this review were obtained from relevant databases and from various scientific journals. The papers collected were published from 2008 to 2012. The focus of the review is to provide an outlook on the utilization of soft computing techniques in sputtering processes. Based on the review, the soft computing techniques which have been applied so far are ANN, GA and Fuzzy Logic. The first finding of this review is that soft computing technique is a promising and more reliable approach to optimize and predict process parameters compared to the traditional methods. The second finding is that the utilizing of soft computing techniques in sputtering processes are still limited and still in exploratory phase as they have not yet been extensively and stably applied. The techniques applied are also limited to ANN, GA and Fuzzy, whereas the exploration into other techniques is also necessary to be conducted in order to seek the most reliable technique and so as to expand the application of soft computing approach. Future research could focus on the exploration of other soft computing techniques for optimization in order to find the best optimization techniques based on the specific processes.

scholarly journals A Novel Ten Check Maximum Power Point Tracking Algorithm for a Standalone Solar Photovoltaic System

Electronics ◽  
2018 ◽  
Vol 7 (11) ◽  
pp. 327 ◽  
Author(s):  
Muhammad Afzal Awan ◽  
Tahir Mahmood
Keyword(s):  
Soft Computing ◽  
Maximum Power ◽  
Photovoltaic System ◽  
Flower Pollination Algorithm ◽  
Global Maximum ◽  
Maximum Power Point ◽  
Partial Shading ◽  
Computing Technique ◽  
Soft Computing Techniques ◽  
Power Point

Optimal energy extraction under partial shading conditions from a photovoltaic (PV) array is particularly challenging. Conventional techniques fail to achieve the global maximum power point (GMPP) under such conditions, while soft computing techniques have provided better results. The main contribution of this paper is to devise an algorithm to track the GMPP accurately and efficiently. For this purpose, a ten check (TC) algorithm was proposed. The effectiveness of this algorithm was tested with different shading patterns. Results were compared with the top conventional algorithm perturb and observe (P&O) and the best soft computing technique flower pollination algorithm (FPA). It was found that the proposed algorithm outperformed them. Analysis demonstrated that the devised algorithm achieved the GMPP efficiently and accurately as compared to the P&O and the FPA algorithms. Simulations were performed in MATLAB/Simulink.

scholarly journals Mobile Road Traffic Management System Using Weighted Sensor

2017 ◽  
Vol 11 (5) ◽  
pp. 147 ◽  
Author(s):  
Solomon Adegbenro Akinboro ◽  
Johnson A Adeyiga ◽  
Adebayo Omotosho ◽  
Akinwale O Akinwumi
Keyword(s):  
Real Time ◽  
Traffic Control ◽  
Urban Areas ◽  
Traffic Management ◽  
Management System ◽  
Road Traffic ◽  
Traffic Information ◽  
Traffic Light ◽  
Real Time Traffic ◽  
Traffic Management System

<p><strong>Vehicular traffic is continuously increasing around the world, especially in urban areas, and the resulting congestion ha</strong><strong>s</strong><strong> be</strong><strong>come</strong><strong> a major concern to automobile users. The popular static electric traffic light controlling system can no longer sufficiently manage the traffic volume in large cities where real time traffic control is paramount to deciding best route. The proposed mobile traffic management system provides users with traffic information on congested roads using weighted sensors. A prototype of the system was implemented using Java SE Development Kit 8 and Google map. The model </strong><strong>was</strong><strong> simulated and the performance was </strong><strong>assessed</strong><strong> using response time, delay and throughput. Results showed that</strong><strong>,</strong><strong> mobile devices are capable of assisting road users’ in faster decision making by providing real-time traffic information and recommending alternative routes.</strong></p>

Keep Moving, Stay Tuned

Transfers ◽  
2018 ◽  
Vol 8 (2) ◽  
pp. 67-86 ◽  
Author(s):  
Marith Dieker
Keyword(s):  
Media Studies ◽  
Traffic Control ◽  
Traffic Management ◽  
Sociotechnical Systems ◽  
The Other ◽  
Traffic Information ◽  
Traffic Jams ◽  
Information Updates ◽  
The 1960S

With the rise of privatized automobility and the increase of traffic jams, new sociotechnical systems have emerged that aim at traffic control. Radio traffic information has been a key element in these systems. Through a qualitative analysis of historical radio broadcasts of the largest Dutch news station between 1960 and 2000, this article explores the changing format and content of traffic information updates. I will show how the rather formal, detailed, and paternalistic narratives of the traffic reports in the 1960s gave way to more informal, witty, yet flow-controlling traffic information discourse in later decades. I will explain the dynamics involved by drawing on mobility and media studies and by developing two distinct notions of flow, one of which builds conceptually on Raymond Williams’s work on mobile privatization, the other is grounded in the field of traffic management. In so doing, this article aims to contribute to a better understanding of the role of public radio broadcasts in our world of privatized automobility.

Deep Learning Based Sentiment Classification on User-Generated Big Data

2020 ◽  
Vol 13 (5) ◽  
pp. 1047-1056
Author(s):  
Akshi Kumar ◽  
Arunima Jaiswal
Keyword(s):  
Neural Network ◽  
Big Data ◽  
Deep Learning ◽  
Soft Computing ◽  
Sentiment Classification ◽  
Convolution Neural Network ◽  
Support Vector ◽  
Computing Technique ◽  
Proposed Model ◽  
Soft Computing Techniques

Background: Sentiment analysis of big data such as Twitter primarily aids the organizations with the potential of surveying public opinions or emotions for the products and events associated with them. Objective: In this paper, we propose the application of a deep learning architecture namely the Convolution Neural Network. The proposed model is implemented on benchmark Twitter corpus (SemEval 2016 and SemEval 2017) and empirically analyzed with other baseline supervised soft computing techniques. The pragmatics of the work includes modelling the behavior of trained Convolution Neural Network on wellknown Twitter datasets for sentiment classification. The performance efficacy of the proposed model has been compared and contrasted with the existing soft computing techniques like Naïve Bayesian, Support Vector Machines, k-Nearest Neighbor, Multilayer Perceptron and Decision Tree using precision, accuracy, recall, and F-measure as key performance indicators. Methods: Majority of the studies emphasize on the utilization of feature mining using lexical or syntactic feature extraction that are often unequivocally articulated through words, emoticons and exclamation marks. Subsequently, CNN, a deep learning based soft computing technique is used to improve the sentiment classifier’s performance. Results: The empirical analysis validates that the proposed implementation of the CNN model outperforms the baseline supervised learning algorithms with an accuracy of around 87% to 88%. Conclusion: Statistical analysis validates that the proposed CNN model outperforms the existing techniques and thus can enhance the performance of sentiment classification viability and coherency.

scholarly journals Sistema de inferencia borroso basado en redes adaptativas para la evaluación de proyectos

2015 ◽  
Vol 19 (2) ◽  
pp. 53
Author(s):  
Anié Bermudez Peña ◽  
José Alejandro Lugo García ◽  
Pedro Yobanis Piñero Pérez
Keyword(s):  
Soft Computing ◽  
Key Management ◽  
Fuzzy Inference ◽  
Management Tool ◽  
Primary Data ◽  
Inference System ◽  
Computing Technique ◽  
Management Indicators ◽  
Soft Computing Techniques ◽  
Automated Tools

In this article, a set of key management indicators related to performance of execution, planning, costs, effectiveness, human resources, data quality, and logistics, are considered for the evaluation of a project. Several automated tools support project managers in this task. However, these tools are still insufficient to accurately assess projects in organizations with continuous improvement management styles and with presence of uncertainty in the primary data. An alternative solution is the introduction of soft computing techniques, allowing gains in robustness, efficiency, and adaptability in these tools. This paper presents an adaptivenetwork- based fuzzy inference system (ANFIS) to optimize projects evaluation made with the Xedro-GESPRO tool. The implementation of the system allowed the adjustment of fuzzy sets parameters in the inference rules for the assessment of projects, based on the automatic calculation of indicators. The contribution of this research lies in the application of ANFIS soft computing technique to optimize the evaluation of projects integrated with the management tool. The results contribute to the improvement of existing decision-making support tools into organizations towards project-oriented production. 

Pilot Performance and Workload Using Traffic Displays to Support Free Flight

1998 ◽  
Vol 42 (1) ◽  
pp. 52-56 ◽  
Author(s):  
M. Ephimia Morphew ◽  
Christopher D. Wickens
Keyword(s):  
Traffic Control ◽  
Traffic Management ◽  
Air Traffic Management ◽  
Air Traffic ◽  
Flight Simulation ◽  
Traffic Information ◽  
Free Flight ◽  
Pilot Performance ◽  
Or Efficiency ◽  
Innovative Solutions

Arising from the need to employ innovative solutions to safely and efficiently maintain air traffic separation in increasingly denser skyways, the concept of Free Flight involves shifting some air traffic management responsibilities from air traffic control specialists on the ground, to pilots in the cockpit. Such a shift in traffic management responsibilities will be critically dependent upon the development of displays to provide traffic and hazard information to pilots in the cockpit (Wickens, Carbonari, Merwin, Morphew, & O'Brien (1997; Battiste (in progress); Johnson, Battiste, Delzell, Holland, Belcher, & Jordan, 1997). This research examined the effect of different information-varying display aids (predictors and threat vectors) for in-cockpit traffic displays, on pilot performance, workload, attentional demands, and flight safety. Fifteen pilots flew a series of traffic avoidance scenarios in a Free Flight simulation designed to assess the effects of different levels of traffic display information on these pilot variables. Three, 2D-coplanar prototype displays were compared which differed in the level of traffic information provided. Analysis of the data revealed that the traffic display with the most predictive information supported increased safety and decreased workload, without appreciable decrements in flight performance or efficiency.

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