Networking Controller Based Real Time Traffic Prediction in Clustered Vehicular Adhoc Networks

The vehicular ad hoc network(VANET)is an emerging network tech-nology that has gained popularity because to its low cost,flexibility,and seamless services.Software defined networking(SDN)technology plays a critical role in network administration in the future generation of VANET withfifth generation(5G)networks.Regardless of the benefits of VANET,energy economy and traffic control are significant architectural challenges.Accurate and real-time trafficflow prediction(TFP)becomes critical for managing traffic effectively in the VANET.SDN controllers are a critical issue in VANET,which has garnered much interest in recent years.With this objective,this study develops the SDNTFP-C technique,a revolutionary SDN controller-based real-time trafficflow forecasting technique for clustered VANETs.The proposed SDNTFP-C technique combines the SDN controller’s scalability,flexibility,and adaptability with deep learning(DL)mod-els.Additionally,a novel arithmetic optimization-based clustering technique(AOCA)is developed to cluster automobiles in a VANET.The TFP procedure is then performed using a hybrid convolutional neural network model with atten-tion-based bidirectional long short-term memory(HCNN-ABLSTM).To optimise the performance of the HCNN-ABLSTM model,the dingo optimization techni-que was used to tune the hyperparameters(DOA).The experimental results ana-lysis reveals that the suggested method outperforms other current techniques on a variety of evaluation metrics.

Identification and Acknowledgment of Programmed Traffic Sign Utilizing Profound Convolutional Neural Organization

Traffic signs are basic security workplaces making the rounds,which expects a huge part in coordinating busy time gridlock direct,ensuring the pros-perity of the road and dealing with the smooth segment of vehicles and indivi-duals by walking,etc.As a segment of the clever transportation structure,the acknowledgment of traffic signs is basic for the driving assistance system,traffic sign upkeep,self-administering driving,and various spaces.There are different assessments turns out achieved for traffic sign acknowledgment in the world.However,most of the works are only for explicit arrangements of traffic signs,for example,beyond what many would consider a possible sign.Traffic sign recognizable proof is generally seen as trying on account of various complexities,for example,extended establishments of traffic sign pictures.Two critical issues exist during the time spent identification(ID)and affirmation of traffic signals.Road signs are occasionally blocked not entirely by various vehicles and various articles are accessible in busy time gridlock scenes which make the signed acknowledgment hard and walkers,various vehicles,constructions,and loads up may frustrate the ID structure by plans like that of road signs.Also concealing information from traffic scene pictures is affected by moving light achieved by environment conditions,time(day-night),and shadowing.Traffic sign revelation and affirmation structure has two guideline sorts out:The essential stage incorpo-rates the traffic sign limitation and the resulting stage portrays the perceived traffic signs into a particular class.

Optimal Routing with Spatial-Temporal Dependencies for Traffic Flow Control in Intelligent Transportation Systems

In Intelligent Transportation Systems(ITS),controlling the trafficflow of a region in a city is the major challenge.Particularly,allocation of the traffic-free route to the taxi drivers during peak hours is one of the challenges to control the trafficflow.So,in this paper,the route between the taxi driver and pickup location or hotspot with the spatial-temporal dependencies is optimized.Initially,the hotspots in a region are clustered using the density-based spatial clustering of applications with noise(DBSCAN)algorithm tofind the hot spots at the peak hours in an urban area.Then,the optimal route is allocated to the taxi driver to pick up the customer in the hotspot.Before allocating the optimal route,each route between the taxi driver and the hot spot is mapped to the number of taxi drivers.Among the map function,the optimal map is selected using the rain opti-mization algorithm(ROA).If more than one map function is obtained as the opti-mal solution,the map between the route and the taxi driver who has done the least number of trips in the day is chosen as thefinal solution This optimal route selec-tion leads to control of the trafficflow at peak hours.Evaluation of the approach depicts that the proposed trafficflow control scheme reduces traveling time,wait-ing time,fuel consumption,and emission.

Design of Fuzzy Logic Control Framework for QoS Routing in MANET

Wireless networks with no infrastructure arise as a result of multiple wireless devices working together.The Mobile Ad hoc Network(MANET)is a system for connecting independently located Mobile Nodes(MNs)via wireless links.A MANET is self-configuring in telecommunications,while MN produces non-infrastructure networks that are entirely decentralized.Both the MAC and routing layers of MANETs take into account issues related to Quality of Service(QoS).When culling a line of optical discernment communication,MANET can be an effective and cost-saving route cull option.To maintain QoS,however,more or fewer challenges must be overcome.This paper proposes a Fuzzy Logic Control(FLC)methodology for specifying a probabilistic QoS guaranteed for MANETs.The framework uses network node mobility to establish the probabil-istic quality of service.Fuzzy Logic(FL)implementations were added to Network Simulator-3(NS-3)and used with the proposed FLC framework for simulation.Researchers have found that for a given node’s mobility,the path’s bandwidth decreases with time,hop count,and radius.It is resolutely based on this fuzzy rule that the priority index for a packet is determined.Also,by avoiding sending pack-ets(PKT)out of source networks when there are no beneficial routes,bandwidth is not wasted.The FLC outperforms the scheduling methods with a wide range of results.To improve QoS within MANETs,it is therefore recommended that FLC is used to synchronize packets.Thus,using these performance metrics,the QoS-responsible routing can opt for more stable paths.Based on network simulation,it is evident that incorporating QoS into routing protocols is meant to improve traf-fic performance,in particular authentic-time traffic.

Study on Recognition Method of Similar Weather Scenes in Terminal Area

Weather is a key factor affecting the control of air traffic.Accurate recognition and classification of similar weather scenes in the terminal area is helpful for rapid decision-making in air trafficflow management.Current researches mostly use traditional machine learning methods to extract features of weather scenes,and clustering algorithms to divide similar scenes.Inspired by the excellent performance of deep learning in image recognition,this paper proposes a terminal area similar weather scene classification method based on improved deep convolution embedded clustering(IDCEC),which uses the com-bination of the encoding layer and the decoding layer to reduce the dimensionality of the weather image,retaining useful information to the greatest extent,and then uses the combination of the pre-trained encoding layer and the clustering layer to train the clustering model of the similar scenes in the terminal area.Finally,term-inal area of Guangzhou Airport is selected as the research object,the method pro-posed in this article is used to classify historical weather data in similar scenes,and the performance is compared with other state-of-the-art methods.The experi-mental results show that the proposed IDCEC method can identify similar scenes more accurately based on the spatial distribution characteristics and severity of weather;at the same time,compared with the actualflight volume in the Guangz-hou terminal area,IDCEC's recognition results of similar weather scenes are con-sistent with the recognition of experts in thefield.

Research on the Influencing Factors of Passenger Traffic at Sanya Airport Based on Gray Correlation Theory

Many factors have an effect on the passenger traffic of Sanya Airport.In this paper,seven factors that have a great influence on passenger demand,namely accommodation and catering turnover,number of overnight visitors received,total tourism revenue,average room opening rate of tourist hotels,flight movements,airport passengerflow and railway station passengerflow,are selected to conduct gray correlation analysis on these factors to provide a research basis for passengerflow prediction.

Dynamic prediction of traffic incident duration on urban expressways: a deep learning approach based on LSTM and MLP

Purpose–Efficient traffic incident management is needed to alleviate the negative impact of traffic incidents.Accurate and reliable estimation of traffic incident duration is of great importance for traffic incident management.Previous studies have proposed models for traffic incident duration prediction;however,most of these studies focus on the total duration and could not update prediction results in real-time.From a traveler’s perspective,the relevant factor is the residual duration of the impact of the traffic incident.Besides,few(if any)studies have used dynamic trafficflow parameters in the prediction models.This paper aims to propose a framework tofill these gaps.Design/methodology/approach–This paper proposes a framework based on the multi-layer perception(MLP)and long short-term memory(LSTM)model.The proposed methodology integrates traffic incident-related factors and real-time trafficflow parameters to predict the residual traffic incident duration.To validate the effectiveness of the framework,traffic incident data and trafficflow data from Shanghai Zhonghuan Expressway are used for modeling training and testing.Findings–Results show that the model with 30-min time window and taking both traffic volume and speed as inputs performed best.The area under the curve values exceed 0.85 and the prediction accuracies exceed 0.75.These indicators demonstrated that the model is appropriate for this study context.The model provides new insights into traffic incident duration prediction.Research limitations/implications–The incident samples applied by this study might not be enough and the variables are not abundant.The number of injuries and casualties,more detailed description of the incident location and other variables are expected to be used to characterize the traffic incident comprehensively.The framework needs to be further validated through a sufficiently large number of variables and locations.Practical implications–The framework can help reduce the impacts of incidents on the safety of efficiency of road traffic once implemented in intelligent transport system and traffic management systems in future practical applications.Originality/value–This study uses two artificial neural network methods,MLP and LSTM,to establish a framework aiming at providing accurate and time-efficient information on traffic incident duration in the future for transportation operators and travelers.This study will contribute to the deployment of emergency management and urban traffic navigation planning.

Research on the classification for road traffic visibility based on the characteristics of driving behaviour–a driving simulator experiment

Purpose–The purpose of this paper is to performfine classification of road traffic visibility based on the characteristics of driving behavior under different visibility conditions.Design/methodology/approach–A driving simulator experiment was conducted to collect data of speed and lane position.ANOVA was used to explore the difference in driving behavior under different visibility conditions.Findings–The results show that only average speed is significantly different under different visibility conditions.With the visibility reducing,the average vehicle speed decreases.The road visibility conditions in a straight segment can be divided intofive levels:less than 20,20-30,35-60,60-140 and more than 140 m.The road visibility conditions in a curve segment can be also divided into four levels:less than 20,20-30,35-60 and more than 60 m.Originality/value–Afine classification of road traffic visibility has been performed,and these classifications help to establish more accurate control measures to ensure road traffic safety under low-visibility conditions.

The research of traffic density extraction method under vehicular ad hoc network environment

Purpose–Traffic density is one of the most important parameters to consider in the traffic operationfield.Owing to limited data sources,traditional methods cannot extract traffic density directly.In the vehicular ad hoc network(VANET)environment,the vehicle-to-vehicle(V2V)and vehicle-to-infrastructure(V2I)interaction technologies create better conditions for collecting the whole time-space and refined traffic data,which provides a new approach to solving this problem.Design/methodology/approach–On that basis,a real-time traffic density extraction method has been proposed,including lane density,segment density and network density.Meanwhile,using SUMO and OMNet11 as traffic simulator and network simulator,respectively,the Veins framework as middleware and the two-way coupling VANET simulation platform was constructed.Findings–Based on the simulation platform,a simulated intersection in Shanghai was developed to investigate the adaptability of the model.Originality/value–Most research studies use separate simulation methods,importing trace data obtained by using from the simulation software to the communication simulation software.In this paper,the tight coupling simulation method is applied.Using real-time data and history data,the research focuses on the establishment and validation of the traffic density extraction model.

Analysis of highway performance under mixed connected and regular vehicle environment

Purpose–This study aims to study the connected vehicle(CV)impact on highway operational performance under a mixed CV and regular vehicle(RV)environment.Design/methodology/approach–The authors implemented a mixed trafficflow model,along with a CV speed control model,in the simulation environment.According to the different traffic characteristics between CVs and RVs,this researchfirst analyzed how the operation of CVs can affect highway capacity under both one-lane and multi-lane cases.A hypothesis was then made that there shall exist a critical CV penetration rate that can significantly show the benefit of CV to the overall traffic.To prove this concept,this study simulated the mixed traffic pattern under various conditions.Findings–The results of this research revealed that performing optimal speed control to CVs will concurrently benefit RVs by improving highway capacity.Furthermore,a critical CV penetration rate should exist at a specified traffic demand level,which can significantly reduce the speed difference between RVs and CVs.The results offer effective insight to understand the potential impacts of different CV penetration rates on highway operation performance.Originality/value–This approach assumes that there shall exist a critical CV penetration rate that can maximize the benefits of CV implementations.CV penetration rate(the proportion of CVs in mixed traffic)is the key factor affecting the impacts of CV on freeway operational performance.The evaluation criteria for freeway operational performance are using average travel time under different given traffic demand patterns.