Transportation systems are experiencing a significant transformation due to the integration of advanced technologies, including artificial intelligence and machine learning. In the context of intelligent transportatio...Transportation systems are experiencing a significant transformation due to the integration of advanced technologies, including artificial intelligence and machine learning. In the context of intelligent transportation systems (ITS) and Advanced Driver Assistance Systems (ADAS), the development of efficient and reliable traffic light detection mechanisms is crucial for enhancing road safety and traffic management. This paper presents an optimized convolutional neural network (CNN) framework designed to detect traffic lights in real-time within complex urban environments. Leveraging multi-scale pyramid feature maps, the proposed model addresses key challenges such as the detection of small, occluded, and low-resolution traffic lights amidst complex backgrounds. The integration of dilated convolutions, Region of Interest (ROI) alignment, and Soft Non-Maximum Suppression (Soft-NMS) further improves detection accuracy and reduces false positives. By optimizing computational efficiency and parameter complexity, the framework is designed to operate seamlessly on embedded systems, ensuring robust performance in real-world applications. Extensive experiments using real-world datasets demonstrate that our model significantly outperforms existing methods, providing a scalable solution for ITS and ADAS applications. This research contributes to the advancement of Artificial Intelligence-driven (AI-driven) pattern recognition in transportation systems and offers a mathematical approach to improving efficiency and safety in logistics and transportation networks.展开更多
The Intelligent Transportation System(ITS),as a vital means to alleviate traffic congestion and reduce traffic accidents,demonstrates immense potential in improving traffic safety and efficiency through the integratio...The Intelligent Transportation System(ITS),as a vital means to alleviate traffic congestion and reduce traffic accidents,demonstrates immense potential in improving traffic safety and efficiency through the integration of Internet of Things(IoT)technologies.The enhancement of its performance largely depends on breakthrough advancements in object detection technology.However,current object detection technology still faces numerous challenges,such as accuracy,robustness,and data privacy issues.These challenges are particularly critical in the application of ITS and require in-depth analysis and exploration of future improvement directions.This study provides a comprehensive review of the development of object detection technology and analyzes its specific applications in ITS,aiming to thoroughly explore the use and advancement of object detection technologies in IoT-based intelligent transportation systems.To achieve this objective,we adopted the Preferred Reporting Items for Systematic Reviews and Meta-Analyses(PRISMA)approach to search,screen,and assess the eligibility of relevant literature,ultimately including 88 studies.Through an analysis of these studies,we summarized the characteristics,advantages,and limitations of object detection technology across the traditional methods stage and the deep learning-based methods stage.Additionally,we examined its applications in ITS from three perspectives:vehicle detection,pedestrian detection,and traffic sign detection.We also identified the major challenges currently faced by these technologies and proposed future directions for addressing these issues.This review offers researchers a comprehensive perspective,identifying potential improvement directions for object detection technology in ITS,including accuracy,robustness,real-time performance,data annotation cost,and data privacy.In doing so,it provides significant guidance for the further development of IoT-based intelligent transportation systems.展开更多
Thedeployment of the Internet of Things(IoT)with smart sensors has facilitated the emergence of fog computing as an important technology for delivering services to smart environments such as campuses,smart cities,and ...Thedeployment of the Internet of Things(IoT)with smart sensors has facilitated the emergence of fog computing as an important technology for delivering services to smart environments such as campuses,smart cities,and smart transportation systems.Fog computing tackles a range of challenges,including processing,storage,bandwidth,latency,and reliability,by locally distributing secure information through end nodes.Consisting of endpoints,fog nodes,and back-end cloud infrastructure,it provides advanced capabilities beyond traditional cloud computing.In smart environments,particularly within smart city transportation systems,the abundance of devices and nodes poses significant challenges related to power consumption and system reliability.To address the challenges of latency,energy consumption,and fault tolerance in these environments,this paper proposes a latency-aware,faulttolerant framework for resource scheduling and data management,referred to as the FORD framework,for smart cities in fog environments.This framework is designed to meet the demands of time-sensitive applications,such as those in smart transportation systems.The FORD framework incorporates latency-aware resource scheduling to optimize task execution in smart city environments,leveraging resources from both fog and cloud environments.Through simulation-based executions,tasks are allocated to the nearest available nodes with minimum latency.In the event of execution failure,a fault-tolerantmechanism is employed to ensure the successful completion of tasks.Upon successful execution,data is efficiently stored in the cloud data center,ensuring data integrity and reliability within the smart city ecosystem.展开更多
The metabolic evolution model of transportation demand for comprehensive transportation systems is put forward on the basis of a metabolic theory of ecology. In the model, the growth rates or changing rates of transpo...The metabolic evolution model of transportation demand for comprehensive transportation systems is put forward on the basis of a metabolic theory of ecology. In the model, the growth rates or changing rates of transportation volumes for the various transportation modes of a city are determined not only by the GDP per capita which reflects the size of the city itself, but also by the relationship of competition and cooperation among transportation modes. The results of empirical analysis for Chinese cities show that the allometric growth exponent in the equation for the variation rate of passenger demand volume on rail is greater than the predicted value of 1/4 in metabolic ecology, whereas the allometric growth relationship is not so evident in the equation for the variation rate of passenger demand volume on road. The changing rate of road transportation is thus mainly affected by the relationship of competition and cooperation among transportation modes for Chinese cities.展开更多
Terahertz(THz)radiation possesses unique properties that make it a promising light source for applications in various fields,particularly spectroscopy and imaging.Ongoing research and development in THz technology has...Terahertz(THz)radiation possesses unique properties that make it a promising light source for applications in various fields,particularly spectroscopy and imaging.Ongoing research and development in THz technology has focused on developing or improving THz sources,detectors,and applications.At the PBP-CMU Electron Linac Laboratory(PCELL)of the Plasma and Beam Physics Research Facility in Chiang Mai University,high-intensity THz radiation has been generated in the form of coherent transition radiation(TR)and investigated since 2006 for electron beams with energies ranging from 8 to 12 MeV.In this study,we investigate and optimize the coherent TR arising from short electron bunches with energies ranging from 8 to 22 MeV using an upgraded linear-accelerator system with a higher radio-frequency(RF)power system.This radiation is then transported from the accelerator hall to the experimental room,in which the spectrometers are located.Electron-beam simulations are conducted to achieve short bunch lengths and small transverse beam sizes at the TR station.Radiation properties,including the radiation spectrum,angular distribution,and radiation polarization,are thoroughly investigated.The electron-bunch length is evaluated using the measuring system.The radiation-transport line is designed to achieve optimal frequency response and high transmission efficiency.A radiation-transmission efficiency of approximately 80-90%can be achieved with this designed system,along with a pulse energy ranging from 0.17 to 0.25μJ.The expected radiation spectral range covers up to 2 THz with a peak power of 0.5-1.25 MW.This coherent,broadband,and intense THz radiation will serve as a light source for THz spectroscopy and THz time-domain spectroscopy applications at the PCELL in the near future.展开更多
A novel maglev transportation system was proposed for large travel range ultra precision motion.The system consists of a levitation subsystem and a propulsion subsystem.During the propulsion subsystem driving the movi...A novel maglev transportation system was proposed for large travel range ultra precision motion.The system consists of a levitation subsystem and a propulsion subsystem.During the propulsion subsystem driving the moving platform along the guideway,the levitation subsystem uses six pairs of electromagnets to steadily suspend the moving platform over the guideway.The model of the levitation system,which is a typical nonlinear multi-input multi-output coupling system and has many inner nonlinear coupling characteristics,was deduced.For testifying the model,the levitation mechanism was firstly controlled by proportional-integral-differential(PID) control,and then a lot of input-output data were collected for model parameter identification.The least-square parameter identification method was used.The identification results prove that the model is feasible and suitable for the real system.展开更多
Based on characteristics of deep sea flexible mining system,a new pump-lockage ore transportation system was designed.According to Bernoulli equation and two-phase hydrodynamics theory,parameters of the new system wer...Based on characteristics of deep sea flexible mining system,a new pump-lockage ore transportation system was designed.According to Bernoulli equation and two-phase hydrodynamics theory,parameters of the new system were obtained and four ore transportation systems were analyzed.The results indicate that the pump head of 1 000 m mining system is 100-150 m and that of 5 000 m mining system is 660-750 m.In addition,based on similarity theory,a model of the new transportation system was made,which can simulate more than 5 000 m actual ore transportation system.So both theory and experiment prove that the new pump-lockage ore transportation system is an ideal design for deep sea flexible mining system.展开更多
Security threats to smart and autonomous vehicles cause potential consequences such as traffic accidents,economically damaging traffic jams,hijacking,motivating to wrong routes,and financial losses for businesses and ...Security threats to smart and autonomous vehicles cause potential consequences such as traffic accidents,economically damaging traffic jams,hijacking,motivating to wrong routes,and financial losses for businesses and governments.Smart and autonomous vehicles are connected wirelessly,which are more attracted for attackers due to the open nature of wireless communication.One of the problems is the rogue attack,in which the attacker pretends to be a legitimate user or access point by utilizing fake identity.To figure out the problem of a rogue attack,we propose a reinforcement learning algorithm to identify rogue nodes by exploiting the channel state information of the communication link.We consider the communication link between vehicle-to-vehicle,and vehicle-to-infrastructure.We evaluate the performance of our proposed technique by measuring the rogue attack probability,false alarm rate(FAR),mis-detection rate(MDR),and utility function of a receiver based on the test threshold values of reinforcement learning algorithm.The results show that the FAR and MDR are decreased significantly by selecting an appropriate threshold value in order to improve the receiver’s utility.展开更多
Sputum transportation from county-level to prefecture-level is an ideal strategy to cover the shortage of the laboratory capability in the resource-poor setting. Here, we firstly evaluated the feasibility of sputum tr...Sputum transportation from county-level to prefecture-level is an ideal strategy to cover the shortage of the laboratory capability in the resource-poor setting. Here, we firstly evaluated the feasibility of sputum transportation system in China by analyzing the culture and molecular diagnosis results from 1982 smear-positive patients with different delay in processing for culture.展开更多
As the main link of ground engineering,crude oil gathering and transportation systems require huge energy consumption and complex structures.It is necessary to establish an energy efficiency evaluation system for crud...As the main link of ground engineering,crude oil gathering and transportation systems require huge energy consumption and complex structures.It is necessary to establish an energy efficiency evaluation system for crude oil gathering and transportation systems and identify the energy efficiency gaps.In this paper,the energy efficiency evaluation system of the crude oil gathering and transportation system in an oilfield in western China is established.Combined with the big data analysis method,the GA-BP neural network is used to establish the energy efficiency index prediction model for crude oil gathering and transportation systems.The comprehensive energy consumption,gas consumption,power consumption,energy utilization rate,heat utilization rate,and power utilization rate of crude oil gathering and transportation systems are predicted.Considering the efficiency and unit consumption index of the crude oil gathering and transportation system,the energy efficiency evaluation system of the crude oil gathering and transportation system is established based on a game theory combined weighting method and TOPSIS evaluation method,and the subjective weight is determined by the triangular fuzzy analytic hierarchy process.The entropy weight method determines the objective weight,and the combined weight of game theory combines subjectivity with objectivity to comprehensively evaluate the comprehensive energy efficiency of crude oil gathering and transportation systems and their subsystems.Finally,the weak links in energy utilization are identified,and energy conservation and consumption reduction are improved.The above research provides technical support for the green,efficient and intelligent development of crude oil gathering and transportation systems.展开更多
Environment perception is one of the most critical technology of intelligent transportation systems(ITS).Motion interaction between multiple vehicles in ITS makes it important to perform multi-object tracking(MOT).How...Environment perception is one of the most critical technology of intelligent transportation systems(ITS).Motion interaction between multiple vehicles in ITS makes it important to perform multi-object tracking(MOT).However,most existing MOT algorithms follow the tracking-by-detection framework,which separates detection and tracking into two independent segments and limit the global efciency.Recently,a few algorithms have combined feature extraction into one network;however,the tracking portion continues to rely on data association,and requires com‑plex post-processing for life cycle management.Those methods do not combine detection and tracking efciently.This paper presents a novel network to realize joint multi-object detection and tracking in an end-to-end manner for ITS,named as global correlation network(GCNet).Unlike most object detection methods,GCNet introduces a global correlation layer for regression of absolute size and coordinates of bounding boxes,instead of ofsetting predictions.The pipeline of detection and tracking in GCNet is conceptually simple,and does not require compli‑cated tracking strategies such as non-maximum suppression and data association.GCNet was evaluated on a multivehicle tracking dataset,UA-DETRAC,demonstrating promising performance compared to state-of-the-art detectors and trackers.展开更多
Cooperative Intelligent Transport System(C-ITS)plays a vital role in the future road traffic management system.A vital element of C-ITS comprises vehicles,road side units,and traffic command centers,which produce a ma...Cooperative Intelligent Transport System(C-ITS)plays a vital role in the future road traffic management system.A vital element of C-ITS comprises vehicles,road side units,and traffic command centers,which produce a massive quantity of data comprising both mobility and service-related data.For the extraction of meaningful and related details out of the generated data,data science acts as an essential part of the upcoming C-ITS applications.At the same time,prediction of short-term traffic flow is highly essential to manage the traffic accurately.Due to the rapid increase in the amount of traffic data,deep learning(DL)models are widely employed,which uses a non-parametric approach for dealing with traffic flow forecasting.This paper focuses on the design of intelligent deep learning based short-termtraffic flow prediction(IDL-STFLP)model for C-ITS that assists the people in various ways,namely optimization of signal timing by traffic signal controllers,travelers being able to adapt and alter their routes,and so on.The presented IDLSTFLP model operates on two main stages namely vehicle counting and traffic flow prediction.The IDL-STFLP model employs the Fully Convolutional Redundant Counting(FCRC)based vehicle count process.In addition,deep belief network(DBN)model is applied for the prediction of short-term traffic flow.To further improve the performance of the DBN in traffic flow prediction,it will be optimized by Quantum-behaved bat algorithm(QBA)which optimizes the tunable parameters of DBN.Experimental results based on benchmark dataset show that the presented method can count vehicles and predict traffic flowin real-time with amaximumperformance under dissimilar environmental situations.展开更多
With the rise of the Internet of Vehicles(IoV)and the number of connected vehicles increasing on the roads,Cooperative Intelligent Transportation Systems(C-ITSs)have become an important area of research.As the number ...With the rise of the Internet of Vehicles(IoV)and the number of connected vehicles increasing on the roads,Cooperative Intelligent Transportation Systems(C-ITSs)have become an important area of research.As the number of Vehicle to Vehicle(V2V)and Vehicle to Interface(V2I)communication links increases,the amount of data received and processed in the network also increases.In addition,networking interfaces need to be made more secure for which existing cryptography-based security schemes may not be sufficient.Thus,there is a need to augment them with intelligent network intrusion detection techniques.Some machine learning-based intrusion detection and anomaly detection techniques for vehicular networks have been proposed in recent times.However,given the expected large network size,there is a necessity for extensive data processing for use in such anomaly detection methods.Deep learning solutions are lucrative options as they remove the necessity for feature selection.Therefore,with the amount of vehicular network traffic increasing at an unprecedented rate in the C-ITS scenario,the need for deep learning-based techniques is all the more heightened.This work presents three deep learning-based misbehavior classification schemes for intrusion detection in IoV networks using Long Short Term Memory(LSTM)and Convolutional Neural Networks(CNNs).The proposed Deep Learning Classification Engines(DCLE)comprise of single or multi-step classification done by deep learning models that are deployed on the vehicular edge servers.Vehicular data received by the Road Side Units(RSUs)is pre-processed and forwarded to the edge server for classifications following the three classification schemes proposed in this paper.The proposed classifiers identify 18 different vehicular behavior types,the F1-scores ranging from 95.58%to 96.75%,much higher than the existing works.By running the classifiers on testbeds emulating edge servers,the prediction performance and prediction time comparison of the proposed scheme is compared with those of the existing studies.展开更多
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 ch...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.展开更多
The PRT (Personal Rapid Transit) refers to a traffic system in which small vehicles automatically travel on a dedicated rail network or road network.It is a branch of monorail traffic and dedicated road traffic.It can...The PRT (Personal Rapid Transit) refers to a traffic system in which small vehicles automatically travel on a dedicated rail network or road network.It is a branch of monorail traffic and dedicated road traffic.It can change the situation of high energy consumption of traditional mountain transportation.It can reduce the amount of machinery used in the construction process to reduce carbon emissions.It is completely powered by electricity and reduces the friction of the cableway to reduce energy consumption.Its construction process uses small amount of traditional building materials such as concrete and steel.It has little damage to the ecological environment of the mountain,and can not damage the carbon sequestration ability of the plant community.It could serve as a means of transporting goods over long distance,reducing the need for big trucks and thus reducing the consumption of fossil fuels.展开更多
Digitalization and decarbonization are projected to be two major trends in the coming decades.As the already widespread process of digitalization continues to progress,especially in energy and transportation systems,m...Digitalization and decarbonization are projected to be two major trends in the coming decades.As the already widespread process of digitalization continues to progress,especially in energy and transportation systems,massive data will be produced,and how these data could support and promote decarbonization has become a pressing concern.This paper presents a comprehensive review of digital technologies and their potential applications in low-carbon energy and transportation systems from the perspectives of infrastructure,common mechanisms and algorithms,and system-level impacts,as well as the application of digital technologies to coupled energy and transportation systems with electric vehicles.This paper also identifies corresponding challenges and future research directions,such as in the field of blockchain,digital twin,vehicle-to-grid,low-carbon computing,and data security and pri-vacy,especially in the context of integrated energy and transportation systems.展开更多
This paper presents a design scheme of intelligent transportation system based on the Internet of things. First, the paper elaborated the related technical and functional demand of intelligent traffic system, designed...This paper presents a design scheme of intelligent transportation system based on the Internet of things. First, the paper elaborated the related technical and functional demand of intelligent traffic system, designed the gateway level model and the overall project. Then, we design gateway hardware circuit according to the overall plan, and design the gateway application software according to the functional requirements. Through the experiment and simulation results show that, the intelligent transportation system gateway based on Internet of things is ability to create ZigBee network through the way of wireless access, GPRS network, Ethernet access based on the wired way, to realizes multimode access, multi-protocol conversion gateway, ad hoc network functions.展开更多
The swift recuperation of communities following natural hazards heavily relies on the efficiency of transporta-tion systems,facilitating the timely delivery of vital resources and manpower to reconstruction sites.This...The swift recuperation of communities following natural hazards heavily relies on the efficiency of transporta-tion systems,facilitating the timely delivery of vital resources and manpower to reconstruction sites.This paper delves into the pivotal role of transportation systems in aiding the recovery of built environments,proposing an evaluative metric that correlates transportation capacity with the speed of post-earthquake recovery.Focusing on optimizing urban population capacity in the aftermath of earthquakes,the study comprehensively examines the impact of pre-earthquake measures such as enhancing building or bridge seismic performance on post-earthquake urban population capacity.The methodology is demonstrated through an analysis of Beijing’s transportation sys-tem,elucidating how enhancements to transportation infrastructure fortify the resilience of built environments.Additionally,the concept of a resource supply rate is introduced to gauge the level of logistical support available after an earthquake.This rate tends to decrease when transportation damage is significant or when the demands for repairs overwhelm available resources,indicating a need for retrofitting.Through sensitivity analysis,this study explores how investments in the built environment or logistical systems can increase the resource supply rate,thereby contributing to more resilient urban areas in the face of seismic challenges.展开更多
The development of Intelligent Railway Transportation Systems necessitates incorporating privacy-preserving mechanisms into AI models to protect sensitive information and enhance system efficiency.Federated learning o...The development of Intelligent Railway Transportation Systems necessitates incorporating privacy-preserving mechanisms into AI models to protect sensitive information and enhance system efficiency.Federated learning offers a promising solution by allowing multiple clients to train models collaboratively without sharing private data.However,despite its privacy benefits,federated learning systems are vulnerable to poisoning attacks,where adversaries alter local model parameters on compromised clients and send malicious updates to the server,potentially compromising the global model’s accuracy.In this study,we introduce PMM(Perturbation coefficient Multiplied by Maximum value),a new poisoning attack method that perturbs model updates layer by layer,demonstrating the threat of poisoning attacks faced by federated learning.Extensive experiments across three distinct datasets have demonstrated PMM’s ability to significantly reduce the global model’s accuracy.Additionally,we propose an effective defense method,namely CLBL(Cluster Layer By Layer).Experiment results on three datasets have confirmed CLBL’s effectiveness.展开更多
基金funded by the Deanship of Scientific Research at Northern Border University,Arar,Saudi Arabia through research group No.(RG-NBU-2022-1234).
文摘Transportation systems are experiencing a significant transformation due to the integration of advanced technologies, including artificial intelligence and machine learning. In the context of intelligent transportation systems (ITS) and Advanced Driver Assistance Systems (ADAS), the development of efficient and reliable traffic light detection mechanisms is crucial for enhancing road safety and traffic management. This paper presents an optimized convolutional neural network (CNN) framework designed to detect traffic lights in real-time within complex urban environments. Leveraging multi-scale pyramid feature maps, the proposed model addresses key challenges such as the detection of small, occluded, and low-resolution traffic lights amidst complex backgrounds. The integration of dilated convolutions, Region of Interest (ROI) alignment, and Soft Non-Maximum Suppression (Soft-NMS) further improves detection accuracy and reduces false positives. By optimizing computational efficiency and parameter complexity, the framework is designed to operate seamlessly on embedded systems, ensuring robust performance in real-world applications. Extensive experiments using real-world datasets demonstrate that our model significantly outperforms existing methods, providing a scalable solution for ITS and ADAS applications. This research contributes to the advancement of Artificial Intelligence-driven (AI-driven) pattern recognition in transportation systems and offers a mathematical approach to improving efficiency and safety in logistics and transportation networks.
文摘The Intelligent Transportation System(ITS),as a vital means to alleviate traffic congestion and reduce traffic accidents,demonstrates immense potential in improving traffic safety and efficiency through the integration of Internet of Things(IoT)technologies.The enhancement of its performance largely depends on breakthrough advancements in object detection technology.However,current object detection technology still faces numerous challenges,such as accuracy,robustness,and data privacy issues.These challenges are particularly critical in the application of ITS and require in-depth analysis and exploration of future improvement directions.This study provides a comprehensive review of the development of object detection technology and analyzes its specific applications in ITS,aiming to thoroughly explore the use and advancement of object detection technologies in IoT-based intelligent transportation systems.To achieve this objective,we adopted the Preferred Reporting Items for Systematic Reviews and Meta-Analyses(PRISMA)approach to search,screen,and assess the eligibility of relevant literature,ultimately including 88 studies.Through an analysis of these studies,we summarized the characteristics,advantages,and limitations of object detection technology across the traditional methods stage and the deep learning-based methods stage.Additionally,we examined its applications in ITS from three perspectives:vehicle detection,pedestrian detection,and traffic sign detection.We also identified the major challenges currently faced by these technologies and proposed future directions for addressing these issues.This review offers researchers a comprehensive perspective,identifying potential improvement directions for object detection technology in ITS,including accuracy,robustness,real-time performance,data annotation cost,and data privacy.In doing so,it provides significant guidance for the further development of IoT-based intelligent transportation systems.
基金supported by the Deanship of Scientific Research and Graduate Studies at King Khalid University under research grant number(R.G.P.2/93/45).
文摘Thedeployment of the Internet of Things(IoT)with smart sensors has facilitated the emergence of fog computing as an important technology for delivering services to smart environments such as campuses,smart cities,and smart transportation systems.Fog computing tackles a range of challenges,including processing,storage,bandwidth,latency,and reliability,by locally distributing secure information through end nodes.Consisting of endpoints,fog nodes,and back-end cloud infrastructure,it provides advanced capabilities beyond traditional cloud computing.In smart environments,particularly within smart city transportation systems,the abundance of devices and nodes poses significant challenges related to power consumption and system reliability.To address the challenges of latency,energy consumption,and fault tolerance in these environments,this paper proposes a latency-aware,faulttolerant framework for resource scheduling and data management,referred to as the FORD framework,for smart cities in fog environments.This framework is designed to meet the demands of time-sensitive applications,such as those in smart transportation systems.The FORD framework incorporates latency-aware resource scheduling to optimize task execution in smart city environments,leveraging resources from both fog and cloud environments.Through simulation-based executions,tasks are allocated to the nearest available nodes with minimum latency.In the event of execution failure,a fault-tolerantmechanism is employed to ensure the successful completion of tasks.Upon successful execution,data is efficiently stored in the cloud data center,ensuring data integrity and reliability within the smart city ecosystem.
基金The Ph.D.Programs Foundation of Ministry of Education of China(No.20060286005)China Postdoctoral Science Foundation(No.20070411018)
文摘The metabolic evolution model of transportation demand for comprehensive transportation systems is put forward on the basis of a metabolic theory of ecology. In the model, the growth rates or changing rates of transportation volumes for the various transportation modes of a city are determined not only by the GDP per capita which reflects the size of the city itself, but also by the relationship of competition and cooperation among transportation modes. The results of empirical analysis for Chinese cities show that the allometric growth exponent in the equation for the variation rate of passenger demand volume on rail is greater than the predicted value of 1/4 in metabolic ecology, whereas the allometric growth relationship is not so evident in the equation for the variation rate of passenger demand volume on road. The changing rate of road transportation is thus mainly affected by the relationship of competition and cooperation among transportation modes for Chinese cities.
基金supported by the National Research Council of Thailand(No.NRCT-5-RSA63004-16)Chiang Mai University.S.Pakluea acknowledges scholarship support from the Science Achievement Scholarship of Thailand(SAST).
文摘Terahertz(THz)radiation possesses unique properties that make it a promising light source for applications in various fields,particularly spectroscopy and imaging.Ongoing research and development in THz technology has focused on developing or improving THz sources,detectors,and applications.At the PBP-CMU Electron Linac Laboratory(PCELL)of the Plasma and Beam Physics Research Facility in Chiang Mai University,high-intensity THz radiation has been generated in the form of coherent transition radiation(TR)and investigated since 2006 for electron beams with energies ranging from 8 to 12 MeV.In this study,we investigate and optimize the coherent TR arising from short electron bunches with energies ranging from 8 to 22 MeV using an upgraded linear-accelerator system with a higher radio-frequency(RF)power system.This radiation is then transported from the accelerator hall to the experimental room,in which the spectrometers are located.Electron-beam simulations are conducted to achieve short bunch lengths and small transverse beam sizes at the TR station.Radiation properties,including the radiation spectrum,angular distribution,and radiation polarization,are thoroughly investigated.The electron-bunch length is evaluated using the measuring system.The radiation-transport line is designed to achieve optimal frequency response and high transmission efficiency.A radiation-transmission efficiency of approximately 80-90%can be achieved with this designed system,along with a pulse energy ranging from 0.17 to 0.25μJ.The expected radiation spectral range covers up to 2 THz with a peak power of 0.5-1.25 MW.This coherent,broadband,and intense THz radiation will serve as a light source for THz spectroscopy and THz time-domain spectroscopy applications at the PCELL in the near future.
基金Projects(50735007,51005253) supported by the National Natural Science Foundation of ChinaProject(2007AA04Z344) supported by the National High-Tech Research and Development Program of China
文摘A novel maglev transportation system was proposed for large travel range ultra precision motion.The system consists of a levitation subsystem and a propulsion subsystem.During the propulsion subsystem driving the moving platform along the guideway,the levitation subsystem uses six pairs of electromagnets to steadily suspend the moving platform over the guideway.The model of the levitation system,which is a typical nonlinear multi-input multi-output coupling system and has many inner nonlinear coupling characteristics,was deduced.For testifying the model,the levitation mechanism was firstly controlled by proportional-integral-differential(PID) control,and then a lot of input-output data were collected for model parameter identification.The least-square parameter identification method was used.The identification results prove that the model is feasible and suitable for the real system.
基金Project(50574100)supported by the National Natural Science Foundation of China
文摘Based on characteristics of deep sea flexible mining system,a new pump-lockage ore transportation system was designed.According to Bernoulli equation and two-phase hydrodynamics theory,parameters of the new system were obtained and four ore transportation systems were analyzed.The results indicate that the pump head of 1 000 m mining system is 100-150 m and that of 5 000 m mining system is 660-750 m.In addition,based on similarity theory,a model of the new transportation system was made,which can simulate more than 5 000 m actual ore transportation system.So both theory and experiment prove that the new pump-lockage ore transportation system is an ideal design for deep sea flexible mining system.
基金This work was partially supported by The China’s National Key R&D Program(No.2018YFB0803600)Natural Science Foundation of China(No.61801008)+2 种基金Beijing Natural Science Foundation National(No.L172049)Scientific Research Common Program of Beijing Municipal Commission of Education(No.KM201910005025)Defense Industrial Technology Development Program(No.JCKY2016204A102)sponsored this research in parts.
文摘Security threats to smart and autonomous vehicles cause potential consequences such as traffic accidents,economically damaging traffic jams,hijacking,motivating to wrong routes,and financial losses for businesses and governments.Smart and autonomous vehicles are connected wirelessly,which are more attracted for attackers due to the open nature of wireless communication.One of the problems is the rogue attack,in which the attacker pretends to be a legitimate user or access point by utilizing fake identity.To figure out the problem of a rogue attack,we propose a reinforcement learning algorithm to identify rogue nodes by exploiting the channel state information of the communication link.We consider the communication link between vehicle-to-vehicle,and vehicle-to-infrastructure.We evaluate the performance of our proposed technique by measuring the rogue attack probability,false alarm rate(FAR),mis-detection rate(MDR),and utility function of a receiver based on the test threshold values of reinforcement learning algorithm.The results show that the FAR and MDR are decreased significantly by selecting an appropriate threshold value in order to improve the receiver’s utility.
基金supported by Bill and Melinda Gabes Foundation Project(51914)
文摘Sputum transportation from county-level to prefecture-level is an ideal strategy to cover the shortage of the laboratory capability in the resource-poor setting. Here, we firstly evaluated the feasibility of sputum transportation system in China by analyzing the culture and molecular diagnosis results from 1982 smear-positive patients with different delay in processing for culture.
基金This work was financially supported by the National Natural Science Foundation of China(52074089 and 52104064)Natural Science Foundation of Heilongjiang Province of China(LH2019E019).
文摘As the main link of ground engineering,crude oil gathering and transportation systems require huge energy consumption and complex structures.It is necessary to establish an energy efficiency evaluation system for crude oil gathering and transportation systems and identify the energy efficiency gaps.In this paper,the energy efficiency evaluation system of the crude oil gathering and transportation system in an oilfield in western China is established.Combined with the big data analysis method,the GA-BP neural network is used to establish the energy efficiency index prediction model for crude oil gathering and transportation systems.The comprehensive energy consumption,gas consumption,power consumption,energy utilization rate,heat utilization rate,and power utilization rate of crude oil gathering and transportation systems are predicted.Considering the efficiency and unit consumption index of the crude oil gathering and transportation system,the energy efficiency evaluation system of the crude oil gathering and transportation system is established based on a game theory combined weighting method and TOPSIS evaluation method,and the subjective weight is determined by the triangular fuzzy analytic hierarchy process.The entropy weight method determines the objective weight,and the combined weight of game theory combines subjectivity with objectivity to comprehensively evaluate the comprehensive energy efficiency of crude oil gathering and transportation systems and their subsystems.Finally,the weak links in energy utilization are identified,and energy conservation and consumption reduction are improved.The above research provides technical support for the green,efficient and intelligent development of crude oil gathering and transportation systems.
基金Supported by National Key Research and Development Program of China(Grant No.2021YFB1600402)National Natural Science Foundation of China(Grant No.52072212)+1 种基金Dongfeng USharing Technology Co.,Ltd.,China Intelli‑gent and Connected Vehicles(Beijing)Research Institute Co.,Ltd.“Shuimu Tsinghua Scholarship”of Tsinghua University of China.
文摘Environment perception is one of the most critical technology of intelligent transportation systems(ITS).Motion interaction between multiple vehicles in ITS makes it important to perform multi-object tracking(MOT).However,most existing MOT algorithms follow the tracking-by-detection framework,which separates detection and tracking into two independent segments and limit the global efciency.Recently,a few algorithms have combined feature extraction into one network;however,the tracking portion continues to rely on data association,and requires com‑plex post-processing for life cycle management.Those methods do not combine detection and tracking efciently.This paper presents a novel network to realize joint multi-object detection and tracking in an end-to-end manner for ITS,named as global correlation network(GCNet).Unlike most object detection methods,GCNet introduces a global correlation layer for regression of absolute size and coordinates of bounding boxes,instead of ofsetting predictions.The pipeline of detection and tracking in GCNet is conceptually simple,and does not require compli‑cated tracking strategies such as non-maximum suppression and data association.GCNet was evaluated on a multivehicle tracking dataset,UA-DETRAC,demonstrating promising performance compared to state-of-the-art detectors and trackers.
文摘Cooperative Intelligent Transport System(C-ITS)plays a vital role in the future road traffic management system.A vital element of C-ITS comprises vehicles,road side units,and traffic command centers,which produce a massive quantity of data comprising both mobility and service-related data.For the extraction of meaningful and related details out of the generated data,data science acts as an essential part of the upcoming C-ITS applications.At the same time,prediction of short-term traffic flow is highly essential to manage the traffic accurately.Due to the rapid increase in the amount of traffic data,deep learning(DL)models are widely employed,which uses a non-parametric approach for dealing with traffic flow forecasting.This paper focuses on the design of intelligent deep learning based short-termtraffic flow prediction(IDL-STFLP)model for C-ITS that assists the people in various ways,namely optimization of signal timing by traffic signal controllers,travelers being able to adapt and alter their routes,and so on.The presented IDLSTFLP model operates on two main stages namely vehicle counting and traffic flow prediction.The IDL-STFLP model employs the Fully Convolutional Redundant Counting(FCRC)based vehicle count process.In addition,deep belief network(DBN)model is applied for the prediction of short-term traffic flow.To further improve the performance of the DBN in traffic flow prediction,it will be optimized by Quantum-behaved bat algorithm(QBA)which optimizes the tunable parameters of DBN.Experimental results based on benchmark dataset show that the presented method can count vehicles and predict traffic flowin real-time with amaximumperformance under dissimilar environmental situations.
基金The work of Vinay Chamola and F.Richard Yu was supported in part by the SICI SICRG Grant through the Project Artificial Intelligence Enabled Security Provisioning and Vehicular Vision Innovations for Autonomous Vehicles,and in part by the Government of Canada's National Crime Prevention Strategy and Natural Sciences and Engineering Research Council of Canada(NSERC)CREATE Program for Building Trust in Connected and Autonomous Vehicles(TrustCAV).
文摘With the rise of the Internet of Vehicles(IoV)and the number of connected vehicles increasing on the roads,Cooperative Intelligent Transportation Systems(C-ITSs)have become an important area of research.As the number of Vehicle to Vehicle(V2V)and Vehicle to Interface(V2I)communication links increases,the amount of data received and processed in the network also increases.In addition,networking interfaces need to be made more secure for which existing cryptography-based security schemes may not be sufficient.Thus,there is a need to augment them with intelligent network intrusion detection techniques.Some machine learning-based intrusion detection and anomaly detection techniques for vehicular networks have been proposed in recent times.However,given the expected large network size,there is a necessity for extensive data processing for use in such anomaly detection methods.Deep learning solutions are lucrative options as they remove the necessity for feature selection.Therefore,with the amount of vehicular network traffic increasing at an unprecedented rate in the C-ITS scenario,the need for deep learning-based techniques is all the more heightened.This work presents three deep learning-based misbehavior classification schemes for intrusion detection in IoV networks using Long Short Term Memory(LSTM)and Convolutional Neural Networks(CNNs).The proposed Deep Learning Classification Engines(DCLE)comprise of single or multi-step classification done by deep learning models that are deployed on the vehicular edge servers.Vehicular data received by the Road Side Units(RSUs)is pre-processed and forwarded to the edge server for classifications following the three classification schemes proposed in this paper.The proposed classifiers identify 18 different vehicular behavior types,the F1-scores ranging from 95.58%to 96.75%,much higher than the existing works.By running the classifiers on testbeds emulating edge servers,the prediction performance and prediction time comparison of the proposed scheme is compared with those of the existing studies.
文摘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.
基金the National Natural Science Foundation of China(51808004)Beijing Municipal Education Commission Basic Research Plan Project(110052971803/041).
文摘The PRT (Personal Rapid Transit) refers to a traffic system in which small vehicles automatically travel on a dedicated rail network or road network.It is a branch of monorail traffic and dedicated road traffic.It can change the situation of high energy consumption of traditional mountain transportation.It can reduce the amount of machinery used in the construction process to reduce carbon emissions.It is completely powered by electricity and reduces the friction of the cableway to reduce energy consumption.Its construction process uses small amount of traditional building materials such as concrete and steel.It has little damage to the ecological environment of the mountain,and can not damage the carbon sequestration ability of the plant community.It could serve as a means of transporting goods over long distance,reducing the need for big trucks and thus reducing the consumption of fossil fuels.
基金supports of Yuheng Zhang,Yifang Zheng,Yuke Zhou,Qiuyuan Ai,Xinjiang Chen,Jiayi Han,Ruiyang Jin,Yihang Jin,Jingyue Sang,Kaiyu Xie,Zhengrun Wu,Yuwen Zheng to this work.This work was supported in part by the National Natural Science Foundation of China under Grants 72271008,72131001,and 52277092.
文摘Digitalization and decarbonization are projected to be two major trends in the coming decades.As the already widespread process of digitalization continues to progress,especially in energy and transportation systems,massive data will be produced,and how these data could support and promote decarbonization has become a pressing concern.This paper presents a comprehensive review of digital technologies and their potential applications in low-carbon energy and transportation systems from the perspectives of infrastructure,common mechanisms and algorithms,and system-level impacts,as well as the application of digital technologies to coupled energy and transportation systems with electric vehicles.This paper also identifies corresponding challenges and future research directions,such as in the field of blockchain,digital twin,vehicle-to-grid,low-carbon computing,and data security and pri-vacy,especially in the context of integrated energy and transportation systems.
文摘This paper presents a design scheme of intelligent transportation system based on the Internet of things. First, the paper elaborated the related technical and functional demand of intelligent traffic system, designed the gateway level model and the overall project. Then, we design gateway hardware circuit according to the overall plan, and design the gateway application software according to the functional requirements. Through the experiment and simulation results show that, the intelligent transportation system gateway based on Internet of things is ability to create ZigBee network through the way of wireless access, GPRS network, Ethernet access based on the wired way, to realizes multimode access, multi-protocol conversion gateway, ad hoc network functions.
基金partially supported by National Natural Science Foundation of China(Grant No.62088101)supported by the Australian Government through the Australian Research Coun-cil’s Discovery Early Career Researcher Award(DE240100207).
文摘The swift recuperation of communities following natural hazards heavily relies on the efficiency of transporta-tion systems,facilitating the timely delivery of vital resources and manpower to reconstruction sites.This paper delves into the pivotal role of transportation systems in aiding the recovery of built environments,proposing an evaluative metric that correlates transportation capacity with the speed of post-earthquake recovery.Focusing on optimizing urban population capacity in the aftermath of earthquakes,the study comprehensively examines the impact of pre-earthquake measures such as enhancing building or bridge seismic performance on post-earthquake urban population capacity.The methodology is demonstrated through an analysis of Beijing’s transportation sys-tem,elucidating how enhancements to transportation infrastructure fortify the resilience of built environments.Additionally,the concept of a resource supply rate is introduced to gauge the level of logistical support available after an earthquake.This rate tends to decrease when transportation damage is significant or when the demands for repairs overwhelm available resources,indicating a need for retrofitting.Through sensitivity analysis,this study explores how investments in the built environment or logistical systems can increase the resource supply rate,thereby contributing to more resilient urban areas in the face of seismic challenges.
基金supported by Systematic Major Project of China State Railway Group Corporation Limited(Grant Number:P2023W002).
文摘The development of Intelligent Railway Transportation Systems necessitates incorporating privacy-preserving mechanisms into AI models to protect sensitive information and enhance system efficiency.Federated learning offers a promising solution by allowing multiple clients to train models collaboratively without sharing private data.However,despite its privacy benefits,federated learning systems are vulnerable to poisoning attacks,where adversaries alter local model parameters on compromised clients and send malicious updates to the server,potentially compromising the global model’s accuracy.In this study,we introduce PMM(Perturbation coefficient Multiplied by Maximum value),a new poisoning attack method that perturbs model updates layer by layer,demonstrating the threat of poisoning attacks faced by federated learning.Extensive experiments across three distinct datasets have demonstrated PMM’s ability to significantly reduce the global model’s accuracy.Additionally,we propose an effective defense method,namely CLBL(Cluster Layer By Layer).Experiment results on three datasets have confirmed CLBL’s effectiveness.
