Research on a TOPSIS energy efficiency evaluation system for crude oil gathering and transportation systems based on a GA-BP neural network

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.

Evaluating the role of transportation system in community seismic resilience

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.

Privacy-Preserving Large-Scale AI Models for Intelligent Railway Transportation Systems:Hierarchical Poisoning Attacks and Defenses in Federated Learning

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.

Existing Case of Transportation System in Turkey and Transportation Trends in Istanbul

Transportation sector is one of the most important elements of a country’s economy with its highway,railway,airway and seaway modes,besides the information and communication infrastructure.Transportation sector has a pattern that affects the society continuously with its economic and social inputs that has a significant role in economies of countries in terms of being an important part of manufacturing process and effects of sizable investments on economy.Demands of more comfortable,more reliable,more safe and more punctual transport in developing economy is an arising trend worldwide and this shows an increase the importance of the transportation sector.Establishment of an efficient and functional transportation system is closely related with traffic safety,intermodal integration and balanced modal distribution.In Turkey,an important improvement has been achieved in these issues,but also some basic constitutive problems are still continuing.These constitutional problems can be summarized as providing traffic safety,integration of innovative implementations to transportation system,enhancing of infrastructure and an effective usage of existing infrastructure.

A Study on Optimizing the Double-Spine Type Flow Path Design for the Overhead Transportation System Using Tabu Search Algorithm

Optimizing Flow Path Design(FPD)is a popular research area in transportation system design,but its application to Overhead Transportation Systems(OTSs)has been limited.This study focuses on optimizing a double-spine flow path design for OTSs with 10 stations by minimizing the total travel distance for both loaded and empty flows.We employ transportation methods,specifically the North-West Corner and Stepping-Stone methods,to determine empty vehicle travel flows.Additionally,the Tabu Search(TS)algorithm is applied to branch the 10 stations into two main layout branches.The results obtained from our proposed method demonstrate a reduction in the objective function value compared to the initial feasible solution.Furthermore,we explore howchanges in the parameters of the TS algorithm affect the optimal result.We validate the feasibility of our approach by comparing it with relevant literature and conducting additional tests on layouts with 20 and 30 stations.

Existing Case of Transportation System in Turkey and Transportation Trends in Istanbul

Transportation sector is one of the most important elements of a country’s economy with its highway,railway,airway and seaway modes,besides the information and communication infrastructure.Transportation sector has a pattern that affects the society continuously with its economic and social inputs that has a significant role in economies of countries in terms of being an important part of manufacturing process and effects of sizable investments on economy.Demands of more comfortable,more reliable,more safe and more punctual transport in developing economy is an arising trend worldwide and this shows an increase the importance of the transportation sector.Establishment of an efficient and functional transportation system is closely related with traffic safety,intermodal integration and balanced modal distribution.In Turkey,an important improvement has been achieved in these issues,but also some basic constitutive problems are still continuing.These constitutional problems can be summarized as providing traffic safety,integration of innovative implementations to transportation system,enhancing of infrastructure and an effective usage of existing infrastructure.

Research on the Interactive Impact of Transportation Systems on Socio-Economic Development

This paper aims to explore the interactive impact between transportation systems and socio-economic development,employing Structural Equation Modeling(SEM)to analyze data from 31 provincial-level administrative regions in China from 2013 to 2022.It comprehensively considers key indicators from the economic,social,and transportation sectors.The paper constructs a model encompassing 5 latent variables and 15 observed variables.Through in-depth analysis,it reveals the promoting role of transportation systems on economic growth and social development,as well as the demand for transportation system construction and optimization driven by socio-economic development levels.The results indicate that an efficient transportation system can not only directly drive economic growth but also indirectly promote social development by improving social welfare and enhancing quality of life.This paper provides new insights into understanding the complex relationship between transportation systems and socio-economic development and holds significant implications for policymakers in optimizing transportation infrastructure to foster economic and social development.

Building trust for traffic flow forecasting components in intelligent transportation systems via interpretable ensemble learning

Traffic flow forecasting constitutes a crucial component of intelligent transportation systems(ITSs).Numerous studies have been conducted for traffic flow forecasting during the past decades.However,most existing studies have concentrated on developing advanced algorithms or models to attain state-of-the-art forecasting accuracy.For real-world ITS applications,the interpretability of the developed models is extremely important but has largely been ignored.This study presents an interpretable traffic flow forecasting framework based on popular tree-ensemble algorithms.The framework comprises multiple key components integrated into a highly flexible and customizable multi-stage pipeline,enabling the seamless incorporation of various algorithms and tools.To evaluate the effectiveness of the framework,the developed tree-ensemble models and another three typical categories of baseline models,including statistical time series,shallow learning,and deep learning,were compared on three datasets collected from different types of roads(i.e.,arterial,expressway,and freeway).Further,the study delves into an in-depth interpretability analysis of the most competitive tree-ensemble models using six categories of interpretable machine learning methods.Experimental results highlight the potential of the proposed framework.The tree-ensemble models developed within this framework achieve competitive accuracy while maintaining high inference efficiency similar to statistical time series and shallow learning models.Meanwhile,these tree-ensemble models offer interpretability from multiple perspectives via interpretable machine-learning techniques.The proposed framework is anticipated to provide reliable and trustworthy decision support across various ITS applications.

Evolution mechanism of demand for comprehensive transportation system based on metabolic ecology

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.

Review of Key Technologies and Applications in Intelligent Transportation Systems

The development of Intelligent Transportation Systems(ITS)is closely intertwined with the growth of every city,serving as a critical component of smart city construction.This paper provides a concise overview of the concept and overall framework of smart transportation.It emphasizes the application of key technologies,including Traffic Element Identification and Perception,data mining,and Smart Transportation System Integration Technology,in the field.Furthermore,the paper elucidates the current practical applications of smart transportation,showcasing its advancements and implementations in real-world scenarios.

Optimizing a Transportation System Using Metaheuristics Approaches (EGD/GA/ACO): A Forest Vehicle Routing Case Study

The large-scale optimization problem requires some optimization techniques, and the Metaheuristics approach is highly useful for solving difficult optimization problems in practice. The purpose of the research is to optimize the transportation system with the help of this approach. We selected forest vehicle routing data as the case study to minimize the total cost and the distance of the forest transportation system. Matlab software helps us find the best solution for this case by applying three algorithms of Metaheuristics: Genetic Algorithm (GA), Ant Colony Optimization (ACO), and Extended Great Deluge (EGD). The results show that GA, compared to ACO and EGD, provides the best solution for the cost and the length of our case study. EGD is the second preferred approach, and ACO offers the last solution.

Levitation mechanism modelling for maglev transportation system

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.

Pump-lockage ore transportation system for deep sea flexible mining system

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.

Authentication of Vehicles and Road Side Units in Intelligent Transportation System

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.

The Feasibility of Sputum Transportation System in China: Effect of Sputum Storage on the Mycobacterial Detection

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.

An Optimal Deep Learning for Cooperative Intelligent Transportation System

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.

Research on Intelligent Transportation System and Its Key Technology based on IOT

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.

Intelligent Framework for Secure Transportation Systems Using Software-Defined-Internet of Vehicles

The Internet of Things plays a predominant role in automating all real-time applications.One such application is the Internet of Vehicles which monitors the roadside traffic for automating traffic rules.As vehicles are connected to the internet through wireless communication technologies,the Internet of Vehicles network infrastructure is susceptible to flooding attacks.Reconfiguring the network infrastructure is difficult as network customization is not possible.As Software Defined Network provide a flexible programming environment for network customization,detecting flooding attacks on the Internet of Vehicles is integrated on top of it.The basic methodology used is crypto-fuzzy rules,in which cryptographic standard is incorporated in the traditional fuzzy rules.In this research work,an intelligent framework for secure transportation is proposed with the basic ideas of security attacks on the Internet of Vehicles integrated with software-defined networking.The intelligent framework is proposed to apply for the smart city application.The proposed cognitive framework is integrated with traditional fuzzy,cryptofuzzy and Restricted Boltzmann Machine algorithm to detect malicious traffic flows in Software-Defined-Internet of Vehicles.It is inferred from the result interpretations that an intelligent framework for secure transportation system achieves better attack detection accuracy with less delay and also prevents buffer overflow attacks.The proposed intelligent framework for secure transportation system is not compared with existing methods;instead,it is tested with crypto and machine learning algorithms.

End-to-End Joint Multi-Object Detection and Tracking for Intelligent Transportation Systems

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.