A Nationwide Evaluation of the State of Practice of Performance Measurements for Intelligent Transportation Systems

State departments of transportation’s (DOTs) decisions to invest resources to expand or implement intelligent transportation systems (ITS) programs or even retire existing infrastructure need to be based on performance evaluations. Nonetheless, an apparent gap exists between the need for ITS performance measurements and the actual implementation. The evidence available points to challenges in the ITS performance measurement processes. This paper evaluated the state of practice of performance measurement for ITS across the US and provided insights. A comprehensive literature review assessed the use of performance measures by DOTs for monitoring implemented ITS programs. Based on the gaps identified through the literature review, a nationwide qualitative survey was used to gather insights from key stakeholders on the subject matter and presented in this paper. From the data gathered, performance measurement of ITS is fairly integrated into ITS programs by DOTs, with most agencies considering the process beneficial. There, however, exist reasons that prevent agencies from measuring ITS performance to greater detail and quality. These include lack of data, fragmented or incomparable data formats, the complexity of the endeavor, lack of data scientists, and difficulty assigning responsibilities when inter-agency collaboration is required. Additionally, DOTs do not benchmark or compare their ITS performance with others for reasons that include lack of data, lack of guidance or best practices, and incomparable data formats. This paper is relevant as it provides insights expected to guide DOTs and other agencies in developing or reevaluating their ITS performance measurement processes.

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.

A deep learning based misbehavior classification scheme for intrusion detection in cooperative intelligent transportation systems

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.

Providing Performance Evaluation Indicators for Intelligent Transportation Systems (The Case Study of Tehran-Karaj Freeway Located in Iran)

Intelligent Transportation Systems (ITS) play a fundamental role in reducing traffic congestion and increasing safety during daily transportation. These systems can also be useful in improving social welfare leading to general satisfaction. Proper performance evaluation can be efficient in improving the performance of these systems, and providing a scientific assessment index system can assist decision-makers in smart communities to plan for the development of ITS. However, the evaluation of these systems requires identifying appropriate indicators of performance evaluation that are consistent with the views of the beneficiaries of these systems. In this paper, performance evaluation indicators of ITS have been identified, and three indicators entitled “environmental and safety”, “assistance in reducing traffic congestion” and “attractive public transport” are presented to evaluate the performance of these systems. Moreover, the intelligent transport systems of the Tehran-Karaj Freeway in Iran are studied, and inferential statistical methods are employed to test the research hypotheses. It is worth noticing that in this study, a one-sample T-test method is used for hypotheses assessment and the SPSS software was used to analyze the findings. Also, the results demonstrated that the performance of ITS in the Tehran-Karaj Freeway regarding the indicators, such as “Declaration of route blocking information due to maintenance or reconstruction” and “Declaration of path geometry conditions” has not been acceptable.

Internet of Things Based Solutions for Transport Network Vulnerability Assessment in Intelligent Transportation Systems

Intelligent Transportation System(ITS)is essential for effective identification of vulnerable units in the transport network and its stable operation.Also,it is necessary to establish an urban transport network vulnerability assessment model with solutions based on Internet of Things(IoT).Previous research on vulnerability has no congestion effect on the peak time of urban road network.The cascading failure of links or nodes is presented by IoT monitoring system,which can collect data from a wireless sensor network in the transport environment.The IoT monitoring system collects wireless data via Vehicle-to-Infrastructure(V2I)channels to simulate key segments and their failure probability.Finally,the topological structure vulnerability index and the traffic function vulnerability index of road network are extracted from the vulnerability factors.The two indices are standardized by calculating the relative change rate,and the comprehensive index of the consequence after road network unit is in a failure state.Therefore,by calculating the failure probability of road network unit and comprehensive index of road network unit in failure state,the comprehensive vulnerability of road network can be evaluated by a risk calculation formula.In short,the IoT-based solutions to the new vulnerability assessment can help road network planning and traffic management departments to achieve the ITS goals.

A Review on Mobile and Sensor Networks Innovations in Intelligent Transportation Systems

Rapid developments of mobile technologies, data acquisition and big data analytics, and their integration with critical application domains such as transportation systems have the potential to produce more efficient, real-time, intelligent and safe transportation infrastructure. To increase the quality of transportation services, wireless sensor networks, mobile phones, crowd sourcing, RFID and Bluetooth technologies are being used. We surveyed innovations that were transformed from ideas in research labs into commercial systems in practical use. In this paper, we present some innovative mobile technologies, services and platforms that are being used in modern transportation applications including traffic data acquisition, traffic management and control, route optimizations, infrastructure redesign, road safety and enhancing user experience.

Analyze the Impact of ITS in Improving the Efficiency of Road Tax and Fee Collection Use of Digital Technologies in the City of Bujumbura

Road transport is currently one of the most important sectors affecting sustainable development and the improvement of the population’s standard of living. In some sub-Saharan African countries, including Burundi, the transport structure is vulnerable, under attack, or even damaged or destroyed. This is prompting decision-makers to look for every possible way to enable dynamic management of the road system, as well as the collection of tax revenues attributable to this sector. To reach this stage, we postulate that the introduction of the Intelligent Transport System (ITS) into the road tax and fee collection process would make a significant contribution (road safety, zero cash on silk Safety Officers, payment of a fine, eradication of road corruption etc.) to the digitization of the various transport sectors. As far as the city of Bujumbura is concerned (our field of intervention), the applicability of the present System could thus meet the expectations of the decision-maker, certain drivers and, by the same token, contribute to the promotion of Digital Technology in Burundi.

Cyber-physical-social System in Intelligent Transportation

A cyber-physical system (CPS) is composed of a physical system and its corresponding cyber systems that are tightly fused at all scales and levels. CPS is helpful to improve the controllability, efficiency and reliability of a physical system, such as vehicle collision avoidance and zero-net energy buildings systems. It has become a hot R&D and practical area from US to EU and other countries. In fact, most of physical systems and their cyber systems are designed, built and used by human beings in the social and natural environments. So, social systems must be of the same importance as their CPSs. The indivisible cyber, physical and social parts constitute the cyber-physical-social system (CPSS), a typical complex system and it's a challengeable problem to control and manage it under traditional theories and methods. An artificial systems, computational experiments and parallel execution (ACP) methodology is introduced based on which data-driven models are applied to social system. Artificial systems, i.e., cyber systems, are applied for the equivalent description of physical-social system (PSS). Computational experiments are applied for control plan validation. And parallel execution finally realizes the stepwise control and management of CPSS. Finally, a CPSS-based intelligent transportation system (ITS) is discussed as a case study, and its architecture, three parts, and application are described in detail. © 2014 Chinese Association of Automation.

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 of Intelligent Transportation System Based on the Internet of Things Frame

According to city public transit problem characteristic, the main body of a paper has been submitted and has worked out one kind of based on the Internet of things frame Intelligent transportation system. That system collects data by vehicle terminal and uploads data to the server through the network and makes data visible to the consumer passing an algorithm in the server. One aspect, the consumer may inquire about public transit vehicle information by Web. On another aspect, the consumer can know public transit vehicle information by station terminal. The experiments have tested that the Intelligent transportation system can offer public transit vehicle information to many consumers with convenient way thereby this system can solve the city mass transit problem.

Establishment and application of performance evaluation model for collection and transportation system of municipal solid waste

On the basis of analyzing the typical waste collection and transportation mode,the evaluation index system for performance of the waste collection and transportation system was proposed with three grades,which related to six factors,such as economic evaluation,high efficient evaluation,environmental impact assessment,resource evaluation,evaluation of security and emergency,evaluation of management and society. With the performance evaluation theory,the performance evaluation model of waste collection and transportation system was constructed,which quantified the grading standard of index and determined the index weight in analytic hierarchy process (AHP). After evaluating the waste collection and transportation system of the main districts of Chongqing city,the results show that the it has an excellent performance evaluation grade with very high performance level of three indices involving evaluation of management and society,environmental impact assessment,evaluation of security and emergency and quite low performance level of two indices that include high efficient evaluation and economic evaluation.

Analyzing the Impact of Blockchain Models for Securing Intelligent Logistics through Unified Computational Techniques

Blockchain technology has revolutionized conventional trade.The success of blockchain can be attributed to its distributed ledger characteristic,which secures every record inside the ledger using cryptography rules,making it more reliable,secure,and tamper-proof.This is evident by the significant impact that the use of this technology has had on people connected to digital spaces in the present-day context.Furthermore,it has been proven that blockchain technology is evolving from new perspectives and that it provides an effective mechanism for the intelligent transportation system infrastructure.To realize the full potential of the accurate and efficacious use of blockchain in the transportation sector,it is essential to understand the most effective mechanisms of this technology and identify the most useful one.As a result,the present work offers a priority-based methodology that would be a useful reference for security experts in managing blockchain technology and its models.The study uses the hesitant fuzzy analytical hierarchy process for prioritizing the different blockchain models.Based on the findings of actual performance,alternative solution A1 which is Private Blockchain model has an extremely high level of security satisfaction.The accuracy of the results has been tested using the hesitant fuzzy technique for order of preference by similarity to the ideal solution procedure.The study also uses guidelines from security researchers working in this domain.

Abusive adversarial agents and attack strategies in cyber-physical systems

The exponential increase in IoT device usage has spawned numerous cyberspace innovations.IoT devices,sensors,and actuators bridge the gap between physical processes and the cyber network in a cyber-physical system(CPS).Cyber-physical system is a complex system from a security perspective due to the heterogeneous nature of its components and the fact that IoT devices can serve as an entry point for cyberattacks.Most adversaries design their attack strategies on systems to gain an advantage at a relatively lower cost,whereas abusive adversaries initiate an attack to inflict maximum damage without regard to cost or reward.In this paper,a sensor spoofing attack is modelled as a malicious adversary attempting to cause system failure by interfering with the feedback control mechanism.It is accomplished by feeding spoofed sensor values to the controller and issuing erroneous commands to the actuator.Experiments on a Simulink-simulated linear CPS support the proof of concept for the proposed abusive ideology,demonstrating three attack strategies.The impact of the evaluations stresses the importance of testing the CPS security against adversaries with abusive settings for preventing cyber-vandalism.Finally,the research concludes by highlighting the limitations of the proposed work,followed by recommendations for the future.

基于三级编码的智能城市网络安全风险评估体系构建研究

[研究目的]城市的智能化发展为城市建设带来了多元的网络安全风险,这些风险已经成为城市发展中的极大隐患,为有效应对该问题,构建了智能城市网络安全风险评估体系。[研究方法]首先基于计量分析对国内外近十年的相关研究进行整理和可视化处理,构建用于评价指标提取的数据集;然后利用扎根理论中的三级编码方法提取智能城市安全风险评估中不同级别的评价指标,并进行编码;最后采用量化分析对指标进行合理性检验和修正,并根据HP-ANP模型进行指标赋权,完成智能城市网络安全风险评估体系的构建。[研究结论]通过对评估体系中影响智能城市网络安全风险要素的关联和重要性分析,提出了智能城市安全建设的对策建议,助力智能城市规避网络安全风险。

Intelligent Parking Management System by Multi-Agent Approach:The Case of Urban Area of Tunis

By coordination and cooperation between multi-agents, this paper proposes the network of intelligent agents which can reduce the search time needed to finding a parking place. Based on multi-agent model, the fined solution is designed to help drivers in finding a parking space at anytime and anywhere. Three services are offered: the search for a vacant place, directions to a parking space and booking a place for parking. The results of this study generated by the platform MATSim transport simulation, show that our approach optimizes the operation of vehicles in a parking need with the aim of reducing congestion, and improve traffic flow in urban area. A comparison between the first method where the vehicles are random and the second method where vehicles are steered to vacant parking spaces shows that the minimization of time looking for a parking space could improve circulation by reducing the number of cars in the morning of 2% and 0.7% of the evening. In addition, the traffic per hour per day was reduced by approximately 4.17%.

重载机车风源系统智能控制研究

针对重载机车提速运行过程中空气中不断扬起的粉尘对风源系统中处理的空气的质量提出了更高的要求。当压缩空气中粉尘颗粒浓度高或压缩空气湿度过大时,使得制动设备无法正常工作甚至损坏,给重载机车的平安行车带来安全隐患。该文通过设计一套智能重载机车风源控制系统,通过智能采集、检测风源系统中空气的洁净度与湿度参数,自动去除压缩空气中的粉尘和水,得到高质量的合格压缩空气,使得处理后的空气的湿度与洁净度符合机车制动系统管路的要求,当处理后的空气不符合要求时发出报警信号,提示工作人员进行排故处理,避免了计划维修的盲目性,提升了风源系统的有效运行效率,保障了重载机车的安全运输。

考虑信息年龄的无人机辅助智能交通系统计算卸载优化

该文考虑无人机(UAV)交通监测与移动边缘计算(MEC)技术结合的智能交通系统。为了保障系统中数据时效性并且降低系统能耗,提出计及信息年龄(AoI)的UAV计算卸载优化方法。首先,建立UAV辅助的MEC系统模型,允许MEC服务器缓存常用的应用程序并为UAV提供计算卸载,以支持UAV执行交通监测任务。通过联合优化UAV任务卸载决策、UAV上下行通信带宽分配以及被卸载任务的计算资源分配,最小化所有UAV与MEC服务器的总能耗,同时满足AoI与资源容量等约束条件。其次,系统能耗最小化问题是混合整数非凸优化问题,因此采用离散化和线性化手段,快速获得问题的近似最优解,并设计离散点生成算法来调节近似误差。最后,仿真结果表明,即使对于大型的非凸问题,所提方法也能够快速得到近似最优解,并且可以在不同的任务场景中满足AoI等约束条件,最大限度降低系统能耗。仿真结果验证了所提方法的有效性。

高校智能制造专业课程思政教学改革实践

智能制造专业的设置具有特殊的历史背景和时代使命,如何通过课程思政改革培养德才兼备的智能制造人才是一项重要任务。为此,分析智能制造专业课程思政存在的问题。从课程思政教学改革的总体设计、教学模式、学习效果评价体系三个方面论述智能制造专业课程思政的教学改革实践,并以智能制造导论为例,阐述如何设计课程思政教学内容。最后,介绍取得的育人效果。该专业对课程思政建设的探索和实践可为新时代其他理工科专业建设提供借鉴与思考。

大庆油田能效评价指标体系及能效综合评价方法研究

针对现行油田主要生产系统能效评价指标及其限值和评价方法对于大庆油田不完全适用的问题,根据工作内容,将油田生产系统划分为机采、集输、注水3个子系统;根据用能的关联性,将油田生产系统划分为机采集输和注水两个子系统,对于这两种子系统的划分,分别建立了能效评价指标体系。体系中的能效评价指标均分为系统级、单元级和设备级3个层级;明确了体系中各项指标的定义、计算方法、基础参数来源及统计口径。基于大庆油田实际情况,根据开采区块类型、驱替方式等,将测试数据进行分组,用统计方法确定每组各个能效指标的优良和合格限值;建立了单项与多项组合的油田生产系统加权能效综合评价方法,该方法综合考虑系统效率或系统单耗及其影响因素,在以较大权重计入系统级指标的同时,以较小的权重计入主要影响因素,即在要求系统指标的同时,也对单元级和设备级指标提出要求,从而分析评定所评价系统能效的提升空间。应用此评价方法对6个机采系统、1个集输和1个机采集输系统进行了能效综合评价。

陆路交通基础设施勘察设计智能化路径探讨

研究目的:陆路交通基础设施勘察设计全流程智能化是提高勘察设计效率、质量,引领行业变革的必经之路。为加快推进勘察设计智能化的进程和一体化需要,本文从勘察设计业务流程出发,覆盖铁路、公路两大行业共性技术领域,包括测绘、勘察、选线、路基、桥梁、隧道六大专业,从技术研究、标准建设、平台研发三个方面,对陆路交通基础设施勘察设计智能化路径进行探讨。研究结论:(1)陆路交通基础设施勘察设计智能化可采用以流程为主线、以数据为核心、以智能技术为驱动、以软件平台为载体的总体技术架构,重点攻克“智能测绘、智能勘察、智能选线、智能设计”4套智能关键技术,建立数字化、智能化标准,研发智能勘察设计平台与交付平台,形成陆路交通基础设施智能勘察设计技术体系;(2)4套智能关键技术可实现从基于北斗地基/星基增强服务的基础设施空间地理与地质信息快速获取、多源数据融合及三维数字化表达、多目标智能优化选线、关键构造物设计方案智能决策、数字化建模与集成设计等方面突破;(3)数字化、智能化标准体系构建是从勘察设计数据体系结构、数据存储、数据传递、智能设计要求与算法、成果交付等维度开展标准化研究;(4)智能勘察设计平台与交付平台研发是基于统一的数据与平台架构,采用自主图形引擎,可实现陆路交通智能勘察设计和成果交付,支撑陆路交通基础设施勘察设计领域高质量发展。