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.

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

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

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.

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.

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.

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.

Continual driver behaviour learning for connected vehicles and intelligent transportation systems: Framework, survey and challenges

Modelling,predicting and analysing driver behaviours are essential to advanced driver assistance systems(ADAS)and the comprehensive understanding of complex driving scenarios.Recently,with the development of deep learning(DL),numerous driver behaviour learning(DBL)methods have been proposed and applied in connected vehicles(CV)and intelligent transportation systems(ITS).This study provides a review of DBL,which mainly focuses on typical applications in CV and ITS.First,a comprehensive review of the state-of-the-art DBL is presented.Next,Given the constantly changing nature of real driving scenarios,most existing learning-based models may suffer from the so-called“catastrophic forgetting,”which refers to their inability to perform well in previously learned scenarios after acquiring new ones.As a solution to the aforementioned issue,this paper presents a framework for continual driver behaviour learning(CDBL)by leveraging continual learning technology.The proposed CDBL framework is demonstrated to outperform existing methods in behaviour prediction through a case study.Finally,future works,potential challenges and emerging trends in this area are highlighted.

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.

YOLO and Blockchain Technology Applied to Intelligent Transportation License Plate Character Recognition for Security

Privacy and trust are significant issues in intelligent transportation systems(ITS).Data security is critical in ITS systems since sensitive user data is communicated to another user over the internet through wireless devices and routes such as radio channels,optical fiber,and blockchain technology.The Internet of Things(IoT)is a network of connected,interconnected gadgets.Privacy issues occasionally arise due to the amount of data generated.However,they have been primarily addressed by blockchain and smart contract technology.While there are still security issues with smart contracts,primarily due to the complexity of writing the code,there are still many challenges to consider when designing blockchain designs for the IoT environment.This study uses traditional blockchain technology with the“You Only Look Once”(YOLO)object detection method to accurately locate and identify license plates.While YOLO and blockchain technologies used for intelligent vehicle license plate recognition are promising,they have received limited research attention.Real-time object identification and recognition would be possible by combining a cutting-edge object detection technique with a regional convolutional neural network(RCNN)built with the tensor flow core open source libraries.This method works reasonably well for identifying any license plate.The Automatic License Plate Recognition(ALPR)approach delivered outstanding results in various datasets.First,with a recognition rate of 96.2%,our system(UFPR-ALPR)surpassed the previously used technology,consisting of 4500 frames and around 150 films.Second,a deep learning algorithm was trained to recognize images of license plate numbers using the UFPR-ALPR dataset.Third,the license plate’s characters were complicated for standard methods to identify because of the shifting lighting correctly.The proposed model,however,produced beneficial outcomes.

An Intelligent Transportation System Application for Smartphones Based on Vehicle Position Advertising and Route Sharing in Vehicular Ad-Hoc Networks

Alerting drivers about incoming emergency vehicles and their routes can greatly improve their travel time in congested cities, while reducing the risk of accidents due to distractions. This paper contributes to this goal by proposing Messiah, an Android application capable of informing regular vehicles about incoming emergency vehicles like ambulances, police cars and fire brigades. This is made possible by creating a network of vehicles capable of directly communicating between them. The user can, therefore, take driving decisions in a timely manner by considering incoming alerts. Using the support of our GRCBox hardware, the application can rely on vehicular ad-hoc network communications in the 5 GHz band, being V2V （vehicle-to-vehicle） communication provided through a combination of Android-based smartphone and our GRCBox device. The application was tested in three different scenarios with different levels of obstruction, showing that it is capable of providing alerts up to 300 meters, and notifying vehicles within less than one second.

Sustainable transit vehicle tracking service,using intelligent transportation system services and emerging communication technologies:A review

The most salient problems of transit vehicle service in Latin American intermediate cities include:the high number of passengers involved in traffic accidents;traffic congestion caused by transit vehicles,and pollution generated by these vehicles,which increases in high congestion scenarios.To improve upon this situation,a research was conducted on the transit vehicle tracking service,which is a basic service for implementing mobility solutions for the aforementioned problems,the most relevant characteristics of this service for the context of Latin American intermediate cities were identified,and an implementation was proposed.This paper presents the four stages of the study:(a)a review of the state-of-the-art of services or systems related to vehicle tracking,including wireless communications technologies,implemented sustainability approaches,usage of special algorithms for efficiency improvement,and the intelligent transportation system(ITS)architecture used as a basis;(b)the process of identifying relevant characteristics of the service for a given context;(c)proposal of an ITS architecture for this service in an intermediate city,its requirements and the suggested technologies;and(d)development of experiments for validating usage of the key suggested technologies.The review allowed to identify the main service characteristics,with regard to vehicle positioning technologies,the recommended wireless communication technology(long range,LoRa),energy consumption considerations,and use of artificial intelligence(AI)to calculate waiting time of users at bus stops.Finally,an ITS architecture for the city of Popayan(Colombian city)considering the aforementioned characteristics is proposed,and the experiments related to the use of these technologies are described in detail.

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.

Whale Optimization Algorithm-Based Deep Learning Model for Driver Identification in Intelligent Transport Systems

Driver identification in intelligent transport systems has immense demand,considering the safety and convenience of traveling in a vehicle.The rapid growth of driver assistance systems(DAS)and driver identification system propels the need for understanding the root causes of automobile accidents.Also,in the case of insurance,it is necessary to track the number of drivers who commonly drive a car in terms of insurance pricing.It is observed that drivers with frequent records of paying“fines”are compelled to pay higher insurance payments than drivers without any penalty records.Thus driver identification act as an important information source for the intelligent transport system.This study focuses on a similar objective to implement a machine learning-based approach for driver identification.Raw data is collected from in-vehicle sensors using the controller area network(CAN)and then converted to binary form using a one-hot encoding technique.Then,the transformed data is dimensionally reduced using the Principal Component Analysis(PCA)technique,and further optimal parameters from the dataset are selected using Whale Optimization Algorithm(WOA).The most relevant features are selected and then fed into a Convolutional Neural Network(CNN)model.The proposed model is evaluated against four different use cases of driver behavior.The results show that the best prediction accuracy is achieved in the case of drivers without glasses.The proposed model yielded optimal accuracy when evaluated against the K-Nearest Neighbors(KNN)and Support Vector Machines(SVM)models with and without using dimensionality reduction approaches.

Precise and efficient Chinese license plate recognition in the real monitoring scene of intelligent transportation system

In this paper, the performance of you only look once(YOLO) series detectors on Chinese license plate recognition(LPR) in the real intelligent transportation system(ITS) monitoring scene is investigated. Specially, a precise and efficient automatic license plate recognition(ALPR) system based on the YOLOv4 detector is proposed. The proposed ALPR system contains three stages including vehicle detection, license plate detection(LPD) and LPR. In vehicle detection stage, YOLOv4 detector is directly applied. In LPD stage, YOLOv4-tiny detector is exploited. In the last stage, the YOLOv4-tiny detector with attention mechanism for LPR is proposed to use. In addition, a large Chinese license plate dataset containing 10 500 images collected from all 31 provinces in the Chinese mainland is created. This Chinese license plate dataset is named Hefei University of Technology license plate version 1(HFUT-LP v1). Particularly, HFUT-LP v1 dataset is collected in the real ITS monitoring scene. In order to compare the performance of different object detection algorithms for ALPR, a variety of object detection algorithms are used to make a comprehensive performance evaluation. Experimental results show that the proposed ALPR system achieves very high accuracy and has very fast processing speed, which is suitable for real-time LPR.

Shaping the future of the application of quantum computing in intelligent transportation system

The intelligent transportation system(ITS)integrates a variety of advanced science and technology to support and monitor road traffic systems and accelerate the urbanization process of various countries.This paper analyzes the shortcomings of ITS,introduces the principle of quantum computing and the performance of universal quantum computer and special-purpose quantum computer,and shows how to use quantum advantages to improve the existing ITS.The application of quantum computer in transportation field is reviewed from three application directions:path planning,transportation operation management,and transportation facility layout.Due to the slow development of the current universal quantum computer,the D-Wave quantum machine is used as a breakthrough in the practical application.This paper makes it clear that quantum computing is a powerful tool to promote the development of ITS,emphasizes the importance and necessity of introducing quantum computing into intelligent transportation,and discusses the possible development direction in the future.

A Multi-Mode Public Transportation System Using Vehicular to Network Architecture

The number of accidents in the campus of Suranaree University of Technology(SUT)has increased due to increasing number of personal vehicles.In this paper,we focus on the development of public transportation system using Intelligent Transportation System(ITS)along with the limitation of personal vehicles using sharing economy model.The SUT Smart Transit is utilized as a major public transportation system,while MoreSai@SUT(electric motorcycle services)is a minor public transportation system in this work.They are called Multi-Mode Transportation system as a combination.Moreover,a Vehicle toNetwork(V2N)is used for developing theMulti-Mode Transportation system in the campus.Due to equipping vehicles with On Board Unit(OBU)and 4G LTE modules,the real time speed and locations are transmitted to the cloud.The data is then applied in the proposed mathematical model for the estimation of Estimated Time of Arrival(ETA).In terms of vehicle classifications and counts,we deployed CCTV cameras,and the recorded videos are analyzed by using You Only Look Once(YOLO)algorithm.The simulation and measurement results of SUT Smart Transit and MoreSai@SUT before the covid-19 pandemic are discussed.Contrary to the existing researches,the proposed system is implemented in the real environment.The final results unveil the attractiveness and satisfaction of users.Also,due to the proposed system,the CO_(2) gas gets reduced when Multi-Mode Transportation is implemented practically in the campus.

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.

Deep Learning Based Autonomous Transport System for Secure Vehicle and Cargo Matching

The latest 6G improvements secured autonomous driving's realism in Intelligent Autonomous Transport Systems(IATS).Despite the IATS's benefits,security remains a significant challenge.Blockchain technology has grown in popularity as a means of implementing safe,dependable,and decentralised independent IATS systems,allowing for more utilisation of legacy IATS infrastructures and resources,which is especially advantageous for crowdsourcing technologies.Blockchain technology can be used to address security concerns in the IATS and to aid in logistics development.In light of the inadequacy of reliance and inattention to rights created by centralised and conventional logistics systems,this paper discusses the creation of a blockchain-based IATS powered by deep learning for secure cargo and vehicle matching(BDL-IATS).The BDL-IATS approach utilises Ethereum as the primary blockchain for storing private data such as order and shipment details.Additionally,the deep belief network(DBN)model is used to select suitable vehicles and goods for transportation.Additionally,the chaotic krill herd technique is used to tune the DBN model’s hyper-parameters.The performance of the BDL-IATS technique is validated,and the findings are inspected under a variety of conditions.The simulationfindings indicated that the BDL-IATS strategy outperformed recent state-of-the-art approaches.

Hybrid Algorithm-Driven Smart Logistics Optimization in IoT-Based Cyber-Physical Systems

Effectively managing complex logistics data is essential for development sustainability and growth,especially in optimizing distribution routes.This article addresses the limitations of current logistics path optimization methods,such as inefficiencies and high operational costs.To overcome these drawbacks,we introduce the Hybrid Firefly-Spotted Hyena Optimization(HFSHO)algorithm,a novel approach that combines the rapid exploration and global search abilities of the Firefly Algorithm(FO)with the localized search and region-exploitation skills of the Spotted Hyena Optimization Algorithm(SHO).HFSHO aims to improve logistics path optimization and reduce operational costs.The algorithm’s effectiveness is systematically assessed through rigorous comparative analyses with established algorithms like the Ant Colony Algorithm(ACO),Cuckoo Search Algorithm(CSA)and Jaya Algo-rithm(JA).The evaluation also employs benchmarking methodologies using standardized function sets covering diverse objective functions,including Schwefel’s,Rastrigin,Ackley,Sphere and the ZDT and DTLZ Function suite.HFSHO outperforms these algorithms,achieving a minimum path distance of 546 units,highlighting its prowess in logistics path optimization.This comprehensive evaluation authenticates HFSHO’s exceptional performance across various logistic optimization scenarios.These findings emphasize the critical significance of selecting an appropriate algorithm for logistics path navigation,with HFSHO emerging as an efficient choice.Through the synergistic use of FO and SHO,HFSHO achieves a 15%improvement in convergence,heightened operational efficiency and substantial cost reductions in logistics operations.It presents a promising solution for optimizing logistics paths,offering logistics planners and decision-makers valuable insights and contributing substantively to sustainable sectoral growth.