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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Smart Parking Management System “MOTAH”

With a surge in the university’s student and staff population, parking problems and congestion have rapidly intensified. The recent inclusion of women drivers, particularly during official working hours, has exacerbated these challenges. This pressing issue underscores the critical necessity for a structured approach to managing university entries and overseeing parking at the gates. The proposed smart parking management system aims to address these concerns by introducing a design concept that restricts unauthorized access and provides exclusive parking privileges to authorized users. Through image processing, the system identifies available parking spaces, relaying real-time information to users via a mobile application. This comprehensive solution also generates detailed reports (daily, weekly, and monthly), aiding university safety authorities in future gate management decisions.

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.

High Performance of a TOA-TDOA Hybrid GSM Location System for Intelligent Transportation Systems

This paper illustrates the performance of a mobile positioning technique applicable to a GSM network.An experimental system of a network-based GSM positioning for ITS has been proposed, and the hybrid TOA-TDOA method based on GSM signaling has been analyzed and used. The performance of the proposed system is showed through simulations in urban and suburban environments. The accuracy for 67% mobile stations is 70 m in urban and 120 m in suburban. The accuracy, coverage and network load of positioning system are also analyzed.

Intelligent Transportation System and Night Delivery Schemes for City Logistics

Intelligent Transportation System （ITS） integrates information and communication technologies with location based technologies into roads, vehicles, traffic and transport management systems. Application of ITS can improve situation in major cities where due to the increasing number of residents and level of motorization traffic congestion represents important issue. Another problem arises from this facts and that is that cities have contradictory needs as grooving need for goods within urban areas as well as need for less vehicles in the same area. The urban logistics activities by private companies within urban areas represent an integral part of city logistics that aims to improve the efficiency of urban freight transportation, reduce traffic congestion, mitigate environmental impacts, reduce costs and fuel consumption. This research presents application of ITS technologies and vehicle routing problem for night delivery scheme planning as one of the core techniques for modelling city logistics.

Telematics as a Transformative Agent for the Zimbabwean Auto Insurance Ecosystem

Zimbabwe has witnessed the evolution of Information Communication Technology (ICT). The vehicle population soared to above 1.2 million hence rendering the Transport and Insurance domains complex. Therefore, there is a need to look at ways that can augment conventional Vehicular Management Information Systems (VMIS) in transforming business processes through Telematics. This paper aims to contextualise the role that telematics can play in transforming the Insurance Ecosystem in Zimbabwe. The main objective was to investigate the integration of Usage-Based Insurance (UBI) with vehicle tracking solutions provided by technology companies like Econet Wireless in Zimbabwe, aiming to align customer billing with individual risk profiles and enhance the synergy between technology and insurance service providers in the motor insurance ecosystem. A triangulation through structured interviews, questionnaires, and literature review, supported by Information Systems Analysis and Design techniques was conducted. The study adopted a case study approach, qualitatively analyzing the complexities of the Telematics insurance ecosystem in Zimbabwe, informed by the TOGAF framework. A case-study approach was applied to derive themes whilst applying within and cross-case analysis. Data was collected using questionnaires, and interviews. The findings of the research clearly show the importance of Telematics in modern-day insurance and the positive relationship between technology and insurance business performance. The study, therefore revealed how UBI can incentivize positive driver behavior, potentially reducing insurance premiums for safe drivers and lowering the incidence of claims against insurance companies. Future work can be done on studying the role of Telematics in combating highway crime and corruption.

Analysis of the Application of Artificial Intelligence in Transportation

With the advancement of the information age,the transportation industry has experienced rapid growth,leading to an expansion in the scale and number of highway constructions.However,this development has also given rise to numerous traffic issues,including frequent vehicle congestion and traffic accidents.To address these problems,it is essential to leverage modern technology for real-time information collection and analysis,providing robust technical support for intelligent transportation systems.This paper focuses on artificial intelligence(AI)technology,explaining its concept and its role in intelligent transportation.It reviews the various application areas and analyzes the use of AI in intelligent transportation.Finally,it proposes strategies for applying AI to promote the healthy development of intelligent transportation systems.

Study on WSN Localization Algorithm and Simulation Model for Intelligent Transportation System

This paper conducts research on the algorithm to improve the location of Wireless Sensor Network （WSN） in Intelligent Transportation System （ITS）. The localization algorithm introduced an improved RSSI vehicle localization algorithm based on multi-path effect and Gaussian white noise. The localization results under different values of Gaussian white noise and different density of beacon nodes are analyzes, and Kalman filtering algorithm is introduced to reduce the influence of signal noise. Finally, a simulation model of ITS is developed to test the algorithm based on mixed noise and Kalman filtering algorithm, which is used to simulate the localization of real vehicles. The simulation shows the algorithm has effect to improve location accuracy and to application