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نتیجه جستجو - Intelligent transportation system

تعداد مقالات یافته شده: 40
ردیف عنوان نوع
1 Data Mining Strategies for Real-Time Control in New York City
استراتژی داده کاوی برای کنترل زمان واقعی در شهر نیویورک-2105
The Data Mining System (DMS) at New York City Department of Transportation (NYCDOT) mainly consists of four database systems for traffic and pedestrian/bicycle volumes, crash data, and signal timing plans as well as the Midtown in Motion (MIM) systems which are used as part of the NYCDOT Intelligent Transportation System (ITS) infrastructure. These database and control systems are operated by different units at NYCDOT as an independent database or operation system. New York City experiences heavy traffic volumes, pedestrians and cyclists in each Central Business District (CBD) area and along key arterial systems. There are consistent and urgent needs in New York City for real-time control to improve mobility and safety for all users of the street networks, and to provide a timely response and management of random incidents. Therefore, it is necessary to develop an integrated DMS for effective real-time control and active transportation management (ATM) in New York City. This paper will present new strategies for New York City suggesting the development of efficient and cost-effective DMS, involving: 1) use of new technology applications such as tablets and smartphone with Global Positioning System (GPS) and wireless communication features for data collection and reduction; 2) interface development among existing database and control systems; and 3) integrated DMS deployment with macroscopic and mesoscopic simulation models in Manhattan. This study paper also suggests a complete data mining process for real-time control with traditional static data, current real timing data from loop detectors, microwave sensors, and video cameras, and new real-time data using the GPS data. GPS data, including using taxi and bus GPS information, and smartphone applications can be obtained in all weather conditions and during anytime of the day. GPS data and smartphone application in NYCDOT DMS is discussed herein as a new concept. © 2014 The Authors. Published by Elsevier B.V. Selection and peer-review under responsibility of Elhadi M. Shakshu Keywords: Data Mining System (DMS), New York City, real-time control, active transportation management (ATM), GPS data
مقاله انگلیسی
2 Solving Vehicle Routing Problem Using Quantum Approximate Optimization Algorithm
حل مسئله مسیریابی خودرو با استفاده از الگوریتم بهینه سازی تقریبی کوانتومی-2022
Intelligent transportation systems (ITS) are a critical component of Industry 4.0 and 5.0, particularly having applications in logistic management. One of their crucial utilization is in supply-chain management and scheduling for optimally routing transportation of goods by vehicles at a given set of locations. This paper discusses the broader problem of vehicle traffic management, more popularly known as the Vehicle Routing Problem (VRP), and investigates the possible use of near-term quantum devices for solving it. For this purpose, we give the Ising formulation for VRP and some of its constrained variants. Then, we present a detailed procedure to solve VRP by minimizing its corresponding Ising Hamiltonian using a hybrid quantum-classical heuristic called Quantum Approximate Optimization Algorithm (QAOA), implemented on the IBM Qiskit platform. We compare the performance of QAOA with classical solvers such as CPLEX on problem instances of up to 15 qubits. We find that performance of QAOA has a multifaceted dependence on the classical optimization routine used, the depth of the ansatz parameterized by p, initialization of variational parameters, and problem instance itself.
Index Terms— Vehicle routing problem | ising model | combinatorial optimization | quantum approximate algorithms | variational quantum algorithms.
مقاله انگلیسی
3 A comprehensive pseudonym changing scheme for improving location privacy in vehicular networks
یک طرح جامع تغییر نام مستعار برای بهبود حریم خصوصی مکان در شبکه های وسایل نقلیه-2022
In an Intelligent Transportation System (ITS), many applications, such as collision avoidance and lane change warning, require real-time information from vehicles on the road. This typically includes detailed status information such as location, speed, heading and a vehicle identifier, which can be used to infer precise movement patterns leading to long-term driver profiling and vehicle tracking. The use of pseudonyms, instead of actual vehicle IDs, allows vehicles to protect their privacy while sharing relevant information. A pseudonym changing scheme (PCS) determines when and how a vehicle should change its pseudonyms to maintain an appropriate privacy level. In this paper, we propose a new comprehensive PCS that utilizes vehicle context and current traffic patterns to leverage the optimum situation for changing pseudonyms. Simulation results indicate that the proposed PCS outperforms existing approaches both in terms of user-centric and adversary-centric performance metrics.
Keywords: Location privacy | Pseudonym changing scheme | VANET | V2V communication | Context-aware
مقاله انگلیسی
4 Beacon Non-Transmission attack and its detection in intelligent transportation systems
حمله عدم انتقال Beacon و تشخیص آن در سیستم های حمل و نقل هوشمند-2022
Message dropping by intermediate nodes as well as RF jamming attack have been studied widely in ad hoc networks. In this paper, we thoroughly investigate a new type of attack in intelligent transportation systems (ITS) defined as Beacon Non-Transmission (BNT) attack in which attacker is not an intermediate vehicle, but rather a source vehicle. In BNT attack, a vehicle suppresses the transmissions of its own periodic beacon packets to get rid of the automated driving misbehavior detection protocols running in ITS, or to mount a Denial-ofService (DoS) attack to cripple the traffic management functionality of ITS. Considering BNT attack as a critical security threat to ITS, we propose two novel and lightweight techniques to detect it. Our first technique bases its detection by assuming a certain distribution of the number of beacons lost from a vehicle while accounting for loss due to channel-error. However, it fails to classify shortish BNT attacks wherein amount of denial and channel-error loss are comparable. Our second technique, suitable for identifying both shortish and longish BNT attacks, considers beacon loss pattern of a vehicle as a time-series data and employs autocorrelation function (ACF) to determine the existence of an attack. In order to trade-off detection accuracy for equitable use of limited computational resources, we propose a random inspection model in which the detection algorithm is executed at random time instances and for randomly selected set of vehicles. We have performed extensive simulations to evaluate the performance of proposed detection algorithms under random inspection and a practical attacker model. The results obtained corroborate the lightweight nature of both techniques, and the efficacy of ACF based technique over simple threshold based technique in terms of higher detection accuracy as well as smaller reaction delay.
keywords: DoS attack | Intrusion detection | Driving anomaly detection | Beacon | Autocorrelation function | Intelligent Transportation Systems
مقاله انگلیسی
5 CREASE: Certificateless and REused-pseudonym based Authentication Scheme for Enabling security and privacy in VANETs
CREASE: طرح احراز هویت مبتنی بر نام مستعار بدون گواهی و استفاده مجدد برای فعال کردن امنیت و حریم خصوصی در VANET-2022
Due to the customers’ growing interest in using various intelligent and connected devices, we are surrounded by the Internet of Things (IoT). It is estimated that the number of IoT devices will exceed 60 billion by 2025. One of the primary reasons for such rapid growth is the Internet of Vehicles (IoV). Internet of Vehicles (IoV) has evolved into an emerging concept in intelligent transportation systems (ITS) that integrates VANETs and the IoT to enhance their capabilities. With the emergence of IoV and the interest shown by customers, Vehicular Ad hoc NETworks (VANETs) are likely to be widely deployed in the near future. However, for this to happen, wide participation of vehicle owners in VANET is needed. The primary concerns of vehicle owners to participate in VANET are privacy and security. In this paper, we present a Certificateless and REused-pseudonym based Authentication Scheme for Enabling security and privacy (CREASE) in VANETs. One of the ways to preserve the privacy of vehicles/drivers is to allow vehicles/drivers to use pseudo identities (pseudonyms) instead of their real identities (such as VIN number or driving license number) in all communications. The pseudonym used by a vehicle needs to be changed frequently to prevent the vehicle from being tracked. Our scheme uses Merkle Hash Tree and Modified Merkle Patricia Trie to efficiently store and manage the pseudonyms assigned to a vehicle. This enables a vehicle to pick and use a random pseudonym from a given set of pseudonyms assigned to it as well as change its pseudonym frequently and securely to ensure privacy. Unlike many of the existing schemes, our scheme does not use certificates and certificate revocation lists for authentication. Moreover, it allows vehicles to get a set of pseudonyms only once from the trusted authority. We present a formal proof of correctness of our scheme and also compare our scheme with some of the other contemporary schemes to show the effectiveness of our scheme.
Keywords: VANETs | Intelligent transportation systems | Authentication | Security | Privacy-preserving authentication
مقاله انگلیسی
6 Blockchain, AI and Smart Grids: The Three Musketeers to a Decentralized EV Charging Infrastructure
بلاکچین ،هوش مصنوعی و شبکه های هوشمند: سه تفنگدار به زیرساخت شارژ EV غیرمتمرکز-2020
The proliferation of Internet of Things (IoT) has brought an array of different services, from smart health-care, to smart transportation, all the way to smart cities. For a truly connected environment, different sectors need to collaborate. One use case of such overlap is between smart grids and Intelligent Transportation System (ITS) giving rise to Electric Vehicles and their charging infrastructure. Being such a lucrative opportunity for investors and the research community, many efforts have been made toward providing the end-user with an extraordinary Quality of Service (QoS). However, given the current protocols and deployment of the Electric Vehicle (EV) charging infrastructure, some key challenges still need to be addressed. In particular, we identify two main EV challenges: (1) vulnerable charging stations and EVs, and (2) non-optimal charging schedules. With these issues in mind, we evaluate the integration of Blockchain and AI with the EV charging infrastructure. Specifically, we discuss the current AI and Blockchain charging solutions available in the market. In addition, we propose a couple of use cases where both technologies complement each other for a secure, efficient and decentralized charging ecosystem. This article serves as starting point for stakeholders and policymakers to help identify potential directions and implementations of better charging systems for EVs.
مقاله انگلیسی
7 A taxonomy of AI techniques for 6G communication networks
طبقه بندی تکنیک های هوش مصنوعی برای شبکه های ارتباطی 6G-2020
With 6G flagship program launched by the University of Oulu, Finland, for full future adaptation of 6G by 2030, many institutes worldwide have started to explore various issues and challenges in 6G communication networks. 6G offers ultra high-reliable and massive ultra-low latency while opening the doors for many applications currently not viable by today’s 4G and 5G communication standards. The current 5G technology has security and privacy issues which makes its usage in limited applications. In such an environment, we believe that AI can offer efficient solutions for the aforementioned issues having low communication overhead cost. Keeping focus on all these issues, in this paper, we presented a comprehensive survey on AI-enabled 6G communication technology, which can be used in wide range of future applications. In this article, we explore how AI can be integrated into different applications such as object localization, UAV communication, surveillance, security and privacy preservation etc. Finally, we discussed a use case that shows the adoption of AI techniques in intelligent transport system.
Keywords: Artificial Intelligence | 6G | Communication networks | Mobile edge computing | Intelligent transportation system
مقاله انگلیسی
8 Machine to machine performance evaluation of grid-integrated electric vehicles by using various scheduling algorithms
ارزیابی عملکرد ماشین به ماشین از وسایل نقلیه برقی شبکه یکپارچه با استفاده از الگوریتم های مختلف برنامه ریزی-2020
For smart cities, electric vehicles (EVs) are promisingly considered as a striving industry due to its pollution-less behaviours and easy-to-maintain characteristics. A seamless management system is necessary to manage the energy between EV and various parties participating in the grid operation. To facilitate the energy system in a distributed and coordinated way, a machine-to-machine (M2M) system can be considered as the key component in future intelligent transportation systems. Due to the ubiquitous range and data speed, a fourth-generation (4G) cellular-based long-term evaluation (LTE) system inspires us to select it as a potential carrier for M2M communication. However, various simulation and analytical modelling end up with the conclusion that the maximum 250 EVs can be connected under an LTE base station. These limitations or scalability limits may result in a terrible mix-up in future smart cities for over dense roads. In this paper, we measured various M2M quality of services performance for exceeding the number of EVs by using three popular algorithms (proportional fair scheduling, modified largest weighted delay first scheduling and exponential scheduling). The result shows that the proportional fair scheduler has the highest packet loss ratio (PLR) and delay time as compared to other two schedulers.
Keywords: DLS | Electric vehicle | Energy management system | EXP | M2M communication | M-LWDF | PF | PLR
مقاله انگلیسی
9 AI-Powered Blockchain : A Decentralized Secure Multiparty Computation Protocol for IoV
بلاکچین با هوش مصنوعی: یک پروتکل محاسباتی محرمانه چند جانبه امن برای IoV-2020
The rapid advancements in autonomous technologies have paved way for vehicular networks. In particular, Vehicular Ad-hoc Network (VANET) forms the basis of the future of Intelligent Transportation System (ITS). ITS represents the communication among vehicles by acquiring and sharing the data. Though congestion control is enhanced by Internet of Vehicles (IoV), there are various security criteria where entire communication can lead to many security and privacy challenges. A blockchain can be deployed to provide the IoV devices with the necessary authentication and security feature for the transfer of data. Blockchain based IoV mechanism eliminates the single source of failure and remains secure at base despite having strong security, the higher level layers and applications are susceptible to attacks. Artificial Intelligence (AI) has the potential to overcome several vulnerabilities of current blockchain technology. In this paper, we propose an AI-Powered Blockchain which provides auto coding feature for the smart contracts making it an intelligent contract. Moreover, it speeds up the transaction verification and optimises energy consumption. The results show that intelligent contracts provide higher security compared to smart contracts considering range of different scenarios.
Index Terms: Blockchain | Artificial Intelligence | Smart contract | Internet of Vehicles | Vehicular Network
مقاله انگلیسی
10 A reinforcement learning model for personalized driving policies identification
یک مدل یادگیری تقویتی برای شناسایی شخصیت های سیاسی محور -2020
Optimizing driving performance by addressing personalized aspects of driving behavior and without posing unrealistic restrictions on personal mobility may have far reaching implications to traffic safety, flow operations and the environment, as well as significant benefits for users. The present work addresses the problem of delivering personalized driving policies based on Reinforcement Learning for enhancing existing Intelligent Transportation Systems (ITS) to the benefit of traffic management and road safety. The proposed framework is implemented on appropriate driving behavior metrics derived from smartphone sensors’ data streams. Aggressiveness, speeding and mobile usage are considered to describe the driving profile per trip and are presented as inputs to the Q-learning algorithm. The implementation of the proposed methodological approach produces personalized quantified driving policies to be exploited for self-improvement. Finally, this paper establishes validation measures of the quality and effectiveness of the produced policies and methodological tools for comparing and classifying the examined drivers.
Keywords: Reinforcement learning | Q-learning | Machine learning | Intelligent transportation systems | Traffic data
مقاله انگلیسی
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