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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 |