IEEE Transactions on Intelligent Transportation Systems
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The theoretical, experimental and operational aspects of electrical and electronics engineering and information technologies as applied to Intelligent Transportation Systems (ITS). Intelligent Transportation Systems are defined as those systems utilizing synergistic technologies and systems engineering concepts to develop and improve transportation systems of all kinds. The scope of this interdisciplinary activity includes the promotion, consolidation and coordination of ITS technical activities among IEEE entities, and providing a focus for cooperative activities, both internally and externally.
Latest Published Articles
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Driving Behavior Analysis through CAN Bus Data in an Uncontrolled Environment
Umberto Fugiglando ; Emanuele Massaro ; Paolo Santi ; Sebastiano Milardo ; Kacem Abida ; Rainer Stahlmann ; Florian Netter ; Carlo RattiTue Jul 17 00:00:00 EDT 2018 Tue Jul 17 00:00:00 EDT 2018 -
MOHA: A Multi-Mode Hybrid Automaton Model for Learning Car-Following Behaviors
Wed Jun 13 00:00:00 EDT 2018 Wed Jun 13 00:00:00 EDT 2018 -
On the Accuracy of Inter-Vehicular Range Measurements Using GNSS Observables in a Cooperative Framework
Tue Jun 12 00:00:00 EDT 2018 Tue Jun 12 00:00:00 EDT 2018 -
PCANet-Based Convolutional Neural Network Architecture for a Vehicle Model Recognition System
Fri Jun 08 00:00:00 EDT 2018 Fri Jun 08 00:00:00 EDT 2018 -
SPERT: A Speed Limit Strategy for Recurrent Traffic Jams
Wed May 30 00:00:00 EDT 2018 Wed May 30 00:00:00 EDT 2018
Popular Articles
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Wheel Defect Detection With Machine Learning
Wed Aug 09 00:00:00 EDT 2017 Wed Aug 09 00:00:00 EDT 2017 -
Traffic Flow Prediction With Big Data: A Deep Learning Approach
Tue Sep 09 00:00:00 EDT 2014 Tue Sep 09 00:00:00 EDT 2014 -
A Review of Motion Planning Techniques for Automated Vehicles
Thu Nov 26 00:00:00 EST 2015 Thu Nov 26 00:00:00 EST 2015 -
Big Data Analytics in Intelligent Transportation Systems: A Survey
Mon Apr 23 00:00:00 EDT 2018 Mon Apr 23 00:00:00 EDT 2018 -
A Survey of the Connected Vehicle Landscape—Architectures, Enabling Technologies, Applications, and Development Areas
Wed Oct 04 00:00:00 EDT 2017 Wed Oct 04 00:00:00 EDT 2017
Publish in this Journal
Meet Our Editors
Editor-in-Chief
Azim Eskandarian
Nicholas and Rebecca Des Champs Professor
Virginia Tech
Blacksburg, VA 24061 USA
eskandarian@vt.edu
Popular Documents (January 2019)
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Wheel Defect Detection With Machine Learning
Publication Year: 2018, Page(s):1176 - 1187
Cited by: Papers (2)Wheel defects on railway wagons have been identified as an important source of damage to the railway infrastructure and rolling stock. They also cause noise and vibration emissions that are costly to mitigate. We propose two machine learning methods to automatically detect these wheel defects, based on the wheel vertical force measured by a permanently installed sensor system on the railway networ... View full abstract»
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Traffic Flow Prediction With Big Data: A Deep Learning Approach
Publication Year: 2015, Page(s):865 - 873
Cited by: Papers (145)Accurate and timely traffic flow information is important for the successful deployment of intelligent transportation systems. Over the last few years, traffic data have been exploding, and we have truly entered the era of big data for transportation. Existing traffic flow prediction methods mainly use shallow traffic prediction models and are still unsatisfying for many real-world applications. T... View full abstract»
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A Review of Motion Planning Techniques for Automated Vehicles
Publication Year: 2016, Page(s):1135 - 1145
Cited by: Papers (117)Intelligent vehicles have increased their capabilities for highly and, even fully, automated driving under controlled environments. Scene information is received using onboard sensors and communication network systems, i.e., infrastructure and other vehicles. Considering the available information, different motion planning and control techniques have been implemented to autonomously driving on com... View full abstract»
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Big Data Analytics in Intelligent Transportation Systems: A Survey
Publication Year: 2019, Page(s):383 - 398Big data is becoming a research focus in intelligent transportation systems (ITS), which can be seen in many projects around the world. Intelligent transportation systems will produce a large amount of data. The produced big data will have profound impacts on the design and application of intelligent transportation systems, which makes ITS safer, more efficient, and profitable. Studying big data a... View full abstract»
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A Survey of the Connected Vehicle Landscape—Architectures, Enabling Technologies, Applications, and Development Areas
Publication Year: 2018, Page(s):2391 - 2406
Cited by: Papers (6)This paper summarizes the state of the art in connected vehicles-from the need for vehicle data and applications thereof, to enabling technologies, challenges, and identified opportunities. Connectivity is increasing around the world and its expansion to vehicles is no exception. With improvements in connectivity, sensing, and computation, the future will see vehicles used as development platforms... View full abstract»
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Wireless Sensor Networks for Condition Monitoring in the Railway Industry: A Survey
Publication Year: 2015, Page(s):1088 - 1106
Cited by: Papers (103)In recent years, the range of sensing technologies has expanded rapidly, whereas sensor devices have become cheaper. This has led to a rapid expansion in condition monitoring of systems, structures, vehicles, and machinery using sensors. Key factors are the recent advances in networking technologies such as wireless communication and mobile ad hoc networking coupled with the technology to integrat... View full abstract»
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A Security Credential Management System for V2X Communications
Benedikt Brecht ; Dean Therriault ; André Weimerskirch ; William Whyte ; Virendra Kumar ; Thorsten Hehn ; Roy GoudyPublication Year: 2018, Page(s):3850 - 3871
Cited by: Papers (1)The U.S. Department of Transportation (USDOT) issued a proposed rule on January 12, 2017 to mandate vehicle-to-vehicle safety communications in light vehicles in the U.S. Cybersecurity and privacy are major challenges for such a deployment. We present a Security Credential Management System (SCMS) for vehicle-to-everything (V2X) communications in this paper, which has been developed by the Crash A... View full abstract»
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Data-Driven Intelligent Transportation Systems: A Survey
Publication Year: 2011, Page(s):1624 - 1639
Cited by: Papers (377)For the last two decades, intelligent transportation systems (ITS) have emerged as an efficient way of improving the performance of transportation systems, enhancing travel security, and providing more choices to travelers. A significant change in ITS in recent years is that much more data are collected from a variety of sources and can be processed into various forms for different stakeholders. T... View full abstract»
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Looking at Vehicles on the Road: A Survey of Vision-Based Vehicle Detection, Tracking, and Behavior Analysis
Publication Year: 2013, Page(s):1773 - 1795
Cited by: Papers (336)This paper provides a review of the literature in on-road vision-based vehicle detection, tracking, and behavior understanding. Over the past decade, vision-based surround perception has progressed from its infancy into maturity. We provide a survey of recent works in the literature, placing vision-based vehicle detection in the context of sensor-based on-road surround analysis. We detail advances... View full abstract»
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Driving Style Recognition for Intelligent Vehicle Control and Advanced Driver Assistance: A Survey
Publication Year: 2018, Page(s):666 - 676
Cited by: Papers (14)Driver driving style plays an important role in vehicle energy management as well as driving safety. Furthermore, it is key for advance driver assistance systems development, toward increasing levels of vehicle automation. This fact has motivated numerous research and development efforts on driving style identification and classification. This paper provides a survey on driving style characterizat... View full abstract»
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Estimated Time of Arrival Using Historical Vessel Tracking Data
Publication Year: 2019, Page(s):7 - 15
Cited by: Papers (1)The growing availability of data coming from ship reporting systems, such as Automatic Identification System (AIS) and Long Range Identification and Tracking (LRIT), is originating an unprecedented set of opportunities to enforce maritime surveillance, ensure the security of the traffic at sea, and manage maritime operations. In this paper, a data-driven methodology is proposed to estimate the ves... View full abstract»
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Obstacle Prediction for Automated Guided Vehicles Based on Point Clouds Measured by a Tilted LIDAR Sensor
Publication Year: 2018, Page(s):2708 - 2720Environment analysis of automatic vehicles needs the detection from 3-D point cloud information. This paper addresses this task when only partial scanning data are available. Our method develops the detection capabilities of autonomous vehicles equipped with 3-D range sensors for navigation purposes. In industrial practice, the safety scanners of automated guided vehicles (AGVs) and a localization... View full abstract»
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A Multimodal ADAS System for Unmarked Urban Scenarios Based on Road Context Understanding
Publication Year: 2015, Page(s):1690 - 1704
Cited by: Papers (18)Comprehensive situational awareness is paramount to the effectiveness of advanced driver assistance systems (ADASs) used in daily urban traffic, particularly for the unmarked roads, which cannot fulfill the requirements of conventional ADAS systems. This paper proposed a stereovision-based multimodal ADAS system designed for expanding the usability of ADAS functions, including lane-keeping assist,... View full abstract»
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Deep Architecture for Traffic Flow Prediction: Deep Belief Networks With Multitask Learning
Publication Year: 2014, Page(s):2191 - 2201
Cited by: Papers (176)Traffic flow prediction is a fundamental problem in transportation modeling and management. Many existing approaches fail to provide favorable results due to being: 1) shallow in architecture; 2) hand engineered in features; and 3) separate in learning. In this paper we propose a deep architecture that consists of two parts, i.e., a deep belief network (DBN) at the bottom and a multitask regressio... View full abstract»
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Challenges Toward Wireless Communications for High-Speed Railway
Bo Ai ; Xiang Cheng ; Thomas Kürner ; Zhang-Dui Zhong ; Ke Guan ; Rui-Si He ; Lei Xiong ; David W. Matolak ; David G. Michelson ; Cesar Briso-RodriguezPublication Year: 2014, Page(s):2143 - 2158
Cited by: Papers (175)High-speed railway (HSR) brings convenience to peoples' lives and is generally considered as one of the most sustainable developments for ground transportation. One of the important parts of HSR construction is the signaling system, which is also called the “operation control system,” where wireless communications play a key role in the transmission of train control data. We discuss in detail the ... View full abstract»
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Driver Behavior Analysis for Safe Driving: A Survey
Publication Year: 2015, Page(s):3017 - 3032
Cited by: Papers (42)Driver drowsiness and distraction are two main reasons for traffic accidents and the related financial losses. Therefore, researchers have been working for more than a decade on designing driver inattention monitoring systems. As a result, several detection techniques for the detection of both drowsiness and distraction have been proposed in the literature. Some of these techniques were successful... View full abstract»
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A Practical Wireless Attack on the Connected Car and Security Protocol for In-Vehicle CAN
Publication Year: 2015, Page(s):993 - 1006
Cited by: Papers (24)Vehicle-IT convergence technology is a rapidly rising paradigm of modern vehicles, in which an electronic control unit (ECU) is used to control the vehicle electrical systems, and the controller area network (CAN), an in-vehicle network, is commonly used to construct an efficient network of ECUs. Unfortunately, security issues have not been treated properly in CAN, although CAN control messages co... View full abstract»
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Real-Time Prediction of Taxi Demand Using Recurrent Neural Networks
Publication Year: 2018, Page(s):2572 - 2581
Cited by: Papers (3)Predicting taxi demand throughout a city can help to organize the taxi fleet and minimize the wait-time for passengers and drivers. In this paper, we propose a sequence learning model that can predict future taxi requests in each area of a city based on the recent demand and other relevant information. Remembering information from the past is critical here, since taxi requests in the future are co... View full abstract»
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GNSS Position Integrity in Urban Environments: A Review of Literature
Publication Year: 2018, Page(s):2762 - 2778
Cited by: Papers (6)Integrity is one criteria to evaluate GNSS performance, which was first introduced in the aviation field. It is a measure of trust which can be placed in the correctness of the information supplied by the total system. In recent years, many GNSS-based applications emerge in the urban environment including liability critical ones, so the concept of integrity attracts more and more attention from ur... View full abstract»
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Traffic Flow Prediction for Road Transportation Networks With Limited Traffic Data
Publication Year: 2015, Page(s):653 - 662
Cited by: Papers (17)Obtaining accurate information about current and near-term future traffic flows of all links in a traffic network has a wide range of applications, including traffic forecasting, vehicle navigation devices, vehicle routing, and congestion management. A major problem in getting traffic flow information in real time is that the vast majority of links is not equipped with traffic sensors. Another pro... View full abstract»
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Potential Cyberattacks on Automated Vehicles
Publication Year: 2015, Page(s):546 - 556
Cited by: Papers (36)Vehicle automation has been one of the fundamental applications within the field of intelligent transportation systems (ITS) since the start of ITS research in the mid-1980s. For most of this time, it has been generally viewed as a futuristic concept that is not close to being ready for deployment. However, recent development of “self-driving” cars and the announcement by car manufacturers of thei... View full abstract»
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Robust Lane Detection and Tracking for Real-Time Applications
Publication Year: 2018, Page(s):4043 - 4048
Cited by: Papers (4)An effective lane-detection algorithm is a fundamental component of an advanced driver assistant system, as it provides important information that supports driving safety. The challenges faced by the lane detection and tracking algorithm include the lack of clarity of lane markings, poor visibility due to bad weather, illumination and light reflection, shadows, and dense road-based instructions. I... View full abstract»
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Traffic Sign Recognition Using a Multi-Task Convolutional Neural Network
Publication Year: 2018, Page(s):1100 - 1111
Cited by: Papers (8)Although traffic sign recognition has been studied for many years, most existing works are focused on the symbol-based traffic signs. This paper proposes a new data-driven system to recognize all categories of traffic signs, which include both symbol-based and text-based signs, in video sequences captured by a camera mounted on a car. The system consists of three stages, traffic sign regions of in... View full abstract»
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Cyber Threats Facing Autonomous and Connected Vehicles: Future Challenges
Publication Year: 2017, Page(s):2898 - 2915
Cited by: Papers (12)Vehicles are currently being developed and sold with increasing levels of connectivity and automation. As with all networked computing devices, increased connectivity often results in a heightened risk of a cyber security attack. Furthermore, increased automation exacerbates any risk by increasing the opportunities for the adversary to implement a successful attack. In this paper, a large volume o... View full abstract»
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Road-Segmentation-Based Curb Detection Method for Self-Driving via a 3D-LiDAR Sensor
Publication Year: 2018, Page(s):3981 - 3991
Cited by: Papers (2)The effective detection of curbs is fundamental and crucial for the navigation of a self-driving car. This paper presents a real-time curb detection method that automatically segments the road and detects its curbs using a 3D-LiDAR sensor. The point cloud data of the sensor are first processed to distinguish on-road and off-road areas. A sliding-beam method is then proposed to segment the road by ... View full abstract»
Aims & Scope
The theoretical, experimental and operational aspects of electrical and electronics engineering and information technologies as applied to Intelligent Transportation Systems (ITS). Intelligent Transportation Systems are defined as those systems utilizing synergistic technologies and systems engineering concepts to develop and improve transportation systems of all kinds. The scope of this interdisciplinary activity includes the promotion, consolidation and coordination of ITS technical activities among IEEE entities, and providing a focus for cooperative activities, both internally and externally.
Meet Our Editors
Editor-in-Chief
Azim Eskandarian
Nicholas and Rebecca Des Champs Professor
Virginia Tech
Blacksburg, VA 24061 USA
eskandarian@vt.edu
Further Links
Aims & Scope
The theoretical, experimental and operational aspects of electrical and electronics engineering and information technologies as applied to Intelligent Transportation Systems (ITS). Intelligent Transportation Systems are defined as those systems utilizing synergistic technologies and systems engineering concepts to develop and improve transportation systems of all kinds. The scope of this interdisciplinary activity includes the promotion, consolidation and coordination of ITS technical activities among IEEE entities, and providing a focus for cooperative activities, both internally and externally.
Persistent Link: https://ieeexplore.ieee.org/servlet/opac?punumber=6979 More »
Frequency: 12
ISSN: 1524-9050
Publication Details:
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Subjects
- Transportation
Contacts
Editor-in-Chief
Azim Eskandarian
Nicholas and Rebecca Des Champs Professor
Virginia Tech
Blacksburg, VA 24061, USA
eskandarian@vt.edu
About this Journal
Contacts
Editor-in-Chief
Azim Eskandarian
Nicholas and Rebecca Des Champs Professor
Virginia Tech
Blacksburg, VA 24061 USA
eskandarian@vt.edu