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Intelligent Transportation Systems, IEEE Transactions on

Issue 2 • Date April 2007

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Displaying Results 1 - 25 of 26
  • Table of contents

    Page(s): C1 - 1
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  • IEEE Transactions on Intelligent Transportation Systems publication information

    Page(s): C2
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  • Video and Seismic Sensor-Based Structural Health Monitoring: Framework, Algorithms, and Implementation

    Page(s): 169 - 180
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    This paper presents the design and application of novel multisensory testbeds for collection, synchronization, archival, and analysis of multimodal data for health monitoring of transportation infrastructures. The framework for data capture from vision and seismic sensors is described, and the important issue of synchronization between these modalities is addressed. Computer-vision algorithms are used to detect and track vehicles and extract their properties. It is noted that the video and seismic sensors in the testbed supply complementary information about passing vehicles. Data fusion between features obtained from these modalities is used to perform vehicle classification. Experimental results of vehicle detection, tracking, and classification obtained with these testbeds are described View full abstract»

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  • Determining Traffic-Flow Characteristics by Definition for Application in ITS

    Page(s): 181 - 187
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    Traffic-flow characteristics such as flow, density, and space mean speed (SMS) are critical to Intelligent Transportation Systems (ITS). For example, flow is a direct measure of throughput, density is an ideal indicator of traffic conditions, and SMS is the primary input to compute travel times. An attractive method to compute traffic-flow characteristics in ITS is expected to meet the following criteria: 1) It should be a one-stop solution, meaning it involves only one type of sensor that is able to determine flow, SMS, and density; 2) it should be accurate, meaning it determines these characteristics by definition rather than by estimation or by using surrogates; 3) it should preserve the fundamental relationship among flow, SMS, and density; and 4) it should be compatible with ITS, meaning it uses ITS data and supports online application. Existing methods may be good for one or some of the above criteria, but none satisfies all of them. This paper tackles the challenge by formulating a method, called the n-t method, which addresses all these criteria. Its accuracy and the fundamental relationship are guaranteed by applying a generalized definition of traffic-flow characteristics. Inputs to the method are time-stamped traffic counts which happen to be the strength of most ITS systems. Some empirical examples are provided to demonstrate the performance of the n-t method View full abstract»

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  • A Traffic Accident Recording and Reporting Model at Intersections

    Page(s): 188 - 194
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    In this paper, we suggested a vision-based traffic accident detection algorithm and developed a system for automatically detecting, recording, and reporting traffic accidents at intersections. A system with these properties would be beneficial in determining the cause of accidents and the features of an intersection that impact safety. This model first extracts the vehicles from the video image of the charge-couple-device camera, tracks the moving vehicles (MVs), and extracts features such as the variation rate of the velocity, position, area, and direction of MVs. The model then makes decisions on the traffic accident based on the extracted features. In a field test, the suggested model achieved a correct detection rate (CDR) of 50% and a detection rate of 60%. Considering that a sound-based accident detection system showed a CDR of 1% and a DR of 66.1%, our result is a remarkable achievement View full abstract»

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  • Elucidating Vehicle Lateral Dynamics Using a Bifurcation Analysis

    Page(s): 195 - 207
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    Issues of stability and bifurcation phenomena in vehicle lateral dynamics are presented. Based on the assumption of constant driving speed, a second-order nonlinear lateral dynamics model is obtained. Local stability and existence conditions for saddle-node bifurcation appearing in vehicle dynamics with respect to the variations in front wheel steering angle are then derived via system linearization and local bifurcation analysis. Bifurcation phenomena occurring in vehicle lateral dynamics might result in spin and/or system instability. A perturbation method is employed to solve for an approximation of system equilibrium near the zero value of the front wheel steering angle, which reveals the relationship between sideslip angle and the applied front wheel angle. Numerical simulations from an example model demonstrate the theoretical results View full abstract»

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  • Conflict Resolution and Train Speed Coordination for Solving Real-Time Timetable Perturbations

    Page(s): 208 - 222
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    During rail operations, unforeseen events may cause timetable perturbations, which ask for the capability of traffic management systems to reschedule trains and to restore the timetable feasibility. Based on an accurate monitoring of train positions and speeds, potential conflicting routes can be predicted in advance and resolved in real time. The adjusted targets (location-time-speed) would be then communicated to the relevant trains by which drivers should be able to anticipate the changed traffic circumstances and adjust the train's speed accordingly. We adopt a detailed alternative graph model for the train dispatching problem. Conflicts between different trains are effectively detected and solved. Adopting the blocking time model, we ascertain whether a safe distance headway between trains is respected, and we also consider speed coordination issues among consecutive trains. An iterative rescheduling procedure provides an acceptable speed profile for each train over the intended time horizon. After a finite number of iterations, the final solution is a conflict-free schedule that respects the signaling and safety constraints. A computational study based on a hourly cyclical timetable of the Schiphol railway network has been carried out. Our automated dispatching system provides better solutions in terms of delay minimization when compared to dispatching rules that can be adopted by a human traffic controller View full abstract»

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  • Maximum Freedom Last Scheduling Algorithm for Downlinks of DSRC Networks

    Page(s): 223 - 232
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    This paper proposes a maximum freedom last (MFL) scheduling algorithm for downlinks, from the roadside unit to the onboard unit (OBU), of dedicated short-range communication networks in intelligent transportation systems, to minimize the system handoff rate under the maximum tolerable delay constraint. The MFL scheduling algorithm schedules the service ordering of OBUs according to their degree of freedom, which is determined by factors such as remaining dwell time of service channel, remaining transmission time, queueing delay, and maximum tolerable delay. The algorithm gives the smallest chance of service to the OBU with the largest remaining dwell time, the smallest remaining transmission time, and the largest weighting factor, which is a function of the queueing delay and the maximum tolerable delay. Simulation results show that the MFL scheduling algorithm outperforms the traditional first-come-first-serve and earliest-deadline-first methods in terms of service failure and system handoff rates View full abstract»

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  • Collision Avoidance for Vehicle-Following Systems

    Page(s): 233 - 244
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    The vehicle-following concept has been widely used in several intelligent-vehicle applications. Adaptive cruise control systems, platooning systems, and systems for stop-and-go traffic employ this concept: The ego vehicle follows a leader vehicle at a certain distance. The vehicle-following concept comes to its limitations when obstacles interfere with the path between the ego vehicle and the leader vehicle. We call such situations dynamic driving situations. This paper introduces a planning and decision component to generalize vehicle following to situations with nonautomated interfering vehicles in mixed traffic. As a demonstrator, we employ a car that is able to navigate autonomously through regular traffic that is longitudinally and laterally guided by actuators controlled by a computer. This paper focuses on and limits itself to lateral control for collision avoidance. Previously, this autonomous-driving capability was purely based on the vehicle-following concept using vision. The path of the leader vehicle was tracked. To extend this capability to dynamic driving situations, a dynamic path-planning component is introduced. Several driving situations are identified that necessitate responses to more than the leader vehicle. We borrow an idea from robotics to solve the problem. Treat the path of the leader vehicle as an elastic band that is subjected to repelling forces of obstacles in the surroundings. This elastic-band framework offers the necessary features to cover dynamic driving situations. Simulation results show the power of this approach. Real-world results obtained with our demonstrator validate the simulation results View full abstract»

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  • Vehicle Classification Based on the Radar Measurement of Height Profiles

    Page(s): 245 - 253
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    The problem of classifying road vehicles according to vehicle type is considered. The proposed solution is based on using vehicle height and length and height profiles obtained by a microwave (MW) radar sensor. We show that if the radar sensor satisfies certain requirements, then a precise feature vector can be extracted, and simple deterministic algorithms can be applied to determine the vehicle class. Field trials using a spread-spectrum MW radar sensor system operating on these principles have been carried out. They confirm that accurate classification of a large number of vehicle classes can be reached View full abstract»

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  • Multiairport Capacity Management: Genetic Algorithm With Receding Horizon

    Page(s): 254 - 263
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    The inability of airport capacity to meet the growing air traffic demand is a major cause of congestion and costly delays. Airport capacity management (ACM) in a dynamic environment is crucial for the optimal operation of an airport. This paper reports on a novel method to attack this dynamic problem by integrating the concept of receding horizon control (RHC) into a genetic algorithm (GA). A mathematical model is set up for the dynamic ACM problem in a multiairport system where flights can be redirected between airports. A GA is then designed from an RHC point of view. Special attention is paid on how to choose those parameters related to the receding horizon and terminal penalty. A simulation study shows that the new RHC-based GA proposed in this paper is effective and efficient to solve the ACM problem in a dynamic multiairport environment View full abstract»

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  • Road-Sign Detection and Recognition Based on Support Vector Machines

    Page(s): 264 - 278
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    This paper presents an automatic road-sign detection and recognition system based on support vector machines (SVMs). In automatic traffic-sign maintenance and in a visual driver-assistance system, road-sign detection and recognition are two of the most important functions. Our system is able to detect and recognize circular, rectangular, triangular, and octagonal signs and, hence, covers all existing Spanish traffic-sign shapes. Road signs provide drivers important information and help them to drive more safely and more easily by guiding and warning them and thus regulating their actions. The proposed recognition system is based on the generalization properties of SVMs. The system consists of three stages: 1) segmentation according to the color of the pixel; 2) traffic-sign detection by shape classification using linear SVMs; and 3) content recognition based on Gaussian-kernel SVMs. Because of the used segmentation stage by red, blue, yellow, white, or combinations of these colors, all traffic signs can be detected, and some of them can be detected by several colors. Results show a high success rate and a very low amount of false positives in the final recognition stage. From these results, we can conclude that the proposed algorithm is invariant to translation, rotation, scale, and, in many situations, even to partial occlusions View full abstract»

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  • Road Selection Using Multicriteria Fusion for the Road-Matching Problem

    Page(s): 279 - 291
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    This paper presents a road selection strategy for novel road-matching methods that are designed to support real-time navigational features within Advanced Driving-Assistance Systems (ADAS). Selecting the most likely segment(s) is a crucial issue for the road-matching problem. The selection strategy merges several criteria using Belief theory. Particular attention is given to the development of belief functions from measurements and estimations of relative distances, headings, and velocities. Experimental results using data from antilock brake system sensors, the differential Global Positioning System receiver, and the accurate digital roadmap illustrate the performances of this approach, particularly in ambiguous situations View full abstract»

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  • Combination of Feature Extraction Methods for SVM Pedestrian Detection

    Page(s): 292 - 307
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    This paper describes a comprehensive combination of feature extraction methods for vision-based pedestrian detection in Intelligent Transportation Systems. The basic components of pedestrians are first located in the image and then combined with a support-vector-machine-based classifier. This poses the problem of pedestrian detection in real cluttered road images. Candidate pedestrians are located using a subtractive clustering attention mechanism based on stereo vision. A components-based learning approach is proposed in order to better deal with pedestrian variability, illumination conditions, partial occlusions, and rotations. Extensive comparisons have been carried out using different feature extraction methods as a key to image understanding in real traffic conditions. A database containing thousands of pedestrian samples extracted from real traffic images has been created for learning purposes at either daytime or nighttime. The results achieved to date show interesting conclusions that suggest a combination of feature extraction methods as an essential clue for enhanced detection performance View full abstract»

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  • Control of Spatiotemporal Congested Traffic Patterns at Highway Bottlenecks

    Page(s): 308 - 320
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    It is shown that the probabilistic feature of traffic breakdown at an on-ramp bottleneck leads to great limitations for reliable applications of a free flow control approach in which free flow should be maintained at the bottleneck. Based on these measured features of traffic breakdown at the bottleneck as well as on the Kerner-Klenov microscopic traffic model in the context of the author's three-phase traffic theory, critical discussions of earlier traffic flow models for freeway control simulations and of ALINEA methods of Papageorgiou for feedback on-ramp metering are made. An alternative congested pattern control approach to feedback on-ramp metering ANCONA introduced by the author in 2004 is numerically studied. In ANCONA, congestion at the bottleneck is allowed to set in. However, ANCONA maintains speeds within a congested pattern higher than about 60 km/h and prevents upstream propagation of the pattern. To reach these goals, after traffic breakdown has occurred spontaneously at the bottleneck, ANCONA tries to return to free flow via reduction of on-ramp inflow. A critical comparison of ANCONA with ALINEA and UP-ALINEA is made View full abstract»

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  • Superresolution of License Plates in Real Traffic Videos

    Page(s): 321 - 331
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    In this paper, a novel method to enhance license plate numbers of moving vehicles in real traffic videos is proposed. A high-resolution image of the number plate is obtained by fusing the information derived from multiple, subpixel shifted, and noisy low-resolution observations. The image to be superresolved is modeled as a Markov random field and is estimated from the observations by a graduated nonconvexity optimization procedure. A discontinuity adaptive regularizer is used to preserve the edges in the reconstructed number plate for improved readability. Experimental results are given on several traffic sequences to demonstrate the robustness of the proposed method to potential errors in motion and blur estimates. The method is computationally efficient as all operations can be implemented locally in the image domain View full abstract»

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  • High-Resolution Estimation of Ranges Using Multiple-Frequency CW Radar

    Page(s): 332 - 339
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    The problem of ranging multiple vehicles traveling at similar speeds using multiple-frequency continuous-wave (CW) radar is considered. A new method based on the compensation of vehicle movement and MUSIC-based time delay estimator is presented. The method is tested using a commercially available multiple-frequency CW radar sensor in real traffic situations. Test results show that the proposed method makes it possible to estimate the positions of two vehicles moving at similar speeds. Test results also demonstrate that under a given bandwidth, the multiple-frequency CW radar can provide about ten times higher resolution as compared with the coherent spread spectrum radar View full abstract»

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  • Real-Time Detection of Driver Cognitive Distraction Using Support Vector Machines

    Page(s): 340 - 350
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    As use of in-vehicle information systems (IVISs) such as cell phones, navigation systems, and satellite radios has increased, driver distraction has become an important and growing safety concern. A promising way to overcome this problem is to detect driver distraction and adapt in-vehicle systems accordingly to mitigate such distractions. To realize this strategy, this paper applied support vector machines (SVMs), which is a data mining method, to develop a real-time approach for detecting cognitive distraction using drivers' eye movements and driving performance data. Data were collected in a simulator experiment in which ten participants interacted with an IVIS while driving. The data were used to train and test both SVM and logistic regression models, and three different model characteristics were investigated: how distraction was defined, which data were input to the model, and how the input data were summarized. The results show that the SVM models were able to detect driver distraction with an average accuracy of 81.1%, outperforming more traditional logistic regression models. The best performing model (96.1% accuracy) resulted when distraction was defined using experimental conditions (i.e., IVIS drive or baseline drive), the input data were comprised of eye movement and driving measures, and these data were summarized over a 40-s window with 95% overlap of windows. These results demonstrate that eye movements and simple measures of driving performance can be used to detect driver distraction in real time. Potential applications of this paper include the design of adaptive in-vehicle systems and the evaluation of driver distraction View full abstract»

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  • Traffic Management Center Use of Incident Detection Algorithms: Findings of a Nationwide Survey

    Page(s): 351 - 358
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    The focus of this paper is the context in which the decision makers for traffic management centers (TMCs) choose whether to include and/or use automatic incident detection (AID) algorithms. A survey was conducted of TMC professionals in positions to make, influence, or provide input to decisions regarding TMC operational policies as well as decisions regarding priorities for future system enhancements. Analysis of the survey results not only provides an understanding of the reasons behind the limited implementation of AID algorithms but also allows a direct comparison between the conventional incident detection methods and the AID technology on the basis of measured and/or perceived performance. It was observed that 90% of the survey respondents feel that the current methods of incident detection are insufficient either at present (70%) or will be so in the future (20%). This finding alone motivates a need to redouble research efforts aimed at developing robust and accurate automatic detection methods. In this regard, this paper presents promising directions to overcome past AID algorithm deficiencies View full abstract»

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  • Stochastic Optimal-Control Approach to Automatic Incident-Responsive Coordinated Ramp Control

    Page(s): 359 - 367
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    This paper presents a stochastic optimal-control-based approach to real-time incident-responsive coordinated ramp control. The proposed coordinated ramp-control methodology includes two major functions, i.e., 1) identification of control-zone-based congestion patterns using the modified generalized-sequential-probability-ratio-testing technology and 2) group-based ramp control using stochastic optimal control coupled with extended Kalman filtering technologies. With the aid of the Paramics microscopic traffic simulator, numerical studies under various simulated incident-induced congestion conditions were conducted. Corresponding numerical results indicate the feasibility of the proposed ramp-control method in responding to diverse lane-blocking incident-induced traffic-congestion conditions View full abstract»

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  • The Selective Random Subspace Predictor for Traffic Flow Forecasting

    Page(s): 367 - 373
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    Traffic flow forecasting is an important issue for the application of Intelligent Transportation Systems. Due to practical limitations, traffic flow data may be incomplete (partially missing or substantially contaminated by noises), which will aggravate the difficulties for traffic flow forecasting. In this paper, a new approach, termed the selective random subspace predictor (SRSP), is developed, which is capable of implementing traffic flow forecasting effectively whether incomplete data exist or not. It integrates the entire spatial and temporal traffic flow information in a transportation network to carry out traffic flow forecasting. To forecast the traffic flow at an object road link, the Pearson correlation coefficient is adopted to select some candidate input variables that compose the selective input space. Then, a number of subsets of the input variables in the selective input space are randomly selected to, respectively, serve as specific inputs for prediction. The multiple outputs are combined through a fusion methodology to make final decisions. Both theoretical analysis and experimental results demonstrate the effectiveness and robustness of the SRSP for traffic flow forecasting, whether for complete data or for incomplete data View full abstract»

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  • ITSS Future Scheduled Conferences

    Page(s): 374
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  • IEEE ITS Magazine

    Page(s): 375
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    Freely Available from IEEE
  • ITSC '07

    Page(s): 376
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    Freely Available from IEEE
  • IEEE Intelligent Transportation Systems Society Information

    Page(s): C3
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Aims & Scope

The IEEE Transactions on ITS is concerned with the design, analysis, and control of information technology as it is applied to transportation systems. The IEEE ITS Transactions is focused on the numerous technical aspects of ITS technologies spanned by the IEEE. Transportation systems are invariably complex, and their complexity arises from many sources. Transportation systems can involve humans, vehicles, shipments, information technology, and the physical infrastructure, all interacting in complex ways. Many aspects of transportation systems are uncertain, dynamic and nonlinear, and such systems may be highly sensitive to perturbations. Controls can involve multiple agents that (and/or who) are distributed and hierarchical. Humans who invariably play critical roles in a transportation system have a diversity of objectives and a wide range of skills and education. Transportation systems are usually large-scale in nature and are invariably geographically distributed.

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Meet Our Editors

Editor-in-Chief
Fei-Yue Wang
Professor
University of Arizona