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Intelligent Transportation Systems, IEEE Transactions on

Issue 1 • Date Feb. 2014

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Displaying Results 1 - 25 of 43
  • Table of contents

    Publication Year: 2014 , Page(s): C1 - C4
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  • IEEE Transactions on Intelligent Transportation Systems publication information

    Publication Year: 2014 , Page(s): C2
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  • Scanning the issue and beyond: Toward ITS knowledge automation

    Publication Year: 2014 , Page(s): 1 - 5
    Cited by:  Papers (1)
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  • Symmetrical SURF and Its Applications to Vehicle Detection and Vehicle Make and Model Recognition

    Publication Year: 2014 , Page(s): 6 - 20
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3170 KB) |  | HTML iconHTML  

    Speeded-Up Robust Features (SURF) is a robust and useful feature detector for various vision-based applications but it is unable to detect symmetrical objects. This paper proposes a new symmetrical SURF descriptor to enrich the power of SURF to detect all possible symmetrical matching pairs through a mirroring transformation. A vehicle make and model recognition (MMR) application is then adopted to prove the practicability and feasibility of the method. To detect vehicles from the road, the proposed symmetrical descriptor is first applied to determine the region of interest of each vehicle from the road without using any motion features. This scheme provides two advantages: there is no need for background subtraction and it is extremely efficient for real-time applications. Two MMR challenges, namely multiplicity and ambiguity problems, are then addressed. The multiplicity problem stems from one vehicle model often having different model shapes on the road. The ambiguity problem results from vehicles from different companies often sharing similar shapes. To address these two problems, a grid division scheme is proposed to separate a vehicle into several grids; different weak classifiers that are trained on these grids are then integrated to build a strong ensemble classifier. The histogram of gradient and SURF descriptors are adopted to train the weak classifiers through a support vector machine learning algorithm. Because of the rich representation power of the grid-based method and the high accuracy of vehicle detection, the ensemble classifier can accurately recognize each vehicle. View full abstract»

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  • Sensor Fusion-Based Vacant Parking Slot Detection and Tracking

    Publication Year: 2014 , Page(s): 21 - 36
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2013 KB) |  | HTML iconHTML  

    This paper proposes a vacant parking slot detection and tracking system that fuses the sensors of an Around View Monitor (AVM) system and an ultrasonic sensor-based automatic parking system. The proposed system consists of three stages: parking slot marking detection, parking slot occupancy classification, and parking slot marking tracking. The parking slot marking detection stage recognizes various types of parking slot markings using AVM image sequences. It detects parking slots in individual AVM images by exploiting a hierarchical tree structure of parking slot markings and combines sequential detection results. The parking slot occupancy classification stage identifies vacancies of detected parking slots using ultrasonic sensor data. Parking slot occupancy is probabilistically calculated by treating each parking slot region as a single cell of the occupancy grid. The parking slot marking tracking stage continuously estimates the position of the selected parking slot while the ego-vehicle is moving into it. During tracking, AVM images and motion sensor-based odometry are fused together in the chamfer score level to achieve robustness against inevitable occlusions caused by the ego-vehicle. In the experiments, it is shown that the proposed method can recognize the positions and occupancies of various types of parking slot markings and stably track them under practical situations in a real-time manner. The proposed system is expected to help drivers conveniently select one of the available parking slots and support the parking control system by continuously updating the designated target positions. View full abstract»

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  • Probabilistic Aircraft Midair Conflict Resolution Using Stochastic Optimal Control

    Publication Year: 2014 , Page(s): 37 - 46
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1292 KB) |  | HTML iconHTML  

    This paper studies the problem of aircraft midair conflict resolution, which is a key technology to enable the coordinated and decentralized air traffic control envisioned in the Next Generation Air Transportation System (NextGen). The method proposed in this paper is based on stochastic optimal control, which is able to incorporate uncertainties in both aircraft and wind dynamics. The proposed numerical algorithm uses a Markov chain (MC) to approximate the continuous-time aircraft and wind dynamics, then the optimal control law is derived based on the MC. The proposed algorithm is able to resolve the conflicts between aircraft and moving convective weather regions. For conflict resolution between pairs of aircraft, a decomposition technique is proposed to reduce the computational complexity of the numerical algorithm. Simulations show that the proposed algorithm provides robustness against uncertainties in the system and is suitable for real applications. View full abstract»

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  • A Car Pooling Model and Solution Method With Stochastic Vehicle Travel Times

    Publication Year: 2014 , Page(s): 47 - 61
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (903 KB) |  | HTML iconHTML  

    Carpooling is one method that can be easily instituted and can help resolve a variety of problems that continue to plague urban areas, ranging from energy demands and traffic congestion to environmental pollution. However, most carpooling organizations currently use a trial-and-error process, in accordance with the projected vehicle travel times, for carpooling, which is neither effective nor efficient. In other words, stochastic disturbances arising from variations in vehicle travel times in actual operations are neglected. In the worst case scenario, where vehicle travel times fluctuate wildly during operations, the planned schedule could be disturbed enough to lose its optimality. Therefore, we constructed a stochastic carpooling model that considers the influence of stochastic travel times. The model is formulated as an integer multiple commodity network flow problem. Since real problem sizes can be large, it could be difficult to find optimal solutions within a reasonable period of time. Therefore, we develop a solution algorithm to solve the model. To test how well the model and the solution algorithm can be applied to the real world, we also developed a simulation-based evaluation method. To test the model and the solution algorithm, a case study is performed based upon data reported from a past study carried out in northern Taiwan. The results show that the model and solution algorithm are good and could be useful for carpooling practices. View full abstract»

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  • Envelope Level Crossing Rate and Average Fade Duration of Nonisotropic Vehicle-to-Vehicle Ricean Fading Channels

    Publication Year: 2014 , Page(s): 62 - 72
    Cited by:  Papers (11)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (795 KB) |  | HTML iconHTML  

    This paper proposes a generic geometry-based stochastic model for nonisotropic scattering vehicle-to-vehicle (V2V) Ricean fading channels. With the proposed model, the level crossing rate (LCR) and average fade duration (AFD) are derived. The resultant expressions are sufficiently general and subsume many well-known existing LCRs and AFDs as special cases. The derived LCR and AFD are further investigated in terms of some important parameters, e.g., the shape of the scattering region (two-ring or ellipse), mean angle, angle spread, and directions of movement of the Tx and Rx (same or opposite direction). More importantly, in this paper, the impact of the vehicular traffic density on the LCR and AFD for nonisotropic scattering V2V Ricean fading channels is investigated for the first time. Excellent agreement is observed between the theoretical LCRs/AFDs and corresponding measured data, thus demonstrating the validity and utility of the proposed model. View full abstract»

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  • Portable Roadside Sensors for Vehicle Counting, Classification, and Speed Measurement

    Publication Year: 2014 , Page(s): 73 - 83
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1892 KB) |  | HTML iconHTML  

    This paper focuses on the development of a portable roadside magnetic sensor system for vehicle counting, classification, and speed measurement. The sensor system consists of wireless anisotropic magnetic devices that do not require to be embedded in the roadway-the devices are placed next to the roadway and measure traffic in the immediately adjacent lane. An algorithm based on a magnetic field model is proposed to make the system robust to the errors created by larger vehicles driving in the nonadjacent lane. These false calls cause an 8% error if uncorrected. The use of the proposed algorithm reduces this error to only 1%. Speed measurement is based on the calculation of the cross correlation between longitudinally spaced sensors. Fast computation of the cross correlation is enabled by using frequency-domain signal processing techniques. An algorithm for automatically correcting for any small misalignment of the sensors is utilized. A high-accuracy differential Global Positioning System is used as a reference to measure vehicle speeds to evaluate the accuracy of the speed measurement from the new sensor system. The results show that the maximum error of the speed estimates is less than 2.5% over the entire range of 5-27 m/s (11-60 mi/h). Vehicle classification is done based on the magnetic length and an estimate of the average vertical magnetic height of the vehicle. Vehicle length is estimated from the product of occupancy and estimated speed. The average vertical magnetic height is estimated using two magnetic sensors that are vertically spaced by 0.25 m. Finally, it is shown that the sensor system can be used to reliably count the number of right turns at an intersection, with an accuracy of 95%. The developed sensor system is compact, portable, wireless, and inexpensive. Data are presented from a large number of vehicles on a regular busy urban road in the Twin Cities, MN, USA. View full abstract»

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  • Analytical Hierarchy Process Using Fuzzy Inference Technique for Real-Time Route Guidance System

    Publication Year: 2014 , Page(s): 84 - 93
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (978 KB) |  | HTML iconHTML  

    This paper focuses on an optimum route search function in the in-vehicle routing guidance system. For a dynamic route guidance system (DRGS), it should provide dynamic routing advice based on real-time traffic information and traffic conditions, such as congestion and roadwork. However, considering all these situations in traditional methods makes it very difficult to identify a valid mathematical model. To realize the DRGS, this paper proposes the analytical hierarchy process (AHP) using a fuzzy inference technique based on the real-time traffic information. This AHP-FUZZY approach is a multicriterion combination system. The nature of the AHP-FUZZY approach is a pairwise comparison, which is expressed by the fuzzy inference techniques, to achieve the weights of the attributes. The hierarchy structure of the AHP-FUZZY approach can greatly simplify the definition of a decision strategy and explicitly represent the multiple criteria, and the fuzzy inference technique can handle the vagueness and uncertainty of the attributes and adaptively generate the weights for the system. Based on the AHP-FUZZY approach, a simulation system is implemented in the route guidance system, and the process is analyzed. View full abstract»

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  • Receiver Autonomous Integrity Monitoring of GNSS Signals for Electronic Toll Collection

    Publication Year: 2014 , Page(s): 94 - 103
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1480 KB) |  | HTML iconHTML  

    Various road user charging mechanisms are used to control traffic and its resulting pollution, as well as revenue sources for reinvestment in the road infrastructure. Among them, electronic toll collection (ETC) systems based on user positions estimated with Global Navigation Satellite Systems (GNSS) are particularly attractive due to their flexibility and reduced roadside infrastructure in comparison to other systems such as tollbooths. Because GNSS positioning may be perturbed by different errors and failures, ETC systems, as liability critical applications, should monitor the integrity of GNSS signals in order to limit the use of faulty positions and the consequent charging errors. The integrity-monitoring systems have been originally designed for civil aviation; hence, they need to be adapted to the ETC requirements. This paper studies the use of receiver autonomous integrity monitoring (RAIM), which are algorithms run within the GNSS receiver and, therefore, are easier to tune to ETC needs than other systems based on external information. The weighted least squares residual RAIM used in civil aviation is analyzed, and an algorithm modification for ETC is proposed. Simulations demonstrate that the proposed RAIM algorithm has a superior level of availability over civil-aviation-based RAIM procedures, particularly in urban environments. View full abstract»

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  • A Wireless Accelerometer-Based Automatic Vehicle Classification Prototype System

    Publication Year: 2014 , Page(s): 104 - 111
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1625 KB) |  | HTML iconHTML  

    Automatic vehicle classification (AVC) systems provide data about vehicle classes that are used for many purposes. This paper describes a prototype axle count and spacing AVC system using wireless accelerometers and magnetometers. The accelerometers detect vehicle axles, and the magnetometers report vehicle arrivals and departures and estimate speed. The prototype system is installed on Interstate 80 at Pinole, CA, USA, and tested under various traffic conditions. Video images and reports from a nearby commercial weigh-in-motion station provide ground truth to evaluate the performance of the system, including classification, axle spacing, and vehicle counts. The results show that the prototype AVC system is reliable in classifying vehicles even under congested traffic with accuracy of 99%. View full abstract»

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  • Study of the Track–Train Continuous Information Transmission Process in a High-Speed Railway

    Publication Year: 2014 , Page(s): 112 - 121
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1540 KB) |  | HTML iconHTML  

    In the experiments and practical applications in a high-speed railway, it is observed that the carrier frequency of the sampled signal in a track circuit reader (TCR) is changed with train speed and goes beyond the upper permissive range prescribed for a jointless track circuit (JTC) in some cases. This can directly affect the availability of train target speed in train control systems and thus has an effect on the generation of the distance-to-go profile. It not only reduces the safety and efficiency of train traveling but also limits the improvement of train speed. To find the primary cause of the deviation in carrier frequency of the sampled signal in TCR (CFSST), this paper models the track-to-train continuous information transmission process using the transmission line theory based on the structures and principles of JTC and TCR. Then, the relation between the deviation in CFSST and the train speed is derived. Experimental results in high-speed railway have verified the correctness of the analysis, and the study can provides a strong theoretical basis for improving the safety level of railway traffic. Moreover, it can be a good reference for other countries where the similar track circuits are applied. View full abstract»

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  • Stochastic Characterization of Information Propagation Process in Vehicular Ad hoc Networks

    Publication Year: 2014 , Page(s): 122 - 135
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1281 KB) |  | HTML iconHTML  

    This paper studies the information propagation process in wireless communication networks formed by vehicles traveling on a highway. Corresponding to different lanes of the highway and different types of vehicles, we consider that vehicles in the network can be categorized into a number of traffic streams, where the vehicles in the same traffic stream have the same speed distribution while the speed distributions of vehicles in different traffic streams are different. We analyze the information propagation process of the aforementioned vehicular network and obtain an analytical formula for the information propagation speed (IPS). Using the formula, one can straightforwardly study the impact of parameters such as radio range, vehicular traffic density, vehicular speed distribution, and the time variation of vehicular speed on the IPS. The accuracy of the analytical results is validated using simulations. View full abstract»

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  • Speed and Texture: An Empirical Study on Optical-Flow Accuracy in ADAS Scenarios

    Publication Year: 2014 , Page(s): 136 - 147
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1881 KB) |  | HTML iconHTML  

    Increasing mobility in everyday life has led to the concern for the safety of automotives and human life. Computer vision has become a valuable tool for developing driver assistance applications that target such a concern. Many such vision-based assisting systems rely on motion estimation, where optical flow has shown its potential. A variational formulation of optical flow that achieves a dense flow field involves a data term and regularization terms. Depending on the image sequence, the regularization has to appropriately be weighted for better accuracy of the flow field. Because a vehicle can be driven in different kinds of environments, roads, and speeds, optical-flow estimation has to be accurately computed in all such scenarios. In this paper, we first present the polar representation of optical flow, which is quite suitable for driving scenarios due to the possibility that it offers to independently update regularization factors in different directional components. Then, we study the influence of vehicle speed and scene texture on optical-flow accuracy. Furthermore, we analyze the relationships of these specific characteristics on a driving scenario (vehicle speed and road texture) with the regularization weights in optical flow for better accuracy. As required by the work in this paper, we have generated several synthetic sequences along with ground-truth flow fields. View full abstract»

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  • Reducing the Error Accumulation in Car-Following Models Calibrated With Vehicle Trajectory Data

    Publication Year: 2014 , Page(s): 148 - 157
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1373 KB) |  | HTML iconHTML  

    With the development of probe vehicle technologies and the emerging connected vehicle technologies, applications and models using trajectory data for calibration and validation significantly increase. However, the error accumulation issue accompanied by the calibration process has not been fully investigated and addressed. This paper explores the mechanism and countermeasures of the error accumulation problems of car-following models calibrated with microscopic vehicle trajectory data. In this paper, we first derive the error dynamic model based on an acceleration-based generic car-following model formulation. The stability conditions for the error dynamic model are found to be different from the model stability conditions. Therefore, adjusting feasible ranges of model parameters in the car-following model calibration to ensure model stability cannot guarantee the error stability. However, directly enforcing those error stability conditions can be ineffective, particularly when explicit formulations are difficult to obtain. To overcome this issue, we propose several countermeasures that incorporate error accumulation indicators into the error measures used in the calibration. Numerical experiments are conducted to compare the traditional and the proposed error measures through the calibration of five representative car-following models, i.e., General Motors, Bando, Gipps, FREeway SIMulation (FRESIM), and intelligent driver model (IDM) models, using field trajectory data. The results indicate that the weighted location mean absolute error (MAE) and the location MAE with crash rate penalty can achieve the best overall error accumulation performance for all five models. Meanwhile, traditional error measures, velocity MAE, and velocity Theil's U also achieve satisfactory error accumulation performance for FRESIM and IDM models, respectively. View full abstract»

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  • Tactical Driving Behavior With Different Levels of Automation

    Publication Year: 2014 , Page(s): 158 - 167
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1010 KB) |  | HTML iconHTML  

    This paper investigated how different types of automation affect tactical driving behavior, depending on trust in the system. Previous research indicates that drivers wait for automation to act, delegating the monitoring of traffic situations. This would be especially true for those who have more trust in automation. Behavioral and gaze data from 30 participants driving an advanced simulator were recorded in four driving conditions, namely, manual driving, intentional car following, adaptive cruise control (ACC), and ACC with adaptive steering. Measures of trust in the systems were recorded with a questionnaire. Three fairly common traffic events requiring a driver response were analyzed. Trust in automation was high among the participants, and no associations between trust levels and behavior could be found. Drivers seem to make informed choices on when to let the automation handle a situation and when to switch it off manually or via the vehicle controls. If drivers did not expect the system to be able to handle the situation, they usually resumed control before the automation reached its limits. If the automation was expected to be able to deal with the situation, control was usually not resumed. In addition, situations were dealt with in a tactically different manner with automation than without. Controlling the car with automation systems is thus accepted by drivers as being a different undertaking than driving in manual mode. View full abstract»

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  • Automated Detection of Driver Fatigue Based on Entropy and Complexity Measures

    Publication Year: 2014 , Page(s): 168 - 177
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (664 KB) |  | HTML iconHTML  

    This paper presents a real-time method based on various entropy and complexity measures for detection and identification of driving fatigue from recorded electroencephalogram (EEG), electromyogram, and electrooculogram signals. The complexity features were used to distinguish whether the subjects are experienced drivers by calculating the Lempel-Ziv complexity of EEG approximate entropy (ApEn). Different threshold values can be set for the two kinds of drivers individually. The entropy-based features, namely, the wavelet entropy (WE), the peak-to-peak value of ApEn (PP-ApEn), and the peak-to-peak value of sample entropy (PP-SampEn), were extracted from the collected signals to estimate the driving fatigue stages. We proposed WE in a sliding window (WES), PP-ApEn in a sliding window (PP-ApEnS), and PP-SampEn in a sliding window (PP-SampEnS) for real-time analysis of driver fatigue. The real-time features obtained by WE, PP-ApEn, and PP-SampEn with sliding window were applied to artificial neural network for training and testing the system, which gives four situations for the fatigue level of the subjects, namely, normal state, mild fatigue, mood swing, and excessive fatigue. Then, the driver fatigue level can be determined in real time. The accuracy of estimation is about 96.5%-99.5%. Receiver operating characteristic (ROC) curve was used to present the performance of the neural network classifier. The area under the ROC curve is 0.9931. The results show that the developed method is valuable for the application of avoiding some traffic accidents caused by driver's fatigue. View full abstract»

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  • Two-Dimensional Sensor System for Automotive Crash Prediction

    Publication Year: 2014 , Page(s): 178 - 190
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2091 KB) |  | HTML iconHTML  

    This paper focuses on the use of magnetoresistive and sonar sensors for imminent collision detection in cars. The magnetoresistive sensors are used to measure the magnetic field from another vehicle in close proximity, to estimate relative position, velocity, and orientation of the vehicle from the measurements. First, an analytical formulation is developed for the planar variation of the magnetic field from a car as a function of 2-D position and orientation. While this relationship can be used to estimate position and orientation, a challenge is posed by the fact that the parameters in the analytical function vary with the type and model of the encountered car. Since the type of vehicle encountered is not known a priori, the parameters in the magnetic field function are unknown. The use of both sonar and magnetoresistive sensors and an adaptive estimator is shown to address this problem. While the sonar sensors do not work at very small intervehicle distance and have low refresh rates, their use during a short initial time duration leads to a reliable estimator. Experimental results are presented for both a laboratory wheeled car door and for a full-scale passenger sedan. The results show that planar position and orientation can be accurately estimated for a range of relative motions at different oblique angles. View full abstract»

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  • Robust Vehicle Sideslip Angle Estimation Through a Disturbance Rejection Filter That Integrates a Magnetometer With GPS

    Publication Year: 2014 , Page(s): 191 - 204
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2596 KB) |  | HTML iconHTML  

    This paper presents a novel method that estimates the vehicle sideslip angle for a wide range of surface frictions and road bank angles by combining measurements of a magnetometer, Global Positioning System (GPS), and inertial measurement unit (IMU). To reject disturbances in the magnetometer, a new stochastic filter is designed and integrated on the Kalman filter framework. The significant latency in a low-cost GPS velocity measurement is addressed by “measurement shifting,” and biases in the IMU measurements are estimated through state augmentation. Dual Kalman filters are employed in the sensor fusing framework. A comprehensive simulation study was conducted to prove the feasibility of the method. Finally, the performance and accuracy are verified through extensive experiments. View full abstract»

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  • Utilizing Microscopic Traffic and Weather Data to Analyze Real-Time Crash Patterns in the Context of Active Traffic Management

    Publication Year: 2014 , Page(s): 205 - 213
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (504 KB) |  | HTML iconHTML  

    This paper investigates the effects of microscopic traffic, weather, and roadway geometric factors on the occurrence of specific crash types for a freeway. The I-70 Freeway was chosen for this paper since automatic vehicle identification (AVI) and weather detection systems are implemented along this corridor. A main objective of this paper is to expand the purpose of the existing intelligent transportation system to incorporate traffic safety improvement and suggest active traffic management (ATM) strategies by identifying the real-time crash patterns. Crashes have been categorized as rear-end, sideswipe, and single-vehicle crashes. AVI segment average speed, real-time weather data, and roadway geometric characteristic data were utilized as explanatory variables in this paper. First, binary logistic regression models were estimated to compare single- with multivehicle crashes and sideswipe with rear-end crashes. Then, a hierarchical logistic regression model that simultaneously fits two conditional logistic regression models for the three crash types has been developed. Results from the models indicate that single-vehicle crashes are more likely to occur in snowy seasons, at moderate slopes, three-lane segments, and under free-flow conditions, whereas the sideswipe crash occurrence differs from rear-end crashes with the visibility situation, segment number of lanes, grades, and their directions (up or down). Furthermore, the innovative way of estimating two conditional logistic regression models simultaneously in the Bayesian framework fits the correlated data structure well. Conclusions from this paper imply that different ATM strategies should be designed for three- and two-lane roadway sections and are also considering the seasonal effects. View full abstract»

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  • Prediction of Traffic Flow at the Boundary of a Motorway Network

    Publication Year: 2014 , Page(s): 214 - 227
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3285 KB) |  | HTML iconHTML  

    For online traffic control at traffic control centers, there is a need for predictions of the traffic flow during a short horizon, for example, 30 min ahead. For this effort, predictions are needed of the traffic inflow into the network at motorways on the network boundary and at on-ramps. This paper presents an adaptive prediction algorithm for the inflows into the network in regular traffic situations based on stochastic control theory. The prediction algorithm is based on an adaptive prediction algorithm of T. Bohlin. The algorithm is designed and tested on traffic flow data of the ring road of Amsterdam. The results show that the algorithm provides robust predictions of traffic demand with relatively small errors for the next 30 min in a large-scale real-time environment. View full abstract»

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  • Text Detection and Recognition on Traffic Panels From Street-Level Imagery Using Visual Appearance

    Publication Year: 2014 , Page(s): 228 - 238
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1499 KB) |  | HTML iconHTML  

    Traffic sign detection and recognition has been thoroughly studied for a long time. However, traffic panel detection and recognition still remains a challenge in computer vision due to its different types and the huge variability of the information depicted in them. This paper presents a method to detect traffic panels in street-level images and to recognize the information contained on them, as an application to intelligent transportation systems (ITS). The main purpose can be to make an automatic inventory of the traffic panels located in a road to support road maintenance and to assist drivers. Our proposal extracts local descriptors at some interest keypoints after applying blue and white color segmentation. Then, images are represented as a “bag of visual words” and classified using Naïve Bayes or support vector machines. This visual appearance categorization method is a new approach for traffic panel detection in the state of the art. Finally, our own text detection and recognition method is applied on those images where a traffic panel has been detected, in order to automatically read and save the information depicted in the panels. We propose a language model partly based on a dynamic dictionary for a limited geographical area using a reverse geocoding service. Experimental results on real images from Google Street View prove the efficiency of the proposed method and give way to using street-level images for different applications on ITS. View full abstract»

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  • Actuator-Redundancy-Based Fault Diagnosis for Four-Wheel Independently Actuated Electric Vehicles

    Publication Year: 2014 , Page(s): 239 - 249
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1116 KB) |  | HTML iconHTML  

    This paper presents a real-time actuator-redundancy-based fault diagnosis approach for four-wheel independently actuated (FWIA) in-wheel motor electric ground vehicles. The tire-road friction coefficient (TRFC), which is usually unknown, needs to be accurately estimated to calculate the in-wheel motor torque and evaluate the fault in real time. An observer is applied to each of the in-wheel motors to generate a TRFC estimate from the respective wheel. The individual estimates of the TRFC are merged using a voting scheme, which can reject the erroneous estimate from the faulty motor. Then, the resulting accurate TRFC estimate is adopted to calculate the residual for each of the in-wheel motors and to detect the possible actuator fault. Experimental results from a prototype FWIA electric ground vehicle are given to show the effectiveness of the proposed fault diagnosis method. View full abstract»

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  • Modeling and Forecasting the Urban Volume Using Stochastic Differential Equations

    Publication Year: 2014 , Page(s): 250 - 259
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1068 KB) |  | HTML iconHTML  

    Traffic flow prediction can be used for the management of traffic control systems and can be applied toward improving traffic light split times at intersections. In this paper, we developed a methodology for the short-term prediction of traffic flow using the stochastic differential equation (SDE). Since the current volume depends on the previous short-term volume and time, we used the Hull-White model. With the proposed method, a flexible short-term prediction of volume is suggested. It is possible to simulate traffic conditions easily and also detect incidents precisely. This method is applied in Tehran's highways, and the obtained results are compared with previous artworks. Our results offered a better fit to the traffic volume. View full abstract»

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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.

Full Aims & Scope

Meet Our Editors

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