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Special Issue "Intelligent Transportation Related Complex Systems and Sensors"

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: 31 July 2020.

Special Issue Editors

Prof. Dr. Kyandoghere Kyamakya
E-Mail Website
Guest Editor
Alpen-Adria-Universität Klagenfurt, Klagenfurt, Austria
Tel. +43 463 2700 3540
Interests: intelligent transportation systems; machine vision; machine learning and pattern recognition; neurocomputing and applications; systems science and nonlinear dynamics; telecommunications systems; robotics and autonomous systems
Special Issues and Collections in MDPI journals
Dr. Jean Chamberlain Chedjou
E-Mail Website
Guest Editor
Alpen-Adria-Universität Klagenfurt, Institute of Smart System Technologies, Klagenfurt, Austria
Interests: dynamic systems in engineering; neurocomputing and applications; optimization and inverse problems; intelligent transportation systems
Special Issues and Collections in MDPI journals
Dr. Fadi Al-Machot
E-Mail Website
Guest Editor
Alpen-Adria-Universität Klagenfurt, Department of Applied Informatics, Klagenfurt, Austria
Interests: machine learning; pattern recognition; image processing; data mining; video understanding; cognitive modeling and recognition
Special Issues and Collections in MDPI journals
Dr. Ahmad Haj Mosa
E-Mail Website
Guest Editor
Alpen-Adria-Universität Klagenfurt, Klagenfurt, Austria
Interests: machine learning; cognitive neuroscience; applied mathematics; machine vision
Special Issues and Collections in MDPI journals
Prof. Dr. Antoine Bagula
E-Mail Website
Guest Editor
University of the Western Cape, ISAT Laboratory, Bellville, South Africa
Interests: internet-of-things; artificial intelligence; blockchain technologies; next generation networks
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

Transportation systems are particularly complex systems as they are mostly “systems of complex systems”. Complex systems are characterized by specific time-dependent interactions among their many constituents/sub-systems/components. As a consequence, they often manifest rich, non-trivial and unexpected behaviour.

Examples of transportation-related complex systems are: road traffic, traffic sensors, autonomous/automated cars, smart cities, smart sensors, virtual sensors, traffic control systems, smart roads, logistics systems, smart mobility systems, etc.

For the mastering and efficient operation of various transportation-related complex systems, a series of sensor/sensing, data quality, online simulation, and modeling and optimization related issues are of extreme high actual interest.

Selected Keywords:

  • Complex systems concepts in transportation
  • System dynamics based modeling and simulation in transportation
  • Online simulation and virtual sensing in intelligent transportation
  • Online (spatio-temporal) traffic modeling (system identification) in relation with virtual traffic sensors
  • Safety performance assessment based on virtual experiments
  • Virtual sensors design principles
  • Virtual sensors modelling techniques
  • Sensor clouds in transportation
  • Cloud virtual sensors in transportation
  • Virtual sensors modelling using neural networks and/or deep learning
  • Self-learning virtual sensor networks in intelligent transportation
  • Compressive sensing for physical and virtual sensors
  • Sensor data quality modeling and prediction in intelligent transportation and smart logistics
  • Effective quality-aware sensor data management in transportation
  • Fault detection and fault correction techniques for both physical and virtual sensors in transportation
  • Predictive maintenance concepts and systems in smart transportation
  • Virtual sensors for automated/autonomous driving
  • Wireless sensor networks in intelligent transportation systems
  • Virtual scanning algorithms for road network surveillance
  • Crowd sensing and related issues for transportation related applications
  • Big sensor data systems for smart cities related transportation systems
  • Multi-sensor fusion approaches

Prof. Dr. Kyandoghere Kyamakya
Dr. Jean Chamberlain Chedjou
Dr. Fadi Al-Machot
Dr. Ahmad Haj Mosa
Prof. Dr. Antoine Bagula
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (8 papers)

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Research

Open AccessArticle
Developing a Neural–Kalman Filtering Approach for Estimating Traffic Stream Density Using Probe Vehicle Data
Sensors 2019, 19(19), 4325; https://doi.org/10.3390/s19194325 - 07 Oct 2019
Abstract
This paper presents a novel model for estimating the number of vehicles along signalized approaches. The proposed estimation algorithm utilizes the adaptive Kalman filter (AKF) to produce reliable traffic vehicle count estimates, considering real-time estimates of the system noise characteristics. The AKF utilizes [...] Read more.
This paper presents a novel model for estimating the number of vehicles along signalized approaches. The proposed estimation algorithm utilizes the adaptive Kalman filter (AKF) to produce reliable traffic vehicle count estimates, considering real-time estimates of the system noise characteristics. The AKF utilizes only real-time probe vehicle data. The AKF is demonstrated to outperform the traditional Kalman filter, reducing the prediction error by up to 29%. In addition, the paper introduces a novel approach that combines the AKF with a neural network (AKFNN) to enhance the vehicle count estimates, where the neural network is employed to estimate the probe vehicles’ market penetration rate. Results indicate that the accuracy of vehicle count estimates is significantly improved using the AKFNN approach (by up to 26%) over the AKF. Moreover, the paper investigates the sensitivity of the proposed AKF model to the initial conditions, such as the initial estimate of vehicle counts, initial mean estimate of the state system, and the initial covariance of the state estimate. The results demonstrate that the AKF is sensitive to the initial conditions. More accurate estimates could be achieved if the initial conditions are appropriately selected. In conclusion, the proposed AKF is more accurate than the traditional Kalman filter. Finally, the AKFNN approach is more accurate than the AKF and the traditional Kalman filter since the AKFNN uses more accurate values of the probe vehicle market penetration rate. Full article
(This article belongs to the Special Issue Intelligent Transportation Related Complex Systems and Sensors)
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Open AccessArticle
A Novel Recurrent Neural Network-Based Ultra-Fast, Robust, and Scalable Solver for Inverting a “Time-Varying Matrix”
Sensors 2019, 19(18), 4002; https://doi.org/10.3390/s19184002 - 16 Sep 2019
Abstract
The concept presented in this paper is based on previous dynamical methods to realize a time-varying matrix inversion. It is essentially a set of coupled ordinary differential equations (ODEs) which does indeed constitute a recurrent neural network (RNN) model. The coupled ODEs constitute [...] Read more.
The concept presented in this paper is based on previous dynamical methods to realize a time-varying matrix inversion. It is essentially a set of coupled ordinary differential equations (ODEs) which does indeed constitute a recurrent neural network (RNN) model. The coupled ODEs constitute a universal modeling framework for realizing a matrix inversion provided the matrix is invertible. The proposed model does converge to the inverted matrix if the matrix is invertible, otherwise it converges to an approximated inverse. Although various methods exist to solve a matrix inversion in various areas of science and engineering, most of them do assume that either the time-varying matrix inversion is free of noise or they involve a denoising module before starting the matrix inversion computation. However, in the practice, the noise presence issue is a very serious problem. Also, the denoising process is computationally expensive and can lead to a violation of the real-time property of the system. Hence, the search for a new ‘matrix inversion’ solving method inherently integrating noise-cancelling is highly demanded. In this paper, a new combined/extended method for time-varying matrix inversion is proposed and investigated. The proposed method is extending both the gradient neural network (GNN) and the Zhang neural network (ZNN) concepts. Our new model has proven that it has exponential stability according to Lyapunov theory. Furthermore, when compared to the other previous related methods (namely GNN, ZNN, Chen neural network, and integration-enhanced Zhang neural network or IEZNN) it has a much better theoretical convergence speed. To finish, all named models (the new one versus the old ones) are compared through practical examples and both their respective convergence and error rates are measured. It is shown/observed that the novel/proposed method has a better practical convergence rate when compared to the other models. Regarding the amount of noise, it is proven that there is a very good approximation of the matrix inverse even in the presence of noise. Full article
(This article belongs to the Special Issue Intelligent Transportation Related Complex Systems and Sensors)
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Open AccessArticle
Artificial Neural Networks for Forecasting Passenger Flows on Metro Lines
Sensors 2019, 19(15), 3424; https://doi.org/10.3390/s19153424 - 05 Aug 2019
Abstract
Forecasting user flows on transportation networks is a fundamental task for Intelligent Transport Systems (ITSs). Indeed, most control and management strategies on transportation systems are based on the knowledge of user flows. For implementing ITS strategies, the forecast of user flows on some [...] Read more.
Forecasting user flows on transportation networks is a fundamental task for Intelligent Transport Systems (ITSs). Indeed, most control and management strategies on transportation systems are based on the knowledge of user flows. For implementing ITS strategies, the forecast of user flows on some network links obtained as a function of user flows on other links (for instance, where data are available in real time with sensors) may provide a significant contribution. In this paper, we propose the use of Artificial Neural Networks (ANNs) for forecasting metro onboard passenger flows as a function of passenger counts at station turnstiles. We assume that metro station turnstiles record the number of passengers entering by means of an automatic counting system and that these data are available every few minutes (temporal aggregation); the objective is to estimate onboard passengers on each track section of the line (i.e., between two successive stations) as a function of turnstile data collected in the previous periods. The choice of the period length may depend on service schedules. Artificial Neural Networks are trained by using simulation data obtained with a dynamic loading procedure of the rail line. The proposed approach is tested on a real-scale case: Line 1 of the Naples metro system (Italy). Numerical results show that the proposed approach is able to forecast the flows on metro sections with satisfactory precision. Full article
(This article belongs to the Special Issue Intelligent Transportation Related Complex Systems and Sensors)
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Open AccessArticle
Spatio-Temporal Synchronization of Cross Section Based Sensors for High Precision Microscopic Traffic Data Reconstruction
Sensors 2019, 19(14), 3193; https://doi.org/10.3390/s19143193 - 19 Jul 2019
Abstract
The next generation of Intelligent Transportation Systems (ITS) will strongly rely on a high level of detail and coverage in traffic data acquisition. Beyond aggregated traffic parameters like the flux, mean speed, and density used in macroscopic traffic analysis, a continuous location estimation [...] Read more.
The next generation of Intelligent Transportation Systems (ITS) will strongly rely on a high level of detail and coverage in traffic data acquisition. Beyond aggregated traffic parameters like the flux, mean speed, and density used in macroscopic traffic analysis, a continuous location estimation of individual vehicles on a microscopic scale will be required. On the infrastructure side, several sensor techniques exist today that are able to record the data of individual vehicles at a cross-section, such as static radar detectors, laser scanners, or computer vision systems. In order to record the position data of individual vehicles over longer sections, the use of multiple sensors along the road with suitable synchronization and data fusion methods could be adopted. This paper presents appropriate methods considering realistic scale and accuracy conditions of the original data acquisition. Datasets consisting of a timestamp and a speed for each individual vehicle are used as input data. As a first step, a closed formulation for a sensor offset estimation algorithm with simultaneous vehicle registration is presented. Based on this initial step, the datasets are fused to reconstruct microscopic traffic data using quintic Beziér curves. With the derived trajectories, the dependency of the results on the accuracy of the individual sensors is thoroughly investigated. This method enhances the usability of common cross-section-based sensors by enabling the deriving of non-linear vehicle trajectories without the necessity of precise prior synchronization. Full article
(This article belongs to the Special Issue Intelligent Transportation Related Complex Systems and Sensors)
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Open AccessArticle
A Virtual In-Cylinder Pressure Sensor Based on EKF and Frequency-Amplitude-Modulation Fourier-Series Method
Sensors 2019, 19(14), 3122; https://doi.org/10.3390/s19143122 - 15 Jul 2019
Abstract
As a crucial and critical factor in monitoring the internal state of an engine, cylinder pressure is mainly used to monitor the burning efficiency, to detect engine faults, and to compute engine dynamics. Although the intrusive type cylinder pressure sensor has been greatly [...] Read more.
As a crucial and critical factor in monitoring the internal state of an engine, cylinder pressure is mainly used to monitor the burning efficiency, to detect engine faults, and to compute engine dynamics. Although the intrusive type cylinder pressure sensor has been greatly improved, it has been criticized by researchers for high cost, low reliability and short life due to severe working environments. Therefore, aimed at low-cost, real-time, non-invasive, and high-accuracy, this paper presents the cylinder pressure identification method also called a virtual cylinder pressure sensor, involving Frequency-Amplitude Modulated Fourier Series (FAMFS) and Extended-Kalman-Filter-optimized (EKF) engine model. This paper establishes an iterative speed model based on burning theory and Law of energy Conservation. Efficiency coefficient is used to represent operating state of engine from fuel to motion. The iterative speed model associated with the throttle opening value and the crankshaft load. The EKF is used to estimate the optimal output of this iteration model. The optimal output of the speed iteration model is utilized to separately compute the frequency and amplitude of the cylinder pressure cycle-to-cycle. A standard engine’s working cycle, identified by the 24th order Fourier series, is determined. Using frequency and amplitude obtained from the iteration model to modulate the Fourier series yields a complete pressure model. A commercial engine (EA211) provided by the China FAW Group corporate R&D center is used to verify the method. Test results show that this novel method possesses high accuracy and real-time capability, with an error percentage for speed below 9.6% and the cumulative error percentage of cylinder pressure less than 1.8% when A/F Ratio coefficient is setup at 0.85. Error percentage for speed below 1.7% and the cumulative error percentage of cylinder pressure no more than 1.4% when A/F Ratio coefficient is setup at 0.95. Thus, the novel method’s accuracy and feasibility are verified. Full article
(This article belongs to the Special Issue Intelligent Transportation Related Complex Systems and Sensors)
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Open AccessArticle
An Adaptive Track Segmentation Algorithm for a Railway Intrusion Detection System
Sensors 2019, 19(11), 2594; https://doi.org/10.3390/s19112594 - 06 Jun 2019
Abstract
Video surveillance-based intrusion detection has been widely used in modern railway systems. Objects inside the alarm region, or the track area, can be detected by image processing algorithms. With the increasing number of surveillance cameras, manual labeling of alarm regions for each camera [...] Read more.
Video surveillance-based intrusion detection has been widely used in modern railway systems. Objects inside the alarm region, or the track area, can be detected by image processing algorithms. With the increasing number of surveillance cameras, manual labeling of alarm regions for each camera has become time-consuming and is sometimes not feasible at all, especially for pan-tilt-zoom (PTZ) cameras which may change their monitoring area at any time. To automatically label the track area for all cameras, video surveillance system requires an accurate track segmentation algorithm with small memory footprint and short inference delay. In this paper, we propose an adaptive segmentation algorithm to delineate the boundary of the track area with very light computation burden. The proposed algorithm includes three steps. Firstly, the image is segmented into fragmented regions. To reduce the redundant calculation in the evaluation of the boundary weight for generating the fragmented regions, an optimal set of Gaussian kernels with adaptive directions for each specific scene is calculated using Hough transformation. Secondly, the fragmented regions are combined into local areas by using a new clustering rule, based on the region’s boundary weight and size. Finally, a classification network is used to recognize the track area among all local areas. To achieve a fast and accurate classification, a simplified CNN network is designed by using pre-trained convolution kernels and a loss function that can enhance the diversity of the feature maps. Experimental results show that the proposed method finds an effective balance between the segmentation precision, calculation time, and hardware cost of the system. Full article
(This article belongs to the Special Issue Intelligent Transportation Related Complex Systems and Sensors)
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Open AccessArticle
A Novel Decentralized Game-Theoretic Adaptive Traffic Signal Controller: Large-Scale Testing
Sensors 2019, 19(10), 2282; https://doi.org/10.3390/s19102282 - 17 May 2019
Cited by 1
Abstract
This paper presents a novel de-centralized flexible phasing scheme, cycle-free, adaptive traffic signal controller using a Nash bargaining game-theoretic framework. The Nash bargaining algorithm optimizes the traffic signal timings at each signalized intersection by modeling each phase as a player in a game, [...] Read more.
This paper presents a novel de-centralized flexible phasing scheme, cycle-free, adaptive traffic signal controller using a Nash bargaining game-theoretic framework. The Nash bargaining algorithm optimizes the traffic signal timings at each signalized intersection by modeling each phase as a player in a game, where players cooperate to reach a mutually agreeable outcome. The controller is implemented and tested in the INTEGRATION microscopic traffic assignment and simulation software, comparing its performance to that of a traditional decentralized adaptive cycle length and phase split traffic signal controller and a centralized fully-coordinated adaptive phase split, cycle length, and offset optimization controller. The comparisons are conducted in the town of Blacksburg, Virginia (38 traffic signalized intersections) and in downtown Los Angeles, California (457 signalized intersections). The results for the downtown Blacksburg evaluation show significant network-wide efficiency improvements. Specifically, there is a 23.6 % reduction in travel time, a 37.6 % reduction in queue lengths, and a 10.4 % reduction in CO 2 emissions relative to traditional adaptive traffic signal controllers. In addition, the testing on the downtown Los Angeles network produces a 35.1 % reduction in travel time on the intersection approaches, a 54.7 % reduction in queue lengths, and a 10 % reduction in CO 2 emissions compared to traditional adaptive traffic signal controllers. The results demonstrate significant potential benefits of using the proposed controller over other state-of-the-art centralized and de-centralized adaptive traffic signal controllers on large-scale networks both during uncongested and congested conditions. Full article
(This article belongs to the Special Issue Intelligent Transportation Related Complex Systems and Sensors)
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Open AccessArticle
Efficient Traffic Video Dehazing Using Adaptive Dark Channel Prior and Spatial–Temporal Correlations
Sensors 2019, 19(7), 1593; https://doi.org/10.3390/s19071593 - 02 Apr 2019
Abstract
In order to restore traffic videos with different degrees of haziness in a real-time and adaptive manner, this paper presents an efficient traffic video dehazing method using adaptive dark channel prior and spatial-temporal correlations. This method uses a haziness flag to measure the [...] Read more.
In order to restore traffic videos with different degrees of haziness in a real-time and adaptive manner, this paper presents an efficient traffic video dehazing method using adaptive dark channel prior and spatial-temporal correlations. This method uses a haziness flag to measure the degree of haziness in images based on dark channel prior. Then, it gets the adaptive initial transmission value by establishing the relationship between the image contrast and haziness flag. In addition, this method takes advantage of the spatial and temporal correlations among traffic videos to speed up the dehazing process and optimize the block structure of restored videos. Extensive experimental results show that the proposed method has superior haze removing and color balancing capabilities for the images with different degrees of haze, and it can restore the degraded videos in real time. Our method can restore the video with a resolution of 720 × 592 at about 57 frames per second, nearly four times faster than dark-channel-prior-based method and one time faster than image-contrast-enhanced method. Full article
(This article belongs to the Special Issue Intelligent Transportation Related Complex Systems and Sensors)
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