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Special Issue "Intelligent Transportation Related Complex Systems and Sensors"

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

Deadline for manuscript submissions: closed (31 July 2020) | Viewed by 79036

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Special Issue Editors

Prof. Dr. Kyandoghere Kyamakya

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Guest Editor

Institute for Smart Systems Technologies, University Klagenfurt, A9020 Klagenfurt, Austria
Interests: intelligent transport systems; telecommunications; neuro-computing; machine learning and pattern recognition; nonlinear dynamics
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Jean Chamberlain Chedjou

E-Mail Website
Guest Editor

Research Center Borstel—Leibniz Lung Center, 23845 Borstel, Germany
Interests: dynamic systems in engineering; neurocomputing and applications; optimization and inverse problems; intelligent transportation systems
Special Issues, Collections and Topics 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, Collections and Topics 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, Collections and Topics in MDPI journals

Prof. Dr. Antoine Bagula

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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, Collections and Topics 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

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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.

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Published Papers (25 papers)

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Editorial

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Open AccessEditorial

Intelligent Transportation Related Complex Systems and Sensors

Kyandoghere Kyamakya

Jean Chamberlain Chedjou

Fadi Al-Machot

Ahmad Haj Mosa

and

Antoine Bagula

Sensors 2021, 21(6), 2235; https://doi.org/10.3390/s21062235 - 23 Mar 2021

Cited by 4 | Viewed by 1968

Abstract

Building around innovative services related to different modes of transport and traffic management, intelligent transport systems (ITSs) are being widely adopted worldwide to improve the efficiency and safety of the transportation system [...] Full article

(This article belongs to the Special Issue Intelligent Transportation Related Complex Systems and Sensors)

Research

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Open AccessArticle

A Low-Cost Automatic Vehicle Identification Sensor for Traffic Networks Analysis

Fernando Álvarez-Bazo

Santos Sánchez-Cambronero

and

Sensors 2020, 20(19), 5589; https://doi.org/10.3390/s20195589 - 29 Sep 2020

Cited by 9 | Viewed by 3016

Abstract

In recent years, different techniques to address the problem of observability in traffic networks have been proposed in multiple research projects, being the technique based on the installation of automatic vehicle identification sensors (AVI), one of the most successful in terms of theoretical results, but complex in terms of its practical application to real studies. Indeed, a very limited number of studies consider the possibility of installing a series of non-definitive plate scanning sensors in the elements of a network, which allow technicians to obtain a better conclusions when they deal with traffic network analysis such as urbans mobility plans that involve the estimation of traffic flows for different scenarios. With these antecedents, the contributions of this paper are (1) an architecture to deploy low-cost sensors network able to be temporarily installed on the city streets as an alternative of rubber hoses commonly used in the elaboration of urban mobility plans; (2) a design of the low-cost, low energy sensor itself, and (3) a sensor location model able to establish the best set of links of a network given both the study objectives and of the sensor needs of installation. A case of study with the installation of as set of proposed devices is presented, to demonstrate its viability. Full article

(This article belongs to the Special Issue Intelligent Transportation Related Complex Systems and Sensors)

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Open AccessArticle

The Effects of the Driver’s Mental State and Passenger Compartment Conditions on Driving Performance and Driving Stress

Víctor Corcoba Magaña

Wilhelm Daniel Scherz

Ralf Seepold

Natividad Martínez Madrid

Xabiel García Pañeda

and

Roberto Garcia

Sensors 2020, 20(18), 5274; https://doi.org/10.3390/s20185274 - 15 Sep 2020

Cited by 19 | Viewed by 4063

Abstract

Globalization has increased the number of road trips and vehicles. The result has been an intensification of traffic accidents, which are becoming one of the most important causes of death worldwide. Traffic accidents are often due to human error, the probability of which increases when the cognitive ability of the driver decreases. Cognitive capacity is closely related to the driver’s mental state, as well as other external factors such as the CO₂ concentration inside the vehicle. The objective of this work is to analyze how these elements affect driving. We have conducted an experiment with 50 drivers who have driven for 25 min using a driving simulator. These drivers completed a survey at the start and end of the experiment to obtain information about their mental state. In addition, during the test, their stress level was monitored using biometric sensors and the state of the environment (temperature, humidity and CO₂ level) was recorded. The results of the experiment show that the initial level of stress and tiredness of the driver can have a strong impact on stress, driving behavior and fatigue produced by the driving test. Other elements such as sadness and the conditions of the interior of the vehicle also cause impaired driving and affect compliance with traffic regulations. Full article

(This article belongs to the Special Issue Intelligent Transportation Related Complex Systems and Sensors)

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Open AccessArticle

Assessment of the Speed Management Impact on Road Traffic Safety on the Sections of Motorways and Expressways Using Simulation Methods

Jacek Oskarbski

Tomasz Kamiński

Kyandoghere Kyamakya

Jean Chamberlain Chedjou

Karol Żarski

and

Małgorzata Pędzierska

Sensors 2020, 20(18), 5057; https://doi.org/10.3390/s20185057 - 05 Sep 2020

Cited by 10 | Viewed by 3500

Abstract

Methods used to evaluate the impact of Intelligent Transport System (ITS) services on road safety are usually based on expert assessments or statistical studies. However, commonly used methods are challenging to apply in the planning process of ITS services. This paper presents the methodology of research using surrogate safety measures calculated and calibrated with the use of simulation techniques and a driving simulator. This approach supports the choice of the type of ITS services that are beneficial for traffic efficiency and road safety. This paper presents results of research on the influence of selected scenarios of variable speed limits on the efficiency and safety of traffic on the sections of motorways and expressways in various traffic conditions. The driving simulator was used to estimate the efficiency of lane-keeping by the driver. The simulation traffic models were calibrated using driving simulator data and roadside sensor data. The traffic models made it possible to determine surrogate safety measures (number of conflicts and their severity) in selected scenarios of using ITS services. The presented studies confirmed the positive impact of Variable Speed Limits (VSLs) on the level of road safety and traffic efficiency. This paper also presents recommendations and plans for further research in this area. Full article

(This article belongs to the Special Issue Intelligent Transportation Related Complex Systems and Sensors)

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Open AccessArticle

Human-Like Obstacle Avoidance Trajectory Planning and Tracking Model for Autonomous Vehicles That Considers the Driver’s Operation Characteristics

and

Sensors 2020, 20(17), 4821; https://doi.org/10.3390/s20174821 - 26 Aug 2020

Cited by 4 | Viewed by 2976

Abstract

Developing a human-like autonomous driving system has gained increasing amounts of attention from both technology companies and academic institutions, as it can improve the interpretability and acceptance of the autonomous system. Planning a safe and human-like obstacle avoidance trajectory is one of the critical issues for the development of autonomous vehicles (AVs). However, when designing automatic obstacle avoidance systems, few studies have focused on the obstacle avoidance characteristics of human drivers. This paper aims to develop an obstacle avoidance trajectory planning and trajectory tracking model for AVs that is consistent with the characteristics of human drivers’ obstacle avoidance trajectory. Therefore, a modified artificial potential field (APF) model was established by adding a road boundary repulsive potential field and ameliorating the obstacle repulsive potential field based on the traditional APF model. The model predictive control (MPC) algorithm was combined with the APF model to make the planning model satisfy the kinematic constraints of the vehicle. In addition, a human driver’s obstacle avoidance experiment was implemented based on a six-degree-of-freedom driving simulator equipped with multiple sensors to obtain the drivers’ operation characteristics and provide a basis for parameter confirmation of the planning model. Then, a linear time-varying MPC algorithm was employed to construct the trajectory tracking model. Finally, a co-simulation model based on CarSim/Simulink was established for off-line simulation testing, and the results indicated that the proposed trajectory planning controller and the trajectory tracking controller were more human-like under the premise of ensuring the safety and comfort of the obstacle avoidance operation, providing a foundation for the development of AVs. Full article

(This article belongs to the Special Issue Intelligent Transportation Related Complex Systems and Sensors)

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Open AccessArticle

Establishment of the Complete Closed Mesh Model of Rail-Surface Scratch Data for Online Repair

and

Sensors 2020, 20(17), 4736; https://doi.org/10.3390/s20174736 - 21 Aug 2020

Cited by 8 | Viewed by 1970

Abstract

Rail surface scratching occurs with increasing frequency, seriously threatening the safety of vehicles and humans. Online repair of rail-surface scratches on damaged rails with scratch depths >1 mm is of increased importance, because direct rail-replacement has the disadvantages of long operation time, high manpower and high material costs. Advanced online repair of rail-surface scratch using three-dimensional (3D) metal printing technology such as laser cladding has become an increasing trend, desperately demanding a solution for the fast and precise establishment of a complete closed mesh model of rail-surface scratch data. However, there have only been limited studies on the topic so far. In this paper, the complete closed mesh model is well established based on a novel triangulation algorithm relying on the topological features of the point-cloud model (PCM) of scratch-data, which is obtained by implementing a scratch-data-computation process following a rail-geometric-feature-fused algorithm of random sample consensus (RANSAC) performed on the full rail-surface PCM constructed by 3D laser vision. The proposed method is universal for all types of normal-speed rails in China. Experimental results show that the proposed method can accurately acquire the complete closed mesh models of scratch data of one meter of 50 Kg/m-rails within 1 min. Full article

(This article belongs to the Special Issue Intelligent Transportation Related Complex Systems and Sensors)

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Open AccessArticle

Data-Driven Analysis of Bicycle Sharing Systems as Public Transport Systems Based on a Trip Index Classification

Mark Richard Wilby

Juan José Vinagre Díaz

Rubén Fernández Pozo

Ana Belén Rodríguez González

José Manuel Vassallo

and

Carmen Sánchez Ávila

Sensors 2020, 20(15), 4315; https://doi.org/10.3390/s20154315 - 02 Aug 2020

Cited by 2 | Viewed by 2711

Abstract

Bicycle Sharing Systems (BSSs) are exponentially increasing in the urban mobility sector. They are traditionally conceived as a last-mile complement to the public transport system. In this paper, we demonstrate that BSSs can be seen as a public transport system in their own right. To do so, we build a mathematical framework for the classification of BSS trips. Using trajectory information, we create the trip index, which characterizes the intrinsic purpose of the use of BSS as transport or leisure. The construction of the trip index required a specific analysis of the BSS shortest path, which cannot be directly calculated from the topology of the network given that cyclists can find shortcuts through traffic lights, pedestrian crossings, etc. to reduce the overall traveled distance. Adding a layer of complication to the problem, these shortcuts have a non-trivial existence in terms of being intermittent, or short lived. We applied the proposed methodology to empirical data from BiciMAD, the public BSS in Madrid (Spain). The obtained results show that the trip index correctly determines transport and leisure categories, which exhibit distinct statistical and operational features. Finally, we inferred the underlying BSS public transport network and show the fundamental trajectories traveled by users. Based on this analysis, we conclude that

90.60 %

of BiciMAD’s use fall in the category of transport, which demonstrates our first statement. Full article

(This article belongs to the Special Issue Intelligent Transportation Related Complex Systems and Sensors)

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Open AccessArticle

Impact on Road Safety and Operation of Rerouting Traffic in Rural Travel Time Information System

Mariusz Kiec

Carmelo D’Agostino

and

Sylwia Pazdan

Sensors 2020, 20(15), 4145; https://doi.org/10.3390/s20154145 - 25 Jul 2020

Cited by 5 | Viewed by 2434

Abstract

The Travel Time Information System (TTIS) is an Intelligent Traffic Control System installed in Poland. As is common, travel time is the only factor in the decision about rerouting traffic, while a route recommendation may consider multiple criteria, including road safety. The aim of the paper is to analyze the safety level of the entire road network when traffic is rerouted on paths with different road categories, intersection types, road environments, and densities of access points. Furthermore, a comparison between traffic operation and road safety performance was carried out, considering travel time and delay, and we predicted the number of crashes for each possible route. The results of the present study allow for maximizing safety or traffic operation characteristics, providing an effective tool in the management of the rural road system. The paper provides a methodology that can be transferred to other TTISs for real-time management of the road network. Full article

(This article belongs to the Special Issue Intelligent Transportation Related Complex Systems and Sensors)

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Open AccessArticle

Estimation of Traffic Stream Density Using Connected Vehicle Data: Linear and Nonlinear Filtering Approaches

Mohammad A. Aljamal

Hossam M. Abdelghaffar

and

Hesham A. Rakha

Sensors 2020, 20(15), 4066; https://doi.org/10.3390/s20154066 - 22 Jul 2020

Cited by 8 | Viewed by 1906

Abstract

The paper presents a nonlinear filtering approach to estimate the traffic stream density on signalized approaches based solely on connected vehicle (CV) data. Specifically, a particle filter (PF) is developed to produce reliable traffic density estimates using CV travel-time measurements. Traffic flow continuity is used to derive the state equation, whereas the measurement equation is derived from the hydrodynamic traffic flow relationship. Subsequently, the PF filtering approach is compared to linear estimation approaches; namely, a Kalman filter (KF) and an adaptive KF (AKF). Simulated data are used to evaluate the performance of the three estimation techniques on a signalized approach experiencing oversaturated conditions. Results demonstrate that the three techniques produce accurate estimates—with the KF, surprisingly, being the most accurate of the three techniques. A sensitivity of the estimation techniques to various factors including the CV level of market penetration, the initial conditions, and the number of particles in the PF is also presented. As expected, the study demonstrates that the accuracy of the PF estimation increases as the number of particles increases. Furthermore, the accuracy of the density estimate increases as the level of CV market penetration increases. The results indicate that the KF is least sensitive to the initial vehicle count estimate, while the PF is most sensitive to the initial condition. In conclusion, the study demonstrates that a simple linear estimation approach is best suited for the proposed application. Full article

(This article belongs to the Special Issue Intelligent Transportation Related Complex Systems and Sensors)

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Open AccessArticle

Empirical Study of Effect of Dynamic Travel Time Information on Driver Route Choice Behavior

Jinghui Wang

and

Hesham Rakha

Sensors 2020, 20(11), 3257; https://doi.org/10.3390/s20113257 - 08 Jun 2020

Cited by 10 | Viewed by 1879

Abstract

The objective of this paper is to study the effect of travel time information on day-to-day driver route choice behavior. A real-world experimental study is designed to have participants repeatedly choose between two alternative routes for five origin-destination pairs over multiple days after providing them with dynamically updated travel time information (average travel time and travel time variability). The results demonstrate that historical travel time information enhances behavioral rationality by 10% on average and reduces inertial tendencies to increase risk seeking in the gain domain. Furthermore, expected travel time information is demonstrated to be more effective than travel time variability information in enhancing rational behavior when drivers have limited experiences. After drivers gain sufficient knowledge of routes, however, the difference in behavior associated with the two information types becomes insignificant. The results also demonstrate that, when drivers lack experience, the faster less reliable route is more attractive than the slower more reliable route. However, with cumulative experiences, drivers become more willing to take the more reliable route given that they are reluctant to become risk seekers once experience is gained. Furthermore, the effect of information on driver behavior differs significantly by participant and trip, which is, to a large extent, dependent on personal traits and trip characteristics. Full article

(This article belongs to the Special Issue Intelligent Transportation Related Complex Systems and Sensors)

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Open AccessArticle

Vehicle Trajectory Prediction Method Based on License Plate Information Obtained from Video-Imaging Detectors in Urban Road Environment

and

Sensors 2020, 20(5), 1258; https://doi.org/10.3390/s20051258 - 25 Feb 2020

Cited by 11 | Viewed by 2456

Abstract

The vehicle license plate data obtained from video-imaging detectors contains a huge volume of information of vehicle trip rules and driving behavior characteristics. In this paper, a real-time vehicle trajectory prediction method is proposed based on historical trip rules extracted from vehicle license plate data in an urban road environment. Using the driving status information at intersections, the vehicle trip chain is acquired on the basis of the topologic graph of the road network and channelization of intersections. In order to obtain an integral and continuous trip chain in cases where data is missing in the original vehicle license plate, a trip chain compensation method based on the Dijkstra algorithm is presented. Moreover, the turning state transition matrix which is used to describe the turning probability of a vehicle when it passes a certain intersection is calculated by a massive volume of historical trip chain data. Finally, a k-step vehicle trajectory prediction model is proposed to obtain the maximum possibility of downstream intersections. The overall method is thoroughly tested and demonstrated in a realistic road traffic scenario with actual vehicle license plate data. The results show that vehicles can reach an average accuracy of 0.72 for one-step prediction when there are only 200 historical training data samples. The proposed method presents significant performance in trajectory prediction. Full article

(This article belongs to the Special Issue Intelligent Transportation Related Complex Systems and Sensors)

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Open AccessArticle

Using Vehicle Interior Noise Classification for Monitoring Urban Rail Transit Infrastructure

and

Sensors 2020, 20(4), 1112; https://doi.org/10.3390/s20041112 - 18 Feb 2020

Cited by 6 | Viewed by 2324

Abstract

This study developed a multi-classification model for vehicle interior noise from the subway system, collected on smartphones. The proposed model has the potential to be used to analyze the causes of abnormal noise using statistical methods and evaluate the effect of rail maintenance work. To this end, first, we developed a multi-source data (audio, acceleration, and angle rate) collection framework via smartphone built-in sensors. Then, considering the Shannon entropy, a 1-second window was selected to segment the time-series signals. This study extracted 45 features from the time- and frequency-domains to establish the classifier. Next, we investigated the effects of balancing the training dataset with the Synthetic Minority Oversampling Technique (SMOTE). By comparing and analyzing the classification results of importance-based and mutual information-based feature selection methods, the study employed a feature set consisting of the top 10 features by importance score. Comparisons with other classifiers indicated that the proposed XGBoost-based classifier runs fast while maintaining good accuracy. Finally, case studies were provided to extend the applications of this classifier to the analysis of abnormal vehicle interior noise events and evaluate the effects of rail grinding. Full article

(This article belongs to the Special Issue Intelligent Transportation Related Complex Systems and Sensors)

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Open AccessArticle

Short Term Traffic State Prediction via Hyperparameter Optimization Based Classifiers

and

Sensors 2020, 20(3), 685; https://doi.org/10.3390/s20030685 - 27 Jan 2020

Cited by 30 | Viewed by 3148

Abstract

Short-term traffic state prediction has become an integral component of an advanced traveler information system (ATIS) in intelligent transportation systems (ITS). Accurate modeling and short-term traffic prediction are quite challenging due to its intricate characteristics, stochastic, and dynamic traffic processes. Existing works in this area follow different modeling approaches that are focused to fit speed, density, or the volume data. However, the accuracy of such modeling approaches has been frequently questioned, thereby traffic state prediction over the short-term from such methods inflicts an overfitting issue. We address this issue to accurately model short-term future traffic state prediction using state-of-the-art models via hyperparameter optimization. To do so, we focused on different machine learning classifiers such as local deep support vector machine (LD-SVM), decision jungles, multi-layers perceptron (MLP), and CN2 rule induction. Moreover, traffic states are evaluated using traffic attributes such as level of service (LOS) horizons and simple if–then rules at different time intervals. Our findings show that hyperparameter optimization via random sweep yielded superior results. The overall prediction performances obtained an average improvement by over 95%, such that the decision jungle and LD-SVM achieved an accuracy of 0.982 and 0.975, respectively. The experimental results show the robustness and superior performances of decision jungles (DJ) over other methods. Full article

(This article belongs to the Special Issue Intelligent Transportation Related Complex Systems and Sensors)

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Open AccessArticle

Abnormal Road Surface Recognition Based on Smartphone Acceleration Sensor

and

Sensors 2020, 20(2), 451; https://doi.org/10.3390/s20020451 - 13 Jan 2020

Cited by 59 | Viewed by 5331

Abstract

In order to identify the abnormal road surface condition efficiently and at low cost, a road surface condition recognition method is proposed based on the vibration acceleration generated by a smartphone when the vehicle passes through the abnormal road surface. The improved Gaussian background model is used to extract the features of the abnormal pavement, and the k-nearest neighbor (kNN) algorithm is used to distinguish the abnormal pavement types, including pothole and bump. Comparing with the existing works, the influence of vehicles with different suspension characteristics on the detection threshold is studied in this paper, and an adaptive adjustment mechanism based on vehicle speed is proposed. After comparing the field investigation results with the algorithm recognition results, the accuracy of the proposed algorithm is rigorously evaluated. The test results show that the vehicle vibration acceleration contains the road surface condition information, which can be used to identify the abnormal road conditions. The test result shows that the accuracy of the recognition of the road surface pothole is 96.03%, and the accuracy of the road surface bump is 94.12%. The proposed road surface recognition method can be utilized to replace the special patrol vehicle for timely and low-cost road maintenance. Full article

(This article belongs to the Special Issue Intelligent Transportation Related Complex Systems and Sensors)

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Open AccessArticle

Design of a Human Evaluator Model for the Ride Comfort of Vehicle on a Speed Bump Using a Neural Artistic Style Extraction

and

Sensors 2019, 19(24), 5407; https://doi.org/10.3390/s19245407 - 08 Dec 2019

Cited by 7 | Viewed by 3350

Abstract

The subjective evaluation of vehicle ride comfort is costly and time-consuming but is crucial for vehicle development. To reduce the cost and time, the objectification of subjective evaluation has been widely studied, and most of the approaches use a regression model between objective metrics and subjective ratings. However, the accuracy of these approaches is highly dependent on the selection of the objective metrics. In most of the methods, it is not clear that the selected metrics are sufficiently significant or whether all significant metrics are included in the selection. This paper presents a method to build a correlation model between measurements and subjective evaluations without using predefined features or objective metrics. A numerical representation of ride comfort was extracted from raw signals based on the idea of the artistic style transfer method. The correlation model was designed based on the extracted numerical representation and subjective ratings. The model has a much better accuracy than any other correlation models in the literature. This better accuracy is contributed to not only by using a neural network, but also by the extraction of the numerical representation of ride comfort using a pre-trained neural network. Full article

(This article belongs to the Special Issue Intelligent Transportation Related Complex Systems and Sensors)

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Open AccessArticle

Developing a Neural–Kalman Filtering Approach for Estimating Traffic Stream Density Using Probe Vehicle Data

Mohammad A. Aljamal

Hossam M. Abdelghaffar

and

Hesham A. Rakha

Sensors 2019, 19(19), 4325; https://doi.org/10.3390/s19194325 - 07 Oct 2019

Cited by 16 | Viewed by 3463

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 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”

Vahid Tavakkoli

Jean Chamberlain Chedjou

and

Kyandoghere Kyamakya

Sensors 2019, 19(18), 4002; https://doi.org/10.3390/s19184002 - 16 Sep 2019

Cited by 7 | Viewed by 2695

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 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

Mariano Gallo

Giuseppina De Luca

, Luca D’Acierno and

Marilisa Botte

Sensors 2019, 19(15), 3424; https://doi.org/10.3390/s19153424 - 05 Aug 2019

Cited by 36 | Viewed by 4306

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 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

Adrian Fazekas

and

Markus Oeser

Sensors 2019, 19(14), 3193; https://doi.org/10.3390/s19143193 - 19 Jul 2019

Cited by 4 | Viewed by 2324

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 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

and

Sensors 2019, 19(14), 3122; https://doi.org/10.3390/s19143122 - 15 Jul 2019

Cited by 8 | Viewed by 3219

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 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

and

Sensors 2019, 19(11), 2594; https://doi.org/10.3390/s19112594 - 06 Jun 2019

Cited by 14 | Viewed by 3576

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 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

Hossam M. Abdelghaffar

and

Hesham A. Rakha

Sensors 2019, 19(10), 2282; https://doi.org/10.3390/s19102282 - 17 May 2019

Cited by 13 | Viewed by 3007

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, 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

and

Sensors 2019, 19(7), 1593; https://doi.org/10.3390/s19071593 - 02 Apr 2019

Cited by 28 | Viewed by 3430

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 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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Review

Jump to: Editorial, Research

Open AccessReview

A Review of Data Analytic Applications in Road Traffic Safety. Part 1: Descriptive and Predictive Modeling

Amir Mehdizadeh

Miao Cai

Qiong Hu

Mohammad Ali Alamdar Yazdi

Nasrin Mohabbati-Kalejahi

and

Sensors 2020, 20(4), 1107; https://doi.org/10.3390/s20041107 - 18 Feb 2020

Cited by 27 | Viewed by 5563

Abstract

This part of the review aims to reduce the start-up burden of data collection and descriptive analytics for statistical modeling and route optimization of risk associated with motor vehicles. From a data-driven bibliometric analysis, we show that the literature is divided into two disparate research streams: (a) predictive or explanatory models that attempt to understand and quantify crash risk based on different driving conditions, and (b) optimization techniques that focus on minimizing crash risk through route/path-selection and rest-break scheduling. Translation of research outcomes between these two streams is limited. To overcome this issue, we present publicly available high-quality data sources (different study designs, outcome variables, and predictor variables) and descriptive analytic techniques (data summarization, visualization, and dimension reduction) that can be used to achieve safer-routing and provide code to facilitate data collection/exploration by practitioners/researchers. Then, we review the statistical and machine learning models used for crash risk modeling. We show that (near) real-time crash risk is rarely considered, which might explain why the optimization models (reviewed in Part 2) have not capitalized on the research outcomes from the first stream. Full article

(This article belongs to the Special Issue Intelligent Transportation Related Complex Systems and Sensors)

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Open AccessReview

A Review of Data Analytic Applications in Road Traffic Safety. Part 2: Prescriptive Modeling

Qiong Hu

Miao Cai

Nasrin Mohabbati-Kalejahi

Amir Mehdizadeh

Mohammad Ali Alamdar Yazdi

and

Sensors 2020, 20(4), 1096; https://doi.org/10.3390/s20041096 - 17 Feb 2020

Cited by 13 | Viewed by 3087

Abstract

In the first part of the review, we observed that there exists a significant gap between the predictive and prescriptive models pertaining to crash risk prediction and minimization, respectively. In this part, we review and categorize the optimization/ prescriptive analytic models that focus on minimizing crash risk. Although the majority of works in this segment of the literature are related to the hazardous materials (hazmat) trucking problems, we show that (with some exceptions) many can also be utilized in non-hazmat scenarios. In an effort to highlight the effect of crash risk prediction model on the accumulated risk obtained from the prescriptive model, we present a simulated example where we utilize four risk indicators (obtained from logistic regression, Poisson regression, XGBoost, and neural network) in the k-shortest path algorithm. From our example, we demonstrate two major designed takeaways: (a) the shortest path may not always result in the lowest crash risk, and (b) a similarity in overall predictive performance may not always translate to similar outcomes from the prescriptive models. Based on the review and example, we highlight several avenues for future research. Full article

(This article belongs to the Special Issue Intelligent Transportation Related Complex Systems and Sensors)

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