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Sensors for Transportation Systems
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Sensors for Transportation Systems

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

Deadline for manuscript submissions: closed (31 March 2019) | Viewed by 27921

Special Issue Editors

Prof. Noelia Correia

E-Mail Website
Guest Editor
Center of Electronics, Optoelectronics, and Telecommunications (CEOT), Faculty of Sciences and Technology (FCT), University of Algarve, 8005-139 Faro, Portugal
Interests: Sensor networks, Internet of Things (IoT), optimization of network design problems, transportation systems, vehicle routing
Dr. Jonathan Rodriguez

E-Mail Website
Guest Editor
Instituto de Telecomunicações, Campus Universitário de Santiago, 3810-193 Aveiro, Portugal
Interests: radio resource management; digital signal processing; cross-layer design; system level simulation methodologies; cooperative communications; energy-efficiency; 5G communications; antennas and electromagnetic computational techniques
Special Issues, Collections and Topics in MDPI journals
Dr. Tomás Mateo Sanguino

E-Mail Website
Guest Editor
Department of Electronic Engineering, Computer Systems and Automatics, University of Huelva, Av. de las Artes s/n, 21007 Huelva, Spain
Interests: road safety; communications; cybersecurity; smart city
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The availability of different affordable sensors, together with the control over these elements that has been enabled by the Internet of Things (IoT), is triggering the development of applications in many sectors, and transportation is undoubtedly one of them. The sensing and networking abilities of IoT nodes are key features to promoting smart, efficient, safe, and scalable solutions for high-quality services, as these enable communication, information processing, and control across transportation systems, allowing for dynamic real-time decisions to be taken.

Sensors can be placed inside transportation systems (e.g, PIRs to detect overcrowding of vehicles) and/or built into highways and surface streets (e.g., impact sensors) to help detect accidents, the amount of cars in each lane, etc. Such systems allow not only drivers to adapt operations in order to increase safety, but also for routes, fleets, and schedules to be dynamically adapted in order to improve the quality of service experienced by users (both drivers and customers) and reduce costs. These systems may require data transmission between vehicles (V2V), or between vehicles and roadside access points (V2R).

The aim of this Special Issue is to bring together innovative developments in areas related to sensors development and sensor-based decision making applied to transportation systems. The topics of interest include, but are not limited to, the following:

  • Sensors for information acquisition
  • Sensing infrastructure planning
  • Sensor-based monitoring and decision making
  • Modeling and analysis of sensor-based transportation systems
  • Remote sensing and real-time tracking
  • Smart roads and road monitoring
  • Connectivity and support (e.g., 5G, NFV, SDN, ...)
  • Contextualization and geo-awareness
  • Vehicle routing and traffic management
  • Crowd sensing
  • Sensor information analysis
  • Security, privacy, and safety
  • Smart and shared mobility
  • Applications of sensor technologies to transportation

Prof. Noelia Correia
Dr. Jonathan Rodriguez
Dr. Tomás Mateo
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 submissions that pass pre-check are 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 2600 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.

Keywords

  • Sensors
  • Transportation systems
  • Vehicles
  • Roads
  • Safety

Published Papers (5 papers)

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Research

29 pages, 6124 KiB  
Article
Analyzing the Impact of Traffic Congestion Mitigation: From an Explainable Neural Network Learning Framework to Marginal Effect Analyses
by Jianping Sun, Jifu Guo, Xin Wu, Qian Zhu, Danting Wu, Kai Xian and Xuesong Zhou
Sensors 2019, 19(10), 2254; https://doi.org/10.3390/s19102254 - 15 May 2019
Cited by 13 | Viewed by 5459
Abstract
Computational graphs (CGs) have been widely utilized in numerical analysis and deep learning to represent directed forward networks of data flows between operations. This paper aims to develop an explainable learning framework that can fully integrate three major steps of decision support: Synthesis [...] Read more.
Computational graphs (CGs) have been widely utilized in numerical analysis and deep learning to represent directed forward networks of data flows between operations. This paper aims to develop an explainable learning framework that can fully integrate three major steps of decision support: Synthesis of diverse traffic data, multilayered traffic demand estimation, and marginal effect analyses for transport policies. Following the big data-driven transportation computational graph (BTCG) framework, which is an emerging framework for explainable neural networks, we map different external traffic measurements collected from household survey data, mobile phone data, floating car data, and sensor networks to multilayered demand variables in a CG. Furthermore, we extend the CG-based framework by mapping different congestion mitigation strategies to CG layers individually or in combination, allowing the marginal effects and potential migration magnitudes of the strategies to be reliably quantified. Using the TensorFlow architecture, we evaluate our framework on the Sioux Falls network and present a large-scale case study based on a subnetwork of Beijing using a data set from the metropolitan planning organization. Full article
(This article belongs to the Special Issue Sensors for Transportation Systems)
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15 pages, 4949 KiB  
Article
Analysis of the Relationship between Turning Signal Detection and Motorcycle Driver’s Characteristics on Urban Roads; A Case Study
by Alfonso Micucci, Luca Mantecchini and Maurizio Sangermano
Sensors 2019, 19(8), 1802; https://doi.org/10.3390/s19081802 - 15 Apr 2019
Cited by 13 | Viewed by 3696
Abstract
The investigations on the effectiveness of the turn signal in motorcyclists understanding of motorists’ potential intentions in potentially dangerous car–motorcycle interactions and on the relationships among some variables that could influence the perception of rear and front turn signal status are examined in [...] Read more.
The investigations on the effectiveness of the turn signal in motorcyclists understanding of motorists’ potential intentions in potentially dangerous car–motorcycle interactions and on the relationships among some variables that could influence the perception of rear and front turn signal status are examined in this paper. The investigations have been based on data pooled from the answers of a survey of 136 motorcycle riders, with special regards to the correct detection of turning indicators. Experimental videos have been realized during in-situ simulations, both in urban and suburban areas, recording vehicular interactions in three-leg road intersections, able to potentially generate crash risks, through a 360-camera mounted on a motorcyclist’s helmet. The blinkers detection rate has been combined with other factors related to motorcyclist’s characteristics and test context (e.g., age, gender, location of the test site, presence of a car behind tester vehicles and if the motorcyclist are also habitual car or bicycle drivers) in a stepwise logistic regression that modelled the odds of detecting the turn signal turned on as a function of significant factors. Within the limits of the proposed methodology, the results highlight the low percentage of correct sighting of the turn indicators and confirm the existence of a relation between the detection of the turn indicators aspect and some of the variables considered (e.g., age, being habitual cyclist or car driver and the presence of a car occluding the views), suggesting the opportunity to further investigate the phenomenon through the use of ad-hoc simulations, in order to highlight connections among the factors that can influence the perception of turning indicators in potentially dangerous contexts for cars and motorcycles. Full article
(This article belongs to the Special Issue Sensors for Transportation Systems)
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17 pages, 8852 KiB  
Article
Measuring Reference-Free Total Displacements of Piles and Columns Using Low-Cost, Battery-Powered, Efficient Wireless Intelligent Sensors (LEWIS2)
by Marlon Aguero, Ali Ozdagli and Fernando Moreu
Sensors 2019, 19(7), 1549; https://doi.org/10.3390/s19071549 - 30 Mar 2019
Cited by 17 | Viewed by 4031
Abstract
Currently, over half of the U.S.’s railroad bridges are more than 100 years old. Railroad managers ensure that the proper Maintenance, Repair, and Replacement (MRR) of rail infrastructure is prioritized to safely adapt to the increasing traffic demand. By 2035, the demand for [...] Read more.
Currently, over half of the U.S.’s railroad bridges are more than 100 years old. Railroad managers ensure that the proper Maintenance, Repair, and Replacement (MRR) of rail infrastructure is prioritized to safely adapt to the increasing traffic demand. By 2035, the demand for U.S. railroad transportation will increase by 88%, which indicates that considerable expenditure is necessary to upgrade rail infrastructure. Railroad bridge managers need to use their limited funds for bridge MRR to make informed decisions about safety. Consequently, they require economical and reliable methods to receive objective data about bridge displacements under service loads. Current methods of measuring displacements are often expensive. Wired sensors, such as Linear Variable Differential Transformers (LVDTs), require time-consuming installation and involve high labor and maintenance costs. Wireless sensors (WS) are easier to install and maintain but are in general technologically complex and costly. This paper summarizes the development and validation of LEWIS2, the second version of the real-time, low-cost, efficient wireless intelligent sensor (LEWIS) for measuring and autonomously storing reference-free total transverse displacements. The new features of LEWIS2 include portability, accuracy, cost-effectiveness, and readiness for field application. This research evaluates the effectiveness of LEWIS2 for measuring displacements through a series of laboratory experiments. The experiments demonstrate that LEWIS2 can accurately estimate reference-free total displacements, with a maximum error of only 11% in comparison with the LVDT, while it costs less than 5% of the average price of commercial wireless sensors. Full article
(This article belongs to the Special Issue Sensors for Transportation Systems)
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16 pages, 5278 KiB  
Article
A Generic Multi-Layer Architecture Based on ROS-JADE Integration for Autonomous Transport Vehicles
by Jon Martin, Oskar Casquero, Brais Fortes and Marga Marcos
Sensors 2019, 19(1), 69; https://doi.org/10.3390/s19010069 - 25 Dec 2018
Cited by 15 | Viewed by 6794
Abstract
The design and operation of manufacturing systems is evolving to adapt to different challenges. One of the most important is the reconfiguration of the manufacturing process in response to context changes (e.g., faulty equipment or urgent orders, among others). In this sense, the [...] Read more.
The design and operation of manufacturing systems is evolving to adapt to different challenges. One of the most important is the reconfiguration of the manufacturing process in response to context changes (e.g., faulty equipment or urgent orders, among others). In this sense, the Autonomous Transport Vehicle (ATV) plays a key role in building more flexible and decentralized manufacturing systems. Nowadays, robotic frameworks (RFs) are used for developing robotic systems such as ATVs, but they focus on the control of the robotic system itself. However, social abilities are required for performing intelligent interaction (peer-to-peer negotiation and decision-making) among the different and heterogeneous Cyber Physical Production Systems (such as machines, transport systems and other equipment present in the factory) to achieve manufacturing reconfiguration. This work contributes a generic multi-layer architecture that integrates a RF with a Multi-Agent System (MAS) to provide social abilities to ATVs. This architecture has been implemented on ROS and JADE, the most widespread RF and MAS framework, respectively. We believe this to be the first work that addresses the intelligent interaction of transportation systems for flexible manufacturing environments in a holistic form. Full article
(This article belongs to the Special Issue Sensors for Transportation Systems)
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24 pages, 7886 KiB  
Article
Real-Time Traffic Sign Detection and Recognition Method Based on Simplified Gabor Wavelets and CNNs
by Faming Shao, Xinqing Wang, Fanjie Meng, Ting Rui, Dong Wang and Jian Tang
Sensors 2018, 18(10), 3192; https://doi.org/10.3390/s18103192 - 21 Sep 2018
Cited by 46 | Viewed by 7189
Abstract
Traffic sign detection and recognition plays an important role in expert systems, such as traffic assistance driving systems and automatic driving systems. It instantly assists drivers or automatic driving systems in detecting and recognizing traffic signs effectively. In this paper, a novel approach [...] Read more.
Traffic sign detection and recognition plays an important role in expert systems, such as traffic assistance driving systems and automatic driving systems. It instantly assists drivers or automatic driving systems in detecting and recognizing traffic signs effectively. In this paper, a novel approach for real-time traffic sign detection and recognition in a real traffic situation was proposed. First, the images of the road scene were converted to grayscale images, and then we filtered the grayscale images with simplified Gabor wavelets (SGW), where the parameters were optimized. The edges of the traffic signs were strengthened, which was helpful for the next stage of the process. Second, we extracted the region of interest using the maximally stable extremal regions algorithm and classified the superclass of traffic signs using the support vector machine (SVM). Finally, we used convolution neural networks with input by simplified Gabor feature maps, where the parameters were the same as the detection stage, to classify the traffic signs into their subclasses. The experimental results based on Chinese and German traffic sign databases showed that the proposed method obtained a comparable performance with the state-of-the-art method, and furthermore, the processing efficiency of the whole process of detection and classification was improved and met the real-time processing demands. Full article
(This article belongs to the Special Issue Sensors for Transportation Systems)
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