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Sensors
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Recent Theories and Applications in Transportation and Mobility
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Recent Theories and Applications in Transportation and Mobility

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

Deadline for manuscript submissions: closed (30 June 2022) | Viewed by 11745

Special Issue Editors

Prof. Dr. Ansar Yasar

E-Mail Website
Guest Editor
Transportation Research Institute (IMOB), Hasselt University, 3500 Hasselt, Belgium
Interests: ITS; vehicular networks; agent-based modeling; modeling and simulation; transportation behavior
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Nafaa Jabeur

E-Mail Website
Guest Editor
Department of Computer Science, German University of Technology in Oman (GUtech), P.O. Box 1816, Athaibah, Muscat PC 130, Oman
Interests: wireless sensor networks; spatial data warehouses; cyber physical systems, internet of things; smart cities; multiagent systems; social networks; spatial data representation, processing, modeling, and visualization; web and mobile catography
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Ahmed Nait-Sidi-Moh

E-Mail Website
Guest Editor
TI Laboratory, Picardie Jules Verne University, Saint Quentin, France
Interests: transportation and mobility; smart logistic; healthcare engineering; sensor networks; internet of things; big-data, modeling; performance evaluation, optimization, simulation, management and decision support

Special Issue Information

Dear Colleagues,

Fast-growing populations and urbanization worldwide are highly increasing the demands for effective and efficient planning, implementation, and control of mobility and transportation services. As the modernization of transportation infrastructures is not being performed at the same pace, commuters are not always able to identify and use the right transportation services at the right time. Goods are not also always delivered from their origins to destinations within the required timeframes and budgets. In order to lessen commuting times, reduce road traffic crashes and their tremendous costs in lives and properties, as well as ensure higher public safety, emerging technologies are being explored and integrated into transportation and mobility solutions.

This Special Issue solicits innovative contributions from academia and industry on recent theories and applications in transportation and mobility. More specifically, we invite researchers, experts, practitioners, and students to submit their original works concerning the use of emergent technologies (such as V2X, machine learning, IoT, drones/UAVs, and blockchain) to enable humans, vehicles, goods, transportation infrastructures, communication networks, as well as data storage and processing facilities to individually and collectively provide citizens, clients, businesses, and governments with the right transportation and mobility services at the right time.

Prof. Dr. Ansar Yasar
Prof. Dr. Nafaa Jabeur
Prof. Dr. Ahmed Nait-Sidi-Moh
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

  • models and architectures for next-generation transportation and mobility
  • communication technologies and protocols
  • modelling, assessment, and prediction of driving behaviour
  • road traffic data analytics
  • mobility as a service (MaaS)
  • blockchain for smart transportation and mobility
  • emergent technologies and applications
  • safety, security, and hazard management
  • sustainable transportation and mobility
  • intelligent transportations systems

Published Papers (4 papers)

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Research

16 pages, 1433 KiB  
Article
Design Methodology of Automotive Time-Sensitive Network System Based on OMNeT++ Simulation System
by Feng Luo, Bowen Wang, Zhenyu Yang, Ping Zhang, Yifei Ma, Zihao Fang, Mingzhi Wu and Zhipeng Sun
Sensors 2022, 22(12), 4580; https://doi.org/10.3390/s22124580 - 17 Jun 2022
Cited by 7 | Viewed by 2954
Abstract
Advances in automotive technology require networks to support a variety of communication requirements, such as reliability, real-time performance, low jitter, and strict delay limits. Time-Sensitive Network (TSN) is a keyframe transmission delay-guaranteed solution based on the IEEE 802 architecture of the automotive Ethernet. [...] Read more.
Advances in automotive technology require networks to support a variety of communication requirements, such as reliability, real-time performance, low jitter, and strict delay limits. Time-Sensitive Network (TSN) is a keyframe transmission delay-guaranteed solution based on the IEEE 802 architecture of the automotive Ethernet. However, most of the existing studies on automotive TSN performance are based on a single mechanism, lacking a complete and systematic research tool. At the same time, the design method should be considered from a global perspective when designing an automotive TSN system, rather than only considering a single mechanism that TSN applies to. This paper discusses the correspondence between traffic types and automotive scenarios and proposes a methodology to target the delay constraint of traffic types as the design goal of automotive TSN networks. To study the performance of automotive TSN under different mechanisms such as time-aware shaper (TAS), credit-based shaper (CBS), cyclic queuing and forwarding (CQF), etc., this paper also develops a systematic automotive TSN simulation system based on OMNeT++. The simulation system plays a crucial role in the whole methodology, including all applicable TSN standards for the automotive field. Lastly, a complex automotive scenario based on zonal architecture provided by a major motor company in Shanghai is analyzed in the simulated system; verifying TSN can guarantee real-time performance and reliability of the in-vehicle network. Full article
(This article belongs to the Special Issue Recent Theories and Applications in Transportation and Mobility)
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27 pages, 1398 KiB  
Article
Energy-Efficient UAV Movement Control for Fair Communication Coverage: A Deep Reinforcement Learning Approach
by Ibrahim A. Nemer, Tarek R. Sheltami, Slim Belhaiza and Ashraf S. Mahmoud
Sensors 2022, 22(5), 1919; https://doi.org/10.3390/s22051919 - 01 Mar 2022
Cited by 15 | Viewed by 2868
Abstract
Unmanned Aerial Vehicles (UAVs) are considered an important element in wireless communication networks due to their agility, mobility, and ability to be deployed as mobile base stations (BSs) in the network to improve the communication quality and coverage area. UAVs can be used [...] Read more.
Unmanned Aerial Vehicles (UAVs) are considered an important element in wireless communication networks due to their agility, mobility, and ability to be deployed as mobile base stations (BSs) in the network to improve the communication quality and coverage area. UAVs can be used to provide communication services for ground users in different scenarios, such as transportation systems, disaster situations, emergency cases, and surveillance. However, covering a specific area under a dynamic environment for a long time using UAV technology is quite challenging due to its limited energy resources, short communication range, and flying regulations and rules. Hence, a distributed solution is needed to overcome these limitations and to handle the interactions among UAVs, which leads to a large state space. In this paper, we introduced a novel distributed control solution to place a group of UAVs in the candidate area in order to improve the coverage score with minimum energy consumption and a high fairness value. The new algorithm is called the state-based game with actor–critic (SBG-AC). To simplify the complex interactions in the problem, we model SBG-AC using a state-based potential game. Then, we merge SBG-AC with an actor–critic algorithm to assure the convergence of the model, to control each UAV in a distributed way, and to have learning capabilities in case of dynamic environments. Simulation results show that the SBG-AC outperforms the distributed DRL and the DRL-EC3 in terms of fairness, coverage score, and energy consumption. Full article
(This article belongs to the Special Issue Recent Theories and Applications in Transportation and Mobility)
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17 pages, 2300 KiB  
Article
HSDDD: A Hybrid Scheme for the Detection of Distracted Driving through Fusion of Deep Learning and Handcrafted Features
by Monagi H. Alkinani, Wazir Zada Khan, Quratulain Arshad and Mudassar Raza
Sensors 2022, 22(5), 1864; https://doi.org/10.3390/s22051864 - 26 Feb 2022
Cited by 12 | Viewed by 2460
Abstract
Traditional methods for behavior detection of distracted drivers are not capable of capturing driver behavior features related to complex temporal features. With the goal to improve transportation safety and to reduce fatal accidents on roads, this research article presents a Hybrid Scheme for [...] Read more.
Traditional methods for behavior detection of distracted drivers are not capable of capturing driver behavior features related to complex temporal features. With the goal to improve transportation safety and to reduce fatal accidents on roads, this research article presents a Hybrid Scheme for the Detection of Distracted Driving called HSDDD. This scheme is based on a strategy of aggregating handcrafted and deep CNN features. HSDDD is based on three-tiered architecture. The three tiers are named as Coordination tier, Concatenation tier and Classification tier. We first obtain HOG features by using handcrafted algorithms, and then at the coordination tier, we leverage four deep CNN models including AlexNet, Inception V3, Resnet50 and VGG-16 for extracting DCNN features. DCNN extracted features are fused with HOG extracted features at the Concatenation tier. Then PCA is used as a feature selection technique. PCA takes both the extracted features and removes the redundant and irrelevant information, and it improves the classification performance. After feature fusion and feature selection, the two classifiers, KNN and SVM, at the Classification tier take the selected features and classify the ten classes of distracted driving behaviors. We evaluate our proposed scheme and observe its performance by using the accuracy metrics. Full article
(This article belongs to the Special Issue Recent Theories and Applications in Transportation and Mobility)
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19 pages, 3801 KiB  
Article
Highway Regional Classification Method Based on Traffic Flow Characteristics for Highway Safety Assessment
by Jongdae Baek
Sensors 2022, 22(1), 86; https://doi.org/10.3390/s22010086 - 23 Dec 2021
Cited by 1 | Viewed by 2536
Abstract
Accurate regional classification of highways is a critical prerequisite to implement a tailored safety assessment. However, there has been inadequate research on objective classification considering traffic flow characteristics for highway safety assessment purposes. We propose an objective and easily applicable classification method that [...] Read more.
Accurate regional classification of highways is a critical prerequisite to implement a tailored safety assessment. However, there has been inadequate research on objective classification considering traffic flow characteristics for highway safety assessment purposes. We propose an objective and easily applicable classification method that considers the administrative divisions of South Korea. We evaluated the feasibility of this method through various theoretical analysis techniques using the data collected from 536 permanent traffic volume counting stations for the national highways in South Korea in 2019. The ratio of the annual average hourly traffic volume to the annual average daily traffic was used as the explanatory variable. The corresponding results of factor and cluster analyses with this ratio showed a 61% concordance with the urban, suburban, and rural areas classified by the administrative divisions. The results of two-sample goodness-of-fit tests also confirmed that the difference in the three distributions of hourly volume ratios was statistically significant. The results of this study can help enhance highway safety and facilitate the development and application of more appropriate highway safety assessment tools, such as Road Assessment Programs or crash prediction models, for specific regions using the proposed method. Full article
(This article belongs to the Special Issue Recent Theories and Applications in Transportation and Mobility)
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