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New Trends and Challenges in Intelligent Transportation Systems Optimisation, Modeling...
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New Trends and Challenges in Intelligent Transportation Systems Optimisation, Modeling and Security

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Information Systems".

Deadline for manuscript submissions: closed (16 September 2021) | Viewed by 12110

Special Issue Editors

Prof. Dr. Nacima Labadie

E-Mail Website
Guest Editor
LOSI - Optimization of Industrial Systems Laboratory, University of Technology of Troyes | UTT, Troyes, France
Interests: transportation; vehicle routing problems; supply chain management; urban logistics; metaheuristic design
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Lyes Khoukhi

E-Mail Website
Guest Editor
ENSICAEN, GREYC Lab, Normandie University, Normandy, France
Interests: connected vehicles; networking; security

Special Issue Information

Dear Colleagues,

Intelligent transportation systems (ITS) play a central role in improving traffic safety, and managing logistics and transport operations. Thanks to this technology, vehicles are now able to communicate with each other thanks to the V2V (vehicle to vehicle) communication system, and also with the nearby road infrastructure V2I (vehicle to infrastructure) with the aim of reducing the number of road accidents, reducing congestion and hence carbon emissions, and increasing road safety and transport operations efficiency.

This special issue aims to gather the last trends and challenges raised in different research communities working on intelligent transportation systems, with a particular focus on the studies dealing with the development of tools issued from Operational Research, Artificial Intelligence, security, and traffic control fields.

An upcoming special issue of the journal Information is dedicated to the subjects described before. Interested authors are invited to submit original work to this special issue coordinated by Prof. Nacima Labadie and Prof. Lyes Khoukhi. Topics of interest include, but are not limited to, the following: Intelligent vehicles, Traffic control and management, Modelling, control and simulation algorithms and techniques, Deep learning and Artificial Intelligence, Logistics and supply chain, Smart mobility.

Prof. Dr. Nacima Labadie
Prof. Dr. Lyes Khoukhi
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. Information is an international peer-reviewed open access monthly 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 1600 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

  • intelligent transportation system
  • transportation technologies
  • transport management
  • connected vehicles

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Published Papers (3 papers)

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Research

22 pages, 1751 KiB  
Article
Study on Customized Shuttle Transit Mode Responding to Spatiotemporal Inhomogeneous Demand in Super-Peak
by Hao Zheng, Xingchen Zhang and Junhua Chen
Information 2021, 12(10), 429; https://doi.org/10.3390/info12100429 - 18 Oct 2021
Cited by 3 | Viewed by 1964
Abstract
Instantaneous mega-traffic flow has long been one of the major challenges in the management of mega-cities. It is difficult for the public transportation system to cope directly with transient mega-capacity flows, and the uneven spatiotemporal distribution of demand is the main cause. To [...] Read more.
Instantaneous mega-traffic flow has long been one of the major challenges in the management of mega-cities. It is difficult for the public transportation system to cope directly with transient mega-capacity flows, and the uneven spatiotemporal distribution of demand is the main cause. To this end, this paper proposed a customized shuttle bus transportation model based on the “boarding-transfer-alighting” framework, with the goal of minimizing operational costs and maximizing service quality to address mega-transit demand with uneven spatiotemporal distribution. The fleet application is constructed by a pickup and delivery problem with time window and transfer (PDPTWT) model, and a heuristic algorithm based on Tabu Search and ALNS is proposed to solve the large-scale computational problem. Numerical tests show that the proposed algorithm has the same accuracy as the commercial solution software, but has a higher speed. When the demand size is 10, the proposed algorithm can save 24,000 times of time. In addition, 6 reality-based cases are presented, and the results demonstrate that the designed option can save 9.93% of fleet cost, reduce 45.27% of vehicle waiting time, and 33.05% of passenger waiting time relative to other existing customized bus modes when encountering instantaneous passenger flows with time and space imbalance. Full article
(This article belongs to the Special Issue New Trends and Challenges in Intelligent Transportation Systems Optimisation, Modeling and Security)
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13 pages, 1934 KiB  
Article
Ship Classification Based on Improved Convolutional Neural Network Architecture for Intelligent Transport Systems
by Lilian Asimwe Leonidas and Yang Jie
Information 2021, 12(8), 302; https://doi.org/10.3390/info12080302 - 28 Jul 2021
Cited by 18 | Viewed by 5635
Abstract
In recent years, deep learning has been used in various applications including the classification of ship targets in inland waterways for enhancing intelligent transport systems. Various researchers introduced different classification algorithms, but they still face the problems of low accuracy and misclassification of [...] Read more.
In recent years, deep learning has been used in various applications including the classification of ship targets in inland waterways for enhancing intelligent transport systems. Various researchers introduced different classification algorithms, but they still face the problems of low accuracy and misclassification of other target objects. Hence, there is still a need to do more research on solving the above problems to prevent collisions in inland waterways. In this paper, we introduce a new convolutional neural network classification algorithm capable of classifying five classes of ships, including cargo, military, carrier, cruise and tanker ships, in inland waterways. The game of deep learning ship dataset, which is a public dataset originating from Kaggle, has been used for all experiments. Initially, the five pretrained models (which are AlexNet, VGG, Inception V3 ResNet and GoogleNet) were used on the dataset in order to select the best model based on its performance. Resnet-152 achieved the best model with an accuracy of 90.56%, and AlexNet achieved a lower accuracy of 63.42%. Furthermore, Resnet-152 was improved by adding a classification block which contained two fully connected layers, followed by ReLu for learning new characteristics of our training dataset and a dropout layer to resolve the problem of a diminishing gradient. For generalization, our proposed method was also tested on the MARVEL dataset, which consists of more than 10,000 images and 26 categories of ships. Furthermore, the proposed algorithm was compared with existing algorithms and obtained high performance compared with the others, with an accuracy of 95.8%, precision of 95.83%, recall of 95.80%, specificity of 95.07% and F1 score of 95.81%. Full article
(This article belongs to the Special Issue New Trends and Challenges in Intelligent Transportation Systems Optimisation, Modeling and Security)
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17 pages, 1390 KiB  
Article
Signal Timing Optimization Model Based on Bus Priority
by Xu Sun, Kun Lin, Pengpeng Jiao and Huapu Lu
Information 2020, 11(6), 325; https://doi.org/10.3390/info11060325 - 15 Jun 2020
Cited by 6 | Viewed by 3125
Abstract
This paper focuses on the optimization problem of a signal timing design based on the concept of bus priority. This optimization problem is formulated in the form of a bi-level programming model that minimizes average passenger delay at intersections and vehicle delay in [...] Read more.
This paper focuses on the optimization problem of a signal timing design based on the concept of bus priority. This optimization problem is formulated in the form of a bi-level programming model that minimizes average passenger delay at intersections and vehicle delay in lanes simultaneously. A solution framework that implements the differential evolution (DE) algorithm is developed to efficiently solve the model. A case study based on a real-world intersection in Beijing, China, is implemented to test the efficiency and applicability of the proposed modeling and computing methods. The experiment’s result shows that the optimization model can not only significantly improve the priority capacity of the buses at the intersection but also reduce the adverse impact of bus-priority approaches on the private vehicles for the intersections. Full article
(This article belongs to the Special Issue New Trends and Challenges in Intelligent Transportation Systems Optimisation, Modeling and Security)
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