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Information
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Soft Computing in Intelligent Transportation System
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Soft Computing in Intelligent Transportation System

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

Deadline for manuscript submissions: closed (10 October 2022) | Viewed by 5066

Special Issue Editors

Dr. Enrique Onieva

E-Mail Website
Guest Editor
Faculty of Engineering, University of Deusto, 48007 Bilbo, Spain
Interests: artificial intelligence; evolutionary algorithms; fuzzy logic; machine learning; deep learning; intelligent transportation systems
Dr. Dmitry Pavlyuk

E-Mail Website
Guest Editor
Transport and Telecommunication Institute, LV-1019 Rīga, Latvia
Interests: multivariate time series analysis; machine learning in transportation; spatial econometrics; spatiotemporal big data modeling; artificial intelligence; intelligent transportation systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Intelligent transportation systems (ITS) combine advanced information and telecommunication technologies and various data sources for improving efficiency and safety of transportation. ITS data are highly diverse and include observations from traffic sensors, video surveillance, navigation systems as well as volunteer messages and social media. This huge amount of imperfect heterogeneous data and vagueness of transportation processes create an excellent environment for application of soft computing techniques. Soft computing techniques like fuzzy set theory, metaheuristic algorithms, neural and probabilistic networks are widely used for solving emerging transportation problems. This issue collects the most recent advances of soft computing with their application for different aspects of transportation.
List of topics:

  • Urban traffic forecasting
  • Autonomous driving
  • Traffic congestion prediction
  • Public transport optimisation
  • Intelligent routing and traffic organization
  • Organization of vehicular adhoc networks
  • Efficient parking facility planning
  • Design, construction and maintenance of transport facilities
  • Logistics ad optimized freight transportation

Dr. Enrique Onieva
Dr. Dmitry Pavlyuk
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 systems
  • artificial intelligence
  • machine learning
  • deep learning
  • spatiotemporal transport modeling

Published Papers (2 papers)

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Research

25 pages, 2268 KiB  
Article
Travel-Time Estimation by Cubic Hermite Curve
by Owen Tamin, Badrul Ikram, Ahmad Lutfi Amri Ramli, Ervin Gubin Moung and Christie Chin Pei Yee
Information 2022, 13(7), 307; https://doi.org/10.3390/info13070307 - 23 Jun 2022
Cited by 2 | Viewed by 2268
Abstract
Travel time is a measure of time taken to travel from one place to another. Global Positioning System (GPS) navigation applications such as Waze and Google Maps are easily accessible presently and allow users to plan a route based on travel time from [...] Read more.
Travel time is a measure of time taken to travel from one place to another. Global Positioning System (GPS) navigation applications such as Waze and Google Maps are easily accessible presently and allow users to plan a route based on travel time from one place to another. However, these applications can only estimate general travel time based on a vehicle’s total distance and average safe speed without considering route curvature. A parametric cubic curve has shown a potential result in travel-time estimation through geometric properties. In this paper, travel time has been estimated using the curvature value obtained from the Hermite Interpolation curve fitted to each section of the selected road. Design speed is determined from the curvature value, and thus an algorithm for travel-time estimation incorporating initial driving information is developed. The proposed method’s accuracy was compared to the existing method’s accuracy using a real-life driving test. This comparison demonstrated that the proposed method estimates travel time more accurately than Google Maps and Waze. Future study can further improve the estimation by embedding traffic data into the algorithm. Full article
(This article belongs to the Special Issue Soft Computing in Intelligent Transportation System)
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16 pages, 8923 KiB  
Article
Methodological Study on the Influence of Truck Driving State on the Accuracy of Weigh-in-Motion System
by Shuanfeng Zhao, Jianwei Yang, Zenghui Tang, Qing Li and Zhizhong Xing
Information 2022, 13(3), 130; https://doi.org/10.3390/info13030130 - 03 Mar 2022
Cited by 2 | Viewed by 2293
Abstract
The weigh-in-motion (WIM) system weighs the entire vehicle by identifying the dynamic forces of each axle of the vehicle on the road. The load of each axle is very important to detect the total weight of the vehicle. Different drivers have different driving [...] Read more.
The weigh-in-motion (WIM) system weighs the entire vehicle by identifying the dynamic forces of each axle of the vehicle on the road. The load of each axle is very important to detect the total weight of the vehicle. Different drivers have different driving behaviors, and when large trucks pass through the weighing detection area, the driving state of the trucks may affect the weighing accuracy of the system. This paper proposes YOLOv3 network model as the basis for this algorithm, which uses the feature pyramid network (FPN) idea to achieve multi-scale prediction and the deep residual network (ResNet) idea to extract image features, so as to achieve a balance between detection speed and detection accuracy. In the paper, spatial pyramid pooling (SPP) network and cross stage partial (CSP) network are added to the original network model to improve the learning ability of the convolutional neural network and make the original network more lightweight. Then the detection-based target tracking method with Kalman filtering + RTS (rauch–tung–striebel) smoothing is used to extract the truck driving status information (vehicle trajectory and speed). Finally, the effective size of the vehicle in different driving states on the weighing accuracy is statistically analyzed. The experimental results show that the method has high accuracy and real-time performance in truck driving state extraction, can be used to analyze the influence of weighing accuracy, and provides theoretical support for personalized accuracy correction of WIM system. At the same time, it is beneficial for WIM system to assist the existing traffic system more accurately and provide a highway health management and effective decision making by providing reliable monitoring data. Full article
(This article belongs to the Special Issue Soft Computing in Intelligent Transportation System)
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