Special Issue "Data-Driven Analysis and Control Methods in ITS and Accident Prevention"

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Transportation".

Deadline for manuscript submissions: 31 October 2021.

Special Issue Editors

Prof. Dr. Changxi Ma
E-Mail Website
Guest Editor
School of Traffic and Transportation, Lanzhou Jiaotong University, Lanzhou, 730070, China
Interests: intelligent transportation systems; traffic control and traffic safety
Dr. Xuecai Xu
E-Mail Website1 Website2
Guest Editor
School of Civil Engineering and Mechanics, Huazhong University of Science and Technology, Wuhan 430040, China
Interests: intelligent transportation systems; traffic control and traffic safety

Special Issue Information

Dear Colleagues,

Emerging techniques such as big data, Internet of Things (IoT), artificial intelligence, blockchain, and hypercomputation have been deeply integrated into the transportation field, enabling data-driven methods to become a potential approach in intelligent transportation systems (ITS). Meanwhile, based on data and driven by new techniques, accident prevention always plays an important role in conventional and intelligent transportation systems. Accordingly, the data have become significant and it is critical to collect, process, and apply data from different sources for intelligent transportation systems and accident prevention.

This Special Issue, “Data-Driven Analysis and Control Methods in ITS and Accident Prevention” will concentrate on the theories, methodologies, and applications of data-driven methods for analysis, modeling, optimization, and control in ITS and accident prevention. Submissions to this Special Issue are encouraged to employ deep learning, reinforcement learning, and other machine learning methods as well as interdisciplinary approaches for data preprocessing, data mining, and data postprocessing. The aim of this Special Issue is to reveal the emerging techniques and the most recent developments of data-driven analysis, modeling, optimization, and control in ITS and accident prevention.

Potential topics include but are not limited to the following:

  • Data-driven analysis methods in ITS and accident prevention;
  • Data-driven modeling methods in ITS and accident prevention;
  • Data-driven optimization methods in ITS;
  • Data-driven control methods in ITS and accident prevention;
  • Emerging and advanced data analysis methods in ITS and accident prevention;
  • Deep learning and reinforcement learning in ITS and accident prevention;
  • Big data and IoT in ITS and accident prevention;
  • Artificial intelligence methods and applications in ITS and accident prevention;
  • Blockchain methods and applications in ITS and accident prevention;
  • Hypercomputation methods and applications in ITS and accident prevention;
  • Data processing and data mining methods in ITS and accident prevention;
  • Other related topics and interdisciplinary approaches in ITS and accident prevention.

Prof. Dr. Changxi Ma
Dr. Xuecai Xu
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 papers will be 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. Sustainability 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 1900 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
  • data-driven analysis
  • system optimization
  • traffic control
  • accident prevention

Published Papers (2 papers)

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Research

Article
Machine Learning Based Short-Term Travel Time Prediction: Numerical Results and Comparative Analyses
Sustainability 2021, 13(13), 7454; https://doi.org/10.3390/su13137454 - 03 Jul 2021
Viewed by 506
Abstract
Due to the increasing traffic volume in metropolitan areas, short-term travel time prediction (TTP) can be an important and useful tool for both travelers and traffic management. Accurate and reliable short-term travel time prediction can greatly help vehicle routing and congestion mitigation. One [...] Read more.
Due to the increasing traffic volume in metropolitan areas, short-term travel time prediction (TTP) can be an important and useful tool for both travelers and traffic management. Accurate and reliable short-term travel time prediction can greatly help vehicle routing and congestion mitigation. One of the most challenging tasks in TTP is developing and selecting the most appropriate prediction algorithm using the available data. In this study, the travel time data was provided and collected from the Regional Integrated Transportation Information System (RITIS). Then, the travel times were predicted for short horizons (ranging from 15 to 60 min) on the selected freeway corridors by applying four different machine learning algorithms, which are Decision Trees (DT), Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Long Short-Term Memory neural network (LSTM). Many spatial and temporal characteristics that may affect travel time were used when developing the models. The performance of prediction accuracy and reliability are compared. Numerical results suggest that RF can achieve a better prediction performance result than any of the other methods not only in accuracy but also with stability. Full article
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Article
Data-Driven Detection Methods on Driver’s Pedal Action Intensity Using Triboelectric Nano-Generators
Sustainability 2020, 12(21), 8926; https://doi.org/10.3390/su12218926 - 27 Oct 2020
Viewed by 504
Abstract
Driver’s driving actions on pedals can be regarded as an expression of driver’s acceleration/deceleration intention. Quickly and accurately detecting driving action intensity on pedals can have great contributions in preventing road traffic accidents and managing the energy consumption. In this paper, we report [...] Read more.
Driver’s driving actions on pedals can be regarded as an expression of driver’s acceleration/deceleration intention. Quickly and accurately detecting driving action intensity on pedals can have great contributions in preventing road traffic accidents and managing the energy consumption. In this paper, we report a pressure-sensitive and self-powered material named triboelectric nano-generators (TENGs). The generated voltage data of TENGs, which is associated with the pedal action, can be collected easily and stored sequentially. According to the characteristics of the voltage data, we have employed a hybrid machine learning method. After collecting signals from TENGs and driving simulator simultaneously, an unsupervised Gaussian mixture model is used to cluster the pedal events automatically using data from simulator. Then, multi-feature candidates of the voltage data from TENGs are extracted and ranked. A supervised random forest model that treats voltage data of TENGs as input data is trained and tested. Results show that data from TENGs can have a high accuracy of more than 90% using the random forest algorithm. The evaluating results demonstrate the accuracy of the proposed data-driven hybrid learning algorithm for recognition of driver’s pedal action intensity. Furthermore, technical and economic characteristics of TENGs and some common sensors are compared and discussed. This work may demonstrate the feasibility of using these data-driven methods on the detection of driver’s pedal action intensity. Full article
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