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Entropy
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Information-Theoretic Methods for Transportation
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Information-Theoretic Methods for Transportation

A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Multidisciplinary Applications".

Deadline for manuscript submissions: closed (31 December 2020) | Viewed by 4938

Special Issue Editors

Dr. Yi Qi

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Guest Editor
Department of Transportation Studies, Texas Southern University, Houston, TX, USA
Interests: intelligent transportation systems; transportation safety; traffic incident management; traffic signal operation
Dr. Sherif Ishak

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Guest Editor
Civil and Environmental Engineering, Old Dominion University, Norfolk, VA, USA
Interests: intelligent transportation systems; traffic operation, control, and safety; human factors and driving behavior; connected and automated vehicles

Special Issue Information

Dear Colleagues,

Enabled by the rapid development and proliferation of the Intelligent Transportation Systems (ITS), in the past few decades, massive amounts of transportation data have become available from different sources over a vast temporal and spatial scale. Huge in size and rich in information, these data collected by the ITS could considerably enhance our understanding of the operation and performance of transportation systems. Recently, many advanced information-theoretic methods have been applied for solving various transportation-related problems, such as freeway incident detection, transportation system performance analysis, transportation safety analysis, and infrastructure management.

Considering the recent advances in the field of information theory (e.g., discovery of hidden connections and prediction of future trends), this Special Issue aims to collect new ideas and improved techniques of information theory that have been successfully applied for solving transportation-related problems. In particular, this Special Issue will accept unpublished original papers and comprehensive reviews focused on (but not restricted to) the following research topics:

  • Entropy-based numerical methods for transportation system performance and network analysis;
  • Algorithms for the analysis of time sequences and entropy calculation applied in transportation;
  • Novel entropy-based numerical methods dedicated to the qualitative analysis of dynamical traffic flow and driving profiles for various ITS applications;
  • Entropy-related artificial intelligence and advanced machine learning methods applied in transportation data analysis.

Dr. Yi Qi
Dr. Sherif Ishak
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. Entropy 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 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

  • Information-theoretic methods
  • Transportation
  • Data analysis
  • Intelligent Transportation Systems (ITS)
  • Information-theoretic techniques
  • Statistics
  • Machine learning
  • Artificial intelligence

Published Papers (2 papers)

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Research

21 pages, 1808 KiB  
Article
Analysis of Factors Contributing to the Severity of Large Truck Crashes
by Jinhong Li, Jinli Liu, Pengfei Liu and Yi Qi
Entropy 2020, 22(11), 1191; https://doi.org/10.3390/e22111191 - 22 Oct 2020
Cited by 19 | Viewed by 2369
Abstract
Crashes that involved large trucks often result in immense human, economic, and social losses. To prevent and mitigate severe large truck crashes, factors contributing to the severity of these crashes need to be identified before appropriate countermeasures can be explored. In this research, [...] Read more.
Crashes that involved large trucks often result in immense human, economic, and social losses. To prevent and mitigate severe large truck crashes, factors contributing to the severity of these crashes need to be identified before appropriate countermeasures can be explored. In this research, we applied three tree-based machine learning (ML) techniques, i.e., random forest (RF), gradient boost decision tree (GBDT), and adaptive boosting (AdaBoost), to analyze the factors contributing to the severity of large truck crashes. Besides, a mixed logit model was developed as a baseline model to compare with the factors identified by the ML models. The analysis was performed based on the crash data collected from the Texas Crash Records Information System (CRIS) from 2011 to 2015. The results of this research demonstrated that the GBDT model outperforms other ML methods in terms of its prediction accuracy and its capability in identifying more contributing factors that were also identified by the mixed logit model as significant factors. Besides, the GBDT method can effectively identify both categorical and numerical factors, and the directions and magnitudes of the impacts of the factors identified by the GBDT model are all reasonable and explainable. Among the identified factors, driving under the influence of drugs, alcohol, and fatigue are the most important factors contributing to the severity of large truck crashes. In addition, the exists of curbs and medians and lanes and shoulders with sufficient width can prevent severe large truck crashes. Full article
(This article belongs to the Special Issue Information-Theoretic Methods for Transportation)
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20 pages, 1242 KiB  
Article
Research on Evaluating the Sustainable Operation of Rail Transit System Based on QFD and Fuzzy Clustering
by Bing Yan, Liying Yu and Jing Wang
Entropy 2020, 22(7), 750; https://doi.org/10.3390/e22070750 - 7 Jul 2020
Cited by 5 | Viewed by 2318
Abstract
The purpose of this study is to evaluate the sustainable operation of rail transit system. In rail transit system, as the most important aspect of negative entropy flow, the effective strategy can offset the increasing entropy of the system and make it have [...] Read more.
The purpose of this study is to evaluate the sustainable operation of rail transit system. In rail transit system, as the most important aspect of negative entropy flow, the effective strategy can offset the increasing entropy of the system and make it have the characteristics of dissipative structure, so as to realize the sustainable operation. At first, this study constructs the Pressure-State-Response (PSR) model to evaluate the sustainable operation of rail transit system. In this PSR model, “pressure” is viewed as customer requirements, which answers the reasons for such changes in rail transit system; “state” refers to the state and environment of system activities, which can be described as the challenges of coping with system pressure; “response” describes the system’s actions to address the challenges posed by customer needs, namely operational strategies. Moreover, then, 13 pressure indices, five state indices and 11 response indices are summarized. In addition, based on quality function deployment (QFD), with 13 pressure indices as input variables, five state indices as customer requirements (CRs) of QFD and 11 response indices as technical attributes (TAs) of QFD, this study proposed the three-phase evaluation method of the sustainable operation of rail transit system to obtain the operational strategy (that is, negative entropy flow): The first phase is to verify that 13 pressure indices can be clustered into five state indices by fuzzy clustering analysis; The second phase is to get the weights of five state indices by evidential reasoning; The third phase is to rate the importance of 11 response indices by integrating fuzzy weighted average and expected value operator. Finally, the proposed model and method of evaluation are applied to the empirical analysis of Shanghai rail transit system. Finally, we come to the conclusion that Shanghai rail transit system should take priority from the following five aspects: “advancement of design standards”, “reliability of subway facilities”, “completeness of operational rules”, “standardization of management operation” and “rationality of passenger flow control”. Full article
(This article belongs to the Special Issue Information-Theoretic Methods for Transportation)
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