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Application of Big Data in Sustainable Transportation
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Application of Big Data in Sustainable Transportation

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

Deadline for manuscript submissions: closed (31 October 2023) | Viewed by 4607

Special Issue Editors

Dr. Chaozhe Jiang

E-Mail Website
Guest Editor
School of Traffic and Transportation, Southwest Jiaotong University, Chengdu, China
Interests: transportation planning and management; transportation engineering; systems engineering; logistics engineering; transportation safety
Dr. Peter Shi

E-Mail Website
Guest Editor
Macquarie Business School, Macquarie University, Guilin 2109, Australia
Interests: supply chain management; logistics management; operaitons management; strategic management; supply chain digitalisation; supply chain risk and resilience
Special Issues, Collections and Topics in MDPI journals
Dr. Chao Wen

E-Mail Website
Guest Editor
School of Transportation and Logistics, Southwest Jiaotong University, Chengdu 610031, China
Interests: railway system analysis; high-speed railway operations; data-driven train delay propagation and recovery; intelligent train dispatching decision-making
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Xin Li

E-Mail Website
Guest Editor
College of Management Science, Chengdu University of Technology, Chengdu, China
Interests: environmental management and policy; resource evaluation and management; logistics engineering

Special Issue Information

Dear Colleagues,

Sustainable transportation refers to a long-term development mode that maximizes reasonable transportation demand with minimal resource input and environmental cost, and has three optimization objectives, namely, saving resources, protecting the environment, and meeting demand. Transportation is closely related to a given area’s population, economy and environment. From the perspective of passenger transportation, transportation includes multiple modes, such as rail transit, highway transportation, civil aviation, etc. From the perspective of freight transportation, transportation includes logistics, supply chain, and finance. Only continuous communication between passenger and freight transport can keep the industrial supply chain flexible and ensure the needs of both production and daily life remain met. Therefore, the sustainable development of transportation plays an essential strategic supporting role in economic development and promoting productivity.

With the rapid development of big data, the perception, prediction, and decision making of passenger and freight traffic flow through big data technology have been studied a lot. However, new thought and explorations are still needed in terms of attaining sustainable transportation. Big data technology can peek into the hidden knowledge in a large amount of data through the analysis of full samples to achieve scientific analysis, prediction, and decision making. For sustainable transportation at the micro level, intelligent perception, prediction, decision-making technology of traffic lights, parking lot information, traffic flow control, and vehicle–road coordination, automatic control, automatic driving, as well as regional development planning, transportation management, transportation planning, transportation safety,  traffic emissions, and double carbon at the macro level al have important value and significance.

Therefore, this Special Issue, entitled “Application of Big Data in Sustainable Transportation”, will address this critical and potentially wide-ranging topic. We encourage the submission original research articles or comprehensive reviews from across diverse disciplines. Examples of contents include but are not limited to:

  1. Sustainable rail transit;
  2. Sustainable highway transportation;
  3. Sustainable civil aviation;
  4. Sustainable logistics;
  5. Sustainable supply chain and finance;
  6. Sustainable big data transportation technology;
  7. Sustainable big data transportation management;
  8. Sustainable big data transportation planning;
  9. Sustainable big data regional development planning;
  10. Sustainable big data automatic driving and control technology;
  11. Sustainable traffic emissions based on big data;
  12. Sustainable transportation safety based on big data.

We look forward to receiving your contributions.

Dr. Chaozhe Jiang
Dr. Peter Shi
Dr. Chao Wen
Prof. Dr. Xin Li
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. 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 2400 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

  • sustainable transportation
  • big data technology
  • sustainable big data
  • sustainability
  • transportation big data
  • supply chain

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Published Papers (3 papers)

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Research

16 pages, 3507 KiB  
Article
Advancing Traffic Simulation Precision and Scalability: A Data-Driven Approach Utilizing Deep Neural Networks
by Ruru Hao and Tiancheng Ruan
Sustainability 2024, 16(7), 2666; https://doi.org/10.3390/su16072666 - 24 Mar 2024
Viewed by 1344
Abstract
In traditional traffic simulation studies, vehicle behavior has typically been modeled using complex analytical frameworks, which often struggle to encompass the full range of variables affecting vehicle operations. Addressing this gap, our research introduces an innovative data-driven framework for traffic simulation that incorporates [...] Read more.
In traditional traffic simulation studies, vehicle behavior has typically been modeled using complex analytical frameworks, which often struggle to encompass the full range of variables affecting vehicle operations. Addressing this gap, our research introduces an innovative data-driven framework for traffic simulation that incorporates human driving data into its decision-making processes. This enables the modeling of diverse vehicle behaviors by taking into account both vehicle-specific characteristics and environmental factors. At the core of this framework are two advanced deep neural networks, convolutional long short-term memory and convolutional gated recurrent unit, which underpin our vehicle traffic simulation model. Utilizing datasets from the Next Generation Simulation project, specifically the I-80 and US-101 road sections, our study further evaluates the framework’s performance through single-step continuous prediction, as well as transferability tests, employing the TransMSEloss function to optimize prediction accuracy. Our findings reveal that the proposed data-driven model significantly outperforms traditional models, achieving an exceptional accuracy of 97.22% in training and 95.76% in testing. Notably, in continuous prediction, our model maintains an 89.57% accuracy up to the fifth step, exceeding the traditional framework’s 82.82% by 5% to 10% at each step. Time cost analysis indicates that while the data-driven framework’s advantages are more pronounced in large-scale simulations, it also demonstrates strong transferability, with a 93.48% accuracy on diverse datasets, showcasing its applicability across different traffic scenarios. This study not only highlights the potential of deep learning in traffic simulation, but also sets a new benchmark for accuracy and scalability in the field. Full article
(This article belongs to the Special Issue Application of Big Data in Sustainable Transportation)
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20 pages, 4646 KiB  
Article
TCN-Informer-Based Flight Trajectory Prediction for Aircraft in the Approach Phase
by Zijing Dong, Boyi Fan, Fan Li, Xuezhi Xu, Hong Sun and Weiwei Cao
Sustainability 2023, 15(23), 16344; https://doi.org/10.3390/su152316344 - 27 Nov 2023
Cited by 1 | Viewed by 1338
Abstract
Trajectory prediction (TP) is a vital operation in air traffic control systems for flight monitoring and tracking. The approach phase of general aviation (GA) aircraft is more of a visual approach, which is related to the safety of the flight and whether to [...] Read more.
Trajectory prediction (TP) is a vital operation in air traffic control systems for flight monitoring and tracking. The approach phase of general aviation (GA) aircraft is more of a visual approach, which is related to the safety of the flight and whether to go around. Therefore, it is important to accurately predict the flight trajectory of the approach phase. Based on the historical flight trajectories of GA aircraft, a TP model is proposed with deep learning after feature extraction in this study, and the hybrid model combines a time convolution network and an improved transformer model. First, feature extraction of the spatiotemporal dimension is performed on the preprocessed flight data by using TCN; then, the extracted features are executed by adopting the Informer model for TP. The performance of the novel architecture is verified by experiments based on real flight trajectory data. The results show that the proposed TCN-Informer architecture performs better according to various evaluation metrics, which means that the prediction accuracies of the hybrid model are better than those of the typical prediction models widely used today. Moreover, it has been verified that the proposed method can provide valuable suggestions for decision-making regarding whether to go around during the approach. Full article
(This article belongs to the Special Issue Application of Big Data in Sustainable Transportation)
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15 pages, 14596 KiB  
Article
Prediction of Aircraft Wake Vortices under Various Crosswind Velocities Based on Convolutional Neural Networks
by Xin He, Rui Zhao, Haoran Gao, Changjiang Yuan and Jingyi Wang
Sustainability 2023, 15(18), 13383; https://doi.org/10.3390/su151813383 - 7 Sep 2023
Cited by 2 | Viewed by 1022
Abstract
In order to overcome the time-consuming computational drawback of using computational fluid dynamics (CFD) for the numerical simulation of aircraft wake vortex evolution under different crosswind velocities, this paper proposes a wake vortex prediction model based on a convolutional neural network (CNN) algorithm. [...] Read more.
In order to overcome the time-consuming computational drawback of using computational fluid dynamics (CFD) for the numerical simulation of aircraft wake vortex evolution under different crosswind velocities, this paper proposes a wake vortex prediction model based on a convolutional neural network (CNN) algorithm. The study focuses on the B737-800 aircraft, and employs CFD numerical simulations to obtain the evolutionary characteristics of wake vortex parameters under crosswind velocities ranging from 0 to 7 m/s. The wake vortex velocity and Q-criterion vorticity values are collected and partitioned into mutually exclusive training and testing datasets. A CNN model is constructed, and the training dataset is used to tune hyperparameters to minimize loss and achieve accurate predictions. After saving the trained model, the desired crosswind velocity value is input to obtain the predicted wake vortex velocity and Q-criterion vorticity values. The results indicate that the convolutional neural network model exhibits an average absolute percentage error of 1.5%, which is 2.3% lower than that of the fully connected neural network model. This suggests that convolutional neural networks can enhance the accuracy of wake vortex predictions, as demonstrated in this study. Compared to traditional CFD methods, the proposed model reduces the computation time by approximately 40 times, effectively improving computational efficiency and offering valuable insight for studies involving numerous numerical simulations, such as analyzing the safety separation between aircraft wake vortices during paired approach procedures. Full article
(This article belongs to the Special Issue Application of Big Data in Sustainable Transportation)
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