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Applied Sciences
Special Issues
Big Data Applications in Transportation
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Big Data Applications in Transportation

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 20 July 2024 | Viewed by 2799

Special Issue Editors

Prof. Dr. Reynold C.K. Cheng

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Guest Editor
Department of Computer Science, The University of Hong Kong, Pokfulam Road, Hong Kong 999077, China
Interests: graph analytics; spatial data analytics; uncertain data management; data science
Dr. Xiaolin Han

E-Mail Website
Guest Editor
School of Computer Science, The Northwestern Polytechnical University, Xi'an 710129, China
Interests: spatial-temporal data mining; urban computing; graph data mining; network representation; deep neural networks; graph neural networks
Dr. Chenhao Ma

E-Mail Website
Guest Editor
School of Data Science, The Chinese University of Hong Kong, Shenzhen 518172, China
Interests: large-scale data management and mining; graph data; traffic data; spatial data management
Prof. Dr. Matthias Renz

E-Mail Website
Guest Editor
Department of Computer Science, Christian Albrechts University, 24105 Kiel, Germany
Interests: spatial AI; spatiotemporal data analytics; graph analytics; transportation analytics; uncertain data management; marine data science; archaeo-data science

Special Issue Information

Dear Colleagues,

A massive amount of traffic data, e.g., GPS data collected from moving vehicles and sensor data collected from highways, enable the development of many big data applications in transportation, e.g., navigation. Hence, more and more researchers are paying attention to topics regarding big transportation data. However, due to the large volume, data uncertainty, and data sparsity issues in big transportation data, it is challenging to manage, storage, and analyze these data.

This Special Issue on ‘Big Data Applications in Transportation’ aims to highlight new and innovative work focused on big data applications in transportation. We invite you to present high-quality research in one or more areas revolving around the current state of the art. This Special Issue intends to explore ‘Big Data Applications in Transportation’, but is not restricted to, Spatial Analysis and Integration, Spatial Data Mining and Knowledge Discovery, Spatial Data Quality and Uncertainty, Spatial Query Processing and Optimization, Spatio-Temporal Data Analysis, Spatio-Temporal Data Management, Spatio-Temporal Disease Spread Modeling, Geographic Information Retrieval, Similarity Searching, Spatial Data Structures and Algorithms, Spatial Information and Society, Spatial Modeling and Reasoning, Spatio-Textual Searching, Spatio-Temporal Sensor Networks, Location-Based Services, Spatio-Temporal Stream Processing, etc.

Prof. Dr. Reynold C.K. Cheng
Dr. Xiaolin Han
Dr. Chenhao Ma
Prof. Dr. Matthias Renz
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. Applied Sciences 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

  • spatial analysis and integration
  • spatial data mining and knowledge discovery
  • spatial data quality and uncertainty
  • spatial query processing and optimization
  • spatio-temporal data analysis
  • spatio-temporal data management
  • spatio-temporal disease spread modeling
  • geographic information retrieval
  • similarity searching
  • spatial data structures and algorithms
  • spatial information and society
  • spatial modeling and reasoning
  • spatio-textual searching
  • spatio-temporal sensor networks
  • location-based services
  • spatio-temporal stream processing
  • machine learning for transportation
  • large-scale AI for transportation
  • autonomous driving
  • distributed spatial-temporal data processing

Published Papers (3 papers)

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Research

19 pages, 9093 KiB  
Article
Route Risk Index for Autonomous Trucks
by Ryan Jones, Raj Bridgelall and Denver Tolliver
Appl. Sci. 2024, 14(7), 2892; https://doi.org/10.3390/app14072892 - 29 Mar 2024
Viewed by 394
Abstract
The proliferation of autonomous trucking demands a sophisticated understanding of the risks associated with the diverse U.S. interstate system. Traditional risk assessment models, while beneficial, do not adequately address the state and regional variations in factors that significantly impact the safety and efficiency [...] Read more.
The proliferation of autonomous trucking demands a sophisticated understanding of the risks associated with the diverse U.S. interstate system. Traditional risk assessment models, while beneficial, do not adequately address the state and regional variations in factors that significantly impact the safety and efficiency of autonomous freight transport. This study addresses the problem by developing a composite risk index that evaluates the safety of U.S. interstate routes for autonomous trucking, considering both state and regional differences in traffic volumes, road conditions, safety records, and weather patterns. The potential for autonomous trucking to transform the freight industry necessitates a risk assessment model that is as dynamic and multifaceted as the system it aims to navigate. This work contributes a regionally sensitive risk index using GIS methodologies, integrating data from national databases, and applying statistical analysis to normalize risk factors. The findings reveal significant state and regional disparities in risk factors, such as the predominance of precipitation-related risks in the Southeast and traffic in the Far West. This work provides a targeted approach to risk assessment for policymakers and infrastructure planners and offers a strategic tool for logistics companies in optimizing autonomous trucking routes. The long-term benefit is a scalable model that can adapt to evolving data inputs and contribute to the broader application of risk assessment strategies in various domains. Full article
(This article belongs to the Special Issue Big Data Applications in Transportation)
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19 pages, 3802 KiB  
Article
Data Quality Analysis and Improvement: A Case Study of a Bus Transportation System
by Shuyan Si, Wen Xiong and Xingliang Che
Appl. Sci. 2023, 13(19), 11020; https://doi.org/10.3390/app131911020 - 06 Oct 2023
Viewed by 948
Abstract
Due to the rapid development of the mobile Internet and the Internet of Things, the volume of generated data keeps growing. The topic of data quality has gained increasing attention recently. Numerous studies have explored various data quality (DQ) problems across several fields, [...] Read more.
Due to the rapid development of the mobile Internet and the Internet of Things, the volume of generated data keeps growing. The topic of data quality has gained increasing attention recently. Numerous studies have explored various data quality (DQ) problems across several fields, with corresponding effective data-cleaning strategies being researched. This paper begins with a comprehensive and systematic review of studies related to DQ. On the one hand, we classify these DQ-related studies into six types: redundant data, missing data, noisy data, erroneous data, conflicting data, and sparse data. On the other hand, we discuss the corresponding data-cleaning strategies for each DQ type. Secondly, we examine DQ issues and potential solutions for a public bus transportation system, utilizing a real-world traffic big data platform. Finally, we provide two representative examples, noise filtering and filling missing values, to demonstrate the DQ improvement practice. The experimental results show that: (1) The GPS noise filtering solution we proposed surpasses the baseline and achieves an accuracy of 97%; (2) The multi-source data fusion method can achieve a 100% missing repair rate (MRR) for bus arrival and departure. The average relative error (ARE) of bus arrival and departure times at stations is less than 1%, and the correlation coefficient (R) is also close to 1. Our research can offer guidance and lessons for enhancing data governance and quality improvement in the bus transportation system. Full article
(This article belongs to the Special Issue Big Data Applications in Transportation)
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19 pages, 4803 KiB  
Article
Efficient Dissemination of Safety Messages in Vehicle Ad Hoc Network Environments
by Jongtae Lim, Dowoong Pyun, Dojin Choi, Kyoungsoo Bok and Jaesoo Yoo
Appl. Sci. 2023, 13(11), 6391; https://doi.org/10.3390/app13116391 - 23 May 2023
Cited by 2 | Viewed by 751
Abstract
The number of people owning vehicles has been steadily growing, resulting in increased numbers of vehicles on the roads, making roads more congested, and increasing the risk of accidents. In addition, heavy rain, snow, and fog have increased due to abnormal weather caused [...] Read more.
The number of people owning vehicles has been steadily growing, resulting in increased numbers of vehicles on the roads, making roads more congested, and increasing the risk of accidents. In addition, heavy rain, snow, and fog have increased due to abnormal weather caused by global warming. These bad weather conditions can also affect the safety of vehicles and drivers. The need to disseminate safety messages on the social Internet of Vehicles due to these problems has been steadily increasing. In this paper, we propose an efficient safety message dissemination scheme that focuses on urban environments with high vehicle density and mobility to address these problems. The proposed scheme reduces packet loss by considering frequent cluster departures and subscriptions through an efficient cluster management technique. In a vehicle-to-vehicle environment, the dissemination of safety messages is divided into intracluster and intercluster emergencies, as well as a general safety message dissemination technique. In a vehicle-to-infrastructure environment, the proposed scheme reduces the number of processing requests and duplicate messages made to roadside units (RSUs) through a request operation process for each vehicle and an RSU scheduling technique. We conducted several performance evaluations of message packet loss and the number of RSU processing requests to demonstrate the superiority of the proposed scheme. Full article
(This article belongs to the Special Issue Big Data Applications in Transportation)
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: Unlocking Maritime Insights: Big Data Applications for Maritime Behaviour Analysis in Dynamic Environments – A Case Study of Kiel Fjord
Authors: Ghassan Al-Falouji; Lukas Haschke; Dirk Nowotka; Sven Tomforde
Affiliation: Kiel University, Germany

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