Advances in Intelligent Transportation Systems

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Transportation and Future Mobility".

Deadline for manuscript submissions: 20 May 2024 | Viewed by 740

Special Issue Editors

Dr. Seoungbum Kim
E-Mail Website
Guest Editor
Department of Urban Engineering, Engineering Research Institute, Gyeongsang National University, Jinju-si 52828, Republic of Korea
Interests: transportation operation and management; traffic safety and accident analysis; transportation infrastructure design; transportation planning; ITSs (intelligent transportation systems)
Dr. Joyoung Lee
E-Mail Website
Guest Editor
Department of Civil & Environmental Engineering, New Jersey Institute of Technology, Newark, NJ 07102, USA
Interests: connected and automated vehicles; intersection control and traffic operations; transportation data analytics and deep learning

Special Issue Information

Dear Colleagues,

Intelligent transportation systems encompass a wide range of technologies aimed at improving the efficiency, safety, and sustainability of transportation. In particular, with the evolution of communication technology and innovative improvements in computation power, the data that can be collected and transmitted from ITSs are being subdivided into individual vehicle units, and as a result, it is possible to present alternatives to contemporary transportation problems that could not be solved with conventional ITSs. Therefore, the objective of this Special Issue is to discuss novel ITS alternatives to solve problems occurring in various transportation fields and new applications using big data collected from various ITS sensors. Topics of interest include, but are not limited to, the following:

  • Cooperative intelligence transportation systems;
  • Vehicle sensor data-based analysis;
  • ITS applications of emerging technologies in traffic operation, road maintenance, road safety, and environment;
  • Automated highway systems;
  • Application of AI and machine learning for ITS;
  • Real-time applications of ITS.

Dr. Seoungbum Kim
Dr. Joyoung Lee
Guest Editors

Manuscript Submission Information

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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

  • cooperative intelligence transportation systems
  • vehicle sensor data-based analysis
  • ITS applications of emerging technologies in traffic operation, road maintenance, road safety, and environment
  • automated highway systems
  • application of AI and machine learning for ITS
  • real-time applications of ITS

Published Papers (2 papers)

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Research

19 pages, 4363 KiB  
Article
A Novel Spatial–Temporal Deep Learning Method for Metro Flow Prediction Considering External Factors and Periodicity
Appl. Sci. 2024, 14(5), 1949; https://doi.org/10.3390/app14051949 - 27 Feb 2024
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Abstract
Predicting metro traffic flow is crucial for efficient urban planning and transit management. It enables cities to optimize resource allocation, reduce congestion, and enhance the overall commuter experience in rapidly urbanizing environments. Nevertheless, metro flow prediction is challenging due to the intricate spatial–temporal [...] Read more.
Predicting metro traffic flow is crucial for efficient urban planning and transit management. It enables cities to optimize resource allocation, reduce congestion, and enhance the overall commuter experience in rapidly urbanizing environments. Nevertheless, metro flow prediction is challenging due to the intricate spatial–temporal relationships inherent in the data and the varying influence of external factors. To model spatial–temporal correlations considering external factors, a novel spatial–temporal deep learning framework is proposed in this study. Firstly, mutual information is utilized to select the highly corrected stations of the examined station. Compared with the traditional correlation calculation methods, mutual information is particularly advantageous for analyzing nonlinear metro flow data. Secondly, metro flow data reflecting the historical trends from different time granularities are incorporated. Additionally, the external factor data that influence the metro flow are also considered. Finally, these multiple sources and dimensions of data are combined and fed into the deep neural network to capture the complex correlations of multi-dimensional data. Sufficient experiments are designed and conducted on the real dataset collected from Xi’an subway to verify the effectiveness of the proposed model. Experimental results are comprehensively analyzed according to the POI information around the subway station. Full article
(This article belongs to the Special Issue Advances in Intelligent Transportation Systems)
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18 pages, 5224 KiB  
Article
A Multi-Agent Driving-Simulation Approach for Characterizing Hazardous Vehicle Interactions between Autonomous Vehicles and Manual Vehicles
Appl. Sci. 2024, 14(4), 1468; https://doi.org/10.3390/app14041468 - 11 Feb 2024
Viewed by 376
Abstract
The advent of autonomous vehicles (AVs) in the traffic stream is expected to innovatively prevent crashes resulting from human errors in manually driven vehicles (MVs). However, substantial safety benefits due to AVs are not achievable quickly because the mixed-traffic conditions in which AVs [...] Read more.
The advent of autonomous vehicles (AVs) in the traffic stream is expected to innovatively prevent crashes resulting from human errors in manually driven vehicles (MVs). However, substantial safety benefits due to AVs are not achievable quickly because the mixed-traffic conditions in which AVs and MVs coexist in the current road infrastructure will continue for a considerably long period of time. The purpose of this study is to develop a methodology to evaluate the driving safety of mixed car-following situations between AVs and MVs on freeways based on a multi-agent driving-simulation (MADS) technique. Evaluation results were used to answer the question ‘What road condition would make the mixed car-following situations hazardous?’ Three safety indicators, including the acceleration noise, the standard deviation of the lane position, and the headway, were used to characterize the maneuvering behavior of the mixed car-following pairs in terms of driving safety. It was found that the inter-vehicle safety of mixed pairs was poor when they drove on a road section with a horizontal curve length of 1000 m and downhill slope of 1% or 3%. A set of road sections were identified, using the proposed evaluation method, as hazardous conditions for mixed car-following pairs consisting of AVs and MVs. The outcome of this study will be useful for supporting the establishment of safer road environments and developing novel V2X-based trafficsafetyinformation content that enables the enhancement of mixed-traffic safety. Full article
(This article belongs to the Special Issue Advances in Intelligent Transportation Systems)
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