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Journals
Infrastructures
Special Issues
Recent Progress in Transportation Infrastructures
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Recent Progress in Transportation Infrastructures

A special issue of Infrastructures (ISSN 2412-3811).

Deadline for manuscript submissions: 31 July 2024 | Viewed by 3678

Special Issue Editors

Prof. Dr. Said M. Easa

E-Mail Website
Guest Editor
Department of Civil Engineering, Toronto Metropolitan University, 350 Victoria St., Toronto, ON M5B 2K3, Canada
Interests: intelligent transportation systems; highway geometric design and safety; human factors in transportation; traffic operations and management; engineering education
Special Issues, Collections and Topics in MDPI journals
Dr. Yuanwen Lai

E-Mail Website
Guest Editor
College of Civil Engineering, Fuzhou University, Fuzhou, China
Interests: theories and methods of urban transportation planning; public transport operation and management; road traffic efficiency based on big data mining
Dr. Yang Ma

E-Mail Website
Guest Editor
School of Automotive and Traffic Engineering, Hefei University of Technology, Hefei, China
Interests: road infrastructure digitization; use and development of LiDAR technology in road and traffic engineering; cooperative vehicle-infrastructure system; microscopic simulation of non-motorized traffic
Dr. Chenzhu Wang

E-Mail Website
Guest Editor
Department of Civil, Environmental and Construction Engineering, University of Central Florida, Orlando, FL, USA
Interests: transportation safety; human factors in transportation; driving bebavhiors under plateau environments; digital twin

Special Issue Information

Dear Colleagues,

Transportation infrastructure (highways, railways, ports, and airports) includes physical elements (e.g., geometric, pavement, and sensors) and soft elements (e.g., algorithms and intelligent systems). In this regard, the design, optimization, management, and maintenance of transportation infrastructures are becoming more challenging in the forthcoming digital and intelligent transportation era. In the meantime, emerging technologies may provide more opportunities for enhancing different aspects of transportation infrastructures. For instance, building information modelling platforms can help manage data flows throughout the lifecycle of transportation infrastructures, thus improving cooperation among different departments. New sensing technologies (e.g., LiDAR) and computer vision algorithms can help automate the process of road asset inventory and infrastructure digitalization, reducing both time and costs for infrastructure administrators. In addition, the boom in artificial intelligence is reshaping methods of infrastructure risk monitoring and enhancing transportation infrastructure safety.

To advance the academic discourse in this area, we welcome papers covering new research related to all transportation modes: highways, railways, ports, and airports. Researchers from academia and industry are invited to submit articles on any topic related to the theme of the Special Issue, including the keywords we listed:

The Special Issue aims at publishing high-quality papers, particularly those that address engineering and scientific aspects related to the topics we listed in the keywords part.

Prof. Dr. Said M. Easa
Dr. Yuanwen Lai
Dr. Yang Ma
Dr. Chenzhu Wang
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. Infrastructures 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 1800 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 infrastructure
  • infrastructure-related data processing and management
  • infrastructure-cooperative algorithms
  • emerging technologies (e.g., blockchain and building information modelling)
  • geometric design of railway tracks
  • railway stations and yards
  • railway maintenance and rehabilitation
  • track drainage
  • railway safety and management
  • classification yards
  • runway and gate capacity
  • air traffic management and safety
  • airport–highway interface
  • baggage handling systems
  • ports and harbour operation
  • intermodal transportation
  • transportation cybersecurity
  • travel demand and network modelling
  • asset management
  • transportation system management
  • transit planning
  • highway geometric design
  • automated transportation systems
  • highway maintenance/management
  • security and monitoring systems
  • sustainable transportation
  • intersections and roundabouts
  • bridge bearing capacity and stability
  • transportation safety and security
  • geographic information systems
  • complete streets (active transport)
  • site impact studies
  • pavement design

Published Papers (2 papers)

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Research

16 pages, 2231 KiB  
Article
Vehicle Driving Safety of Underground Interchanges Using a Driving Simulator and Data Mining Analysis
by Zhen Liu, Qifeng Yang, Anlue Wang and Xingyu Gu
Infrastructures 2024, 9(2), 28; https://doi.org/10.3390/infrastructures9020028 - 02 Feb 2024
Viewed by 1303
Abstract
In the process of driving in an underground interchange, drivers are faced with many challenges, such as being in a closed space, visual changes alternating between light and dark conditions, complex road conditions in the confluence section, and dense signage, which directly affect [...] Read more.
In the process of driving in an underground interchange, drivers are faced with many challenges, such as being in a closed space, visual changes alternating between light and dark conditions, complex road conditions in the confluence section, and dense signage, which directly affect the safety and comfort of drivers in an underground interchange. Thus, driving simulation, building information modeling (BIM), and data mining were used to analyze the impact of underground interchange safety facilities on driving safety and comfort. Acceleration disturbance and steering wheel comfort loss values were used to assist the comfort analysis. The CART algorithm, classification decision trees, and neural networks were used for data mining, which uses a dichotomous recursive partitioning technique where multiple layers of neurons are superimposed to fit and replace very complex nonlinear mapping relationships. Ten different scenarios were designed for comparison. Multiple linear regression combined with ANOVA was used to calculate the significance of the control variables for each scenario on the evaluation index. The results show that appropriately reducing the length of the deceleration section can improve driving comfort, setting reasonable reminder signs at the merge junction can improve driving safety, and an appropriate wall color can reduce speed oscillation. This study indicates that the placement of traffic safety facilities significantly influences the safety and comfort of driving in underground interchanges. This study may provide support for the optimization of the design of underground interchange construction and internal traffic safety facilities. Full article
(This article belongs to the Special Issue Recent Progress in Transportation Infrastructures)
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12 pages, 2124 KiB  
Article
Comparative Analysis of Machine-Learning Models for Recognizing Lane-Change Intention Using Vehicle Trajectory Data
by Renteng Yuan, Shengxuan Ding and Chenzhu Wang
Infrastructures 2023, 8(11), 156; https://doi.org/10.3390/infrastructures8110156 - 25 Oct 2023
Viewed by 1788
Abstract
Accurate detection and prediction of the lane-change (LC) processes can help autonomous vehicles better understand their surrounding environment, recognize potential safety hazards, and improve traffic safety. This study focuses on the LC process, using vehicle trajectory data to select a model for identifying [...] Read more.
Accurate detection and prediction of the lane-change (LC) processes can help autonomous vehicles better understand their surrounding environment, recognize potential safety hazards, and improve traffic safety. This study focuses on the LC process, using vehicle trajectory data to select a model for identifying vehicle LC intentions. Considering longitudinal and lateral dimensions, the information extracted from vehicle trajectory data includes the interactive effects among target and adjacent vehicles (54 indicators) as input parameters. The LC intention of the target vehicle serves as the output metric. This study compares three widely recognized machine-learning models: support vector machines (SVM), ensemble methods (EM), and long short-term memory (LSTM) networks. The ten-fold cross-validated method was used for model training and evaluation. Classification accuracy and training complexity were used as critical metrics for evaluating model performance. A total of 1023 vehicle trajectories were extracted from the CitySim dataset. The results indicate that, with an input length of 150 frames, the XGBoost and LightGBM models achieve an impressive overall classification performance of 98.4% and 98.3%, respectively. Compared to the LSTM and SVM models, the results show that the two ensemble models reduce the impact of Types I and III errors, with an improved accuracy of approximately 3.0%. Without sacrificing recognition accuracy, the LightGBM model exhibits a sixfold improvement in training efficiency compared to the XGBoost model. Full article
(This article belongs to the Special Issue Recent Progress in Transportation Infrastructures)
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