Intelligent Transportation System and Road Infrastructure Design

A special issue of Future Transportation (ISSN 2673-7590).

Deadline for manuscript submissions: 30 April 2024 | Viewed by 1499

Special Issue Editors

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Guest Editor
Durham School of Architectural Engineering and Construction, University of Nebraska-Lincoln, Lincoln, NE 68588, USA
Interests: civil infrastructure systems; pavement sensing technology; pavements
Nebraska Transportation Center, Department of Civil Engineering, University of Nebraska-Lincoln, Lincoln, NE 68588, USA
Interests: ITS; traffic safety modeling; driver behavior; traffic operation and simulation

Special Issue Information

Dear Colleagues,

The rapid development of the modern transportation network is accompanied by challenges in traffic and road infrastructure, including safety, congestion, maintenance, mode accommodation, environmental impacts, and more. In particular, the design and condition of the road infrastructure (e.g., pavements, signage, pathway, parking facility) can greatly affect the behavior of drivers and their ability to respond to potential hazards and crash impacts. For some of these challenges, the integration of advanced intelligent transportation system (ITS) technologies into highway infrastructure design offers innovative solutions. ITS technologies augment traditional infrastructure improvement approaches and provide real-time traffic information (e.g., traffic flow, road conditions, parking availability), helping to create safer and more efficient, reliable, and sustainable transportation systems.

This Special Issue examines the latest advancements and trends in the fields of pavements, highway infrastructure, and road traffic, exploring the ways in which ITS can improve the design and functionality of highway infrastructure. Specifically, there is a broad spectrum of topics that are of interest, including but not limited to:

  • Resilient and sustainable highway infrastructure
  • Performance evaluation of pavement materials
  • Innovative solutions to long lasting pavements
  • Merging technology in pavement evaluation
  • The impact of ITS technologies (e.g., sensors, communication) on road safety and mobility
  • ITS data-driven methods to infrastructure design for multi-modal transportation (walking, cycling, automobile, public transit, etc.) and connections between modes
  • Integration of sensor and communication technologies for real-time traffic and road monitoring and management
  • Sustainable transport infrastructure focused on reducing emission pollution and promoting clean energy
  • Road infrastructure readiness to support emerging transportation systems such as shared mobility services, connected and automated vehicles, and electric vehicles
  • Development of smart cities and sustainable transportation systems
  • Opportunities and challenge of utilizing the huge potential of ITS data for traffic management and infrastructure design.

Dr. Chunhsing Ho
Dr. Li Zhao
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at 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. Future Transportation is an international peer-reviewed open access quarterly 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 1000 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.


  • civil infrastructure
  • pavements
  • ITS
  • traffic safety
  • driver behavior

Published Papers (1 paper)

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20 pages, 4477 KiB  
A Methodology to Detect Traffic Data Anomalies in Automated Traffic Signal Performance Measures
by Bangyu Wang, Grant G. Schultz, Gregory S. Macfarlane, Dennis L. Eggett and Matthew C. Davis
Future Transp. 2023, 3(4), 1175-1194; - 02 Oct 2023
Viewed by 949
Automated traffic signal performance measures (ATSPMs) have garnered significant attention for their ability to collect and evaluate real-time and historical data at signalized intersections. ATSPM data are widely utilized by traffic engineers, planners, and researchers in various application scenarios. In working with ATSPM [...] Read more.
Automated traffic signal performance measures (ATSPMs) have garnered significant attention for their ability to collect and evaluate real-time and historical data at signalized intersections. ATSPM data are widely utilized by traffic engineers, planners, and researchers in various application scenarios. In working with ATSPM data in Utah, it was discovered that five types of ATSPM data anomalies (data switching, data shifting, data missing under 6 months, data missing over 6 months, and irregular curves) were present in the data. To address the data issues, this paper presents a method that enables transportation agencies to automatically detect data anomalies in their ATSPM datasets. The proposed method utilizes the moving average and standard deviation of a moving window to calculate the z-score for traffic volume data points at each timestamp. Anomalies are flagged when the z-score exceeds 2, which is based on the data falling within two standard deviations of the mean. The results demonstrate that this method effectively identifies anomalies within ATSPM systems, thereby enhancing the usability of data for engineers, planners, and all ATSPM users. By employing this method, transportation agencies can improve the efficiency of their ATSPM systems, leading to more accurate and reliable data for analysis. Full article
(This article belongs to the Special Issue Intelligent Transportation System and Road Infrastructure Design)
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