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Modelling
Special Issues
Advanced Modelling Techniques in Transportation Engineering
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Advanced Modelling Techniques in Transportation Engineering

A special issue of Modelling (ISSN 2673-3951).

Deadline for manuscript submissions: 31 October 2025 | Viewed by 1377

Special Issue Editors

Dr. Konstantinos Gkyrtis

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Guest Editor
Department of Civil Engineering, Democritus University of Thrace (D.U.Th.), Xanthi, Greece
Interests: road-pavement engineering; pavement materials and structures; road asset condition assessment; emerging technologies and nondestructive testing; pavement performance prediction; road safety evaluation; smart road systems
Special Issues, Collections and Topics in MDPI journals
Dr. Andreas Nikiforiadis

E-Mail Website
Guest Editor
Department of Civil Engineering, Democritus University of Thrace (D.U.Th.), Kimmeria Campus, 67100 Xanthi, Greece
Interests: transport planning; active mobility; shared mobility; micromobility
Special Issues, Collections and Topics in MDPI journals
Dr. George N. Botzoris

E-Mail Website
Guest Editor
Department of Civil Engineering, Democritus University of Thrace (D.U.Th.), 67100 Xanthi, Greece
Interests: travel behavior analysis and modeling; analysis and forecast of transport demand; transport economics and feasibility methods; public transport planning and policy; traffic analysis and management
Prof. Dr. Alexandros Kokkalis

E-Mail Website
Guest Editor
Department of Civil Engineering, Democritus University of Thrace (D.U.Th.), 67100 Xanthi, Greece
Interests: road geometric design; road safety assessment and human factors; driving simulators; road infrastructure design and management; road functionality; pavements
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Advanced models and analysis techniques have been integrated into all aspects of transportation engineering over the last decade. Data science provides a multitude of ways to expedite the design, planning, operation, and maintenance of transportation infrastructure and systems. Research trends have proven that the estimation accuracy is usually a matter of concern for the transportation engineering discipline, something which becomes even more complex because of the behavioral components of transportation system end-users. This Special Issue aims to act as a collection of the most recent advances in the modelling procedures of transportation-related data, with the aim of optimizing engineering judgement and decision-making procedures. This Special Issue welcomes contributions (articles, reviews, etc.) related, but not limited, to the following aspects:

  • Impacts of emerging technologies and mobility services on transportation system performance;
  • Road and traffic safety analysis;
  • Black spot identification;
  • Road material modelling;
  • Road resiliency and pavement infrastructure status;
  • Long-term pavement performance prediction;
  • Methodologies and applications of advanced travel behavior models;
  • The use of models for rail and air transport management.

Dr. Konstantinos Gkyrtis
Dr. Andreas Nikiforiadis
Dr. George N. Botzoris
Prof. Dr. Alexandros Kokkalis
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. Modelling 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 1200 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

  • traffic engineering
  • road pavement engineering
  • road safety modelling
  • sustainable urban mobility
  • behavioral aspects in transportation engineering
  • infrastructure resiliency and condition assessment

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

20 pages, 1252 KiB  
Article
Probability-Constrained Path Planning for UAV Logistics Using Mixed Integer Linear Programming
by Zhongxiang Chen, Shengchun Wang, Kaige Chen and Xiaoke Zhang
Modelling 2025, 6(3), 82; https://doi.org/10.3390/modelling6030082 - 15 Aug 2025
Viewed by 396
Abstract
In three-dimensional (3D) logistics environments, finding optimal paths for unmanned aerial vehicles (UAVs) is challenging due to positioning inaccuracies that require ground-based corrections. These inaccuracies are exacerbated in harsh environments, leading to a significant risk of correction failure. This research proposes a multi-objective [...] Read more.
In three-dimensional (3D) logistics environments, finding optimal paths for unmanned aerial vehicles (UAVs) is challenging due to positioning inaccuracies that require ground-based corrections. These inaccuracies are exacerbated in harsh environments, leading to a significant risk of correction failure. This research proposes a multi-objective mixed integer programming model (MILP) that transforms dynamic uncertainties into binary constraints, utilizing a hierarchical sequencing strategy in the Gurobi optimizer to efficiently identify optimal paths. Our simulations indicate that achieving an 80% mission success probability necessitates an optimal path of 104,946 m with nine corrections. For a 100% success rate, the path length increases to 105,874 m, with corrections remaining constant. These results validate the model’s effectiveness in navigating environments with probabilistic constraints, highlighting its potential for addressing complex logistical challenges. Full article
(This article belongs to the Special Issue Advanced Modelling Techniques in Transportation Engineering)
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28 pages, 8266 KiB  
Article
SpatioConvGRU-Net for Short-Term Traffic Crash Frequency Prediction in Bogotá: A Macroscopic Spatiotemporal Deep Learning Approach with Urban Factors
by Alejandro Sandoval-Pineda and Cesar Pedraza
Modelling 2025, 6(3), 71; https://doi.org/10.3390/modelling6030071 - 25 Jul 2025
Viewed by 458
Abstract
Traffic crashes represent a major challenge for road safety, public health, and mobility management in complex urban environments, particularly in metropolitan areas characterized by intense traffic flows, high population density, and strong commuter dynamics. The development of short-term traffic crash prediction models represents [...] Read more.
Traffic crashes represent a major challenge for road safety, public health, and mobility management in complex urban environments, particularly in metropolitan areas characterized by intense traffic flows, high population density, and strong commuter dynamics. The development of short-term traffic crash prediction models represents a fundamental line of analysis in road safety research within the scientific community. Among these efforts, macro-level modeling plays a key role by enabling the analysis of the spatiotemporal relationships between diverse factors at an aggregated zonal scale. However, in cities like Bogotá, predicting short-term traffic crashes remains challenging due to the complexity of these spatiotemporal dynamics, underscoring the need for models that more effectively integrate spatial and temporal data. This paper presents a strategy based on deep learning techniques to predict short-term spatiotemporal traffic crashes in Bogotá using 2019 data on socioeconomic, land use, mobility, weather, lighting, and crash records across TMAU and TAZ zones. The results showed that the strategy performed with a model called SpatioConvGru-Net with top performance at the TMAU level, achieving R2 = 0.983, MSE = 0.017, and MAPE = 5.5%. Its hybrid design captured spatiotemporal patterns better than CNN, LSTM, and others. Performance improved at the TAZ level using transfer learning. Full article
(This article belongs to the Special Issue Advanced Modelling Techniques in Transportation Engineering)
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