Advanced Modelling Techniques in Transportation Engineering

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

Deadline for manuscript submissions: 31 October 2025 | Viewed by 757

Special Issue Editors


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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

E-Mail Website
Guest Editor

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

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

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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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Published Papers (1 paper)

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Research

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 306
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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