Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
2.2
1.5
Journals
Modelling
Special Issues
Advanced Modelling Techniques in Transportation Engineering
share announcement

Advanced Modelling Techniques in Transportation Engineering

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

Deadline for manuscript submissions: 31 May 2026 | Viewed by 7008

Special Issue Editors

Dr. Konstantinos Gkyrtis

E-Mail Website
Guest Editor
Department of Civil Engineering, Democritus University of Thrace (D.U.Th.), GR-67100 Xanthi, Greece
Interests: pavement condition assessment; non-destructive evaluation; deflectometric testing; smart monitoring 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 250 words) can be sent to the Editorial Office for assessment.

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 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 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

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

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

Published Papers (7 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

34 pages, 10503 KB  
Article
Multi-Objective Trajectory Optimization for Autonomous Vehicles Based on an Improved Driving Risk Field
by Jianping Gao, Wenju Liu, Pan Liu, Peiyi Bai and Chengwei Xie
Modelling 2026, 7(2), 75; https://doi.org/10.3390/modelling7020075 - 17 Apr 2026
Viewed by 196
Abstract
Trajectory planning in dynamic multi-vehicle interaction environments faces three critical challenges, including the difficulty of quantifying spatial risk distributions, the complexity of characterizing behavioral uncertainty arising from the multimodal maneuvers of surrounding vehicles, and the challenge of simultaneously optimizing multiple competing objectives such [...] Read more.
Trajectory planning in dynamic multi-vehicle interaction environments faces three critical challenges, including the difficulty of quantifying spatial risk distributions, the complexity of characterizing behavioral uncertainty arising from the multimodal maneuvers of surrounding vehicles, and the challenge of simultaneously optimizing multiple competing objectives such as safety, efficiency, comfort, and energy consumption. To address these challenges, this paper proposes an Improved Driving Risk Field-based Multi-objective Trajectory Optimization (IDRF-MTO) method. First, a joint spatiotemporal social attention mechanism achieves unified modeling of spatial interactions, temporal dependencies, and spatiotemporal coupling, combined with a lateral–longitudinal intent strategy for multimodal trajectory prediction. Second, an improved dynamic risk field model is constructed comprising three components: a vehicle risk field that incorporates spatial orientation and motion direction factors for anisotropic risk representation, along with a collision tendency factor that converts objective risk into effective risk; a predicted trajectory risk field that achieves anticipatory quantification of future risk from surrounding vehicles through confidence-weighted fusion; and a driving environment risk field that encapsulates road geometry, static obstacles, and environmental conditions. Finally, a multi-objective cost function embedding risk field gradients is formulated, and multi-objective coordinated optimization is realized through a three-dimensional spatiotemporal situation graph with adaptive safety sampling. Simulation results demonstrate that the proposed method enhances safety while simultaneously improving comfort and efficiency and reducing energy consumption, exhibiting excellent planning performance in complex dynamic environments. Full article
(This article belongs to the Special Issue Advanced Modelling Techniques in Transportation Engineering)
►▼ Show Figures

Graphical abstract

26 pages, 3594 KB  
Article
Evaluating Public Transportation Criteria and Congestion Using Multi-Criteria Assessment and Simulation Modeling
by Kasin Ransikarbum, Naraphorn Paoprasert and Pornthep Anussornnitisarn
Modelling 2026, 7(2), 73; https://doi.org/10.3390/modelling7020073 - 13 Apr 2026
Viewed by 334
Abstract
Congestion in urban transportation is a significant challenge, often exacerbated by increasing private vehicle use and limitations in public transport. This study introduces a two-stage approach combining multi-criteria assessment and traffic simulation to examine current conditions and propose improvements. Initially, data on five [...] Read more.
Congestion in urban transportation is a significant challenge, often exacerbated by increasing private vehicle use and limitations in public transport. This study introduces a two-stage approach combining multi-criteria assessment and traffic simulation to examine current conditions and propose improvements. Initially, data on five primary and twenty-one secondary factors affecting public transport choice are assessed using the Best–Worst Method (BWM). The findings reveal that convenience is prioritized by working professionals, while travel cost is most important to students. A baseline simulation model is established using a case study at Kaset Intersection in Bangkok. Incorporating weighted preferences into the simulation aims to enhance public transport and encourage private car users to switch modes through potential traffic management policies. Additionally, a micro-simulation assesses the impacts of decreased traffic density, revealing that a reduction in traffic density can shorten overall travel time by about 2.04 s, based on regression analysis. The results suggest policies to improve public transport, reduce traffic density, and enhance urban transport system performance. Full article
(This article belongs to the Special Issue Advanced Modelling Techniques in Transportation Engineering)
►▼ Show Figures

Figure 1

14 pages, 418 KB  
Article
Traffic Accident Risk Assessment at Urban Signalized Intersections Using Cellular Automata Modeling
by Laila Taoufiq, Omar Bamaarouf, Abdelmajid Kadiri and Rachid Marzoug
Modelling 2026, 7(2), 57; https://doi.org/10.3390/modelling7020057 - 17 Mar 2026
Viewed by 415
Abstract
Traffic accidents at urban intersections represent a major road safety concern, particularly those caused by traffic signal violations. To analyze accident mechanisms and develop effective prevention strategies, this study employs a cellular automata model to investigate the relationship between accident probability [...] Read more.
Traffic accidents at urban intersections represent a major road safety concern, particularly those caused by traffic signal violations. To analyze accident mechanisms and develop effective prevention strategies, this study employs a cellular automata model to investigate the relationship between accident probability Pac and traffic parameters at signalized intersections. Simulation results reveal a nonlinear relationship between Pac and traffic demand. The accident probability reaches a maximum under free-flow conditions and subsequently decreases as congestion increases, eventually stabilizing at a nearly constant level under highly congested traffic. Additionally, collision risk increases with lane-changing probability Pchg, especially upstream of the intersection. High traffic speeds significantly elevate both accident probability and severity. Finally, the results indicate that extending traffic signal cycle durations is not an effective strategy for reducing accident risk. Overall, the proposed model provides a useful framework for estimating accident risk under different traffic conditions and supporting traffic management, including control decisions aimed at improving road safety. Full article
(This article belongs to the Special Issue Advanced Modelling Techniques in Transportation Engineering)
►▼ Show Figures

Figure 1

21 pages, 4838 KB  
Article
Data-Driven Prediction of Punchout Occurrence in CRCP Using an Optimized Gradient Boosting Model
by Ali Juma Alnaqbi, Ghazi G. Al-Khateeb and Waleed Zeiada
Modelling 2026, 7(1), 38; https://doi.org/10.3390/modelling7010038 - 13 Feb 2026
Viewed by 487
Abstract
Punchouts distress represents a major structural deficiency in Continuously Reinforced Concrete Pavements (CRCPs), contributing to premature deterioration, reduced ride quality, and increased maintenance demands. To improve the prediction of punchout occurrence, this study develops a hybrid data-driven modeling approach that combines Gradient Boosting [...] Read more.
Punchouts distress represents a major structural deficiency in Continuously Reinforced Concrete Pavements (CRCPs), contributing to premature deterioration, reduced ride quality, and increased maintenance demands. To improve the prediction of punchout occurrence, this study develops a hybrid data-driven modeling approach that combines Gradient Boosting Machines (GBMs) with Particle Swarm Optimization (PSO). The proposed framework utilizes 395 observations obtained from 33 CRCP sections in the Long-Term Pavement Performance (LTPP) database, incorporating structural, climatic, traffic, and performance-related variables. PSO was applied to systematically tune key GBM hyperparameters, including the number of boosting iterations, learning rate, and tree complexity, in order to enhance predictive accuracy. Model performance was evaluated using five-fold cross-validation, where the optimized PSO-GBM model achieved an average RMSE of 1.09 and an R2 value of 0.947, outperforming conventional GBM as well as Random Forest, Support Vector Regression, Artificial Neural Networks, and Linear Regression models. Variable importance and sensitivity analyses revealed that Layer 3 thickness, pavement age, annual average daily traffic, and precipitation play dominant roles in punchout development. The consistency of residual distributions and the stability of hyperparameter sensitivity trends further confirm the robustness of the proposed framework. Overall, the results demonstrate that integrating evolutionary optimization with ensemble learning provides an effective tool for modeling complex pavement distresses and offers practical support for proactive maintenance planning and long-term management of CRCP infrastructure. Full article
(This article belongs to the Special Issue Advanced Modelling Techniques in Transportation Engineering)
►▼ Show Figures

Figure 1

30 pages, 1142 KB  
Article
A Generalizable, Data-Driven Agent-Based Transport Simulation Framework: Towards Land Use and Transport Interaction Models in Brazil
by Ígor Godeiro de Oliveira Maranhão and Romulo Dante Orrico Filho
Modelling 2025, 6(4), 145; https://doi.org/10.3390/modelling6040145 - 10 Nov 2025
Viewed by 938
Abstract
Agent-based models (ABMs) in transport represent a paradigm shift from traditional aggregate and equilibrium-based approaches. By modeling individual behaviors of a heterogeneous population, an ABM offers a more realistic representation of urban phenomena and extends sensitivity to different policy interventions. Despite this, ABM [...] Read more.
Agent-based models (ABMs) in transport represent a paradigm shift from traditional aggregate and equilibrium-based approaches. By modeling individual behaviors of a heterogeneous population, an ABM offers a more realistic representation of urban phenomena and extends sensitivity to different policy interventions. Despite this, ABM implementation faces several challenges such as limited reproducibility, uneven global implementation, and high technical and financial costs, particularly relevant in the Global South. The proposed framework addresses these gaps by implementing a modular, transparent, publicly shared data-driven approach, reducing hierarchies and relationships definitions while ensuring reproducibility. Utilizing nationally available data to generate a synthetic population, activity plans, multimodal network and agent simulations in MATSim, the framework was applied in the Metropolitan Area of Fortaleza, a region with approximately 4 million people in Brazil. Despite inherent data limitations characteristic of developing contexts, the framework demonstrated performance compatible with strategic planning applications. Traffic assignment validation showed a mean absolute error of 301 vehicles during morning peak hours and 423 vehicles for the 24 h period, which are acceptable for scenario-based policy analysis. Beyond the potential to democratize access to robust urban planning models in similar data-constrained scenarios worldwide, this study presents pathways to foster national dialogue toward improved data collection practices for disaggregated transport model implementation. Full article
(This article belongs to the Special Issue Advanced Modelling Techniques in Transportation Engineering)
►▼ Show Figures

Figure 1

20 pages, 1252 KB  
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 2005
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)
►▼ Show Figures

Figure 1

28 pages, 8266 KB  
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 1470
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)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-7
Back to TopTop