Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
7.7
3.3
Journals
Sustainability
Special Issues
Transportation Systems and Infrastructures Planning, Optimization, and Management
sustainability-logo
Submit to Special Issue Submit Abstract to Special Issue Review for Sustainability Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Transportation Systems and Infrastructures Planning, Optimization, and Management

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Transportation".

Deadline for manuscript submissions: 31 January 2026 | Viewed by 3251

Special Issue Editors

Dr. Andrea Pompigna

E-Mail Website
Guest Editor
Faculty of Technological & Innovation Sciences, Universitas Mercatorum, Piazza Mattei, 10, 00186 Rome, Italy
Interests: traffic flow theory; traffic data management and analysis; transportation modelling and simulation; traffic engineering operations and highway capacity; time-dependent queue theory in transportation engineering; technologies and applications for smart and sustainable roads
Dr. Giulia Del Serrone

E-Mail Website
Guest Editor
Department of Civil, Constructional and Environmental Engineering, University of Rome La Sapienza, Via Eudossiana 18, 00184 Rome, Italy
Interests: road alignment; big data analysis and management; civil and environmental engineering; road infrastructure design; probe vehicles; road safety
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Urban and interurban mobility face growing challenges due to population growth, urbanization, and increased demand for transportation. These pressures necessitate innovative and sustainable solutions to optimize transportation systems and infrastructures, improve efficiency, and reduce environmental impacts. This Special Issue focuses on transportation system and infrastructures optimization, transport planning, and traffic management, aiming to provide a holistic perspective that addresses both operational efficiency and environmental sustainability.

Advancements in technology, including intelligent transportation systems (ITSs), machine learning, and real-time data analysis, offer new opportunities for optimizing traffic management and transport networks. This Issue seeks to expand the current literature by presenting novel models and methodologies that enhance operational efficiency, promote equitable access to mobility services, and minimize the ecological footprint of transportation systems and infrastructures. It emphasizes strategic transport planning that integrates sustainability goals by balancing socio-economic, environmental, and technological factors.

We invite scientific contributions exploring advanced methods and case studies in areas such as capacity optimization, sustainable transport planning, real-time traffic management, and multimodal transport integration. By fostering scientific discourse and innovation, this Special Issue aims to contribute to the development of resilient, efficient, and equitable mobility systems.

Dr. Andrea Pompigna
Dr. Giulia Del Serrone
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. Sustainability 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 2400 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

  • transportation system and infrastructures optimization
  • transport planning
  • traffic and infrastructure management
  • sustainable mobility
  • intelligent transportation systems (ITS)
  • traffic simulation
  • congestion mitigation
  • multimodal integration
  • big data in transportation
  • road safety and risk 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 (5 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

25 pages, 5850 KiB  
Article
Simulation-Based Modeling of the Impact of Left-Turn Bay Overflow on Signalized Intersection Capacity
by Deana Breški and Biljana Maljković
Sustainability 2025, 17(12), 5397; https://doi.org/10.3390/su17125397 - 11 Jun 2025
Viewed by 265
Abstract
The motorized vehicle methodology in the Highway Capacity Manual (HCM) does not account for the effect of left-turn bay overflow, which is stated as a limitation of the methodology. In this study, an adjustment factor was developed to quantify the impact of left-turn [...] Read more.
The motorized vehicle methodology in the Highway Capacity Manual (HCM) does not account for the effect of left-turn bay overflow, which is stated as a limitation of the methodology. In this study, an adjustment factor was developed to quantify the impact of left-turn bay length on the through lane capacity at signalized intersections. The adjustment factor was modeled based on a large number of scenarios generated using the CORSIM microsimulation model. These scenarios covered intersection geometries typical for two-phase signal control and included a wide range of traffic parameters (number of lanes, traffic volume, left-turn volume, left-turn bay length, cycle length, and green ratio). By comparing the capacity values obtained with a short left-turn bay to those with an infinitely long bay under identical other traffic conditions, it was possible to develop an adjustment factor that reflects the impact of turn bay overflow. A regression-based model was created and validated, showing very good agreement with the simulated values. The new adjustment factor provides an enhancement of the HCM estimation methodology that improves the accuracy of capacity and delay estimates in intersection evaluations as well as supports more effective intersection design and sustainable mobility. More accurate capacity estimation reduces congestion, travel delays, and vehicle stopping, directly contributing to sustainable transportation goals, lowering emissions, and supporting environmentally responsible urban mobility systems. Full article
(This article belongs to the Special Issue Transportation Systems and Infrastructures Planning, Optimization, and Management)
►▼ Show Figures

Figure 1

21 pages, 1309 KiB  
Article
Quantum-Inspired Spatio-Temporal Inference Network for Sustainable Car-Sharing Demand Prediction
by Nihad Brahimi, Huaping Zhang and Zahid Razzaq
Sustainability 2025, 17(11), 4987; https://doi.org/10.3390/su17114987 - 29 May 2025
Viewed by 386
Abstract
Accurate car-sharing demand prediction is a key factor in enhancing the operational efficiency of shared mobility systems. However, mobility data often exhibit temporal, spatial, and spatio-temporal interdependencies that pose significant challenges for conventional models. These models typically struggle to capture nonlinear and high-dimensional [...] Read more.
Accurate car-sharing demand prediction is a key factor in enhancing the operational efficiency of shared mobility systems. However, mobility data often exhibit temporal, spatial, and spatio-temporal interdependencies that pose significant challenges for conventional models. These models typically struggle to capture nonlinear and high-dimensional patterns. Existing methods struggle to model entangled relationships across these modalities and lack scalability in dynamic urban environments. This paper presents the Quantum-Inspired Spatio-Temporal Inference Network (QSTIN), an enhanced approach that builds upon our previously proposed Explainable Spatio-Temporal Inference Network (eX-STIN). QSTIN integrates a Quantum-Inspired Neural Network (QINN) into the fusion module, generating complex-valued feature representations. This enables the model to capture intricate, nonlinear dependencies across heterogeneous mobility features. Additionally, Quantum Particle Swarm Optimization (QPSO) is applied at the final prediction stage to optimize output parameters and improve convergence stability. Experimental results indicate that QSTIN consistently outperforms both conventional baseline models and the earlier eX-STIN in predictive accuracy. By enhancing demand prediction, QSTIN supports efficient vehicle allocation and planning, reducing energy use and emissions and promoting sustainable urban mobility from both environmental and economic perspectives. Full article
(This article belongs to the Special Issue Transportation Systems and Infrastructures Planning, Optimization, and Management)
►▼ Show Figures

Figure 1

21 pages, 4151 KiB  
Article
Research on Resource Consumption Standards for Highway Electromechanical Equipment Based on Monte Carlo Model
by Linxuan Liu, Wei Tian, Xiaomin Dai and Liang Song
Sustainability 2025, 17(10), 4640; https://doi.org/10.3390/su17104640 - 19 May 2025
Viewed by 319
Abstract
The increasing complexity of highway electromechanical systems has created a critical need to improve the accuracy of resource consumption standards. Traditional deterministic methods often fail to capture inherent variability in resource usage, resulting in significant discrepancies between budget estimates and actual costs. To [...] Read more.
The increasing complexity of highway electromechanical systems has created a critical need to improve the accuracy of resource consumption standards. Traditional deterministic methods often fail to capture inherent variability in resource usage, resulting in significant discrepancies between budget estimates and actual costs. To address this issue for a specific device, this study develops a probabilistic framework based on Monte Carlo simulation, using manual barrier gate installation as a case study. First, probability distribution models for key parameters were established by collecting and statistically analyzing field data. Next, Monte Carlo simulation generated 100,000 pseudo-observations, yielding mean labor consumption of 1.08 workdays (SD 0.29), expansion bolt usage of 6.02 sets (SD 0.97), and equipment shifts of 0.20 (SD 0.10). Comparison with the “Highway Engineering Budget Standards” (JTG/T 3832-2018) revealed deviations of 1% to 4%, and comparison with market bid prices showed errors below 2%. These results demonstrate that the proposed method accurately captures dynamic fluctuations in resource consumption, aligning with both national norms and actual tender data. In conclusion, the framework offers a robust and adaptable tool for cost estimation and resource allocation in highway electromechanical projects, enhancing budgeting accuracy and reducing the risk of cost overruns. Full article
(This article belongs to the Special Issue Transportation Systems and Infrastructures Planning, Optimization, and Management)
►▼ Show Figures

Figure 1

17 pages, 2907 KiB  
Article
ST_AGCNT: Traffic Speed Forecasting Based on Spatial–Temporal Adaptive Graph Convolutional Network with Transformer
by Rongjun Cheng, Mengxia Liu and Yuanzi Xu
Sustainability 2025, 17(5), 1829; https://doi.org/10.3390/su17051829 - 21 Feb 2025
Cited by 1 | Viewed by 736
Abstract
Traffic speed prediction is difficult because of the complicated dynamic spatiotemporal correlations. Recent studies in spatiotemporal models have achieved impressive outcomes for traffic speed prediction. But many studies use graphs in graph convolutional networks to learn spatial features that are often static. Additionally, [...] Read more.
Traffic speed prediction is difficult because of the complicated dynamic spatiotemporal correlations. Recent studies in spatiotemporal models have achieved impressive outcomes for traffic speed prediction. But many studies use graphs in graph convolutional networks to learn spatial features that are often static. Additionally, effectively modeling long-range temporal features is crucial for prediction accuracy. In order to overcome these challenges, a Spatial–Temporal Adaptive Graph Convolutional Network with Transformer (ST_AGCNT) is designed in this paper. Specifically, an adaptive graph convolution network (AGCN) is designed to extract spatial dependency. An adaptive graph that fuses predefined matrices and learnable matrix is proposed to learn the correlations between nodes. The predefined matrices provide the model with richer prior information, while the learnable matrix can extract the dynamic nature of the nodes. And a temporal transformer (TT) is proposed to extract the long-range temporal dependency. In addition, to learn more information to achieve better results, different historical segments are modeled. Experiments conducted on a real-world traffic dataset confirm the effectiveness of the proposed model when compared to other baseline models. This model demonstrated excellent performance in prediction tasks across different time steps, effectively accomplishing traffic speed forecasting. It provides data support for improving traffic efficiency and reducing resource waste, contributing to the sustainable development of traffic management. Full article
(This article belongs to the Special Issue Transportation Systems and Infrastructures Planning, Optimization, and Management)
►▼ Show Figures

Figure 1

21 pages, 3769 KiB  
Article
Effects of Lane Imbalance on Capacity Drop and Emission in Expressway Merging Areas: A Simulation Analysis
by Kai Zhang, Jian Rong, Yacong Gao and Yue Chen
Sustainability 2024, 16(23), 10388; https://doi.org/10.3390/su162310388 - 27 Nov 2024
Cited by 1 | Viewed by 1016
Abstract
Lane imbalance does not provide sufficient space for merging vehicles to adjust their speed and change lanes smoothly. This leads to improper driving behavior that disrupts mainline traffic flow stability, resulting in capacity drops and increased vehicle emissions. However, quantitative analyses, specifically the [...] Read more.
Lane imbalance does not provide sufficient space for merging vehicles to adjust their speed and change lanes smoothly. This leads to improper driving behavior that disrupts mainline traffic flow stability, resulting in capacity drops and increased vehicle emissions. However, quantitative analyses, specifically the effects of lane imbalance on capacity and emissions, remain limited. Existing traffic simulation platforms struggle to capture the effects of geometric design changes on capacity. To address these gaps, we developed a simulation method incorporating interactions between geometric design and traffic flow demand into an XGBoost model, enhancing the predictive accuracy for driving behavior parameters. Implemented within the TESS NG platform, this model enables real-time adjustments in driving behavior parameters as traffic demand varies under different lane balance conditions. The simulation results indicated a 42.4% capacity drop and a 34.9% increase in CO2 emissions when the balanced merging area was shifted to lane imbalance. Conversely, shifting to lane balance increases capacity by 8.2% and reduces CO2 emissions by 39.8% under severe congestion conditions. Under lane imbalance, vehicle speeds are lower across all traffic demand levels. When the demand exceeds 1300 pcu/h/ln, lane changes occur closer to the end of the acceleration lane, with higher speed differentials. These insights underscore the potential of lane balance optimization to mitigate capacity drops and emissions, providing a valuable simulation approach for the design and evaluation of merging areas. Full article
(This article belongs to the Special Issue Transportation Systems and Infrastructures Planning, Optimization, and Management)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-5
Back to TopTop