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Advances in Data-Driven Transportation Systems: Emerging Trends, Challenges, and Applications

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

Deadline for manuscript submissions: 31 August 2026 | Viewed by 16975

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Special Issue Editors

Dr. Yi Li

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Guest Editor

School of Transportation Engineering, Chang’an University, Xi'an, China
Interests: sustainable transportation systems; resilient transportation infrastructure network modeling and optimization; low-carbon transportation economy

Dr. Meng Meng

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Guest Editor

School of Management, University of Bath, Bath, UK
Interests: sustainable transportation; network modelling; travel behaviour analysis

Dr. Ziyue Yuan

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Guest Editor

School of Traffic and Transportation Engineering, Central South University, Changsha, China
Interests: transportation; emergency management

Special Issue Information

Dear Colleagues,

We are excited to invite you to contribute to our upcoming Special Issue, titled ‘Advances in Data-Driven Transportation Systems: Emerging Trends, Challenges, and Applications’. This Special Issue aims to explore how emerging data-driven methodologies can promote the sustainable development of transportation systems.

In an era where digital technologies continuously reshape operational processes, understanding the implications, challenges, and opportunities presented by data-driven solutions in transportation is essential. This Special Issue seeks to gather original articles and research that investigate how big data analytics, intelligent transportation systems, and advanced modeling techniques transform transportation planning, operations, and policy-making.

The increasing availability of transportation data has opened new avenues for enhancing user experience, optimizing traffic management, and promoting sustainable mobility. We encourage the submission of articles that not only present theoretical and empirical research, but that also demonstrate the practical implementations of data-driven methods in real-world transportation contexts.

We are particularly interested in contributions that address, but are not limited to, the following topics:

Data analytics and predictive modeling for real-time traffic management;
Intelligent transportation systems (ITS) and smart city applications;
AI-driven approaches to optimize mobility and reduce congestion;
Sustainable and eco-friendly routing supported by data insights;
Safety, risk assessment, and incident detection using data-driven methods;
Integrating autonomous and connected vehicles into existing transportation networks;
Big data fusion techniques for multi-modal transportation planning;
Emerging technologies and innovations in public transport management;
Future trends and evolving challenges in data-centric mobility solutions.

This Special Issue aims to provide a platform for interdisciplinary dialog and foster new ideas and approaches that challenge traditional practices in transportation management. We welcome original research articles, comprehensive reviews, and case studies that contribute to the theoretical and methodological advancement of the field.

We look forward to receiving your insightful contributions and advancing the discourse in this exciting and rapidly developing area.

Dr. Yi Li
Dr. Meng Meng
Dr. Ziyue Yuan
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. 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

low-carbon/resilient transportation systems
data-driven optimization
sustainable transport
emergency management
infrastructure management
network analysis

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Published Papers (12 papers)

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Research

20 pages, 1042 KB

Open AccessArticle

Evaluating Bus Driver Compliance with Speed Adjustment Commands Under Different Driving Conditions: A Driving Simulator-Based Study

by Weiya Chen, Haochen Wang and Duo Li

Sustainability 2026, 18(6), 2977; https://doi.org/10.3390/su18062977 - 18 Mar 2026

Viewed by 321

Abstract

While bus transit plays a critical role in promoting urban transport sustainable development, the phenomenon of bus bunching has brought severe challenges. To alleviate bus bunching, speed control strategies have been widely used to improve the stability of bus headway distribution. However, existing research mainly focuses on developing optimized models with more flexible speed adjustments; a critical yet often ignored fundamental assumption behind these models is that all bus drivers can strictly adhere to the speed instructions issued by the bus dispatch center. To further explore how the compliance of bus drivers affects the implementation of speed adjustment instructions, this study designs a driving simulation experiment under different driving conditions. Modeled after a real bus line in Changsha, China, the designed simulator study incorporates three external variables, weather conditions, road conditions and command types, with behavioral data from 48 professional drivers analyzed via linear mixed-effects models. The results have shown that road conditions and command types emerged as main factors affecting compliance patterns. Specifically, congestion reduced average speeds by 5.1 km/h, especially affecting female drivers who showed 15.9% Command Compliance Index (it has been designed to quantify execution efficiency and will be referred to as CCI hereafter) reduction versus 10.6% for males. Compared to high-speed instructions, the execution efficiency of low-speed instructions increased by 12.3%, with drivers exceeding target speeds during 45.69% of sections to balance speed profiles. It is notable that the fog density had a minimal impact on efficiency, with only about 2% difference in efficiency. Despite standardized operational norms minimizing individual behavioral heterogeneity, significant group-level demographic variations persisted. Male drivers consistently maintained higher compliance with speed adjustment commands across all driving conditions; drivers under 40 and over 50 had a 3.3% higher CCI than middle-aged drivers; and prior bus bunching exposure increased compliance by 3.3%. High-CCI bus drivers strategically balanced headway distribution through controlled overspeeding. These findings provide empirical foundations for optimizing speed control strategies based on road sections. This study explores ways to enhance the attractiveness of public transit and promote sustainable development. Full article

(This article belongs to the Special Issue Advances in Data-Driven Transportation Systems: Emerging Trends, Challenges, and Applications)

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25 pages, 5755 KB

Open AccessArticle

Revealing Freight Vehicle Trip Chains and Travel Behavior: Insights from Heavy Duty Vehicle GPS Data

by Bo Yu, Gaofeng Gu, Yuandong Liu and Yi Li

Sustainability 2026, 18(3), 1303; https://doi.org/10.3390/su18031303 - 28 Jan 2026

Viewed by 475

Abstract

High-quality, well-structured trip chain data are essential for analyzing the daily activity patterns, travel behaviors, and logistical decisions of commercial vehicles, as well as for supporting sustainability-oriented freight management and low-carbon urban logistics. This study introduces a novel methodology for analyzing truck travel patterns using extensive GPS data, focusing on identifying freight trip chains and enhancing urban freight systems. A road-constrained clustering approach was developed to accurately identify vehicle stops and truck stop locations, addressing limitations in previous studies that struggled with misclassification. A trip chain reconstruction methodology was formulated, key characteristics were extracted and clustering techniques were applied to categorize trucks based on their travel behavior. A case study in Chongqing demonstrates that the proposed method outperforms traditional clustering algorithms, reducing misclassification rates in stop location identification. The findings reveal consistent trip chain patterns and distinct travel behaviors within truck groups. This research presents a data-driven framework that provides a foundation for optimizing logistics, fleet management, and low-carbon freight system planning. By enhancing the accuracy of trip chain analysis, this methodology contributes to the design of energy-efficient and sustainable urban freight systems, helping reduce emissions and foster eco-friendly logistics solutions. Full article

(This article belongs to the Special Issue Advances in Data-Driven Transportation Systems: Emerging Trends, Challenges, and Applications)

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24 pages, 3151 KB

Open AccessArticle

Sustainable Mixed-Traffic Micro-Modeling in Intelligent Connected Environments: Construction and Simulation Analysis

by Yang Zhao, Xiaoqiang Zhang, Haoxing Zhang, Xue Lei, Jianjun Wang and Mei Xiao

Sustainability 2026, 18(2), 960; https://doi.org/10.3390/su18020960 - 17 Jan 2026

Viewed by 439

Abstract

Sustainable urban mobility necessitates traffic regimes that enhance operational efficiency and improve traffic safety and flow stability; the rise in intelligent connected vehicles (ICVs) provides a salient mechanism to meet this imperative. This paper aims to investigate the mixed traffic flow characteristics in an intelligent connected environment, using one-way single-lane, double-lane, and three-lane straight highways as modeling objects. Combining the different driving characteristics of human-driven vehicles (HDVs) and ICVs, a single-lane mixed traffic flow model and a multi-lane mixed traffic flow model are established based on the intelligent driver model (IDM) and flexible symmetric two-lane cellular automata model (FSTCAM). The mixed traffic flow in the intelligent connected environment is then simulated using MATLAB R2021a. The research results indicate that the integration of ICVs can improve the speed, flow, and critical density of traffic flow. The increase in the proportion of ICVs can reduce the congestion ratio and speed difference between front and rear vehicles at the same density. As the proportion of ICVs increases, the frequency of lane-changing for HDVs gradually increases, while the frequency of lane-changing for ICVs gradually decreases. The overall lane-changing frequency shows a trend of first increasing and then decreasing. In addition, with the continuous infiltration of ICVs, the area of road congestion gradually decreases, and congestion is significantly alleviated. The speed fluctuation of following vehicles gradually decreases. When the infiltration rate reaches a high level, vehicles travel at a stable speed and remain in a relatively steady state. The findings substantiate the potential of ICV-enabled operations to advance efficiency-oriented and stability-enhancing urban mobility and to inform evidence-based traffic management and policy design. Full article

(This article belongs to the Special Issue Advances in Data-Driven Transportation Systems: Emerging Trends, Challenges, and Applications)

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29 pages, 7175 KB

Open AccessArticle

Exploring the Interaction of Transit Accessibility, Housing Affordability, and Low-Income Household Displacement: A Statistical and Spatial Analysis of Tennessee Counties

by Jing Guo, Candace Brakewood, Abubakr Ziedan and Wei Hao

Sustainability 2026, 18(2), 859; https://doi.org/10.3390/su18020859 - 14 Jan 2026

Viewed by 612

Abstract

Urban sustainability depends on balancing transportation accessibility, housing affordability, and social equity. Displacement—defined in this study as the population-level loss of low-income households from a census block over time—poses a growing challenge to inclusive urban development. This study examines statistical relationships and spatial patterns linking transit accessibility, housing affordability, and low-income household displacement across the four largest counties in Tennessee. Negative binomial regression models are used to quantify relationships between transit accessibility, housing affordability, and displacement, revealing that housing affordability is consistently linked to displacement, while the effects of transit accessibility vary substantially across counties. Bivariate Local Indicators of Spatial Association (LISA) identify localized clusters where displacement coincides with transit or housing constraints, and Multivariate Cluster Typology Analysis classifies census blocks into distinct typologies, highlighting region-specific trade-offs between accessibility and affordability. Together, the results demonstrate that displacement dynamics are highly context dependent, underscoring the need for place-based and sustainability-oriented policy responses. The findings provide an empirical basis for integrating transportation and housing strategies to reduce displacement risks and support equitable and sustainable urban development in diverse metropolitan contexts. Full article

(This article belongs to the Special Issue Advances in Data-Driven Transportation Systems: Emerging Trends, Challenges, and Applications)

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27 pages, 4018 KB

Open AccessArticle

Developing a Simulation-Based Traffic Model for King Abdulaziz University Hospital, Saudi Arabia

by Mohaimin Azmain, Alok Tiwari, Jamal Abdulmohsen Eid Abdulaal and Abdulrhman M. Gbban

Sustainability 2025, 17(24), 10985; https://doi.org/10.3390/su172410985 - 8 Dec 2025

Viewed by 1243

Abstract

Transportation management within university campuses presents distinct challenges due to highly fluctuating traffic patterns. King Abdulaziz University (KAU), which attracts over 350,000 trips daily, is experiencing substantial congestion-related issues. This study focuses specifically on King Abdulaziz University Hospital (KAUH), a major trip generator on campus characterized by significant temporal variations in travel demand. The objective of this research is to develop a validated and operational traffic demand model using PTV VISUM 2025. A four-step framework was implemented, where campus gates were defined as trip production sources and 13 parking areas were designated as trip attractions. The morning peak-hour, identified as 7:15 AM to 8:15 AM, was selected for analysis due to the highest observed inflow of vehicles. Traffic surveys were conducted at seven bidirectional stations along key links to support Origin–Destination (O–D) matrix estimation and calibration. Both static and dynamic traffic assignment methods were applied to assess model performance. Model validity was evaluated using the R² statistic, percentage deviations, and the GEH measure of fit. The results demonstrate that both the equilibrium static assignment and the dynamic stochastic assignment achieved strong levels of accuracy, with R² = 0.98 and 86% of links exhibiting GEH values below 5, alongside average GEH scores of 3.2 and 2.7, respectively. This dual-model approach provides a robust analytical foundation for KAU, enabling long-term strategic planning through static assignment outputs and supporting short-term, peak-hour operational management through dynamic assignment results. Full article

(This article belongs to the Special Issue Advances in Data-Driven Transportation Systems: Emerging Trends, Challenges, and Applications)

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18 pages, 1818 KB

Open AccessArticle

Estimating AADT Using Statewide Traffic Data Programs: Missing Data Impact

by Muhammad Faizan Rehman Qureshi and Ahmed Al-Kaisy

Sustainability 2025, 17(21), 9896; https://doi.org/10.3390/su17219896 - 6 Nov 2025

Viewed by 1821

Abstract

State highway agencies usually measure Annual Average Daily Traffic (AADT) using traffic data from permanent detector stations within their system-wide traffic monitoring programs. Agencies also estimate the AADT at many other locations using short-term counts. Traffic counters at the permanent stations frequently malfunction, leading to periods of inaccurate or missing data. Addressing missing data in estimating AADT by highway agencies is important for sustainable infrastructure management. This study used extensive traffic data from permanent detector stations in the state of Montana to examine the effect of missing data on the accuracy of AADT estimation. On a rotational basis, one station was used to test the accuracy of AADT estimation, while the remaining stations (training stations) were used to develop the traffic adjustment factors. Data truncation at the training stations was conducted using two sampling techniques and three scenarios of data availability. The study results showed that the increase in AADT estimation error (inaccuracy) was not linearly proportional to the increase in the amount of missing data. Given the extreme scenarios of missing data examined in this study and the relatively lower effect on AADT estimation error, it can be concluded that the current practice in treating missing data does not involve a considerable compromise in the accuracy of AADT estimation. This highlights the robustness of the current estimation practice, suggesting that it can be effectively applied in statewide traffic monitoring programs without a significant loss of accuracy. Full article

(This article belongs to the Special Issue Advances in Data-Driven Transportation Systems: Emerging Trends, Challenges, and Applications)

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29 pages, 2702 KB

Open AccessArticle

Machine Learning Methods Benchmarking for Predicting Flight Delays: An Efficiency Meta-Analysis

by Hélio da Silva Queiróz Júnior, Viviane Falcão, Francisco Gildemir Ferreira da Silva, Izabelle Marie Trindade Bezerra and Joab Kleber Lucena Machado

Sustainability 2025, 17(21), 9887; https://doi.org/10.3390/su17219887 - 5 Nov 2025

Cited by 1 | Viewed by 2783

Abstract

Predicting delays in commercial flights is an increasing challenge due to rising air traffic demand, which generates additional costs and operational complexity. This study synthesizes and evaluates machine learning approaches for flight delay predictions, aiming to identify the most accurate prediction logic and assess the role of sample size in model performance. A systematic literature review was conducted, followed by a meta-analysis of 1077 studies published between 2015 and 2025. The studies were classified by prediction logic (binary classification or regression) and evaluated in terms of model effectiveness using Data Envelopment Analysis and Tobit regression to determine the influence of explanatory variables. The results show that binary classification approaches achieved higher average accuracy than regression models did, with confidence intervals validating their relative effectiveness. Furthermore, findings indicate that the use of more complex models does not guarantee improved predictive performance, suggesting that researchers should prioritize robust variable selection rather than constantly adopting increasingly complex methods. This work provides a comprehensive overview of machine learning methods for flight delay predictions and highlights implications for optimizing airport operations and enhancing passenger experience through the adoption of more reliable predictive strategies. Full article

(This article belongs to the Special Issue Advances in Data-Driven Transportation Systems: Emerging Trends, Challenges, and Applications)

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25 pages, 4445 KB

Open AccessArticle

Enhancing Urban Traffic Modeling Using Google Traffic and Field Data: A Case Study in Flood-Prone Areas of Loja, Ecuador

by Yasmany García-Ramírez and Corina Fárez

Sustainability 2025, 17(21), 9718; https://doi.org/10.3390/su17219718 - 31 Oct 2025

Viewed by 1135

Abstract

Urban mobility plays a critical role in ensuring resilience during natural disasters such as floods, yet developing reliable traffic models remains challenging for medium-sized cities with limited monitoring infrastructure. This study developed a hybrid traffic modeling approach that integrates Google Traffic data with field measurements to address incomplete digital coverage in flood-prone areas of Loja, Ecuador. The methodology involved collecting 1501 field speed measurements and 235,690 Google Typical Traffic observations using exclusively open-source tools and freely available data sources. Adjustment factors ranging from 0.25 to 0.97 revealed systematic discrepancies between Google Traffic estimates and field observations, highlighting the need for local calibration. The resulting traffic network model encompassing 4966 nodes and 5425 edges accurately simulated flood impacts, with the most critical scenario (Thursday 17–19, 100% road impact) showing travel time increases of 1123% and congestion index deterioration from 1.79 to 21.69. Statistical validation confirmed significant increases in both travel times (p = 0.0231) and distances (p = 0.0207) under flood conditions across five representative routes. This research demonstrates that accurate traffic models can be developed through intelligent integration of heterogeneous data sources, providing a scalable solution for enhancing urban mobility analysis and emergency preparedness in resource-constrained cities facing climate-related transportation challenges. Full article

(This article belongs to the Special Issue Advances in Data-Driven Transportation Systems: Emerging Trends, Challenges, and Applications)

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22 pages, 8095 KB

Open AccessArticle

Characterizing the Evolution of Multi-Scale Communities in Urban Road Networks

by Yifan Wang, Yi Li, Xingwa Song, Shilong Wang and Ning Wang

Sustainability 2025, 17(20), 9355; https://doi.org/10.3390/su17209355 - 21 Oct 2025

Cited by 1 | Viewed by 1107

Abstract

The growing abundance of traffic data offers new opportunities to uncover dynamic traffic patterns in urban road networks, providing valuable insights for promoting sustainable mobility. By leveraging these data, road segments can be grouped into communities to capture the spatiotemporal correlations driving the dynamic evolution of traffic states. However, existing distance-based methods lack the capacity to facilitate multi-scale analysis of urban traffic patterns and are limited in capturing the heterogeneity of road regions. To address this gap, in this study, we introduce a traffic-data-driven approach to detect road segment communities and extract multi-scale traffic patterns. Here, traffic data are mapped onto a dual graph of urban road networks, with node correlations weighted using Dynamic Time Warping (DTW). A hierarchical community detection algorithm is then applied to identify multi-scale communities, revealing the spatiotemporal structure of urban traffic dynamics. The robustness and effectiveness of the proposed method were tested on the road network of Chengdu. The results show that the method successfully integrates the topological structure with traffic data, capturing multi-scale spatial autocorrelation communities. By characterizing the evolution of traffic patterns, our method has potential applications in traffic prediction, traffic control, and urban planning applications, contributing to sustainable urban transportation through congestion mitigation and efficiency enhancement. Full article

(This article belongs to the Special Issue Advances in Data-Driven Transportation Systems: Emerging Trends, Challenges, and Applications)

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33 pages, 12683 KB

Open AccessArticle

Analysis of Traffic Conflict Characteristics and Key Factors Influencing Severity in Expressway Interchange Diverging Areas: Insights from a Chinese Freeway Safety Study

by Feng Tang, Zhizhen Liu, Zhengwu Wang and Ning Li

Sustainability 2025, 17(18), 8419; https://doi.org/10.3390/su17188419 - 19 Sep 2025

Viewed by 2246

Abstract

Conflicts in freeway interchange diverging areas remain poorly understood, particularly their characteristics and severity determinants. To address this gap, we extracted over 20,000 vehicle trajectories from UAV footage at 16 interchange divergence zone across five multi-lane expressways using a YOLOX–DeepSORT method. From these trajectories, we identified longitudinal and lateral conflicts and classified their severity into minor, moderate, and severe levels using a two-dimensional extended time-to-collision metric. Subsequently, we incorporated 19 macroscopic traffic-flow and microscopic driver-behavior variables into four conflict-severity models–multivariate logistic regression, random forest, CatBoost, and XGBoost—and conducted to identify the key determinants of conflict severity based on the optimal models. The results indicate that lateral conflicts last longer and pose higher collision risks than longitudinal ones. Furthermore, moderate conflicts are most prevalent, whereas severe conflicts are concentrated within 300 m upstream of exit ramps. Specifically, for longitudinal conflicts, the most influential factors include speed difference, target-vehicle speed, truck involvement, traffic density, and exit behavior. In contrast, for lateral conflicts, the most critical factors include lane-change frequency, speed difference, target-vehicle speed, distance to the exit ramp, and truck proportion. Overall, these findings support the development of hazardous-driving warning systems and proactive safety management strategies in interchange diverging areas. Full article

(This article belongs to the Special Issue Advances in Data-Driven Transportation Systems: Emerging Trends, Challenges, and Applications)

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27 pages, 4949 KB

Open AccessArticle

Resolving the Classic Resource Allocation Conflict in On-Ramp Merging: A Regionally Coordinated Nash-Advantage Decomposition Deep Q-Network Approach for Connected and Automated Vehicles

by Linning Li and Lili Lu

Sustainability 2025, 17(17), 7826; https://doi.org/10.3390/su17177826 - 30 Aug 2025

Viewed by 1272

Abstract

To improve the traffic efficiency of connected and automated vehicles (CAVs) in on-ramp merging areas, this study proposes a novel region-level multi-agent reinforcement learning framework, Regionally Coordinated Nash-Advantage Decomposition Deep Q-Network with Conflict-Aware Q Fusion (RC-NashAD-DQN). Unlike existing vehicle-level control methods, which suffer from high computational overhead and poor scalability, our approach abstracts on-ramp and main road areas as region-level control agents, achieving coordinated yet independent decision-making while maintaining control precision and merging efficiency comparable to fine-grained vehicle-level approaches. Each agent adopts a value–advantage decomposition architecture to enhance policy stability and distinguish action values, while sharing state–action information to improve inter-agent awareness. A Nash equilibrium solver is applied to derive joint strategies, and a conflict-aware Q-fusion mechanism is introduced as a regularization term rather than a direct action-selection tool, enabling the system to resolve local conflicts—particularly at region boundaries—without compromising global coordination. This design reduces training complexity, accelerates convergence, and improves robustness against communication imperfections. The framework is evaluated using the SUMO simulator at the Taishan Road interchange on the S1 Yongtaiwen Expressway under heterogeneous traffic conditions involving both passenger cars and container trucks, and is compared with baseline models including C-DRL-VSL and MADDPG. Extensive simulations demonstrate that RC-NashAD-DQN significantly improves average traffic speed by 17.07% and reduces average delay by 12.68 s, outperforming all baselines in efficiency metrics while maintaining robust convergence performance. These improvements enhance cooperation and merging efficiency among vehicles, contributing to sustainable urban mobility and the advancement of intelligent transportation systems. Full article

(This article belongs to the Special Issue Advances in Data-Driven Transportation Systems: Emerging Trends, Challenges, and Applications)

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23 pages, 8057 KB

Open AccessArticle

Strategies for Coordinated Merging of Vehicles at Ramps in New Hybrid Traffic Environments

by Zhizhen Liu, Xinyue Liu, Qile Li, Zhaolei Zhang, Chao Gao and Feng Tang

Sustainability 2025, 17(10), 4522; https://doi.org/10.3390/su17104522 - 15 May 2025

Cited by 4 | Viewed by 2338

Abstract

With the advancement of autonomous driving technology, transportation systems are inevitably confronted with mixed traffic flows consisting of connected and automated vehicles (CAVs) and human-driven vehicles (HDVs). Current research has predominantly focused on implementing homogeneous control strategies for ramp merging vehicles in such scenarios, which, however, may result in the oversight of specific requirements in fine-grained traffic scenarios. Therefore, a classified cooperative merging strategy is proposed to address the challenges of microscopic decision-making in hybrid traffic environments where HDVs and CAVs coexist. The optimal cooperating vehicle on the mainline is first selected for the target ramp vehicle based on the principle of minimizing time differences. Three merging strategies—joint coordinated control, partial cooperation, and speed limit optimization—are then established according to the pairing type between the cooperating and ramp vehicles. Optimal deceleration and lane-changing decisions are implemented using the average speed change rate within the control area to achieve cooperative merging. Validation via a SUMO-based simulation platform demonstrates that the proposed strategy reduces fuel consumption by 6.32%, NO_x emissions by 9.42%, CO₂ emissions by 9.37%, and total delay by 32.15% compared to uncontrolled merging. These results confirm the effectiveness of the proposed strategy in mitigating energy consumption, emissions, and vehicle delays. Full article

(This article belongs to the Special Issue Advances in Data-Driven Transportation Systems: Emerging Trends, Challenges, and Applications)

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