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Sustainable Urban Mobility: Road Safety and Traffic Engineering

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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: 15 July 2026 | Viewed by 20284

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Special Issue Editors

Dr. Evangelos Paschalidis

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Guest Editor

Transport and Mobility Laboratory, School of Architecture, Civil and Environmental Engineering (ENAC), École Polytechnique Fédérale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland
Interests: discrete choice modelling; road safety; sustainable mobility; micromobility; human factors
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Prof. Dr. Socrates Basbas

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Guest Editor

Laboratory of Transportation Planning, Transportation Engineering & Highway Engineering, Department of Transportation & Hydraulic Engineering, School of Rural & Surveying Engineering, Faculty of Engineering, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
Interests: transport planning; active mobility; walkability; accessibility; micromobility; cycling; pedestrians
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

As urban areas continue to develop, ensuring sustainable, safe, and efficient transportation systems is crucial for supporting economic vitality, reducing environmental impacts, improving road safety, and enhancing quality of life. This Special Issue on "Sustainable Urban Mobility: Road Safety and Traffic Engineering" invites research on the latest innovations, challenges, best practices, and findings in the areas of road safety and traffic engineering in relation to sustainable urban mobility.

Some key themes of this issue include sustainable transport planning, smart mobility and autonomous solutions, micromobility, and innovative traffic management strategies. In particular, we welcome submissions that address topics such as sustainable infrastructure design, the integration of non-motorized modes, electric and public transport modes, smart infrastructure, autonomous vehicles, and novel approaches in traffic safety and monitoring.

This Special Issue aims to bring together multidisciplinary insights to support the development of urban transport systems that prioritize safety, sustainability, and resilience. By contributing to this issue, researchers can help shape a safer, greener, and more accessible future for urban mobility.

Dr. Evangelos Paschalidis
Prof. Dr. Socrates Basbas
Guest Editors

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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

sustainable urban transport
road safety
traffic engineering
smart mobility
pedestrian safety
cyclist safety
micromobility
autonomous vehicles
traffic management
infrastructure design

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Published Papers (9 papers)

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Research

29 pages, 3842 KB

Open AccessArticle

From Private Cars to Micromobility: Network Modeling and Environmental Assessment of Short-Distance Trips in Izmir

by Emre Ogutveren and Soner Haldenbilen

Sustainability 2026, 18(7), 3523; https://doi.org/10.3390/su18073523 - 3 Apr 2026

Viewed by 340

Abstract

Urban transportation systems face increasing sustainability challenges due to the dominance of private-car use, particularly for short-distance trips. This study investigates the potential of micromobility to replace private-car travel on short-distance journeys and evaluates the resulting impacts on urban transportation networks and environmental sustainability. The analysis focuses on the Bornova district of Izmir and is based on a face-to-face survey conducted with 502 private-vehicle users. Survey data were analyzed using descriptive statistics, chi-square tests and a binary logit regression model to identify factors influencing the willingness to adopt micromobility. Within the surveyed sample of private-car users, modal-shift rates were estimated as 35% for trips up to 5 km and 33% for trips between 5 and 10 km. These rates were applied to the private-car demand and distance matrices developed for the year 2030 within the scope of the Izmir Transportation Master Plan, resulting in a revised private-car demand matrix and a separate demand matrix representing potential micromobility users. Network assignments were performed in the PTV VISUM modeling environment. Assignment results demonstrate notable network-level changes following micromobility integration. The total length of road segments with micromobility traffic volumes exceeding a threshold of 10 veh/h was calculated at 292.5 km. Environmental impacts were evaluated using a life-cycle assessment (LCA) framework, revealing an approximate 5.5% reduction in total life-cycle CO₂ emissions. Overall, the findings provide quantitative evidence supporting micromobility as an effective component of sustainable urban transport strategies and offer guidance for local governments in infrastructure planning and policy development. Full article

(This article belongs to the Special Issue Sustainable Urban Mobility: Road Safety and Traffic Engineering)

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22 pages, 3528 KB

Open AccessArticle

Characterizing Interaction Patterns and Quantifying Associated Risks in Urban Interchange Merging Areas: A Multi-Driver Simulation Study

by Haorong Peng

Sustainability 2026, 18(4), 2029; https://doi.org/10.3390/su18042029 - 16 Feb 2026

Viewed by 476

Abstract

Interchange merging areas are critical safety hotspots in urban road networks, where complex vehicle interactions challenge traffic safety and efficiency. Improving safety performance at these locations is essential for developing sustainable, resilient, and intelligent urban transportation systems. To overcome the limitations of single-driver simulators, this study developed a multi-driver simulation platform based on Unity3D (Version 2022.3.1f1c1), enabling real-time interaction among multiple human drivers. High-resolution trajectory data were collected from 231 valid interaction events. An eight-direction relative position model was employed to classify behaviors into four patterns: longitudinal, lateral, front cut-in, and rear cut-in. Risk was quantified using time-exposed and time-integrated Anticipated Collision Time metrics, with events subsequently clustered into low (n = 138), medium (n = 67), and high-risk (n = 26) categories. An ordered logit regression model identified key risk factors. The results quantitatively demonstrate that interaction risk escalates significantly with abrupt speed changes (OR = 16.22) and late-stage occurrence of speed extremes (OR = 6.76) in the interacting vehicle, as well as large initial speed differences (OR = 2.45). Conversely, stable speed regulation and adaptive acceleration by the subject vehicle proved to be potent mitigating factors. These findings provide actionable insights for the development of intelligent collision warning systems and the sustainable design of interchange infrastructure. Full article

(This article belongs to the Special Issue Sustainable Urban Mobility: Road Safety and Traffic Engineering)

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22 pages, 3994 KB

Open AccessArticle

Sustainable Safety Planning on Two-Lane Highways: A Random Forest Approach for Crash Prediction and Resource Allocation

by Fahmida Rahman, Cidambi Srinivasan, Xu Zhang and Mei Chen

Sustainability 2026, 18(2), 635; https://doi.org/10.3390/su18020635 - 8 Jan 2026

Viewed by 406

Abstract

During the safety planning stage, accurate crash prediction tools are critical for prioritizing countermeasures and allocating resources effectively. Traditional statistical approaches, while long applied in this field, often depend on distributional assumptions that may introduce bias and limit model accuracy. To address these issues, studies have started exploring Machine Learning (ML)-based techniques for crash prediction, particularly for higher functional class roads. However, the application of ML models on two-lane highways remains relatively limited. This study aims to develop an approach to integrate traffic, geometric, and critically, speed-based factors in crash prediction using Random Forest (RF) and SHapley Additive exPlanations (SHAP) techniques. Comparative analysis shows that the RF model improves crash prediction accuracy by up to 25% over the traditional Zero-Inflated Negative Binomial model. SHAP analysis identified AADT, segment length, and average speed as the three most influential predictors of crash frequency, with speed emerging as a key operational factor alongside traditional exposure measures. The strong influence of speed in the RF–SHAP results depicts its critical role in the safety performance of two-lane highways and highlights the value of incorporating detailed operating characteristics into crash prediction models. Overall, the proposed RF–SHAP framework advances roadway safety assessment by offering both predictive accuracy and interpretability, allowing agencies to identify high-impact factors, prioritize countermeasures, and direct resources more efficiently. In doing so, the approach supports sustainable safety management by enabling evidence-based investments, promoting optimal use of limited transportation funds, and contributing to safer, more resilient mobility systems. Full article

(This article belongs to the Special Issue Sustainable Urban Mobility: Road Safety and Traffic Engineering)

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32 pages, 11980 KB

Open AccessArticle

Decentralized Multi-Agent Reinforcement Learning with Visible Light Communication for Robust Urban Traffic Signal Control

by Manuel Augusto Vieira, Gonçalo Galvão, Manuela Vieira, Mário Véstias, Paula Louro and Pedro Vieira

Sustainability 2025, 17(22), 10056; https://doi.org/10.3390/su172210056 - 11 Nov 2025

Viewed by 1362

Abstract

The rapid growth of urban vehicle and pedestrian flows has intensified congestion, delays, and safety concerns, underscoring the need for sustainable and intelligent traffic management in modern cities. Traditional centralized traffic signal control systems often face challenges of scalability, heterogeneity of traffic patterns, and limited real-time adaptability. To address these limitations, this study proposes a decentralized Multi-Agent Reinforcement Learning (MARL) framework for adaptive traffic signal control, where Deep Reinforcement Learning (DRL) agents are deployed at each intersection and trained on local conditions to enable real-time decision-making for both vehicles and pedestrians. A key innovation lies in the integration of Visible Light Communication (VLC), which leverages existing LED-based infrastructure in traffic lights, streetlights, and vehicles to provide high-capacity, low-latency, and energy-efficient data exchange, thereby enhancing each agent’s situational awareness while promoting infrastructure sustainability. The framework introduces a queue–request–response mechanism that dynamically adjusts signal phases, resolves conflicts between flows, and prioritizes urgent or emergency movements, ensuring equitable and safer mobility for all users. Validation through microscopic simulations in SUMO and preliminary real-world experiments demonstrates reductions in average waiting time, travel time, and queue lengths, along with improvements in pedestrian safety and energy efficiency. These results highlight the potential of MARL–VLC integration as a sustainable, resilient, and human-centered solution for next-generation urban traffic management. Full article

(This article belongs to the Special Issue Sustainable Urban Mobility: Road Safety and Traffic Engineering)

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27 pages, 3291 KB

Open AccessArticle

Risk Perception Accuracy Among Urban Cyclists: Behavioral and Infrastructural Influences in Loja, Ecuador

by Yasmany García-Ramírez and Corina Fárez

Sustainability 2025, 17(16), 7432; https://doi.org/10.3390/su17167432 - 17 Aug 2025

Viewed by 1732

Abstract

Urban cycling faces the challenge of cyclist vulnerability due to infrastructural deficiencies and complex traffic environments. This study evaluates the accuracy of risk perception among 153 urban cyclists in Loja, Ecuador, using a mixed-methods design that integrates self-reported behaviors (Cycling Behavior Questionnaire—CBQ), visual assessments of 12 road segments, and objective risk classifications derived from the CycleRAP methodology. Results show a notable misalignment between perceived and actual risk, with consistent underestimation of extreme-risk scenarios and overestimation of low-risk ones. The combined CBQ score (violations + errors) emerged as the strongest predictor of inaccurate risk perception in decision tree models, explaining 28.75% of the model’s predictive power. Interestingly, cycling experience did not improve accuracy; frequent cyclists with high violation/error scores and older age showed the poorest perception, while young cyclists with moderate behavior scores exhibited higher accuracy. These results suggest that the relationship between cycling experience and risk assessment is more complex than commonly assumed. Findings highlight the need for behavioral interventions to correct misperceptions, alongside infrastructural measures that address objective hazards. Given the limited number of road segments and moderate sample size, subgroup analyses may be underpowered and should be interpreted with caution. Full article

(This article belongs to the Special Issue Sustainable Urban Mobility: Road Safety and Traffic Engineering)

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23 pages, 9667 KB

Open AccessArticle

Analysis of Traffic Conflicts on Slow-Moving Shared Paths in Shenzhen, China

by Lingyi Miao, Feifei Liu and Yuanchang Deng

Sustainability 2025, 17(9), 4095; https://doi.org/10.3390/su17094095 - 1 May 2025

Cited by 3 | Viewed by 2335

Abstract

The rapid growth of e-bikes has intensified traffic conflicts on slow-moving shared paths in China. This study analyzed traffic safety of pedestrians and non-motorized vehicles and examined the factors influencing conflict severity utilizing traffic conflict techniques. Video-based surveys were conducted on six shared paths in Shenzhen, and conflict trajectory was extracted by Petrack software (Version 0.8). The minimum Time to Collision and Yaw Rate Ratio were selected as conflict indicators. Fuzzy c-means clustering was employed to classify conflicts into three severity levels: 579 potential conflicts, 435 minor conflicts, and 150 serious conflicts. Nineteen feature variables related to road environment, traffic operation, conflict sample information, and conflict behavior were considered. A SMOTE random forest model was constructed to explore critical influencing factors systematically. The results identified ten key factors affecting conflict severity. The increase in conflict severity is associated with the rise in pedestrian proportion and flow, and the decrease in e-bike proportion and flow. Male participants and pedestrians are more likely to engage in serious conflicts, while illegal lane occupation and wrong-way travel further elevate the severity level. These findings can provide references for traffic engineers and planners to enhance the safety management of shared paths and contribute to sustainable non-motorized transport. Full article

(This article belongs to the Special Issue Sustainable Urban Mobility: Road Safety and Traffic Engineering)

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21 pages, 1836 KB

Open AccessArticle

Assessing the Impact of a Low-Emission Zone on Air Quality Using Machine Learning Algorithms in a Business-As-Usual Scenario

by Marta Doval-Miñarro, María C. Bueso and Pedro Antonio Guillén-Alcaraz

Sustainability 2025, 17(8), 3582; https://doi.org/10.3390/su17083582 - 16 Apr 2025

Cited by 4 | Viewed by 5726

Abstract

The proliferation of low-emission zones (LEZs) across Europe is anticipated to accelerate in the coming years as a measure to enhance air quality in urban areas. Nevertheless, there is a lack of a standardized methodology to evaluate their effectiveness, and some of the proposed strategies may not adequately address air quality issues or overlook meteorological considerations. In this study, we employ three machine learning (ML) algorithms to forecast NO₂, PM₁₀ and PM_2.5 concentrations in the air in Madrid in 2022 (post-LEZ) based on data from the period 2015–2018 (pre-LEZ) under a business-as-usual scenario, accounting for seasonal and meteorological factors. According to the models, the reductions in NO₂ concentrations in 2022 varied from 29 to 35% in contrast to a scenario without the LEZ, which is coherent with the observed decrease in 2022 in traffic volume inside the area limited by the LEZ. However, no clear improvement was observed for PM₁₀ and PM_2.5 concentrations. Full article

(This article belongs to the Special Issue Sustainable Urban Mobility: Road Safety and Traffic Engineering)

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20 pages, 2680 KB

Open AccessArticle

Evaluating the Environmental and Safety Impacts of Eco-Driving in Urban and Highway Environments

by Marios Sekadakis, Maria Ioanna Sousouni, Thodoris Garefalakis, Maria G. Oikonomou, Apostolos Ziakopoulos and George Yannis

Sustainability 2025, 17(6), 2762; https://doi.org/10.3390/su17062762 - 20 Mar 2025

Cited by 6 | Viewed by 3967

Abstract

The present study aims to investigate the benefits of eco-driving in urban areas and on highways through an experiment conducted in a driving simulator. Within a group of 39 participants aged 18–30, multiple driving scenarios were conducted, both without and with eco-driving guides, to assess the impact of eco-driving behavior on environmental sustainability and safety outcomes. Data on pollutant emissions, including carbon dioxide (CO₂), carbon monoxide (CO), and nitrogen oxides (NO_x), as well as crash probability, were collected during the experiment. The relationships between driving behavior and pollutant emissions were estimated using linear regression models, while binary logistic regression models were employed to assess crash probability. The analysis revealed that eco-driving led to a significant reduction in pollutant emissions, with CO₂ emissions decreasing by 1.42%, CO by 98.2%, and NO_x by 20.7% across both urban and highway environments, with a more substantial impact in urban settings due to lower average speeds and smoother driving patterns. Furthermore, eco-driving reduced crash probability by 90.0%, with urban areas exhibiting an 86.8% higher crash likelihood compared to highways due to higher traffic density and more complex driving conditions. These findings highlight the dual benefit of eco-driving in reducing environmental impact and improving road safety. This study supports the integration of eco-driving techniques into transportation policies and driver education programs to foster sustainable and safer driving practices. Full article

(This article belongs to the Special Issue Sustainable Urban Mobility: Road Safety and Traffic Engineering)

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22 pages, 2874 KB

Open AccessArticle

Priority-Driven Resource Allocation with Reuse for Platooning in 5G Vehicular Network

by Tae-Woo Kim, Sanghoon Lee, Dong-Hyung Lee and Kyung-Joon Park

Sustainability 2025, 17(4), 1747; https://doi.org/10.3390/su17041747 - 19 Feb 2025

Cited by 2 | Viewed by 2187

Abstract

Recently, Vehicle-to-Everything (V2X) communication has emerged as a critical technology for enhancing the safety and traffic management of autonomous vehicles. Developing a resource allocation algorithm that enables autonomous vehicles to perceive and react to their surroundings in real time through fast and reliable communication is of paramount importance. This paper proposes a novel resource allocation algorithm that minimizes the degradation of communication performance for non-platoon vehicles while ensuring low-latency, high-reliability communication within vehicle platoons. The proposed algorithm prioritizes platoon vehicles and enhances resource efficiency by simultaneously applying interference-based and distance-based resource reuse techniques. Performance evaluations conducted using the Simu5G simulator demonstrate that the proposed algorithm consistently maintains the average resource allocation rate and delay for both platoon and non-platoon vehicles, even as the number of platoons increases. Specifically, in a congested environment with 60 general vehicles and five platoons, the proposed algorithm achieves an average resource allocation rate of over 90%, significantly outperforming existing algorithms such as Max-C/I, which achieves only 58%, and the priority-based algorithm with 54%, ensuring reliable communication for all vehicles. Full article

(This article belongs to the Special Issue Sustainable Urban Mobility: Road Safety and Traffic Engineering)

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