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Safer Roads Ahead: Exploring the Latest Innovations and Advancements in...
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Safer Roads Ahead: Exploring the Latest Innovations and Advancements in Road Design and Safety Technology, 2nd Edition

A Special Issue of Infrastructures (ISSN 2412-3811).

Deadline for manuscript submissions: closed (31 August 2026) | Viewed by 5829

Editors

Dr. Dimitrios Nikolaou

E-Mail Website
Guest Editor
Department of Transportation Planning and Engineering, National Technical University of Athens (NTUA), Athens, Greece
Interests: traffic engineering; road safety; crash analysis; statistical and econometric methods; machine learning; spatial analyses; data science
Special Issues, Collections and Topics in MDPI journals
Dr. Panagiotis Papantoniou

E-Mail Website
Guest Editor
Department of Surveying and Geoinformatics Engineering, University of West Attica (UNIWA), Athens, Greece
Interests: road safety; driver behaviour; road design; sustainable mobility; intelligent transport systems; planning of transportation systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Safe road design is fundamental to ensuring road safety, as it plays a crucial role in preventing road crashes and accommodating evolving transportation technologies. As urbanization increases and new mobility solutions emerge, it is essential to rethink traditional road designs to meet future demands. Incorporating innovative features such as smart infrastructure, adaptive road systems, and safety-focused design for vulnerable road users (VRUs) can significantly enhance the resilience of transportation networks. Furthermore, road design should also consider environmental factors, human behaviours, and the integration of advanced technologies to create safer and more efficient roads. With the rise of connected and autonomous vehicles (CAVs), future road designs will need to adapt to new traffic patterns and technological advancements, ensuring that all road users are better protected and supported. This proactive approach to road design is essential not only for enhancing current road safety but also for preparing transportation systems to meet future challenges.

This Special Issue is the 2nd edition of "Safer Roads Ahead: Exploring the Latest Innovations and Advancements in Road Design and Safety Technology" and aims to present state-of-the-art research related to the latest innovations and advancements in road design and safety technology, with a focus on creating safer, more efficient, and resilient road systems for the future.

Potential topics for submissions include but are not limited to the following:

  • Emerging road design approaches to enhance traffic safety;
  • Smart roads and adaptive infrastructure;
  • Advanced safety features for vulnerable road users (VRUs) (e.g., pedestrians, cyclists, e-scooter riders);
  • Data-driven approaches for identifying high-risk road segments;
  • AI-enhanced traffic management and predictive analytics for crash prevention;
  • The role of intelligent transport systems (ITSs) in improving traffic flow, safety, and incident management;
  • Systems and interventions related to connected and autonomous vehicles (CAVs);
  • Human factors and behavioural insights in road safety design;
  • Environmental and climate considerations in road design for resilient infrastructure.

Dr. Dimitrios Nikolaou
Dr. Panagiotis Papantoniou
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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Infrastructures is an international peer-reviewed open access monthly 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 1800 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

  • road/traffic safety
  • road design
  • smart infrastructure
  • smart traffic
  • transportation systems

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Related Special Issue

Published Papers (5 papers)

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Research

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25 pages, 1371 KB  
Article
Evaluating Risky Driving Behavior Using a Naturalistic Driving Dataset: A Hybrid Modelling Approach
by Eleni Maria Theodoraki, Thodoris Garefalakis, Eva Michelaraki and George Yannis
Infrastructures 2026, 11(8), 266; https://doi.org/10.3390/infrastructures11080266 - 1 Aug 2026
Viewed by 464
Abstract
Driver behavior is a critical factor in road safety, contributing to the majority of traffic crashes. The i-DREAMS project introduced the concept of a Safety Tolerance Zone (STZ) to enhance driving safety through real-time and post-trip interventions. This study develops and evaluates three [...] Read more.
Driver behavior is a critical factor in road safety, contributing to the majority of traffic crashes. The i-DREAMS project introduced the concept of a Safety Tolerance Zone (STZ) to enhance driving safety through real-time and post-trip interventions. This study develops and evaluates three hybrid machine learning models—(i) Deep Neural Network–Random Forest (DNN-RF), (ii) Convolutional Neural Network–Long Short-Term Memory (CNN-LSTM), and (iii) Recurrent Neural Network–AdaBoost (RNN-AdaBoost)—to classify risky driving behavior into three safety levels using naturalistic driving data from Belgium and the UK. The dataset includes 69 drivers, 15,389 trips, and 265,512 min of driving data. Among the models tested, the DNN-RF model demonstrated the highest accuracy, reaching 98% in Belgium and 97% in the United Kingdom, outperforming other approaches. Feature importance analysis identified harsh acceleration and braking as the most critical factors in Belgium, while total trip distance and harsh acceleration were predominant in the UK. To enhance model transparency, we applied the Local Interpretable Model-agnostic Explanations (LIME) algorithm, providing valuable insights into model predictions. The findings support the potential of hybrid deep learning models in improving road safety by accurately detecting risky driving behaviors. These insights can inform targeted interventions and driver assistance technologies to mitigate crash risks and promote safer driving practices. Full article
(This article belongs to the Special Issue Safer Roads Ahead: Exploring the Latest Innovations and Advancements in Road Design and Safety Technology, 2nd Edition)
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20 pages, 7709 KB  
Article
Human-Centered Optimization of Expressway Interchange Guide-Sign Infrastructure in Complex Road Networks: Evidence from Visual Behavior, Physiological Responses, and Driving Simulation
by Yanshuang Zhi, Shuzhen Lou, Hongfu Wu, Yingying Luo and Yanqun Yang
Infrastructures 2026, 11(8), 265; https://doi.org/10.3390/infrastructures11080265 - 31 Jul 2026
Viewed by 404
Abstract
Expressway interchange guide signs must support rapid route decisions under limited viewing time, high information load, and potential vehicle occlusion. This study developed a human-centered framework for optimizing guide-sign information and presentation in complex expressway networks by integrating an on-road field experiment, driving [...] Read more.
Expressway interchange guide signs must support rapid route decisions under limited viewing time, high information load, and potential vehicle occlusion. This study developed a human-centered framework for optimizing guide-sign information and presentation in complex expressway networks by integrating an on-road field experiment, driving simulation, eye-movement measures, physiological responses, and driving behavior. The field experiment involved ten drivers unfamiliar with the Fuzhou expressway network and yielded 119 valid sign-reading episodes. A separate simulation experiment with 60 licensed drivers examined character heights of 55, 65, and 75 cm and roadside, median-side, and dual-side installation under three ordinal traffic background conditions. Drivers generally initiated visual search in the upper-left region of sign panels and focused on route-relevant place names and directional arrows. Descriptive observations suggested that usable control-point information could support route inference when the destination was absent, whereas the absence of both destination and usable control-point information was accompanied by more sustained deceleration and more dispersed visual search. Effective viewing distance increased across the tested character-height conditions. At a design speed of 120 km/h and an assumed recognition time of 2.6 s, the required viewing distance was approximately 86.7 m. The mean viewing distance for the 55 cm condition was below this requirement, whereas the 65 and 75 cm conditions exceeded it. The findings were applied to Xiuzhai Interchange. In a dynamic screen-based route-choice test, 24 of 25 participants made acceptable choices. Overall, guide signs in complex networks should be designed as coordinated information systems integrating control-point hierarchy, information continuity, character height, and installation position. Full article
(This article belongs to the Special Issue Safer Roads Ahead: Exploring the Latest Innovations and Advancements in Road Design and Safety Technology, 2nd Edition)
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34 pages, 1989 KB  
Article
Auditing iRAP’s ViDA Risk Engine: A Two-Stage Surrogate Learning and Orthogonalized Heterogeneity Framework for Modelled Road Safety
by Amirhossein Hassani, Borna Abramović, Muhammad Shahid and Marko Ševrović
Infrastructures 2026, 11(4), 129; https://doi.org/10.3390/infrastructures11040129 - 5 Apr 2026
Viewed by 1291
Abstract
Road safety studies commonly use machine learning to predict crashes or to estimate crash-based treatment effects. This study instead audits the modelled fatal-and-serious-injury (FSI) risk produced by the iRAP ViDA risk engine. We analyse 147,466 segments (100 m each) from 12 surveys grouped [...] Read more.
Road safety studies commonly use machine learning to predict crashes or to estimate crash-based treatment effects. This study instead audits the modelled fatal-and-serious-injury (FSI) risk produced by the iRAP ViDA risk engine. We analyse 147,466 segments (100 m each) from 12 surveys grouped into four European reporting groups. In Stage 1, gradient-boosted trees reproduce the engine’s risk surface under road-grouped cross-validation(R2 ≈ 0.92 with flows and survey identifiers), and Shapley-based attributions identify which coded attributes drive modelled risk at 396 hotspots (top-three segments per road). In Stage 2, a causal-forest double machine learning estimator adjusts for 38 covariates to estimate segment-level conditional contrasts between modelled risk and six retrofittable treatments across all eligible segments. Simple absolute and relative reduction thresholds translate these associations into 1170 association-based candidate upgrades. On 321 over-lapping hotspots, the candidate upgrades show moderate agreement with iRAP’s Safer Roads Investment Plan (Recall = 0.77; Precision = 0.66; Cohen’s κ = 0.40). All results are conditional associations on a calibrated risk engine whose totals are anchored to project- or network-level fatality totals or fatality estimates used in calibration, not causal effects on observed crashes. Full article
(This article belongs to the Special Issue Safer Roads Ahead: Exploring the Latest Innovations and Advancements in Road Design and Safety Technology, 2nd Edition)
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17 pages, 3846 KB  
Article
Exploratory Analysis of Young Drivers’ Speed and Vehicle Lateral Positioning on Simulated Rural and Highway Roads
by Konstantinos Gkyrtis, George Botzoris and Alexandros Kokkalis
Infrastructures 2026, 11(3), 106; https://doi.org/10.3390/infrastructures11030106 - 20 Mar 2026
Cited by 2 | Viewed by 853
Abstract
Young drivers are often involved in speed-related crashes, particularly on rural and highway roads. This is usually due to high speeds, unstable control of vehicle positioning, complex road designs, and limited visibility. This study explores how young drivers select their speed and position [...] Read more.
Young drivers are often involved in speed-related crashes, particularly on rural and highway roads. This is usually due to high speeds, unstable control of vehicle positioning, complex road designs, and limited visibility. This study explores how young drivers select their speed and position their vehicle on different types of roads under daytime and nighttime conditions using a driving simulator. Thirty civil engineering students aged 18 to 24 participated in four simulated scenarios: a rural road during the day, rural road at night, highway during the day, and highway at night. They also completed a structured questionnaire about their driving experience, confidence, and perception of risk. Vehicle speed, lateral position, and acceleration were analyzed using descriptive statistics and linear regression. The results indicate that driving on highways resulted in higher speeds and increased lateral wander. Additionally, driver experience and familiarity with the road affected speed choice and vehicle position. Compliance with speed limits was linked to more consistent lane positioning. These findings give important insights into the behavior of young drivers and may suggest ways to improve infrastructure design, visibility, and speed management strategies, thereby helping to reduce crash risk. Full article
(This article belongs to the Special Issue Safer Roads Ahead: Exploring the Latest Innovations and Advancements in Road Design and Safety Technology, 2nd Edition)
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24 pages, 719 KB  
Systematic Review
Traffic Calming Measures in Urban Environment: A Systematic Review
by Mahdi Sadeqi Bajestani and Ali Pirdavani
Infrastructures 2026, 11(5), 148; https://doi.org/10.3390/infrastructures11050148 - 27 Apr 2026
Viewed by 2040
Abstract
Speed is a key determinant of crash risk and injury severity, particularly on urban and secondary roads with frequent interactions between vulnerable road users. Traffic calming measures (TCMs) encompass physical, regulatory, perceptual, and technological interventions and aim to reduce operating speeds and improve [...] Read more.
Speed is a key determinant of crash risk and injury severity, particularly on urban and secondary roads with frequent interactions between vulnerable road users. Traffic calming measures (TCMs) encompass physical, regulatory, perceptual, and technological interventions and aim to reduce operating speeds and improve safety and liveability. This study systematically evaluates the effectiveness of TCMs in reducing speed and improving safety outcomes on urban roads, following PRISMA 2020 guidelines. It encompasses the identification, screening, and synthesis of articles from the Scopus, ScienceDirect, and SpringerLink databases, published between January 2020 and February 2026. Risk of bias in the included studies was assessed qualitatively by the co-authors. The assessment was conducted independently, with discrepancies resolved through discussion. A total of 91 studies were included in the review. Evidence from field studies, driving simulator experiments, and analytical, simulation, and computation-based evaluations is reviewed and structured within a three-cluster taxonomy comprising physical and geometrical measures, regulatory and perceptual interventions, and digital and technological approaches. The synthesis indicates that physically self-enforcing measures yield the most consistent reductions in speed. At the same time, regulatory and digital interventions can deliver meaningful safety benefits when implemented at scale with credible governance. Perceptual and advisory measures show more varying and context-dependent effects. The evidence base is limited by heterogeneity in study designs, short-term evaluations, and inconsistent reporting across studies. Full article
(This article belongs to the Special Issue Safer Roads Ahead: Exploring the Latest Innovations and Advancements in Road Design and Safety Technology, 2nd Edition)
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