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Emerging Transportation Safety and Operations: Practical Perspectives, 2nd Edition

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A special issue of Vehicles (ISSN 2624-8921).

Deadline for manuscript submissions: closed (28 February 2026) | Viewed by 30383

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Special Issue Editors

Prof. Dr. Deogratias Eustace

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Guest Editor

Department of Civil and Environmental Engineering and Engineering Mechanics, University of Dayton, Dayton, OH, USA
Interests: highway safety; traffic operations; emerging mobility services; travel demand modeling; ITS applications; CAV/AV impacts on traffic safety; non-motorized transportation; statistical applications in transportation engineering
Special Issues, Collections and Topics in MDPI journals

Dr. Bhaven Naik

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Guest Editor

Traffic Safety/ITS/Traffic Signals, V3 Companies, Columbus, OH 43210, USA
Interests: traffic microsimulation modelling; highway safety and human factors research; traffic operations; signal system design and optimization; applications of ITS; geometric design; CV/AV technologies; statistical modeling and analysis
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Heng Wei

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Guest Editor

Department of Civil and Architectural Engineering and Construction Management, University of Cincinnati, Cincinnati, OH, USA
Interests: ITS; CAV impacts on transportation control system and infrastructure design; safety operations and management as well as environment; AI and advanced computing and communication technologies in transportation infrastructure systems; GIS application; vehicle routing modeling and optimization; advanced technologies in highway safety
Special Issues, Collections and Topics in MDPI journals

Dr. Parth Bhavsar

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Guest Editor

Department of Civil and Environmental Engineering, The Southern Polytechnic College of Engineering and Engineering Technology (SPCEET), Kennesaw State University, Marietta, GA 30060, USA
Interests: transportation data analytics; intelligent transportation system
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Worldwide, it is estimated that traffic-related crashes (accidents) cause about 1.3 million deaths per year, with an additional 20–50 million people sustaining various types of injuries. Therefore, road safety is a public health issue. For many years, traffic safety professionals and researchers have believed that highway traffic-related deaths and injuries are preventable. Traffic engineers believe that transportation automation technologies such as advanced driver assistance systems, automated driving vehicles, connected vehicles, and autonomous vehicles have the potential to reduce crashes, prevent injuries, save lives, and improve traffic operations. In recent years, concerted efforts have been made to improve road safety worldwide. One of the major recognized efforts is a global multi-country effort known as Vision Zero, which was started in Sweden and has now spread all over the world. This global movement aims to use road safety systemic approach measures to end traffic-related fatalities and serious injuries.

For this Special Issue of Vehicles entitled “Emerging Transportation Safety and Operations: Practical Perspectives,” we are seeking original contributions within this research area. Topics include but are not limited to applications of safety methods along with emerging technologies, the evaluation of traffic studies, before–after studies of safety countermeasures, operation-based safety and other impact studies, emerging trends in traffic safety and operations, surrogate measures, and applications of data-driven safety and operation methods with CAV-generated data, third-party data or other synergized data sources.

The publications in the first edition, which we believe may be of interest to you, can be found here: https://www.mdpi.com/journal/vehicles/special_issues/VP5V2662T4.

Prof. Dr. Deogratias Eustace
Dr. Bhaven Naik
Prof. Dr. Heng Wei
Dr. Parth Bhavsar
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. Vehicles 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

traffic safety
surrogate measures
injury severity
crash severity
connected/automated vehicle safety
safety methods
intelligent transportation systems

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Published Papers (14 papers)

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33 pages, 3518 KB

Open AccessArticle

Assessing Low Autonomous Vehicle Penetration Effects on Mobility and Safety at a Rural Signalized Intersection Under Adverse Weather Conditions

by Talha Ahmed, Pan Lu and Ying Huang

Vehicles 2026, 8(4), 76; https://doi.org/10.3390/vehicles8040076 - 2 Apr 2026

Viewed by 294

Abstract

Adverse weather conditions significantly degrade mobility and safety at rural signalized intersections, where high approach speeds and limited driver expectancy amplify operational and crash risks. While autonomous vehicles (AVs) have the potential to improve traffic performance, it takes a significant duration to penetrate. During this period, mixed traffic with human drivers and AVs will dominate. In this mixed traffic, the impacts of AVs at low penetration levels on adverse weather remain insufficiently understood, particularly in rural contexts. This study presents a simulation-based assessment of the effects of low AV penetration on mobility and safety at a rural signalized intersection under varying weather conditions. A calibrated microsimulation model was developed using PTV VISSIM to represent clear, rain, and snow scenarios with autonomous vehicles introduced at low penetration rates within conventional traffic. Mobility performance was evaluated using delay, travel time, and average speed, while safety impacts were assessed through surrogate safety measures extracted using the Surrogate Safety Assessment Model (SSAM), including time-to-collision and post-encroachment time. Results indicate that low levels of AV penetration of 10% can improve overall mobility performance compared with conventional traffic, particularly under adverse weather conditions. Safety outcomes show a reduction in conflict frequency and severity under low AV penetration, with more pronounced benefits observed during degraded weather scenarios. Further AV penetration from 10% to 25% may not significantly improve in a rural environment. The findings suggest that early-stage AV deployment may offer measurable mobility and safety benefits at rural signalized intersections, even before widespread adoption. This study provides practical insights for transportation agencies and policymakers regarding the potential role of low-penetration AV integration in enhancing rural traffic operations and safety under adverse weather conditions. Full article

(This article belongs to the Special Issue Emerging Transportation Safety and Operations: Practical Perspectives, 2nd Edition)

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22 pages, 4204 KB

Open AccessArticle

Evaluating Harsh Braking Events as a Surrogate Measure of Crash Risk Using Connected-Vehicle Telematics

by Md Tufajjal Hossain, Joyoung Lee, Dejan Besenski and Lazar Spasovic

Vehicles 2026, 8(3), 68; https://doi.org/10.3390/vehicles8030068 - 20 Mar 2026

Viewed by 377

Abstract

On heavily traveled highway corridors, traffic congestion, lane merges, toll facilities, and complex interchanges frequently trigger sudden and aggressive deceleration, commonly referred to as harsh braking (HB). Such maneuvers reflect near-miss driving conditions that may precede crashes. Traditional traffic safety analyses rely primarily on historical crash records, a reactive approach that limits agencies’ ability to identify and address emerging risks in a timely manner. Because HB events are continuously captured by connected-vehicle telematics, they provide an opportunity to evaluate roadway safety risk more proactively. This study investigates the applicability of harsh braking events as a surrogate indicator of crash risk on New Jersey interstate highways. The analysis uses more than 8.5 million connected-vehicle telemetry records from Drivewyze and approximately 45,000 police-reported crashes collected between July and December 2024. HB events were identified using a deceleration threshold of 6 ft/s² (approximately 0.2 g) and spatially matched to one-mile highway segments along with crash data. Spatial analysis shows that both HB events and crashes are highly concentrated along major corridors, including I-95, I-80, I-78, and I-287, with notable clustering near toll plazas and complex interchange areas. Temporal patterns indicate that harsh braking activity increases substantially during late fall, likely reflecting seasonal congestion and adverse weather conditions. To quantify the relationship between HB events and crash frequency, Negative Binomial (NB) and Zero-Inflated Negative Binomial (ZINB) regression models were estimated at the segment level. Results reveal a positive and statistically significant association between HB events and crash counts. In the preferred ZINB model, each additional HB event is associated with approximately a one percent increase in expected crash frequency. While the effect of individual events is small, repeated harsh braking activity corresponds to a meaningful increase in crash risk; for example, an increase of 10 HB events corresponds to an expected crash frequency of about 10% higher. Overall, the findings suggest that connected-vehicle HB data can complement traditional crash records by providing early indications of elevated risk. Incorporating HB monitoring into highway safety programs may support proactive identification of hazardous locations and more timely deployment of targeted countermeasures. Full article

(This article belongs to the Special Issue Emerging Transportation Safety and Operations: Practical Perspectives, 2nd Edition)

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31 pages, 28983 KB

Open AccessArticle

Safety Validation of Connected Autonomous Driving Systems in Urban Intersections Using the SUNRISE Safety Assurance Framework

by Mohammed Shabbir Ali, Alexis Warsemann, Pierre Merdrignac, Mohamed-Cherif Rahal, Amar Mokrani and Wael Jami

Vehicles 2026, 8(3), 55; https://doi.org/10.3390/vehicles8030055 - 11 Mar 2026

Viewed by 484

Abstract

Ensuring the safety of Autonomous Driving Systems (ADS) at urban intersections remains challenging due to complex interactions between vehicles and traffic management infrastructure. This study validates an ADS equipped with connected perception using Infrastructure-to-Vehicle (I2V) communication within a combined virtual and hybrid testing approach. The validation follows the overall structure and methodology of the SUNRISE Safety Assurance Framework (SAF), which is applied in detail where required by the scope of the study. Five representative urban intersection scenarios, covering both nominal driving conditions and safety-critical edge cases, are evaluated using virtual simulations in MATLAB/Simulink (2014b) and hybrid experiments integrating OMNeT++ (5.7.1)/Veins (5.2)/SUMO (1.12.0) with real-world components. Key Performance Indicators (KPIs) related to safety, decision-making, longitudinal control, passenger comfort, and V2X communication performance are analyzed. The results show strong consistency between virtual and hybrid testing, with ego vehicle speed deviations below 2 km/h and trigger distance differences under 3 m. V2X communication achieves a near-perfect Cooperative Awareness Message (CAM) delivery ratio, with an average latency of approximately 142 ms. While this latency remains within the tolerance of the deployed ADS, the overall end-to-end delay highlights opportunities for further optimization. The study demonstrates how the SUNRISE SAF can effectively structure ADS validation, identifies critical scenarios such as right-of-way violations by non-priority obstacles, and provides insights into improving connectivity handling and low-speed braking behavior for Cooperative, Connected, and Automated Mobility (CCAM) systems in urban environments. Full article

(This article belongs to the Special Issue Emerging Transportation Safety and Operations: Practical Perspectives, 2nd Edition)

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23 pages, 9975 KB

Open AccessArticle

Leveraging LiDAR Data and Machine Learning to Predict Pavement Marking Retroreflectivity

by Hakam Bataineh, Dmitry Manasreh, Munir Nazzal and Ala Abbas

Vehicles 2026, 8(1), 23; https://doi.org/10.3390/vehicles8010023 - 20 Jan 2026

Viewed by 750

Abstract

This study focused on developing and validating machine learning models to predict pavement marking retroreflectivity using Light Detection and Ranging (LiDAR) intensity data. The retroreflectivity data was collected using a Mobile Retroreflectometer Unit (MRU) due to its increasing acceptance among states as a compliant measurement device. A comprehensive dataset was assembled spanning more than 1000 miles of roadways, capturing diverse marking materials, colors, installation methods, pavement types, and vehicle speeds. The final dataset used for model development focused on dry condition measurements and roadway segments most relevant to state transportation agencies. A detailed synchronization process was implemented to ensure the accurate pairing of retroreflectivity and LiDAR intensity values. Using these data, several machine learning techniques were evaluated, and an ensemble of gradient boosting-based models emerged as the top performer, predicting pavement retroreflectivity with an R² of 0.94 on previously unseen data. The repeatability of the predicted retroreflectivity was tested and showed similar consistency as the MRU. The model’s accuracy was confirmed against independent field segments demonstrating the potential for LiDAR to serve as a practical, low-cost alternative for MRU measurements in routine roadway inspection and maintenance. The approach presented in this study enhances roadway safety by enabling more frequent, network-level assessments of pavement marking performance at lower cost, allowing agencies to detect and correct visibility problems sooner and helping to prevent nighttime and adverse weather crashes. Full article

(This article belongs to the Special Issue Emerging Transportation Safety and Operations: Practical Perspectives, 2nd Edition)

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16 pages, 1209 KB

Open AccessArticle

Comparative Analysis of Machine Learning and Statistical Models for Railroad–Highway Grade Crossing Safety

by Erickson Senkondo, Deo Chimba, Masanja Madalo, Afia Yeboah and Shala Blue

Vehicles 2025, 7(4), 163; https://doi.org/10.3390/vehicles7040163 - 17 Dec 2025

Cited by 1 | Viewed by 1441

Abstract

Railroad-highway grade crossings (RHGCs) are critical points of conflict between roadway and rail systems, contributing to over 2000 crashes and 250 fatalities annually in the United States. This study applied machine learning methods (ML) techniques to model and predict crash frequency at RHGCs, using a comprehensive dataset from the Federal Railroad Administration (FRA) and Tennessee Department of Transportation (TDOT). The dataset included 807 validated crossings, incorporating roadway geometry, traffic volumes, rail characteristics, and control features. Five ML models—Random Forest, XGBoost, PSO-Elastic Net, Transformer-CNN, and Autoencoder-MLP—were developed and compared to a traditional Negative Binomial (NB) regression model. Results showed that ML models significantly outperformed the NB model in predictive accuracy, with the Transformer-CNN achieving the lowest Mean Squared Error (21.4) and Mean Absolute Error (3.2). Feature importance analysis using SHAP values consistently identified Annual Average Daily Traffic (AADT), Truck Traffic Percentage, and Number of Lanes as the most influential predictors, findings that were underrepresented or statistically insignificant in the NB model. Notably, the NB model failed to detect the nonlinear relationships and interaction effects that ML algorithms captured effectively. While only three variables were statistically significant in the NB model, ML models revealed a broader spectrum of critical crash determinants, offering deeper interpretability and higher sensitivity. These findings emphasize the superiority of machine learning approaches in modeling RHGC safety and highlight their potential to support data-driven interventions and policy decisions for reducing crash risks at grade crossings. Full article

(This article belongs to the Special Issue Emerging Transportation Safety and Operations: Practical Perspectives, 2nd Edition)

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17 pages, 2962 KB

Open AccessArticle

Fusion of Simulation and AI Methods for Understanding HOV/HOT Lane Operational Flow Dynamics

by Deo Chimba, Therezia Matongo, Hellen Shita, Erickson Senkondo, Masanja Madalo and Afia Yeboah

Vehicles 2025, 7(4), 139; https://doi.org/10.3390/vehicles7040139 - 28 Nov 2025

Viewed by 711

Abstract

This study investigated the impact of converting High Occupancy Vehicle (HOV) lanes to High Occupancy Toll (HOT) lanes on fundamental traffic flow characteristics, focusing on speed, density, and flow relationships. A 25-mile HOV corridor along I-24 Westbound in Nashville, Tennessee was evaluated using both microscopic simulation via VISSIM and data-driven machine learning through a Multi-Layer Perceptron (MLP) neural network. Four operational scenarios were assessed: (1) HOV lanes without enforcement, (2) HOV lanes with effective occupancy enforcement, (3) HOT lanes with limited access points, and (4) HOT lanes with intermediate access points. Flow-density and speed-flow relationships were modeled using Greenshields theory to extract key traffic performance thresholds including free-flow speed, jam density, and maximum flow. Results indicate that while free-flow speeds were generally consistent across scenarios (ranging from 71 to 80 mph), HOV and HOT lanes exhibited higher values compared to general-purpose lanes. Capacity increases were observed following HOV-to-HOT conversions, especially when intermediate access points were introduced. The MLP neural network successfully replicated nonlinear flow relationships and predicted maximum flow near 2000 vph with a jam density of approximately 215 vpmpl—values that closely matched simulation outputs. Both the VISSIM and MLP-derived diagrams demonstrated curve shapes and capacity thresholds that were highly consistent with Highway Capacity Manual (HCM) standards for freeway segments. However, slightly higher thresholds were observed for HOV/HOT lanes, suggesting their potential for improved operational performance under managed conditions. The integration of simulation and machine learning offers a robust framework for evaluating managed lane conversions and informing data-driven policy. Beyond the scenario-specific findings, the study demonstrates an innovative hybrid methodology that links detailed microsimulation with an explainable neural network model, providing a concise and scalable approach for analyzing managed-lane operations. This combined framework highlights the contribution of integrating simulation and AI to enhance the analytical depth and practical relevance of traffic flow studies. Full article

(This article belongs to the Special Issue Emerging Transportation Safety and Operations: Practical Perspectives, 2nd Edition)

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19 pages, 1145 KB

Open AccessArticle

Speed Prediction Models for Tangent Segments Between Horizontal Curves Using Floating Car Data

by Giulia Del Serrone and Giuseppe Cantisani

Vehicles 2025, 7(3), 68; https://doi.org/10.3390/vehicles7030068 - 5 Jul 2025

Cited by 1 | Viewed by 1425

Abstract

The integration of connected autonomous vehicles (CAVs), advanced driver assistance systems (ADAS), and conventional vehicles necessitates the development of robust methodologies to enhance traffic efficiency and ensure safety across heterogeneous traffic streams. A comprehensive understanding of vehicle interactions and operating speed variability is essential to support informed decision-making in traffic management and infrastructure design. This study presents operating speed models aimed at estimating the 85th percentile speed (V₈₅) on straight road segments, utilizing floating car data (FCD) for both calibration and validation purposes. The dataset encompasses approximately 2000 km of the Italian road network, characterized by diverse geometric features. Speed observations were analyzed under three traffic conditions: general traffic, free-flow, and free-flow with dry pavement. Results indicate that free-flow conditions improve the model’s explanatory power, while dry pavement conditions introduce greater speed variability. Initial models based exclusively on geometric parameters exhibited limited predictive accuracy. However, the inclusion of posted speed limits significantly enhanced model performance. The most influential predictors identified were the V₈₅ on the preceding curve and the length of the straight segment. These findings provide empirical evidence to inform road safety evaluations and geometric design practices, offering insights into driver behavior in mixed-traffic environments. The proposed model supports the development of data-driven strategies for the seamless integration of automated and non-automated vehicles. Full article

(This article belongs to the Special Issue Emerging Transportation Safety and Operations: Practical Perspectives, 2nd Edition)

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24 pages, 6492 KB

Open AccessArticle

Time-Dependent Shortest Path Optimization in Urban Multimodal Transportation Networks with Integrated Timetables

by Yong Peng, Aizhen Ma, Dennis Z. Yu, Ting Zhao and Chester Xiang

Vehicles 2025, 7(2), 43; https://doi.org/10.3390/vehicles7020043 - 9 May 2025

Cited by 2 | Viewed by 3130

Abstract

Urban transportation systems evolve toward greater diversification, scalability, and complexity. To address the escalating issue of urban traffic congestion, leveraging modern information technologies to enhance the integration of multiple transportation modes and maximize overall efficiency has emerged as a promising strategy. This study focuses on the decision making problem of urban multimodal transportation travel paths, integrating the time-varying characteristics of public transportation schedules and networks. We consider passengers’ diverse needs and systematically investigate how to optimize travel paths to minimize travel time while adhering to constraints, such as the number of interchanges and travel costs. To address this NP-hard problem, we propose and implement two optimization algorithms: a variable-length coding genetic algorithm (V-GA) and a full permutation coding genetic algorithm (F-GA). Detailed numerical analysis validates the effectiveness of both algorithms, with the V-GA demonstrating significant advantages over the F-GA in terms of solution efficiency. Our findings provide novel perspectives and methodologies for optimizing urban multimodal transportation travel paths, offering robust theoretical foundations and practical tools for enhancing urban traffic planning and travel service efficiency. Full article

(This article belongs to the Special Issue Emerging Transportation Safety and Operations: Practical Perspectives, 2nd Edition)

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19 pages, 3338 KB

Open AccessArticle

Comparison of Machine Learning Models to Predict Nighttime Crash Severity: A Case Study in Tyler, Texas, USA

by Raja Daoud, Matthew Vechione, Okan Gurbuz, Prabha Sundaravadivel and Chi Tian

Vehicles 2025, 7(1), 20; https://doi.org/10.3390/vehicles7010020 - 18 Feb 2025

Cited by 4 | Viewed by 1914

Abstract

Driving at night is riskier in terms of crash involvement than it is during the day. Fortunately, it is clearly established that illumination on roadways can reduce the number and severity of nighttime crashes. However, state and municipal departments of transportation (DOTs) lack the available illumination data. Therefore, the objective of this research is threefold, as follows: (i) to develop machine learning models that use readily available roadway characteristic data to predict the severity of nighttime crashes; (ii) determine the effect that illumination has on crash severity; and (iii) develop a tool to assist DOT decision makers in collecting illumination data. To accomplish this objective, we have extracted data from the Texas Department of Transportation (TxDOT) Crash Record Information System (CRIS) database, which was then further split into a training and a test dataset. Then, seven machine learning techniques, namely binary logistic regression, k-nearest neighbors, naïve Bayes, random forest, artificial neural network, Extreme Gradient Boosting (XGBoost), and a Long Short-Term Memory (LSTM) model, were all applied to the unseen test data. The random forest model produced the most promising results by predicting severe crashes with 97.6% accuracy. In addition, we conducted a pilot study to test the collection of illumination data using a light meter. In the future, we aim to complete the development of a smartphone application, which can be used in conjunction with the random forest model presented in this paper, to collect crowdsourced illumination data and predict nighttime crash hotspots. This may assist DOT decision makers to prioritize funding for illumination at the hot spots. Full article

(This article belongs to the Special Issue Emerging Transportation Safety and Operations: Practical Perspectives, 2nd Edition)

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17 pages, 2590 KB

Open AccessArticle

Analyzing Crash Severity: Human Injury Severity Prediction Method Based on Transformer Model

by Yalan Jiang, Xianguo Qu, Weiwei Zhang, Wenfeng Guo, Jiejie Xu, Wangpengfei Yu and Yang Chen

Vehicles 2025, 7(1), 5; https://doi.org/10.3390/vehicles7010005 - 15 Jan 2025

Cited by 10 | Viewed by 7940

Abstract

Traffic accident-related injuries and fatalities are a serious global public health and social development challenge. The accurate prediction of crash severity improves road safety and reduces casualties, as well as serving road managers and policy makers. Prediction models need to learn and analyze the various characteristic factors of traffic accidents and capture from them the inherent complex relationship between accident characteristics and the severity of traffic accidents. However, most accident prediction studies lack analytical predictions of injury severity, and predictive models rely on the content and quality of accident datasets. To increase the robustness and accuracy of prediction models, this paper leverages a Transformer-based architecture for the severity prediction of traffic collisions from human injury severity. This framework learns both text and sequence data from accident datasets. After comparative analysis, the framework can achieve the prediction of human injury severity under different data categories and show good prediction performance at low injury severity levels using only textual data or sequence data. Full article

(This article belongs to the Special Issue Emerging Transportation Safety and Operations: Practical Perspectives, 2nd Edition)

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25 pages, 3488 KB

Open AccessArticle

Emerging Decision-Making for Transportation Safety: Collaborative Agent Performance Analysis

by Jack Maguire-Day, Saba Al-Rubaye, Anirudh Warrier, Muhammet A. Sen, Huw Whitworth and Mohammad Samie

Vehicles 2025, 7(1), 4; https://doi.org/10.3390/vehicles7010004 - 15 Jan 2025

Cited by 7 | Viewed by 2801

Abstract

This paper addresses the challenge of improving decision-making capabilities and safety in autonomous vehicles (AVs) using Agent-Based Modelling (ABM). The study evaluates ABM’s effect on Advanced Driver Assistance Systems (ADASs) in challenging driving situations, like lane merging, by incorporating it into a simulation framework designed for autonomous vehicles. Identifying emergent behaviours that enhance safety and efficiency, verifying the efficacy of ABM in AV decision-making, and investigating the function of hardware acceleration to enable practical application in ADASs are some of the major achievements. According to the simulation results, ABM can greatly improve AV performance, providing a practical and scalable means of enhancing safety in future transportation systems. Full article

(This article belongs to the Special Issue Emerging Transportation Safety and Operations: Practical Perspectives, 2nd Edition)

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19 pages, 4236 KB

Open AccessArticle

Implementation of Re-Simulation-Based Integrated Analysis System to Evaluate and Improve Autonomous Driving Algorithms

by Soobin Jeon, Junehong Park and Dongmahn Seo

Vehicles 2024, 6(4), 2209-2227; https://doi.org/10.3390/vehicles6040108 - 22 Dec 2024

Cited by 2 | Viewed by 2538

Abstract

Autonomous driving technology requires rigorous testing and validation of perception, decision-making, and control algorithms to ensure safety and reliability. Although existing simulators and testing tools play critical roles in algorithm evaluation, they struggle to satisfy the demands of complex, real-time systems. This study proposes a re-simulation-based integrated analysis system designed to overcome these challenges by providing advanced visualization, algorithm-testing, re-simulation, and data-handling capabilities. The proposed system features a comprehensive visualization module for real-time analysis of diverse sensor data and ego vehicle information, offering intuitive insights to researchers. Additionally, it includes a flexible algorithm-testing framework that abstracts simulator-specific dependencies, enabling seamless integration and evaluation of algorithms in various scenarios. The system also introduces robust re-simulation capabilities, enhancing algorithm validation using iterative testing based on real-world or simulated sensor data. To address the computational demands of high-frequency sensor data, the system employs optimized data-handling mechanisms based on shared memory, thereby significantly reducing latency and improving scalability. The proposed system overcomes critical challenges faced by existing alternatives by providing a robust, efficient, and scalable solution for testing and validating autonomous-driving algorithms, ultimately accelerating the development of safe and reliable autonomous vehicles. Full article

(This article belongs to the Special Issue Emerging Transportation Safety and Operations: Practical Perspectives, 2nd Edition)

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12 pages, 1492 KB

Open AccessArticle

Are Safety Corridors Effective in Mitigating Safety? An Ohio-Based Case Study Evaluating Their Effectiveness

by Sudesh Ramesh Bhagat, Bernard Ndeogo Issifu, Devon Destocki, Bhaven Naik and Deogratias Eustace

Vehicles 2024, 6(4), 1963-1974; https://doi.org/10.3390/vehicles6040096 - 24 Nov 2024

Viewed by 2299

Abstract

Distracted driving remains a major concern on highways, with it contributing to severe and fatal crashes, particularly on high-speed routes, prompting numerous states to implement targeted initiatives aimed at combating traffic violations that significantly contribute to fatal and injury-inducing crashes. Among these initiatives is the highway safety corridor program, a collaborative endeavor between the state departments of transportation and law enforcement agencies. Highway safety corridors employ a combination of engineering interventions and heightened law enforcement presence to address risky driver behavior and mitigate the occurrence of crashes. Despite the longstanding existence of safety corridors, research on their effectiveness remains relatively limited, with existing studies indicating only moderate success rates. This study is dedicated to evaluating the effectiveness of ten highway safety corridors in Ohio, where the state recently launched its inaugural highway safety corridor program targeting distracted driving. Utilizing 2023 crash data, this Empirical Bayes’ before-and-after study seeks to gauge the impact of these safety corridors on enhancing roadway transportation safety. Upon assessing all crash types within Ohio’s distracted driving safety corridors that provided sufficient data for a before–after study, it was determined that the adoption of safety corridors generally led to a reduction in crashes ranging from 2% to 49%. The significance and magnitude of crash reduction may vary if specific crash types or severity levels are considered. Full article

(This article belongs to the Special Issue Emerging Transportation Safety and Operations: Practical Perspectives, 2nd Edition)

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Open AccessSystematic Review

Integration of Connected Autonomous Vehicles in the Transportation Networks: A Systematic Review

by Fabricio Esteban Espinoza-Molina, Gustavo Javier Aguilar Miranda, Jaqueline Balseca and J. P. Díaz-Samaniego

Vehicles 2025, 7(3), 98; https://doi.org/10.3390/vehicles7030098 - 12 Sep 2025

Viewed by 2161

Abstract

Connected Autonomous Vehicles (CAVs) are expected to reshape transportation systems, yet their role in enhancing network robustness remains underexplored. This research intends to fill this gap by conducting a systematic review based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses protocol (PRISMA) to analyze 21 peer-reviewed publications identified from Scopus, Web of Science, and ScienceDirect. Articles were classified into five thematic areas: (1) system robustness, (2) infrastructure adaptation, (3) traffic flow and behavior, (4) security and communication, and (5) environmental impact. The results show that CAVs have the potential to improve robustness in transportation networks, thus helping the efficiency of transportation networks, reducing cyber vulnerability, and mitigating environmental impact. However, despite several advantages, CAVs also present challenges, including new infrastructure or updates to cybersecurity standards. This review contributes to the literature by consolidating current approaches, highlighting knowledge gaps, and offering methodological insights to guide research and policy development toward resilient, sustainable, and connected mobility systems. Full article

(This article belongs to the Special Issue Emerging Transportation Safety and Operations: Practical Perspectives, 2nd Edition)

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