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Emerging Transportation Safety and Operations: Practical Perspectives

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

Deadline for manuscript submissions: closed (15 March 2024) | Viewed by 20050

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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
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Dr. Bhaven Naik

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Guest Editor

Department of Civil Engineering, Ohio University, Athens, OH, 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

E-Mail Website
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
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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
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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, there has been concerted efforts 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 now spread all over the world. This global movement aims at using the 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, 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, applications of data-driven safety and operation methods with CAV-generated data, third-party data or other synergized data sources.

Prof. Dr. Deogratias Eustace
Dr. Bhaven Naik
Prof. Dr. Heng Wei
Prof. 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 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Vehicles is an international peer-reviewed open access quarterly 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 1600 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 (10 papers)

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Research

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20 pages, 9991 KiB

Open AccessArticle

Required Field of View of a Sensor for an Advanced Driving Assistance System to Prevent Heavy-Goods-Vehicle to Bicycle Accidents

by Ernst Tomasch, Heinz Hoschopf, Karin Ausserer and Jannik Rieß

Vehicles 2024, 6(4), 1922-1941; https://doi.org/10.3390/vehicles6040094 - 19 Nov 2024

Viewed by 377

Abstract

Accidents involving cyclists and trucks are among the most severe road accidents. In 2021, 199 cyclists were killed in accidents involving a truck in the EU. The main accident situation is a truck turning right and a cyclist going straight ahead. A large proportion of these accidents are caused by the inadequate visibility in an HGV (Heavy Goods Vehicle). The blind spot, in particular, is a significant contributor to these accidents. A BSD (Blind Spot Detection) system is expected to significantly reduce these accidents. There are only a few studies that estimate the potential of assistance systems, and these studies include a combined assessment of cyclists and pedestrians. In the present study, accident simulations are used to assess a warning and an autonomously intervening assistance system that could prevent truck to cyclist accidents. The main challenges are local sight obstructions such as fences, hedges, etc., rule violations by cyclists, and the complexity of correctly predicting the cyclist’s intentions, i.e., detecting the trajectory. Taking these accident circumstances into consideration, a BSD system could prevent between 26.3% and 65.8% of accidents involving HGVs and cyclists. Full article

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

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14 pages, 9865 KiB

Open AccessArticle

The CornerGuard: Seeing around Corners to Prevent Broadside Collisions

by Victor Xu and Sheng Xu

Vehicles 2024, 6(3), 1468-1481; https://doi.org/10.3390/vehicles6030069 - 27 Aug 2024

Viewed by 1028

Abstract

Nearly 3700 people are killed in broadside collisions in the U.S. every year. To reduce broadside collisions, we created and tested the CornerGuard, a prototype system that senses around a corner to alert a car driver of an impending collision with a pedestrian or automobile that is not in the line of sight (LOS). The CornerGuard leverages a microwave-transceiving radar sensor mounted on the car and a curved radio wave reflector installed at the corner to sense around the corner and detect a broadside collision threat. The car’s speed is constantly read by an onboard diagnostics (OBD) system to allow the sensor to differentiate between static objects and objects approaching around the corner. Field testing demonstrated that the CornerGuard can effectively and consistently detect threats at a consistent range without blind spots under broad weather conditions. Our proof of concept study shows that the CornerGuard can be enhanced to be readily integrated into automobile construction and street infrastructure. Full article

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

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19 pages, 6962 KiB

Open AccessArticle

Impacts of a Toll Information Sign and Toll Lane Configuration on Queue Length and Collision Risk at a Toll Plaza with a High Percentage of Heavy Vehicles

by Farnaz Zahedieh and Chris Lee

Vehicles 2024, 6(3), 1249-1267; https://doi.org/10.3390/vehicles6030059 - 23 Jul 2024

Viewed by 792

Abstract

This study assessed the impacts of a toll information sign with different toll lane configurations on queue length and collision risk at a toll plaza with an estimated high percentage of heavy vehicles (HVs). The toll information sign displays information about different toll payment methods for cars and HVs upstream of the toll booth. The impacts were assessed for the toll plaza of the Gordie Howe International Bridge under construction at the Windsor–Detroit international border crossing using a traffic simulation model. Results show that the toll information sign upstream of the toll plaza and converting the toll lanes with multiple toll payment methods to electronic toll collection (ETC)-only lanes reduced queue length and collision risk. However, increasing the number of HV-only lanes for a higher percentage of HVs increased lane-change collision risk. Thus, it is recommended that toll lane configurations be changed based on the percentage of HVs to reduce collision risk at a toll plaza. Full article

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

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15 pages, 10256 KiB

Open AccessArticle

Radar-Based Pedestrian and Vehicle Detection and Identification for Driving Assistance

by Fernando Viadero-Monasterio, Luciano Alonso-Rentería, Juan Pérez-Oria and Fernando Viadero-Rueda

Vehicles 2024, 6(3), 1185-1199; https://doi.org/10.3390/vehicles6030056 - 9 Jul 2024

Cited by 1 | Viewed by 1940

Abstract

The introduction of advanced driver assistance systems has significantly reduced vehicle accidents by providing crucial support for high-speed driving and alerting drivers to imminent dangers. Despite these advancements, current systems still depend on the driver’s ability to respond to warnings effectively. To address this limitation, this research focused on developing a neural network model for the automatic detection and classification of objects in front of a vehicle, including pedestrians and other vehicles, using radar technology. Radar sensors were employed to detect objects by measuring the distance to the object and analyzing the power of the reflected signals to determine the type of object detected. Experimental tests were conducted to evaluate the performance of the radar-based system under various driving conditions, assessing its accuracy in detecting and classifying different objects. The proposed neural network model achieved a high accuracy rate, correctly identifying approximately 91% of objects in the test scenarios. The results demonstrate that this model can be used to inform drivers of potential hazards or to initiate autonomous braking and steering maneuvers to prevent collisions. This research contributes to the development of more effective safety features for vehicles, enhancing the overall effectiveness of driver assistance systems and paving the way for future advancements in autonomous driving technology. Full article

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

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19 pages, 4690 KiB

Open AccessArticle

Meta-Feature-Based Traffic Accident Risk Prediction: A Novel Approach to Forecasting Severity and Incidence

by Wei Sun, Lili Nurliynana Abdullah, Puteri Suhaiza Sulaiman and Fatimah Khalid

Vehicles 2024, 6(2), 728-746; https://doi.org/10.3390/vehicles6020034 - 25 Apr 2024

Viewed by 1591

Abstract

This study aims to improve the accuracy of predicting the severity of traffic accidents by developing an innovative traffic accident risk prediction model—StackTrafficRiskPrediction. The model combines multidimensional data analysis including environmental factors, human factors, roadway characteristics, and accident-related meta-features. In the model comparison, the StackTrafficRiskPrediction model achieves an accuracy of 0.9613, 0.9069, and 0.7508 in predicting fatal, serious, and minor accidents, respectively, which significantly outperforms the traditional logistic regression model. In the experimental part, we analyzed the severity of traffic accidents under different age groups of drivers, driving experience, road conditions, light and weather conditions. The results showed that drivers between 31 and 50 years of age with 2 to 5 years of driving experience were more likely to be involved in serious crashes. In addition, it was found that drivers tend to adopt a more cautious driving style in poor road and weather conditions, which increases the margin of safety. In terms of model evaluation, the StackTrafficRiskPrediction model performs best in terms of accuracy, recall, and ROC–AUC values, but performs poorly in predicting small-sample categories. Our study also revealed limitations of the current methodology, such as the sample imbalance problem and the limitations of environmental and human factors in the study. Future research can overcome these limitations by collecting more diverse data, exploring a wider range of influencing factors, and applying more advanced data analysis techniques. Full article

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

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18 pages, 6428 KiB

Open AccessArticle

Connected Automated and Human-Driven Vehicle Mixed Traffic in Urban Freeway Interchanges: Safety Analysis and Design Assumptions

by Anna Granà, Salvatore Curto, Andrea Petralia and Tullio Giuffrè

Vehicles 2024, 6(2), 693-710; https://doi.org/10.3390/vehicles6020032 - 11 Apr 2024

Cited by 2 | Viewed by 1833

Abstract

The introduction of connected automated vehicles (CAVs) on freeways raises significant challenges, particularly in interactions with human-driven vehicles, impacting traffic flow and safety. This study employs traffic microsimulation and surrogate safety assessment measures software to delve into CAV–human driver interactions, estimating potential conflicts. While previous research acknowledges that human drivers adjust their behavior when sharing the road with CAVs, the underlying reasons and the extent of associated risks are not fully understood yet. The study focuses on how CAV presence can diminish conflicts, employing surrogate safety measures and real-world mixed traffic data, and assesses the safety and performance of freeway interchange configurations in Italy and the US across diverse urban contexts. This research proposes tools for optimizing urban layouts to minimize conflicts in mixed traffic environments. Results reveal that adding auxiliary lanes enhances safety, particularly for CAVs and rear-end collisions. Along interchange ramps, an exclusive CAV stream performs similarly to human-driven ones in terms of longitudinal conflicts, but mixed traffic flows, consisting of both CAVs and human-driven vehicles, may result in more conflicts. Notably, when CAVs follow human-driven vehicles in near-identical conditions, more conflicts arise, emphasizing the complexity of CAV integration and the need for careful safety measures and roadway design considerations. Full article

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

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27 pages, 6591 KiB

Open AccessArticle

Enhancing Urban Intersection Efficiency: Utilizing Visible Light Communication and Learning-Driven Control for Improved Traffic Signal Performance

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

Vehicles 2024, 6(2), 666-692; https://doi.org/10.3390/vehicles6020031 - 4 Apr 2024

Cited by 2 | Viewed by 1811

Abstract

This paper introduces an approach to enhance the efficiency of urban intersections by integrating Visible Light Communication (VLC) into a multi-intersection traffic control system. The main objectives include the reduction in waiting times for vehicles and pedestrians, the improvement of overall traffic safety, and the accommodation of diverse traffic movements during multiple signal phases. The proposed system utilizes VLC to facilitate communication among interconnected vehicles and infrastructure. This is achieved by utilizing streetlights, headlamps, and traffic signals for transmitting information. By integrating VLC localization services with learning-driven traffic signal control, the multi-intersection traffic management system is established. A reinforcement learning scheme, based on VLC queuing/request/response behaviors, is utilized to schedule traffic signals effectively. Agents placed at each intersection control traffic lights by incorporating information from VLC-ready cars, including their positions, destinations, and intended routes. The agents devise optimal strategies to improve traffic flow and engage in communication to optimize the collective traffic performance. An assessment of the multi-intersection scenario through the SUMO urban mobility simulator reveals considerable benefits. The system successfully reduces both waiting and travel times. The reinforcement learning approach effectively schedules traffic signals, and the results highlight the decentralized and scalable nature of the proposed method, especially in multi-intersection scenarios. The discussion emphasizes the possibility of applying reinforcement learning in everyday traffic scenarios, showcasing the potential for the dynamic identification of control actions and improved traffic management. Full article

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

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14 pages, 2872 KiB

Open AccessArticle

Driving Standardization in Infrastructure Monitoring: A Role for Connected Vehicles

by Raj Bridgelall

Vehicles 2023, 5(4), 1878-1891; https://doi.org/10.3390/vehicles5040101 - 18 Dec 2023

Cited by 1 | Viewed by 1547

Abstract

This study tackles the urgent need for efficient condition monitoring of road and rail infrastructure, which is integral to a nation’s economic vitality. Traditional methods proved both costly and inadequate, resulting in network gaps and accelerated infrastructure decay. Employing connected vehicles with integrated sensors and cloud computing capabilities can provide a cost-effective, sustainable solution for comprehensive infrastructure monitoring. In advocating for international standardization, this study furnishes compelling evidence—encompassing trends in transportation, economics, and patent landscapes—that underscores the necessity and advantages of such standards. The analysis confirmed that trucks and rail will remain dominant in freight transport as infrastructure limitations intensify. A noteworthy finding is the absence of patented solutions in this domain, which simplifies the path toward global standardization. By integrating data from diverse sources, agencies can optimize maintenance triggers and allocate funds more strategically, thus preserving vital transportation networks. These insights not only offer an effective alternative to current practices but also have the potential to influence policymaking and industry standards for infrastructure monitoring. Full article

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

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26 pages, 4274 KiB

Open AccessArticle

Enhancing Safety Assessment of Automated Driving Systems with Key Enabling Technology Assessment Templates

by Martin Skoglund, Fredrik Warg, Anders Thorsén and Mats Bergman

Vehicles 2023, 5(4), 1818-1843; https://doi.org/10.3390/vehicles5040098 - 13 Dec 2023

Viewed by 1819

Abstract

The emergence of Automated Driving Systems (ADSs) has transformed the landscape of safety assessment. ADSs, capable of controlling a vehicle without human intervention, represent a significant shift from traditional driver-centric approaches to vehicle safety. While traditional safety assessments rely on the assumption of a human driver in control, ADSs require a different approach that acknowledges the machine as the primary driver. Before market introduction, it is necessary to confirm the vehicle safety claimed by the manufacturer. The complexity of the systems necessitates a new comprehensive safety assessment that examines and validates the hazard identification and safety-by-design concepts and ensures that the ADS meets the relevant safety requirements throughout the vehicle lifecycle. The presented work aims to enhance the effectiveness of the assessment performed by a homologation service provider by using assessment templates based on refined requirement attributes that link to the operational design domain (ODD) and the use of Key Enabling Technologies (KETs), such as communication, positioning, and cybersecurity, in the implementation of ADSs. The refined requirement attributes can serve as safety-performance indicators to assist the evaluation of the design soundness of the ODD. The contributions of this paper are: (1) outlining a method for deriving assessment templates for use in future ADS assessments; (2) demonstrating the method by analysing three KETs with respect to such assessment templates; and (3) demonstrating the use of assessment templates on a use case, an unmanned (remotely assisted) truck in a limited ODD. By employing assessment templates tailored to the technology reliance of the identified use case, the evaluation process gained clarity through assessable attributes, assessment criteria, and functional scenarios linked to the ODD and KETs. Full article

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

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Review

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17 pages, 11024 KiB

Open AccessReview

An Overview of the Efficiency of Roundabouts: Design Aspects and Contribution toward Safer Vehicle Movement

by Konstantinos Gkyrtis and Alexandros Kokkalis

Vehicles 2024, 6(1), 433-449; https://doi.org/10.3390/vehicles6010019 - 25 Feb 2024

Cited by 3 | Viewed by 4868

Abstract

Transforming intersections into roundabouts has shown that a sufficient degree of road safety and traffic capacity can be achieved. Because of the lower speeds at the area of a roundabout, drivers tend to become more easily adaptive to any kind of conflict with the surrounding environment. Despite the contribution to safety, the design elements of roundabouts are not uniformly fixed on a worldwide scale because of different traffic volumes, vehicle dimensions, drivers’ attitude, etc. The present study provides a brief overview of the contribution of roundabouts to road safety and the interactions between safety and the design elements of roundabouts. In addition, discussion points about current challenges and prospects are elaborated, including findings from the environmental assessment of roundabouts; their use and performance on the era of autonomous vehicles that will dominate in the near future; as well as the role and importance of simulation studies towards the improvement of the design and operation of roundabouts in favor of safer vehicle movement. The criticality of roundabouts, in terms of their geometric design as well as the provided road safety, lies upon the fact that roundabouts are currently used for the conventional vehicle fleet, which will be gradually replaced by new vehicle technologies. Such an action will directly impact the criteria for road network design and/or redesign, thereby continuously fostering new research initiatives. Full article

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

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