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Focus on Traffic Safety: From Artificial Intelligence Approaches to Other Advances

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A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (20 March 2022) | Viewed by 34430

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Prof. Dr. Paweł Droździel

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Department of Transport, Combustion Engine and Ecology, Faculty of Mechanical Engineering, Lublin University of Technology, Lublin, Poland
Interests: safety and social issues in the operation of road transport vehicles; sustainable transport; economic aspects of the operation and construction of means of transport; problems of operation of vehicles; tribology aspects in vehicles; problems of start-ups of the diesel engine
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Prof. Ing. Radovan Madleňák

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Department of Communications, University of Zilina, 01026 Zilina, Slovakia
Interests: transportation planning; logistics; marketing; e-business; human-computer interaction; communication; IT infrastructure; network architecture
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Assoc. Prof. Dr. Saugirdas Pukalskas

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Department of Automotive Engineering, Faculty of Transport Engineering, Vilnius Gediminas Technical University, Vilnius, Lithuania
Interests: road safety; hybrid vehicle; internal combustion engines

Assoc. Prof. Dr. Drago Sever

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Department of Traffic and Transportation Engineering, Faculty of Civil, Transportation Engineering and Architecture, University of Maribor, Maribor, Slovenia
Interests: theory of traffic flows; dimensioning of traffic infrastructure; traffic and transportation infrastructure; traffic calming; sustainable mobility; mobility patterns; public passenger transport; traffic safety

Assoc. Prof. Dr. Marcin Ślęzak

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Motor Transport Institute, General Director, Warsaw, Poland
Interests: combustion engines; vehicle diagnostics and environmental protection; road safety; autonomous vehicles and electromobility

Special Issue Information

Dear Colleagues,

Vehicle technologies are rapidly moving forward; therefore, vehicle safety today is incomparable to that of 20 or 30 years ago, let alone that of vehicles manufactured even earlier than that. It is generally now quite difficult to get into a fatal or severe accident driving a modern vehicle at the permitted speed. However, lots of road accidents still happen every day, with people killed or injured. Furthermore, there are always vulnerable road users on our roads, whose safety mainly depends on drivers’ attention and awareness level.

The safest way to move is by maintaining speed which is as low as possible—such as walking or at least running speed, in which no one can suffer serious physical harm in the event of an accident. Moreover, making sure that wearing a helmet is the prevalent behavior on our roads would lead to an easier implementation of Vision Zero. Unfortunately, humanity will probably never return to such speeds again. Therefore, what is the future of road users, and what kind of technologies and opportunities will ensure their safety? These questions are not only more relevant than ever before today, but they also remain open to sustainable ideas and modern technologies.

This is thus a call for everyone to help us to move forward, solve issues of traffic safety, and protect each other.

Assoc. Prof. Dr. Paweł Droździel
Prof. Ing. Radovan Madleňák
Assoc. Prof. Dr. Saugirdas Pukalskas
Assoc. Prof. Dr. Drago Sever
Assoc. Prof. Dr. Marcin Ślęzak
Guest Editors

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Keywords

traffic safety
road safety
speed limits
eye tracking
safety analysis of road networks
safety potential
autonomous vehicle
road safety audit
collisions
traffic accidents

Published Papers (12 papers)

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Research

20 pages, 3596 KiB

Open AccessArticle

Analysis of Statistical and Artificial Intelligence Algorithms for Real-Time Speed Estimation Based on Vehicle Detection with YOLO

by Héctor Rodríguez-Rangel, Luis Alberto Morales-Rosales, Rafael Imperial-Rojo, Mario Alberto Roman-Garay, Gloria Ekaterine Peralta-Peñuñuri and Mariana Lobato-Báez

Appl. Sci. 2022, 12(6), 2907; https://0-doi-org.brum.beds.ac.uk/10.3390/app12062907 - 11 Mar 2022

Cited by 14 | Viewed by 4595

Abstract

Automobiles have increased urban mobility, but traffic accidents have also increased. Therefore, road safety is a significant concern involving academics and government. Transit studies are the main supply for studying road accidents, congestion, and flow traffic, allowing the understanding of traffic flow. They require special equipment (sensors) to measure the car’s speed. With technological advances, artificial intelligence, and videos, it is possible to estimate the speed in real-time without modifying the installed urban infrastructure. We need to employ public databases that provide reliable monocular videos to generate automated traffic studies. The problem of speed estimation with a monocular camera involves synchronizing data recording, tracking, and detecting the vehicles over the road considering the lanes and distance between cars. Usually, a set of constraints are considered, such as camera calibration, flat roads, including methods based on the homography and augmented intrusion lines, patterns or regions, or prior knowledge about the actual dimensions of some of the objects. In this paper, we present a system that generates a dataset from videos recorded from a highway—obtaining 532 samples; we separated the vehicle’s detection by lane, estimating its speed. We use this data set to compare five different statistical methods and three machine learning methods to evaluate their accuracy in estimating the cars’ speed in real-time. Our vehicle estimation requires a feature extraction process using YOLOv3 and Kalman filter to detect and track vehicles. The Linear Regression Model (LRM) yielded the best results obtaining a Mean Absolute Error (MAE) of 1.694 km/h for the center lane and 0.956 km/h for the last lane. The results were compared with several state-of-the-art works, having competitive performance. Hence, LRM is fast estimating speed in real time and does not require high computational resources allowing a future hardware implementation. Full article

(This article belongs to the Special Issue Focus on Traffic Safety: From Artificial Intelligence Approaches to Other Advances)

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21 pages, 3632 KiB

Open AccessArticle

Application of the Theory of Planned Behavior in Autonomous Vehicle-Pedestrian Interaction

by Farrukh Hafeez, Usman Ullah Sheikh, Abdullahi Abubakar Mas’ud, Saud Al-Shammari, Muhammad Hamid and Ameer Azhar

Appl. Sci. 2022, 12(5), 2574; https://0-doi-org.brum.beds.ac.uk/10.3390/app12052574 - 01 Mar 2022

Cited by 11 | Viewed by 3174

Abstract

Automobile manufacturers, alongside technology providers, researchers, and public agencies, are conducting extensive testing to design autonomous vehicles (AVs) algorithms that will provide a complete understanding of road users, specifically pedestrians. Pedestrian behavior and actions determination are highly unpredictable depending on behavioral beliefs, context, and socio-demographic variables. Context includes everything that potentially affects one’s behavior; in AVs–pedestrian interaction, context may consist of weather conditions, road structure, social factors norms, and traffic volume. These influencing elements, therefore, need to be focused on during the development of pedestrian interaction algorithms. For this purpose, the pedestrian behavior questionnaire for FAVs (PBQF) is designed based on the theory of planned behavior (TPB). A total of almost 1000 voluntary participants completed this multilingual survey. As socio-demographic values and physiological perception varies with local norms, regions, and ethnicity, participants from 27 countries were therefore chosen to account for this variation. One of the key findings of this study is the influence of pedestrian attributes and the context on pedestrian behavior. Pedestrian action cannot be understood without visual observation of the pedestrian themselves and their context. The findings showed that pedestrians build communication with vehicles based on their driving styles. The vehicle’s driving style leads pedestrians to think that the vehicle is human-driven or autonomous. The results also revealed that pedestrians use several cues to show their intention. The general perception of AVs was also analyzed, and the communication between AVs and pedestrians with different displaying options was investigated. Full article

(This article belongs to the Special Issue Focus on Traffic Safety: From Artificial Intelligence Approaches to Other Advances)

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14 pages, 295 KiB

Open AccessArticle

Comprehensive Analysis of Traffic Accidents in Seoul: Major Factors and Types Affecting Injury Severity

by Hyeonchoel Jeong, Inhi Kim, Keejun Han and Jungeun Kim

Appl. Sci. 2022, 12(4), 1790; https://0-doi-org.brum.beds.ac.uk/10.3390/app12041790 - 09 Feb 2022

Cited by 11 | Viewed by 2673

Abstract

Accident and fatality rates of traffic accidents worldwide are steadily increasing every year; thus, considerable effort has been made to prevent traffic accidents and prepare countermeasures. This study aims to identify the major factors and types that affect the severity of traffic accidents in Seoul by utilizing the Seoul Metropolitan Government’s traffic accident dataset. To achieve this, we perform a comprehensive analysis by adopting various machine learning techniques—not only supervised learning methods but also unsupervised learning methods. As a result of the experiment, we derived several critical factors that were found to affect the severity of traffic accidents via supervised learning methods (i.e., ensemble-based and regression-based algorithms) and discovered dominant accident types via unsupervised learning methods (i.e., clustering-based algorithms). One of our primary findings is that, in contrast to common sense, environmental factors such as weather, season, and day of the week do not significantly affect the severity of traffic accidents in Seoul. Moreover, all methods highlight the importance of pedestrian-related factors, implying that it is highly necessary to prepare more meticulous institutional measures for pedestrians to reduce the negative influence of serious traffic accidents in Seoul. Full article

(This article belongs to the Special Issue Focus on Traffic Safety: From Artificial Intelligence Approaches to Other Advances)

18 pages, 2164 KiB

Open AccessArticle

Prediction of Run-Off Road Crash Severity in South Korea’s Highway through Tree Augmented Naïve Bayes Learning

by Hyungkyu Kim, Jin-Tae Kim, Somyoung Shin, Hyerin Lee and Joonbeom Lim

Appl. Sci. 2022, 12(3), 1120; https://0-doi-org.brum.beds.ac.uk/10.3390/app12031120 - 21 Jan 2022

Cited by 2 | Viewed by 1623

Abstract

The run-off road crash (RORC) is a representative type of lethal crash. The severity of RORC has increased owing to a combination of factors, such as roadside geometry, traffic conditions, and weather/climatic conditions. In this study, a model for estimating the RORC severity was developed based on various factors, including fixed objects, roadway geometry, traffic conditions, and road traffic environment. To develop the model, the accident data of crashes with roadside fixed objects on highways, as well as information on fixed object-related variables and roadway geometry-related variables, were collected. To improve the model in terms of implementing a close reflection of the real world, a learning method with tree augmented naïve Bayes (TAN), which takes into account the causal links between variables, was applied. The results of the analysis showed that the severity of crashes with roadside fixed objects increased sharply when the vertical slope was ≥4%, the radius of the curve was ≥250 m, the distance between the fixed object and the roadway was less than 3 m, or the density of fixed objects installation was greater than 2 for every 10 m. The proposed model allows for an analysis of sections with a high RORC severity on the roadways in operation and provides improvement measures to reduce the severity of RORC. Full article

(This article belongs to the Special Issue Focus on Traffic Safety: From Artificial Intelligence Approaches to Other Advances)

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16 pages, 432 KiB

Open AccessArticle

Mining Road Traffic Rules with Signal Temporal Logic and Grammar-Based Genetic Programming

by Federico Pigozzi, Eric Medvet and Laura Nenzi

Appl. Sci. 2021, 11(22), 10573; https://0-doi-org.brum.beds.ac.uk/10.3390/app112210573 - 10 Nov 2021

Cited by 7 | Viewed by 1694

Abstract

Traffic systems, where human and autonomous drivers interact, are a very relevant instance of complex systems and produce behaviors that can be regarded as trajectories over time. Their monitoring can be achieved by means of carefully stated properties describing the expected behavior. Such properties can be expressed using Signal Temporal Logic (STL), a specification language for expressing temporal properties in a formal and human-readable way. However, manually authoring these properties is a hard task, since it requires mastering the language and knowing the system to be monitored. Moreover, in practical cases, the expected behavior is not known, but it has instead to be inferred from a set of trajectories obtained by observing the system. Often, those trajectories come devoid of human-assigned labels that can be used as an indication of compliance with expected behavior. As an alternative to manual authoring, automatic mining of STL specifications from unlabeled trajectories would enable the monitoring of autonomous agents without sacrificing human-readability. In this work, we propose a grammar-based evolutionary computation approach for mining the structure and the parameters of an STL specification from a set of unlabeled trajectories. We experimentally assess our approach on a real-world road traffic dataset consisting of thousands of vehicle trajectories. We show that our approach is effective at mining STL specifications that model the system at hand and are interpretable for humans. To the best of our knowledge, this is the first such study on a set of unlabeled real-world road traffic data. Being able to mine interpretable specifications from this kind of data may improve traffic safety, because mined specifications may be helpful for monitoring traffic and planning safety promotion strategies. Full article

(This article belongs to the Special Issue Focus on Traffic Safety: From Artificial Intelligence Approaches to Other Advances)

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18 pages, 1457 KiB

Open AccessArticle

Applying Heuristics to Generate Test Cases for Automated Driving Safety Evaluation

by Leonard Stepien, Silvia Thal, Roman Henze, Hiroki Nakamura, Jacobo Antona-Makoshi, Nobuyuki Uchida and Pongsathorn Raksincharoensak

Appl. Sci. 2021, 11(21), 10166; https://0-doi-org.brum.beds.ac.uk/10.3390/app112110166 - 29 Oct 2021

Cited by 11 | Viewed by 2783

Abstract

Comprehensive safety evaluation methodologies for automated driving systems that account for the large complexity real traffic are currently being developed. This work adopts a scenario-based safety evaluation approach and aims at investigating an advanced methodology to generate test cases by applying heuristics to naturalistic driving data. The targeted requirements of the generated test cases are severity, exposure, and realism. The methodology starts with the extraction of scenarios from the data and their split in two subsets—containing the relatively more critical scenarios and, respectively, the normal driving scenarios. Each subset is analysed separately, in regard to the parameter value distributions and occurrence of dependencies. Subsequently, a heuristic search-based approach is applied to generate test cases. The resulting test cases clearly discriminate between safety critical and normal driving scenarios, with the latter covering a wider spectrum than the former. The verification of the generated test cases proves that the proposed methodology properly accounts for both severity and exposure in the test case generation process. Overall, the current study contributes to fill a gap concerning the specific applicable methodologies capable of accounting for both severity and exposure and calls for further research to prove its applicability in more complex environments and scenarios. Full article

(This article belongs to the Special Issue Focus on Traffic Safety: From Artificial Intelligence Approaches to Other Advances)

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26 pages, 1608 KiB

Open AccessArticle

Investigating Rural Single-Vehicle Crash Severity by Vehicle Types Using Full Bayesian Spatial Random Parameters Logit Model

by Fulu Wei, Zhenggan Cai, Zhenyu Wang, Yongqing Guo, Xin Li and Xiaoyan Wu

Appl. Sci. 2021, 11(17), 7819; https://0-doi-org.brum.beds.ac.uk/10.3390/app11177819 - 25 Aug 2021

Cited by 10 | Viewed by 1692

Abstract

The effect of risk factors on crash severity varies across vehicle types. The objective of this study was to explore the risk factors associated with the severity of rural single-vehicle (SV) crashes. Four vehicle types including passenger car, motorcycle, pickup, and truck were considered. To synthetically accommodate unobserved heterogeneity and spatial correlation in crash data, a novel Bayesian spatial random parameters logit (SRP-logit) model is proposed. Rural SV crash data in Shandong Province were extracted to calibrate the model. Three traditional logit approaches—multinomial logit model, random parameter logit model, and random intercept logit model—were also established and compared with the proposed model. The results indicated that the SRP-logit model exhibits the best fit performance compared with other models, highlighting that simultaneously accommodating unobserved heterogeneity and spatial correlation is a promising modeling approach. Further, there is a significant positive correlation between weekend, dark (without street lighting) conditions, and collision with fixed object and severe crashes and a significant negative correlation between collision with pedestrians and severe crashes. The findings can provide valuable information for policy makers to improve traffic safety performance in rural areas. Full article

(This article belongs to the Special Issue Focus on Traffic Safety: From Artificial Intelligence Approaches to Other Advances)

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24 pages, 7371 KiB

Open AccessArticle

Estimation of Optimal Speed Limits for Urban Roads Using Traffic Information Big Data

by Hyungkyu Kim and Doyoung Jung

Appl. Sci. 2021, 11(12), 5710; https://0-doi-org.brum.beds.ac.uk/10.3390/app11125710 - 20 Jun 2021

Cited by 6 | Viewed by 2617

Abstract

The use of an inconsistent speed limit determination method can cause low speed limit compliance. Therefore, we developed an objective methodology based on engineering judgment considering the traffic accident rate in road sections, the degree of roadside development, and the geometric characteristics of road sections in urban roads. The scope of this study is one-way roads with two or more lanes in cities, and appropriate sections were selected among all roads in Seoul. These roads have speed limits of the statutory maximum speed of 80 km/h or lower and are characterized by various speeds according to the function of the road, the roadside development, and traffic conditions. The optimal speed limits of urban roads were estimated by applying the characteristics of variables as adjustment factors based on the statutory maximum speed limit. As a result of investigating and testing various influence variables, the function of roads, the existence of median, the level of curbside parking, the number of roadside access points, and the number of traffic breaks were selected as optional variables that influence the operating speed. The speed limit of one-way roads with two or more lanes in Seoul was approximately 10 km/h lower than the current speed limit. The existing speed limits of the roads were applied uniformly considering only the functional road class. However, considering the road environment, the speed limit should be applied differently for each road. In the future, if the collection scope and real-time collection of road environment information can be determined, the GIS visualization of traffic safety information will be possible for all road sections and the safety of road users can be ensured. Full article

(This article belongs to the Special Issue Focus on Traffic Safety: From Artificial Intelligence Approaches to Other Advances)

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16 pages, 2217 KiB

Open AccessArticle

Research on Safety Prediction of Sector Traffic Operation Based on a Long Short Term Memory Model

by Wenying Lyu, Honghai Zhang, Junqiang Wan and Lei Yang

Appl. Sci. 2021, 11(11), 5141; https://0-doi-org.brum.beds.ac.uk/10.3390/app11115141 - 01 Jun 2021

Cited by 6 | Viewed by 2121

Abstract

Traffic safety has been thought of as a basic feature of transportation, recent developments in civil aviation have emphasized the need for risk identification and safety prediction. This study aims to increase en-route flight safety through the development of prediction models for flight conflicts. Firstly, flight conflicts time series and traffic parameters are extracted from historical ADS-B data. In the second step, a Long Short-Term Memory (LSTM) model is trained to make a one-step-ahead prediction on the flight conflict time series. The results show that the LSTM model has the greatest prediction effect (MAE 0.3901) with comparison to other models. Based on that, we add traffic parameters (volume, density, velocity) into the LSTM model as new input variables and issue a comprehensive analysis of the relative predictive power of traffic parameters. The accuracy of prediction model is validated with a mean error of less than 3%. Based on the improvements of model performance brought by traffic parameters, LSTM models with a single traffic parameter are proposed for further discussion. The results illustrate that volume is the most important factor in promoting prediction accuracy and density has an advantage of improvement in the aspect of model stability. Full article

(This article belongs to the Special Issue Focus on Traffic Safety: From Artificial Intelligence Approaches to Other Advances)

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14 pages, 4637 KiB

Open AccessArticle

The Influence of Quality Management System on the Operation of Periodical Technical Inspection Stations

by Juraj Hudec, Branislav Šarkan, Renáta Czödörová and Jacek Caban

Appl. Sci. 2021, 11(11), 4854; https://0-doi-org.brum.beds.ac.uk/10.3390/app11114854 - 25 May 2021

Cited by 4 | Viewed by 2221

Abstract

This article deals with the minimum requirements for quality management at Periodical Technical Inspection Stations (PTIS) for road vehicles in the Slovak Republic, as well as in selected countries of the European Union. Specifically, it contains research performed at all of the PTIS in the Slovak Republic with a focus on the established quality management system and the number of employees of companies operating these PTIS, as well as similar research in selected countries of the European Union. Based on the research results, the article contains an assessment of the influence of the implemented quality management system on the operation of PTIS. The analysis of the results showed that 86.7% of PTIs have an established system to meet the minimum requirements for quality management through a documented procedure (internal regulation), and 13.3% of the PTI have the certified quality management system according to the STN EN ISO/IEC 9001 standard. Unfortunately, no PTI in the Slovak Republic has the fulfilment of quality management requirements according to the standard STN EN ISO/IEC 17020. Full article

(This article belongs to the Special Issue Focus on Traffic Safety: From Artificial Intelligence Approaches to Other Advances)

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14 pages, 987 KiB

Open AccessArticle

Analysis of Solutions Improving Safety of Cyclists in the Road Traffic

by Przemysław Skoczyński

Appl. Sci. 2021, 11(9), 3771; https://0-doi-org.brum.beds.ac.uk/10.3390/app11093771 - 22 Apr 2021

Cited by 6 | Viewed by 3033

Abstract

Cycling safety management is particularly important due to the increasing use of this mode of transport and increasing car traffic flows. Cyclists—travelling on roadways or sharing the space with pedestrians—are exposed to considerable risks for their safety, as well as for the safety of other road users. Comprehensive and effective management of bicycle traffic safety is therefore essential for the protection of this group of road users. The article presents procedures, the implementation of which is aimed at increasing the safety of cyclists and effective implementation of measures to achieve that. The analyses aimed at the selection of appropriate measures and solutions to improve safety of cyclists in the road traffic have been presented. The procedures include: Selection of devices and measures, risk assessment and estimation of the potential to reduce the risk of collisions/accidents involving cyclists, selection of investment measures and devices reducing the risk of cyclists and other road users, implementation methods and monitoring of selected measures reducing the risk of collisions/accidents involving cyclists. The proposals contained in the article fill in the gap existing in this area of knowledge. The analyses conducted and the presented results show that the construction of infrastructure for cyclists is not the only way to improve the safety of this group of road users. Due to the high costs and deficit of the road surface in the road-street cross-section—other forms of measures in this area should also be analyzed. Full article

(This article belongs to the Special Issue Focus on Traffic Safety: From Artificial Intelligence Approaches to Other Advances)

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24 pages, 659 KiB

Open AccessArticle

Model Validation and Scenario Selection for Virtual-Based Homologation of Automated Vehicles

by Stefan Riedmaier, Daniel Schneider, Daniel Watzenig, Frank Diermeyer and Bernhard Schick

Appl. Sci. 2021, 11(1), 35; https://0-doi-org.brum.beds.ac.uk/10.3390/app11010035 - 23 Dec 2020

Cited by 19 | Viewed by 3916

Abstract

Due to the rapid progress in the development of automated vehicles over the last decade, their market entry is getting closer. One of the remaining challenges is the safety assessment and type approval of automated vehicles, as conventional testing in the real world would involve an unmanageable mileage. Scenario-based testing using simulation is a promising candidate for overcoming this approval trap. Although the research community has recognized the importance of safeguarding in recent years, the quality of simulation models is rarely taken into account. Without investigating the errors and uncertainties of models, virtual statements about vehicle safety are meaningless. This paper describes a whole process combining model validation and safety assessment. It is demonstrated by means of an actual type-approval regulation that deals with the safety assessment of lane-keeping systems. Based on a thorough analysis of the current state-of-the-art, this paper introduces two approaches for selecting test scenarios. While the model validation scenarios are planned from scratch and focus on scenario coverage, the type-approval scenarios are extracted from measurement data based on a data-driven pipeline. The deviations between lane-keeping behavior in the real and virtual world are quantified using a statistical validation metric. They are then modeled using a regression technique and inferred from the validation experiments to the unseen virtual type-approval scenarios. Finally, this paper examines safety-critical lane crossings, taking into account the modeling errors. It demonstrates the potential of the virtual-based safeguarding process using exemplary simulations and real driving tests. Full article

(This article belongs to the Special Issue Focus on Traffic Safety: From Artificial Intelligence Approaches to Other Advances)

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