Road Behavior Research Articles

Rural Road Assessment Method for Sustainable Territorial Development

In Latin America, initiatives have been advocated for developing rural roads that facilitate optimal conditions free from dust, mud, and noise. The criteria for assessing public investment do not align with the requirements of rural infrastructure. Indeed, in rural areas, the territorial conditions such as openness to rural–urban markets, access to education and health, environmental protection, culture, and identity are more important than transportation times or traffic volume. Hence, a multicriteria evaluation method is proposed to prioritize the rural road improvements and maximize their contribution to sustainable territorial development. The roads with the highest sustainable contribution are optimized using a multi-objective decision-making analysis and prioritized based on a Manhattan distance. In addition, a fuzzy cognitive map analyzes the dynamic behavior of the optimal roads. Based on this proposal, a case study is applied where fifteen roads are selected from a sample of 101 in the Araucanía Region, Chile. For this, 16 evaluation criteria, 27 indicators, and sustainability’s social, environmental, technical, and economic dimensions are considered. The results detect reduced one-dimensional contributions despite identifying 15 optimal roads that collectively enhance sustainability. Two roads stand out for their long-term sustainability contribution, which are influenced by economic criteria of zonal productivity, tourism, and road maintenance. Thus, this method can help public agencies rank the roads that must be the subject of development projects.

Analysis of Influencing Factor for Rear-end Crash on Toll Road: An Examination Using Self-reported Questionnaires

Background Rear-end crashes on toll roads have been a significant issue in Indonesia. This type of crash exists and needs a mitigation strategy. However, limited research exists examining the mechanism and contributing factors of these crashes in Indonesia. This research aimed to describe how rear-end crashes have occurred on toll roads in Indonesia, describe associated risk factors, develop a model for assessing the risk of rear-end crashes on Indonesian toll roads and identify potential mitigation strategies. Materials and Methods A questionnaire was developed and assessed for validity and reliability. It was distributed (online) to 220 drivers who had experienced a rear-end crash or near-crash in the past year. Results The findings from this study showed that contributing factors are driver (55%), environment (33%), and vehicle (12%). Subject vehicles usually consisted of private vehicles, while the lead (struck) vehicles included private vehicles (91.8%), trucks (7.27%), and buses (0.93%). Discussion This study also found two phenomena of local unique behavior in Indonesian toll roads that lead to rear-end crashes, such as “Lane Hogger” and driving under the speed limit (trucks) that lead other vehicles to rear-end crashes. Conclusion Factors contributing to rear-crash risk on Indonesian toll roads were driving experience (ownership driving license 15-20 years), breaking driving rules, driving in the right lane, and using an MPV. The findings from this study can improve potential mitigation strategies, including developing focused driving rules, traffic signs on toll roads, and crash avoidance technology.

Do enhanced school zone policies improve pedestrians' safety? A deep learning-based case study of Osan City, South Korea

This study investigates the effectiveness of strengthened penalty policies in South Korean school zones by analyzing the changes in road users' behaviors, focusing on pedestrian-vehicle interactions. This study employed three surrogate safety measurements: vehicle speed, Pedestrian Safety Margins (PSM), and Predicted Collision Risk (PCR) level. The comprehensive analysis covers a spectrum of behaviors, from simple to complex, assessing the policy's impact in urban environments. The findings reveal several important insights. First, the policy enforcement resulted in a positive impact on vehicle speeds, with average speeds aligning with posted speed limits. Second, an increase was observed in yielding behaviors, particularly in school zones. However, much of this behavior appeared to be cosmetic, emphasizing the need for more safety-oriented yielding practices. Finally, the policy enforcement had a mixed impact on PCR levels, with a reduction in danger levels in school zones, yet an unexpected increase in danger levels in non-school zones. This study provides valuable insights into the effectiveness of strengthened penalty policies in school zones, particularly the development of a safe and sustainable urban environment.

Pedestrian Behavior in Static and Dynamic Virtual Road Crossing Experiments

Virtual studies involving pedestrians have gained relevance due to the advantage of not exposing them to actual risk, and simulation setups have benefitted from rapid technical advancements, becoming increasingly complex and immersive. However, it remains unclear whether complex setups affecting participants’ freedom of movement impact their decision-making. This research evaluated the effects of a more realistic approach to studying pedestrian crossing behavior by comparing a perception-action task requiring participants to walk effectively along a semi-virtual crosswalk with a similar experiment using static crossing conditions. Using a CAVE system, two real-world streets were modeled in two different virtual scenarios, varying vehicle speed patterns and distance from the crosswalk. Visual stimuli were presented to two groups of 30 participants, with auditory stimuli adapted accordingly. The impact of various factors on participants’ crossing decisions was evaluated by examining the percentage of crossings, crossing start time, and time-to-passage. Overall, the experimental approach did not significantly affect participants’ crossing decisions.

Discrete element method simulation of high-speed vehicle collisions with road barrier systems

AbstractThe behavior of road or perimeter protection barriers under vehicle impact are usually investigated based on crash tests and finite element (FE) numerical approaches, which are ether expensive or time-consuming. Several studies have proposed to reduce the computation time of the numerical analysis by substituting the complex FE models of vehicles using simplified mass–spring–damper system models. However, these models have drawbacks since consideration of different vehicle impact angles is difficult and they are unable to correctly simulate the risk of high-speed vehicle collision running over the barrier. In this paper, a new approach is proposed to simulate the collision of vehicles with barriers based on the discrete element method (DEM). Here, to save computation time only a handful of 3D non-spherical particles are used to represent the barrier and vehicle. These particles are generated based on the super-quadric function, which is capable of generating a variety of shapes needed for the model. The contact detection and evaluation are carried out based on discrete function representation of the particles with uniform sampling. The bond between two discrete elements is defined using a nonlinear cohesive beam model since the distance between the elements is relatively large. The simulation results obtained based on this approach are more accurate and complete than the simplified mass–spring models and computationally more efficient than the FE model.

Location, location, location! An application of situational action theory to phone use while driving in different environments

Situational action theory (SAT) is a comprehensive theory that explains offending behaviour from the context of the interaction between interpersonal factors, such as morals, self-control, and habits, with situational factors, such as environmental risk and social conditions. Illegal hand-held mobile phone use while driving (MPUWD), a prevalent and risky road behaviour, has yet to be examined under the SAT framework. Therefore, by utilising SAT, this exploratory study sought to investigate the interpersonal and situational factors preceding hand-held MPUWD in three distinct road environments (highways, built-up areas and stopped-at-traffic lights). To achieve this, Queensland drivers aged over 18 who owned a mobile phone (N = 821, Mage = 41.7 years, SD = 20.9 years) were invited to complete an online survey. Repeated measures ANOVAs demonstrated significant mean differences for participants’ perceived risks associated with MPUWD and subsequent likelihood of offending across the three environments, with traffic lights being more conducive to MPUWD behaviours. Three hierarchical multiple linear regressions also revealed that age, attitude, risk of judgement, and self-control were significant predictors of MPUWD across all environments, while the results for gender, risk of injury and risk of being apprehended varied across the different driving contexts. Together, these factors explained 38.5–39.6 % of the variance in the perceived likelihood of illegally engaging in MPUWD. Overall, the findings indicated that the application of SAT has predictive utility in determining MPUWD engagement and can provide a more comprehensive framework for approaching and intervening in MPUWD behaviour.

The Mediating Role of Traffic Safety Awareness on Road Safety Attitude and Behavior of Drivers

This research study aimed to determine the mediating role of traffic safety awareness on the relationship between road safety attitude and behavior of drivers. The researcher surveyed 378 licensed drivers selected using a stratified random sampling technique. The study utilized Mean, Pearson Product Moment Correlation, Medgraph using Sobel z-test and Regression. The study found a very high level of Traffic Safety Awareness, a moderate level of Driving behavior, and high Road Safety Attitudes among licensed drivers. A significant correlation was observed between road safety attitude and drivers’ behavior. There is also a high significance on the relationship between road safety attitude and traffic safety awareness, as well as between traffic safety awareness and drivers’ driving behavior. The mediation analysis examined the role of traffic safety awareness as a mediator in the relationship between road safety attitude and driver behavior. The results point towards partial mediation, where the mediator variable partially explains the relationship between the independent and dependent variables.

A Road Behavior Pattern-Detection Model in Querétaro City Streets by the Use of Shape Descriptors

In this research, a proposed model aims to automatically identify patterns of spatial and temporal behavior of moving objects in video sequences. The moving objects are analyzed and characterized based on their shape and observable attributes in displacement. To quantify the moving objects over time and form a homogeneous database, a set of shape descriptors is introduced. Geometric measurements of shape, contrast, and connectedness are used to represent each moving object. The proposal uses Granger’s theory to find causal relationships from the history of each moving object stored in a database. The model is tested in two scenarios; the first is a public database, and the second scenario uses a proprietary database from a real scenario. The results show an average accuracy value of 78% in the detection of atypical behaviors in positive and negative dependence relationships.

HELMET RULE VIOLATION DETECTION USING DEEP LEARNING

The widespread non-compliance of helmet-wearing regulations among motorcyclists in India poses a significant threat to road safety. This paper introduces a new approach to address this issue through the developing a Helmet Rule Violation Detection system. Given the challenges faced by traffic police in enforcing helmet regulations, the proposedsystem leverages computer vision techniques to automatically identify motorcyclists without helmets. The methodology involves the detection of moving vehicles through thresholding, followed by a classification process based on area and aspect ratio to distinguish between motorcyclists and non-motorcyclists. The subsequent identification of helmet violation is achieved by isolating relevant blobs and employing a classification mechanism. By automating this process, the system aims to reduce the burden on traffic police and enhance the overall safety of road users. The proposed approach not only contributes to the mitigation of helmet rule violations but also promotes responsible road behavior, fostering a safer and more compliant traffic environment in India.

Rheological Properties of Silica-Fume-Modified Bioasphalt and Road Performance of Mixtures.

The objective of this research is to enhance the high-temperature antirutting and antiaging characteristics of bioasphalt. In this study, silica fume (SF) was selected to modify bioasphalt. The dosage of bio-oil in bioasphalt was 5%, and the dosage of SF was 2%, 4%, 6%, 8%, and 10% of bioasphalt. The high- and low-temperature characteristics, aging resistance, and temperature sensitivity of Bio + SF were evaluated by temperature sweep (TS), the multiple stress creep recovery (MSCR) test, the bending beam rheology (BBR) test, and the viscosity test. Meanwhile, the road behavior of the Bio + SF mixture was evaluated using the rutting test, low-temperature bending beam test, freeze-thaw splitting test, and fatigue test. The experimental results showed that the dosage of SF could enhance the high-temperature rutting resistance, aging resistance, and temperature stability of bioasphalt. The higher the dosage of SF, the more significant the enhancement effect. However, incorporating SF weakened bioasphalt's low-temperature cracking resistance properties. When the SF dosage was less than 8%, the low-temperature cracking resistance of Bio + SF was still superior to that of matrix asphalt. Compared with matrix asphalt mixtures, the dynamic stability, destructive strain, freeze-thaw splitting strength ratio, and fatigue life of 5%Bio + 8%SF mixtures increased by 38.4%, 49.1%, 5.9%, and 68.9%, respectively. This study demonstrates that the development of SF-modified bioasphalt could meet the technical requirements of highway engineering. Using SF and bio-oil could decrease the consumption of natural resources and positively reduce environmental pollution.

Investigating The Influence Of Driver Misconceptions On Road Behaviour Using A Novel Lattice Hydrodynamic Model

Investigating The Influence Of Driver Misconceptions On Road Behaviour Using A Novel Lattice Hydrodynamic Model

Assessing road vulnerability in seismic conditions: a comprehensive study

Road infrastructure is indispensable for societal functionality, yet it is highly susceptible to the devastating impacts of seismic events. This paper focuses on enhancing the resilience of road systems by systematically identifying, quantifying, and assessing factors contributing to their vulnerability during earthquakes. The core objective of this study is to develop and validate a seismic vulnerability index for road sections, which aims to evaluate and classify the susceptibility of road infrastructure to earthquake-induced damage comprehensively. This systematic approach is pivotal for guiding effective mitigation strategies and prioritization efforts. To fulfill this objective, the study employs the Analytic Hierarchy Process (AHP) to introduce a novel methodology for calculating the seismic vulnerability index, incorporating various factors that influence road vulnerability. This method allows for the accurate classification of road sections into distinct levels of susceptibility, providing a solid foundation for implementing targeted interventions and enhancing road resilience. Further, the paper validates the theoretical model through several case studies, demonstrating the practical applicability and effectiveness of the seismic vulnerability index in real-world scenarios. Additionally, the use of Geographic Information System (GIS) technology to simulate earthquake scenarios within an urban road network offers valuable insights into the potential seismic behavior of roads. These simulations are crucial for identifying critical areas that require intervention and for planning resilience-building efforts more effectively. By proposing a comprehensive framework that combines rigorous analysis, empirical validation, and advanced simulation techniques, this paper seeks to make a significant contribution to the field of infrastructure resilience. It aims to advance the understanding of road vulnerability in seismic conditions and supports the development of strategic approaches for enhancing the resilience of road infrastructure against earthquakes. Through the development and validation of a seismic vulnerability index, this study meets its primary objective, providing a valuable resource for researchers, policymakers, and practitioners in disaster management and infrastructure planning.

Enhanced Road Safety with Photoluminescent Pedestrian Crossings in Urban Contexts

The safety of roads in urban areas is a major concern for governments, demanding innovative solutions to enhance pedestrian safety. This paper introduces a novel approach to crosswalks by integrating resin with photoluminescent additives, offering a significant boost to road safety. A thorough methodology was employed to assess its effectiveness, covering mechanical, lighting, and vibroacoustic aspects, alongside a photogrammetric analysis of real-world experiments. The material exhibited noteworthy mechanical properties, displaying consistent tensile strength, load capacity, and strain values with a remarkable Shore A hardness. After 20 min, luminance values peaked at 68 mcd/m2, surpassing standard vehicle headlights at 100 m. Additionally, vibroacoustic analysis highlighted a noticeable relationship between vehicle speed and sound bandwidth, indicating the system’s potential to alert pedestrians. Tests revealed that the proposed system significantly decreased the average vehicle speed by 36.96% compared to conventional crosswalks, with a 27.80% reduction when drivers yielded to pedestrians. Furthermore, a survey involving 35 participants, focusing on the knowledge of road safety regulations, behavior, signage, and visibility, found positive results regarding accident reduction. The estimations indicate potential decreases of 26.26% in injuries and 35.4% in fatalities due to improved road conditions, 26.58% in injuries and 53.16% in fatalities resulting from reduced average speeds, and 52.56% in injuries and 79.91% in fatalities through enhanced road education. This underscores the multifaceted impact of the system on urban road safety.

A Genetic Algorithm-based Decision Framework to Incorporate Climate Impact on Pavement Maintenance Planning

A recent trend of increase in the vulnerable behavior of roads to potential climate events is observed worldwide. However, a few studies conducted incorporate climate impact into road maintenance and rehabilitation. To address this, a genetic algorithm (GA) based optimization approach is proposed in this study with a climate risk index (CRI) in terms of criticality of roads, probability of occurrence of a climate event, and existing severity level of pavement. Criticality is defined by road functional class, availability of alternative routes and land use. Probability is determined by historical events and topography, while severity is defined by existing pavement condition. The CRI is incorporated as a generic constraint to the GA-based optimization model to maximize the average network condition under a given budget. To demonstrate this, a case study is conducted using twenty roads in different climatic conditions in Sri Lanka. The results show that in 25% and 50% of required total budget conditions there is a clear separation between priority roads IRI and non-priority roads IRI due to the generic constraint. This is an indication that the optimization model effectively prioritizes roads when there is a budget constraint. This concludes that the proposed approach can be utilized to make the most of the available budget for road maintenance by prioritizing roads that are highly vulnerable to climate events without compromising the overall network condition. Further, the proposed maintenance optimization approach can be extended to long term maintenance planning economically for developing countries.

Road safety attitude and behaviour among motorcycle riders in Ghana: A focus on traffic locus of control and health belief.

Road traffic accident is a leading cause of death and various life deformities worldwide. This burden is even higher among motorcycle riders in lower-to-middle-income countries. Despite the various interventions made to address the menace, the fatalities continue to be on the ascendency. One major area that has received little attention is the attitude and behaviour of motorcycle riders. The present study aimed to examine the contribution of traffic Locus of Control (LoC) and health belief on road safety attitude and behaviour. 317 motorcycle riders participated in the study. The participants completed a questionnaire comprising various sections such as motorcycle riding behaviour, road safety attitude, risk perception, the intention to use helmets, and traffic LoC. The results showed a significant positive correlation between road safety attitude and behaviour (r (295) = .33, p < .001). Drifting towards internal LoC was associated with more positive behaviour on the roads (r (295) = -.23, p < .001). Intention to use helmet, health motivation, perceived susceptibility, perceived benefits, and perceived barriers were the factors in the health belief model that were associated with road safety attitude (r (295) = .404, p < .001). Finally, the multiple linear regression model showed that road safety attitude and traffic LoC made significant contributions to road user behaviour [F(3, 293) = 13.73, p < .001]. These findings have important implications towards shaping responsible behaviour among motorcycle riders.

Simulation modelling of traffic flow of the east-west latitudinal corridor for socio-economic development of the Novosibirsk region

The article presents the study’s results to assess the degree of impact of different categories of objects on traffic flows in the city of Novosibirsk (Russia). The main feature of the area is its intersection with the Trans-Siberian Railway. The highway is a transport corridor linking states in the European part of the continent with the countries of the Asia-Pacific region. Using structurization, analysis, synthesis, and simulation modeling allowed us to obtain a quantitative and qualitative description of the key objects whose behavior significantly affects the implementation of traffic flows in the study area. Formal rail, road, and pedestrian behavior models are studied and presented. Such models became the basis of simulation modeling, allowing us to estimate the degree of influence of modernization of the Trans-Siberian Railway and the objects of street road infrastructure of Novosibirsk adjacent to the relevant sections of the railway.

Augmented Reality (AR) as a New Educational Medium for Children: Using Road Safety Content

Road accidents, often involving one or more vehicles, can result in property damage, injuries, or even fatalities. These incidents can occur due to a range of factors, including human errors, adverse weather conditions, or mechanical failures, despite precautions taken by road users. Consequently, early education on road safety is crucial, especially for children. This research focuses on assessing children's knowledge of road accidents in Malaysia and evaluating the impact of augmented reality (AR) experiences on their understanding. To support this project, we developed an AR application using the Pendidikan Keselamatan Jalan Raya (PKJR) Year 2 Guidebook as a medium for students to learn about road safety. Selected pages in the book serve as markers, enabling students to engage with AR content while using the book. The study aims to determine the effectiveness of the AR application as an educational tool for children. A group of school children participated in testing this AR application, and their experiences were assessed using an adapted Game Experience Questionnaire (GEQ) model. The results indicate that after using the AR application, the group of 8-year-old children exhibited improved awareness of the road environment. This paper also explores the emotional aspects of participant experiences as emphasized by the GEQ. These findings highlight the potential of AR as an innovative tool for educating children about road safety, challenging traditional approaches, and promoting safer road behaviors among young individuals. Furthermore, this project aligns with the strategies outlined in the Malaysian Road Safety Plan 2020-2030, particularly in reducing risks to pedestrians. In a broader context, this study contributes to the evolving landscape of technology-driven safety initiatives, underscoring the importance of adapting contemporary educational mediums to address complex societal issues like road accidents. In conclusion, we discuss the potential for future studies involving road safety education and interactive technology.

MmWave scattering properties of roads on rough asphalt and concrete surfaces

Abstract. In this work, a synthetic aperture radar setup is used for analyzing the mmWave scattering of road surfaces in the automotive 77 GHz band in the laboratory. With this setup, samples of concrete roads in two different surface conditions are investigated, determining the variances in reflectivity depending on material composition and surface structure. Afterward, the distribution of these variations is fitted using probability density functions, namely normal and rayleigh distribution fits. Consequently, the diffuse scattering behavior of concrete roads can be described mathematically. Additionally, previously presented porous asphalt roads are compared and fitted analogously to get a summary of the scattering for all common road surfaces in Germany. Furthermore, a validation of the measurement and the processing by analyzing particularly generated reference samples is performed.

Quantitative Analysis of Dangerous Driving Behavior through Drone Footage using Mathematical Modeling

This research paper presents a method for analyzing driving using aerial drone images of traffic scenes. By applying principles of motion and calculus we introduce an approach that provides insights into driving dynamics without relying on complex vehicle markings or annotations. This method offers a perspective on road behaviors, allowing for an understanding of potential risks and ultimately enhancing road safety. In our experiment we examined a series of drone images capturing traffic scenarios. We provided a procedure and demonstration of applications of physics and calculus to reconstruct traffic scenes. We constructed a trajectory mechanism that uses all of said concepts to form a concrete plan to reconstruct scenes, using real drone footage with bird’s eye view. The combination of concepts with aerial image analysis showcases the potential for objective and data driven assessments of reckless driving behaviors. Overall this paper serves as a starting point for advancements, in utilizing models to analyze traffic patterns. It contributes to promoting road practices and improving transportation management through evidence based approaches.

A multi-modal spatial–temporal model for accurate motion forecasting with visual fusion

The multi-source visual information from ring cameras and stereo cameras provides a direct observation of the road, traffic conditions, and vehicle behavior. However, relying solely on visual information may not provide a complete environmental understanding. It is crucial for intelligent transportation systems to effectively utilize multi-source, multi-modal data to accurately predict the future motion trajectory of vehicles accurately. Therefore, this paper presents a new model for predicting multi-modal trajectories by integrating multi-source visual feature. A spatial–temporal cross attention fusion module is developed to capture the spatiotemporal interactions among vehicles, while leveraging the road’s geographic structure to improve prediction accuracy. The experimental results on the realistic dataset Argoverse 2 demonstrate that, in comparison to other methods, ours improves the metrics of minADE (Minimum Average Displacement Error), minFDE (Minimum Final Displacement Error), and MR (Miss Rate) by 1.08%, 3.15%, and 2.14% , respectively, in unimodal prediction. In multimodal prediction, the improvements are 5.47%, 4.46%, and 6.50%. Our method effectively captures the temporal and spatial characteristics of vehicle movement trajectories, making it suitable for autonomous driving applications.