Virtual Road Research Articles

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.

Fatigue life prediction of rubber suspension bushings based on virtual road load spectrum pulses

Rubber suspension bushings have mainly been studied under single- or multi-axis load cycling, while research on rubber suspension bushings under random load cycling is not deep enough. This study conducted uniaxial tensile and fatigue tests on N60 rubber dumbbell-shaped plate samples to obtain stress–strain curves and S–N curves. Using the uniaxial tensile test data, a Mooney–Rivlin hyperelastic constitutive model was fitted using Abaqus software, and a finite element model of the rubber suspension bushing was established. A 3D virtual road surface was constructed using Adams software, and characteristic features of typical road surfaces were extracted for statistical analysis, resulting in the generation of a load spectrum showing variations in force over time. Based on the Miner linear damage accumulation theory, finite element fatigue life prediction of the rubber suspension bushing was conducted using pulses from the 232 s region of the load spectrum. The predicted fatigue life was compared to the results from benchtop fatigue testing, resulting in a relative error of less than 6.443 %. The findings suggest that the method is effective in predicting the fatigue life of rubber suspension bushings under random loads.

STTraj2Vec: A spatio-temporal trajectory representation learning approach

Computing trajectory similarity plays a critical role in various spatio-temporal applications that involve trajectory analysis. In recent years, trajectory representation learning has been extensively studied and applied for trajectory similarity calculation. However the majority of existing algorithms for trajectory representation generally have two problems. The first problem is the emphasis of spatial similarity over temporal similarity, and even to discard the temporal dimension of spatio-temporal trajectories. As a result, the outputs of these approaches cannot fully represent the similarity of spatio-temporal trajectories. The second problem is the introduction of additional information, such as the topology of the road network, which increases the uncertainty of capturing the spatio-temporal correlation of trajectories and prevents their application in scenarios where it is difficult to obtain such information. This poses a significant challenge when dealing with complex and time-varying traffic networks. This paper proposes a novel method, named STTraj2Vec (Spatio-temporal Trajectory 2 Vector), which relies only on spatio-temporal trajectories to capture their similarity without spatio-temporal separation. This takes into account the whole spatio-temporal trajectory information. In this method, an extended clustering algorithm is introduced, which maps the trajectory into a point-region quadtree, and constructs a time-varying virtual network structure based on the point-region quadtree. In this method, an extended clustering algorithm is introduced, which maps each trajectory into a point-region quadtree, and then completes density clustering through the adjacency relation of leaf nodes to construct a time-varying virtual network structure. This virtual network structure not only considers the spatial proximity, but also the time, so as to reflect the spatio-temporal characteristics of the trajectory more accurately. Then, a novel spatially and temporally integrated random walk algorithm is designed, which carries out spatiotemporal random walk on the virtual network structure, to capture the spatiotemporal characteristics of the trajectory, and thus obtains the representation of all nodes in the virtual network. Furthermore, each trajectory is converted into a sequence of vectors on the virtual road network according to the latitude, longitude, and time of the trajectory points. Finally, based on these node representations and trajectories, a transformer model with ranking loss is employed to capture the distinct contributions of the various locations and times to the similarity computation and encode each sequence of vectors into target vectors. Experiments on two public datasets show that STTraj2Vec is superior to the state-of-the-art methods in terms of effectiveness for top-k trajectory similarity search and trajectory clustering, while exhibiting low parameter sensitivity and high model robustness.

Application of immersive virtual reality for assessing chronic neglect in individuals with stroke: the immersive virtual road-crossing task

ABSTRACT Background Neglect can be a long-term consequence of chronic stroke that can impede an individual’s ability to perform daily activities, but chronic and discrete forms can be difficult to detect. We developed and evaluated the “immersive virtual road-crossing task” (iVRoad) to identify and quantify discrete neglect symptoms in chronic stroke patients. Method The iVRoad task requires crossing virtual intersections and placing a letter in a mailbox placed either on the left or right. We tested three groups using the HTC Vive Pro Eye: (1) chronic right hemisphere stroke patients with (N = 20) and (2) without (N = 20) chronic left-sided neglect, and (3) age and gender-matched healthy controls (N = 20). We analyzed temporal parameters, errors, and head rotation to identify group-specific patterns, and applied questionnaires to measure self-assessed pedestrian behavior and usability. Results Overall, the task was well-tolerated by all participants with fewer cybersickness-induced symptoms after the VR exposure than before. Reaction time, left-sided errors, and lateral head movements for traffic from left most clearly distinguished between groups. Neglect patients committed more dangerous crossings, but their self-rated pedestrian behavior did not differ from that of stroke patients without neglect. This demonstrates their reduced awareness of the risks in everyday life and highlights the clinical relevance of the task. Conclusions Our findings suggest that a virtual road crossing task, such as iVRoad, has the potential to identify subtle symptoms of neglect by providing virtual scenarios that more closely resemble the demands and challenges of everyday life. iVRoad is an immersive, naturalistic virtual reality task that can measure clinically relevant behavioral variance and identify discrete neglect symptoms.

Development and optimization of simulation models and methods for controlling virtual artillery units in game scenarios

In the realm of modern video game development, special attention is given to the simulation of artillery systems, which play a crucial role in various military-themed games. This research presents a mathematical model for simulating the actions of a virtual artillery system. The model is designed to manage the execution of combat tasks, including targeting destruction with a specified number of shells and incorporating the strategic movement between firing positions to minimize detection and attack by enemy forces in the game. The model presumes that all shots are effective and equates the number of firing positions to the number of shots, with a minimum of one shot per position. The model's dynamics do not allow for returning to previous positions, adding a layer of complexity and realism to the gameplay. Movement simulations between positions are designed along virtual roads of varying quality, enhancing the strategic elements of the game. A method for determining the optimal strategy for the artillery system's actions has been developed, introducing the concept of the current structure of combat task execution. This problem-solving approach falls within the realm of Pareto-oriented tasks or dynamic programming challenges. The computational method of the model is based on a general algorithm, underpinned by specialized additional algorithms. Results from this model demonstrate the feasibility of completing combat tasks effectively, with a maximum of two shots per firing position. The research differentiates between defensive and offensive tactics in gameplay, suggesting that while a strategy involving ten shots per target aligns with defensive gameplay, a strategy with four shots per target aligns with offensive actions. Consequently, the "shot-and-scoot" tactic in an offensive context can be aptly termed as “hid-and-shot”.

IMPLEMENTATION OF INFORMATION TECHNOLOGIES IN INSTITUTIONS OF VOCATIONAL EDUCATION

The article covers the theoretical, methodical and practical aspects of the implementation of information technologies in vocational education and training. The purpose of the article is to find factors for improving the educational process, management, methods and information work via the introduction of information/information and communication technologies in institutions of vocational and technical education. The following methods were used in the research process: theoretical and substantive analysis of primary sources and advanced pedagogical experience, analysis, generalization and systematization of scientific approaches in the use of information and communication technologies in the educational process of vocational education institutions, as well as methods of modelling and designing the “Teacher’s Road Map”. It has been established that information/information and communication technologies in vocational education are an important area of activity of an educational institution and a tool for increasing the teacher’s productivity. It has been found out that the pedagogical terms contributing to the effectiveness of the use of information technologies in vocational education institutions are a high level of information culture of teachers and students; implementation of innovative technologies including information and communication pedagogical ones based on subject-subject interaction; ensuring the reflection of the subjects of the educational process, their capability of adequate self-assessment of their personality. A virtual road map for the teacher has been developed as an electronic public resource (for example, a website). The map shows all mandatory points and routes that a teacher should take during educational activities in a vocational education institution. The main directions in the teacher’s virtual road map which are consistent with the similar ones of the teacher’s activity in a real educational situation are defined. The virtual road map covers educational and methodical work, has a hyperlink to the plan of methodical work of the cycle commission for the year, a hyperlink to methodical manuals or guides on the organization work in a vocational education institution; provides teachers with system suggestions and hyperlinks to electronic educational resources; a hyperlink to cloud storage for downloading documents for advanced training courses or internships; certificates, diplomas regarding participation in scientific and methodical events and self-education; a hyperlink to the cloud storage for uploading planning documents and methodological developments for review and approval. The virtual road map is differentiated by subject area which allows for providing meaningful instructions to occupational safety teachers, road safety teachers, industrial training masters, and teachers of special disciplines. The result of the research is the development of a meaningful module “Digital technologies in education: cloud services, online platforms for the creation of an educational environment by the teacher and the improvement of the qualifications of the pedagogical staff of the vocational educational institution.” It has been concluded that due to the testing of the information educational environment model at the Kostyantyniv Higher Vocational School, the approaches to the internal management of the institution, the organization of methodical, educational and study work, the forms and methods of teaching the profession, the culture of designing theoretical and industrial training lessons, the system assessment of educational achievements of students, and ways of involving parents in the educational process at the institution have been enhanced.

How do bicyclists respond to vehicles with adaptive headlamp systems? A nighttime study in an immersive virtual environment

Introduction: The risk of motor vehicle-bicyclist crashes and fatalities is greater during nighttime than daytime lighting conditions, even though there are fewer cyclists on roadways at night. Vehicle Adaptive Headlamp Systems (AHS) aim to increase the visibility of bicyclists for drivers by directing a spotlight to illuminate bicyclists on or near the roadway. AHS technology also serves to alert bicyclists to the approaching vehicle by illuminating the road beneath the rider and by projecting a warning icon on the roadway. Method: Here, we examined how bicyclists respond to different AHS designs using a large screen, immersive virtual environment. Participants bicycled along a virtual road during nighttime lighting conditions and were overtaken by vehicles with and without an AHS system. The experiment included five treatment conditions with five different AHS designs. In each design a box of white light was projected beneath the rider; in four of the designs an icon was also projected on the road that varied in color (white or red) and position (to the left of the rider at midline or to the left of the front wheel). Participants in the control condition experienced only non-AHS vehicles. Results: We found that riders in all AHS treatment conditions moved significantly farther away from overtaking vehicles with AHS systems, whereas riders in the control condition did not significantly move away from overtaking vehicles without AHS systems. Practical Applications: The experiment demonstrates that AHS has potential to increase bicycling safety by influencing riders to steer away from overtaking vehicles.

An Electric Bus Battery Swapping Station Location Method Based on Global Optimized Peak Traffic Flow

The electric bus is an extremely important part of urban public transportation and has a huge impact on the ecosystem. However, the battery capacity is still a tough problem, and electric buses often face a booming demand for charging during peak periods. This paper focuses on the problem of electric bus battery swapping station (BSS) location. Based on the traffic flow assignment theory, this paper proposes a hybrid traffic assignment method based on GA and Frank–Wolfe algorithm, which has proved to be closer to the global optimum than the traditional method. This paper proposes a BSS selection model considering service quality as an evaluating indicator and a simulation is made based on a virtual road network. Compared with the traditional method, the result from the hybrid method is more suitable for electric buses when considering the situation at peak hours.

Driver drowsiness modeling based on spatial factors and electroencephalography using machine learning methods: A simulator study

Driver drowsiness is one of the leading causes of fatal road traffic accidents (RTA). While studies have illustrated the effectiveness of spatial criteria on driver drowsiness, the effects of these factors on drowsy driver behavior have yet to be mapped or spatially modeled properly. Meanwhile, driver behavior has been known as a major factor in the RTA occurrence. This paper investigated the effect of four spatial criteria of chainage, distance traveled after a horizontal curve, horizontal curvature, and slope on drowsy driver behavior in a simulated driving task. Accordingly, the drowsiness level on a monotone 108 km virtual expressway in northeastern Iran was determined by modeling the EEG data of 20 subjects. Four machine learning (ML) algorithms, namely bagged decision trees (BDT), multi-layer perceptron (MLP), random forest (RF), and support vector regression (SVR), were used to analyze electroencephalogram (EEG) signals and to estimate driver drowsiness based on spatial criteria. The results of the receiver operating characteristic curve (ROC) showed that BDT (0.925), RF (0.919), MLP (0.807), and SVR (0.706) had the highest accuracy in estimating drowsiness level on the virtual road using EEG signals, respectively. Using the estimated drowsiness level by BDT and spatial criteria values, ML algorithms were trained to map driver drowsiness on the Tehran-Mashhad road. Based on ROC results, MLP (0.921), BDT (0.918), RF (0.917), and SVR (0.896) had the most accurate performance in mapping driver drowsiness, respectively. Generated drowsiness level maps were validated, and the estimations of BDT and RF were more consistent with real drowsy driving accident black spots in the study area. In conclusion, mapping driver drowsiness by applying ML algorithms and spatial criteria detects high-risk areas and helps to improve road safety.

How children judge affordances when walking and bicycling across virtual roads: Does mode of locomotion matter?

This investigation examined whether the mode of locomotion matters in how 8-, 10-, 12-, and 14-year-old children (N = 91) judge dynamic affordances in a complex perception-action task with significant safety risks. The primarily European American children in the sample came from the area of Iowa City, Iowa and were balanced for gender. The same children crossed a single lane of continuous traffic on foot and on bike (order counterbalanced) in identical immersive virtual environments. We found that although 8-year-olds chose significantly larger gaps when crossing on bike than on foot, these gaps were not large enough to compensate for their delay in entering the gap and their slowness in crossing the road. As a result, they ended up with less time to spare when exiting the roadway on bike than on foot. In contrast, 14-year-olds exhibited no difference in their gap choices on bike than on foot, nor did they exhibit a difference in their timing of entry into the gap. However, they crossed the road much more quickly on bike, resulting in significantly more time to spare when crossing on bike than on foot. The 10- and 12-year-olds' performance fit neatly between that of the 8- and 14-year-olds. We conclude that as children gained better control over the bike with age, they were better able to match their gap decisions with their crossing movements such that bicycling afforded even safer road-crossing than walking for 14-year-olds. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Development of Monocular Vision based Autonomous Car

Abstract: The evolution of the artificial intelligence has served as the catalyst in the field of technology. We can now develop things which was once just an imagination. One of such creation is the birthof Autonomous cars. Days have come one can do their work even touching steering wheel, accelerator you will still be able to reach your target destination safely. Self-driving car (Autonomous cars) is a robotic vehicle that is designed to travel between destinations without human intervention. It is capable of sensing environment and navigate without human input. Autonomous cars must have control systems that are capable of analysing sensor data to distinguish between different cars on the road. The potential benefits of autonomous cars include reduced mobility and infrastructure costs, increased safety, increased mobility, increased customer satisfaction and reduced crime. Self-driving cars rely on software which needs to be thoroughly tested. Testing self-drivingcar software in real traffic is not only expensive but also dangerous and has already caused fatalities. Virtual tests, in which self-driving car software is tested in computer simulations, offer a more efficient and safer alternative compared to naturalistic field operational tests. However, creating suitable test scenarios is laborious and difficult. In this paper we combine procedural content generation, a technique commonly employed in modern video games, and search-based testing, a testing technique proven to be effective in many domains, in order to automatically create challenging virtual scenarios for testing self-driving car soft- ware. Our as Fault prototype implements this approach to generate virtual roads for testing lane keeping, one of the defining features of autonomous driving. Evaluation on two different self-driving car software systems demonstrates that as Fault can generate effective virtual road networks that succeed in revealing software failures, which manifest as cars departing their lane. Compared to random testing as Faultwas not only more efficient, but also caused up to twice as many lane departures

Turning at Intersections Using Virtual LiDAR Signals Obtained from a Segmentation Result

We implemented a novel visual navigation method for autonomous mobile robots, which is based on the results of semantic segmentation. The novelty of this method lies in its control strategy used for a robot during road-following: the robot moves toward a target point determined through semantic information. Previous implementations of the method sometimes failed to turn at an intersection owing to a fixed value of the turning angle. To address this issue, this study proposes a novel method for turning at an intersection using a control method based on a target point, which was originally developed for road-following. Here, an intersection is modeled as consisting of multiple straight roads. Evaluation using the CARLA simulator showed that the proposed method could accurately estimate the parameters representing a virtual road composing an intersection. In addition, run experiments conducted at the Ikuta Campus of Meiji University using an actual robot confirmed that the proposed method could appropriately make turns at intersections.

Effects of exposure to immersive computer-generated virtual nature and control environments on affect and cognition

Previous research has shown that exposure to immersive virtual nature environments is able to induce positive affective and physiological effects. However, research on the effects on cognitive performance is scarce. Additionally, the effects of virtual nature exposure compared to a virtual control environment with a comparable amount of virtual objects have not been examined so far. Therefore, we conducted an experiment with 27 participants to study the psychological effects of such exposure. The virtual nature consisted of a 3D model of a typical forest environment, whereas the control environment was an abstract replication of the virtual forest environment. In both environments, a virtual wooden cart was used to transport the users from the start to the end of the virtual road. The typical background noise of moving such a cart was integrated into both environments as well. In addition, the virtual nature environment included typical forest sounds in the background, whereas the control condition did not have such background sounds. Both environments were compared with regard to their effects on cognitive performance (using trail making tests (TMTA, TMTB, and TMTB-A) as well as digit span forward and digit span backward tests), perceived restorativeness, mood, stress, sense of presence, and simulator sickness. The results showed that in comparison to the control environment, exposure to the virtual nature resulted in significantly higher cognitive performance, higher perceived restorativeness, higher positive affect, higher sense of presence, lower perceived stress, and lower simulator sickness.

Performance Evaluation of Traffic Speed Deflectometer Based on Virtual Standard Test Road

It is challenging to find actual standard test roads (ASTRs) to evaluate the measuring performance of the Traffic Speed Deflectometer (TSD), leading to misjudgment of its evaluation results. To address this issue, a method to construct virtual standard test roads (VSTRs) using in-service roads is proposed. Firstly, the principle of TSD based on the Laser-Doppler effect to measure pavement deflection is introduced, and the shortcomings of traditional evaluation methods are reviewed and summarized. Secondly, an adaptive construction method for VSTRs is proposed based on the K-means clustering algorithm. It layers the Falling Weight Deflectometer (FWD) measurements on in-service roads and reconstructs them as virtual roads with stable deflections but different ranges. Thirdly, two new indexes, the coefficient of variation <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$CV_{k}$ </tex-math></inline-formula> and the influence coefficient of speed <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$ICS_{k}$ </tex-math></inline-formula> , are proposed to replace the traditional indexes, such as repeatability and correlation coefficient. Finally, the feasibility of evaluating TSD performance based on VSTRs and the new indexes is verified by experiments. Results show that the newly constructed VSTRs solve the problem that the ASTRs are challenging to obtain, reducing the experimental requirements for evaluating TSD, and the new indexes achieve an efficient and scientific evaluation of TSD.

Development of System for Collecting User-specified Training Data for Autonomous Driving Based on Virtual Road Environment

Development of System for Collecting User-specified Training Data for Autonomous Driving Based on Virtual Road Environment

From Human to Autonomous Driving: A Method to Identify and Draw Up the Driving Behaviour of Connected Autonomous Vehicles

The driving behaviour of Connected and Automated Vehicles (CAVs) may influence the final acceptance of this technology. Developing a driving style suitable for most people implies the evaluation of alternatives that must be validated. Intelligent Virtual Drivers (IVDs), whose behaviour is controlled by a program, can test different driving styles along a specific route. However, multiple combinations of IVD settings may lead to similar outcomes due to their high variability. The paper proposes a method to identify the IVD settings that can be used as a reference for a given route. The method is based on the cluster analysis of vehicular data produced by a group of IVDs with different settings driving along a virtual road scenario. Vehicular data are clustered to find IVDs representing a driving style to classify human drivers who previously drove on the same route with a driving simulator. The classification is based on the distances between the different vehicular signals calculated for the IVD and recorded for human drivers. The paper includes a case study showing the practical use of the method applied on an actual road circuit. The case study demonstrated that the proposed method allowed identifying three IVDs, among 29 simulated, which have been subsequently used as a reference to cluster 26 human driving styles. These representative IVDs, which ideally replicate the driving style of human drivers, can be used to support the development of CAVs control logic that better fits human expectations. A closing discussion about the flexibility of the method in terms of the different natures of data collection, allowed for depicting future applications and perspectives.

PG-Based Vehicle-In-the-Loop Simulation for System Development and Consistency Validation

The concern over safety features in autonomous vehicles is increasing due to the rapid development and increasing use of autonomous driving technology. The safety evaluations performed for an autonomous driving system cannot depend only on existing safety verification methods, due to the lack of scenario reproducibility and the dynamic characteristics of the vehicle. Vehicle-In-the-Loop Simulation (VILS) utilizes both real vehicles and virtual simulations for the driving environment to overcome these drawbacks and is a suitable candidate for ensuring reproducibility. However, there may be differences between the behavior of the vehicle in the VILS and vehicle tests due to the implementation level of the virtual environment. This study proposes a novel VILS system that displays consistency with the vehicle tests. The proposed VILS system comprises virtual road generation, synchronization, virtual traffic manager generation, and perception sensor modeling, and implements a virtual driving environment similar to the vehicle test environment. Additionally, the effectiveness of the proposed VILS system and its consistency with the vehicle test is demonstrated using various verification methods. The proposed VILS system can be applied to various speeds, road types, and surrounding environments.

Resilience assessment and enhancement of urban road networks subject to traffic accidents: a network-scale optimization strategy

This study is aimed at investigating the resilience degradation caused by traffic accidents and developing relevant resilience optimization strategies. A two-stage accident resilience triangle framework was proposed by comparing the differences between natural disasters and traffic accidents. To maximize system resilience, a network-wide traffic signal optimization model was presented. Spillback constraints and equilibrium constraints were established to enhance the capacity of urban-road networks to minimize congestion escalation, in addition to rapid recovery. A two-level algorithm based on greedy strategy and gradient descent was designed to solve the proposed non-linear programming model. In the experiment, a virtual road network was constructed based on the Simulation of Urban Mobility (SUMO) platform for validation and sensitivity analysis. The experimental results revealed that: (1) Compared to the traditional resilience framework, the proposed two-stage accident resilience framework can more reasonably describe the change mechanism of road network resilience under disturbance. (2) The proposed resilience-based traffic signal optimization model improved the system resilience under different conditions of traffic demand, accident severity, and rescue time in terms of the maximum performance degradation and recovery time. Precisely, the resilience loss is reduced by a maximum of 1.4%. Finally, the proposed model was further implemented with field data. The resilience improvement was significant during the evening rush hour. The results of this study contribute toward transportation resilience research and accident rescue strategies with respect to traffic management and control.

Over-speeding trend across self-reported driving aberrant behaviors: A simulator study.

The aim of the present study is to investigate the relation between self-reported aberrant behaviors as measured by using the Italian version of the Manchester Driver Behavior Questionnaire (DBQ) and actual driving performance during a virtual simulation, focusing particularly on over-speeding. Individual variables are considered based on participants’ behavior, and driving styles are derived from both the self-report questionnaire and the kinematic variables obtained through a moped simulator after the simulated driving task. The experiment was carried out on an Italian sample of 79 individuals aged between 18 and 35 who had to drive throughout virtual road environments. A cluster analysis of the kinematic variables provided by the simulator was used to individuate two different groups of drivers: 45 fell into the cluster named “Prudent” and 34 participants fell into the “Imprudent” cluster. The Prudent participants were characterized by lower acceleration, lower speed, better overall evaluations, and a smaller number of accidents. Correlations showed that self-report responses correlated positively with performance variables in terms of acceleration, speed, and over-speeding. Furthermore, the results from a MANOVA supported and complemented this evidence by emphasizing the usefulness of the integrated approach employed. Overall, these results reflect the suitability of experimental sample-splitting into two clusters, pointing out the appropriateness and relevance of self-report DBQ use with particular emphasis on Ordinary Violations and Lapses. The integrated use of the driving simulator and the self-report DBQ instrument with reference to driving behavior made it possible to support previous theoretical considerations regarding the relations between on-road aberrant behaviors and over-speeding behaviors. It also enabled the addition of evidence on the effectiveness of the simulator in detecting drivers’ actual performance. These results are relevant to allow the integration of useful information to expand intervention and training designs that can be used to reduce risky behavior and promote road safety.

Extracting interrelated information from road-related social media data

The social media data (SMD) have been viewed as a potential and promising information source of road conditions. However, most existing SMD-based sensing approaches (SMDSAs) either ignore interrelations among information items (e.g., name, direction, and status of the road) or rely on rigid grammar rules to establish entities’ interrelations. Additionally, current SMDSAs in the transportation domain are unable to link the extracted text-formatted information with domain-specific models (e.g., virtual road model, VRM). In order to fill such gaps, this work proposes an improved SMDSA of road conditions, which involves a three-stage (i.e., SMD classification, relation inference, and entity pair recognition) interrelated information extraction model, as well as a semantic converter to feed the SMD-provided text-formatted information into VRMs. The proposed SMDSA is demonstrated by the newly annotated datasets of tweets in Lexington, USA. The three-stage interrelated information extraction model outperforms conventional rule-based methods and deep-learning algorithms (e.g., Text CNN, Bi-LSTM, Piecewise CNN, and Capsule Net). The SMD-enabled VRM also preliminarily shows its capacity to optimize signal timings during incidents that change the road network topology. This work contributes to circumventing the reliance on human-made rules during SMDSAs’ development, bridging user-generated SMD with operable VRMs for potential real-world road management, and providing a standard tweet dataset annotated with interrelation triplets to help promote SMDSA studies.