Traffic Situation Research Articles

Broken into two – unusual internal injuries due to a road traffic accident

We present a case of a motorcyclist involved in a multi-vehicle motor vehicle accident (MVA), where he was rear-ended by a motorcar, collided head-on into a second motorcar and was subsequently flung off his motorcycle. There was no suggestion of a runover. At autopsy, the torso showed extensive brush abrasions with no open wounds. Internally, there were devastating injuries, with complete separation of the thoracic and abdominal viscera. Although the road traffic situation in Singapore has been improving with declining incidence of MVAs, motorcyclists continue to make up a disproportionate majority of injuries and fatalities. The spectrum of injuries encountered is vast, with injuries to the head and extremities most commonly described. Thoracic and intra-abdominal injuries can also occur, typically consisting of rib fractures and injuries to solid organs. This case highlights an unusual pattern of injury sustained in an MVA without runover and is postulated to be a result of rapid deceleration.

Using an immersive virtual reality bicycle simulator to evaluate hazard detection and anticipation of overt and covert traffic situations in young bicyclists

Using an immersive virtual reality bicycle simulator to evaluate hazard detection and anticipation of overt and covert traffic situations in young bicyclists

A Collaborative Monitoring Method for Traffic Situations under Urban Road Emergencies

The complex and diverse urban road traffic environments make it difficult to accurately assess road traffic situations. This paper proposes a collaborative monitoring method for urban road traffic situational assessment during emergency events. This method is applied to a monitoring network mapped by road geographic relations. When an emergency event is captured by a monitoring node in the network, road traffic situational awareness is completed by an activation function. Then, the Incidence matrix of the emergency event is constructed based on the node degree of this monitoring node. The collaborative node set and collaborative monitoring area are formed dynamically from this Incidence matrix. Finally, the AHP and EM combination weight calculation method based on Game Theory (GT-AHP-EM) is used to fuse the data of various information in the collaborative monitoring area to predict the current road traffic trend. The experiments show that the collaborative monitoring method can effectively assess road traffic conditions and enhance the accuracy of road traffic trend prediction.

Categorized review of drive simulators and driver behavior analysis focusing on ACT-R architecture in autonomous vehicles

Driving a vehicle in a safe manner is a highly specialized task that depends on the driver’s cognitive ability, skills, attention, fast processing, interpretation of traffic situations and rules, and above all, sound decision making. By driver, we imply either a human or an intelligent agent. In parallel to numerous yet fragmented studies that have addressed the individual effect of the above factors, there has been a continuous quest towards a unifying perspective to articulate the driving task within the framework of a cognitive architecture. Adaptive Control of the Thought—Rational (ACT-R) as a theory of human cognition is among the most promising and complete cognitive architectures that has been widely used to model human behavior and decision making. It is sensible to suggest that it can realistically be employed to model human behavior in driving, and by the same token, it can be configured as architecture for an autonomous driving system. In this paper, we present a directed review of drive simulators, and cognitive architectures, notably ACT-R architecture. Resources including but not limited to Google, Google Scholar, Crossref, Science Direct, and Mendeley are examined, compiled, and presented in this document. The results are classified into seven categories: (1) simulation environment; (2) review of ongoing drive simulators; (3) driving simulation data analysis; (4) driving performance analysis; (5) driving simulation validation; (6) driver behavior analysis; and (7) driving simulation experiments. The review could be beneficial to researchers and stakeholders in Advanced Driver Assistance Systems (ADAS) and Driverless cars.

A model study of low-frequency noise exposure indoors due to road traffic

Indoor low-frequency noise levels due to road traffic has been modelled for facade examples consisting of a lightweight steel facade, a concrete facade and two types of windows. Possible audibility of heavy vehicles passing by has been investigated as well as the dependence of the exposure level on driving speed and distance to road. The results show that pass-by events may be audible at low frequencies for cases complying with building standards and noise guideline values exemplified by Swedish regulation. Moreover, the A-weighted levels may be dominated by low frequency noise, and the frequency of occurrence of pass-by traffic noise events may be sufficiently high to create disturbance for typical traffic situations. Furthermore, it is shown that the contribution of pass-by events to the equivalent level indoors may increase when the driving speed is lowered.

A Data-Driven Method for Arrival Sequencing and Scheduling Problem

Decision support tools for arrival sequencing and scheduling could assist air traffic controllers in managing the arrival aircraft in terminal areas. However, one critical issue is that the current method for dealing with the arrival sequencing and scheduling problem does not consider the dynamic traffic situation and the human working experience, which results in a deviation between the scheduled and actual landing sequences. This paper develops a data-driven method to address this issue. Firstly, the random forest model is applied to predict the estimated time of arrival (ETA). During the ETA prediction, the trajectory, operation, and airport-related factors that could increase the prediction accuracy are considered. Secondly, the landing sequence is obtained by sorting the predicted ETAs. Thirdly, two optimization methods are proposed to generate the scheduled time of arrival (STA). The former uses the predicted ETAs as inputs and then directly optimizes the landing sequence and the STA. The latter uses both the predicted ETA and the landing sequence as inputs for further optimization. Finally, these proposed methods are evaluated with three sets of historical data on arrival operations at Changsha Huanghua International Airport (ZGHA). The results show that the RF-based ETA prediction method could improve scheduling performance. Moreover, the proposed optimization methods could provide controllers with a more appropriate decision advisory. Such advisories could simultaneously reduce the operation efficiency indicators (average/maximum delay or dwell time) and the operation complexity indicators (Kendall rank correlation or position shift).

New Parking Lot Selection Approach Based on the Multi-Criteria Decision Making (MCDM) Methods: Health Criteria

To find a parking space, valet parking drivers have to travel a lot, which leads to carbon dioxide (CO2) emissions. In order to reduce these emissions, it is essential to understand a user’s needs and criteria when searching for a parking space. Several selection criteria are considered when allocating a parking space. Recent research on parking space management mentions several parameters that have an impact on the choice of a parking space: namely, the traffic situation, the availability of each parking lot in question, and the cost of parking, etc. In this article, we discuss a new criterion: the physical condition of the driver in the management of parking spaces; the identification of the driver’s bodily fragility. We also propose MCDM as a parking space allocation model that best meets the cost–benefit convention. This reflection leads us to evaluate MCDM methods in the field of intelligent parking management. Therefore, we conducted a comparison between the most recent multi-criteria decision making methods used by researchers, namely, CODA, EDAS, TOPSIS, and WASPAS. The CRITIC method was used in this paper to objectively determine the weight of each criterion. A new approach is proposed to evaluate and select the best MCDM method. Indeed, we propose a method that computes the “average inter-item correlation SW”, a combination of the “average inter-item correlation” and the SW coefficient. This approach allows us to efficiently compute the correlation between a method and the set of methods while favoring the cells with the best ranking. A case study is presented to illustrate the MCDM approach to parking space allocation and evaluation. The proposed system provides drivers with services such as intelligent parking decisions, taking into account the human aspect while reducing energy consumption, driving time, and traffic congestion caused by searching for available parking spaces.

The statistical assessment of the traffic situation based on sample data of traffic accidents in the urban agglomeration

The article analyzes the accident rate on the roads at the entrance sections of the Belgorod agglomeration road network with the help of a documentary study of traffic accident records for the statistical assessment of the road traffic situation. It has been established that collisions and hitting a pedestrian are particular types of traffic accidents. The results of the analysis showed that Tuesday and Sunday are among the peak days of the week in terms of the probability of traffic accidents. Calculations based on mathematical statistics and probability theory have shown that there is a correlation between the number of accidents and the proposed characteristic hours of the day. To improve road safety, an information model was developed, an information model for reducing traffic accidents in places of concentration of traffic accidents.

A Multi-Head Self-Attention Transformer-Based Model for Traffic Situation Prediction in Terminal Areas

Terminal operations management is an important part of air traffic management. Accurately detecting and predicting the operational status of the terminal area can help formulate more appropriate and efficient management methods. To achieve more accurate results in predicting the traffic situation, a ConvTrans-TCN (Convolutional Transformer with Temporal Convolutional Network) model is proposed in this paper. The model first constructs the feature extraction part using the causal-convolution multi-head self-attention module. It can effectively model the long-term dependency in the sequence and match the local patterns of the sequence, and it enhances the performance of feature extraction. Then the TCN (Temporal Convolutional Network) module is used to build the information fusion part to complete the fusion of feature data. The TCN architecture can accurately learn long-term and short-term dependencies in time series, and it has sufficient memory. Finally, the situation prediction is obtained by a feedforward neural network. The experiment’s results prove that this model is feasible and it performs better than the common models such as LSTM, BP, which can help air traffic managers to identify the operational status of the terminal area and provide decision support.

HISS: A Pedestrian Trajectory Planning Framework Using Receding Horizon Optimization

The paper proposes a generative pedestrian trajectory modeling framework named HISS - Human Interactions in Shared Space. The trajectory modeling framework is based on a receding horizon optimization approach utilizing pedestrian behavior and interactions that seeks to capture pedestrian trajectory planning and execution. The benefit of the proposed dynamic optimization trajectory generation approach is that it requires minimal calibration data under a variety of traffic scenarios. In this paper, we formalize several pedestrian-pedestrian interaction scenarios and implement trajectories’ conflict avoidance through mixed integer linear programming (MILP). We validate the proposed framework on two benchmark datasets - DUT and TrajNet++. The paper shows that when the framework’s parameters are tuned to certain initial conditions and pedestrian behavior and interaction rules, the framework generates pedestrian trajectories similar to those observable in real-world scenarios, justifying the framework’s capability to provide explanations and solutions to various traffic situations. This feature makes the proposed framework useful for modelers and urban city planners in making policy decisions.

Bezpečnosť účastníkov cestnej premávky a ich vplyv na vnímanie dopravnej situácie

When analysing an accident scene, one of the significant tasks is to evaluate the driver's ability to perceive the traffic situation according to different aspects. Currently, there are various eye movement measurement systems designed to perform a high-quality reconstruction of the direction of eye movement of road users depending on time. In addition to the basic physiology of the perception of road users, the paper deals with using the Viewpointsystem® measurement system designed to analyse eye movement when perceiving the traffic situation. The article assesses the development of traffic accidents in the Slovak Republic according to the consequences of traffic accidents and categories of road users. As part of determining the decisive moment for a collision situation, an analysis of the direction of the eye movement of the driver of the vehicle during a dangerous traffic situation while driving on a road section located in a rural area is performed.

The view from driver’s seat Effects of driver’s agency and the first person perspective on responsibility attribution in traffic situations

The view from driver’s seat Effects of driver’s agency and the first person perspective on responsibility attribution in traffic situations

Simplified model of children-pedestrian crossing speed at signalized crosswalks

Road accidents are one of the main causes of child mortality, and research results show that children do not have an adequate perception of traffic risks and are prone to insecure and impulsive reactions in traffic situations. The data clearly show that educational and restrictive measures need to be complemented by engineering measures that shape pedestrian transport infrastructure (especially crosswalks) according to the needs of vulnerable traffic users. The influence of children's age, gender, group movement, supervision, distractions and the influence of traffic infrastructure has been widely studied.In articles previously published by the authors, on the basis of field research involving child pedestrians at zebra crosswalks in the vicinity of primary schools in two Croatian and one Italian city, several parameters were identified as significant for the development of the linear regression model for the speed of child pedestrians. Statistical analysis showed that the statistically significant parameters are related to the design of the conflict zone of the transport infrastructure, as well as traffic regulation. As the developed model is considered complex with regard to the number of parameters, a simplified model will be developed in the following of this article. In the first phase, a simplified model with exclusively infrastructural parameters will be developed that would allow a simple application in solving various engineering and planning problems in traffic infrastructure, and its accuracy will be tested. In subsequent phases, models including other main/simple parameters will be developed and tested and the models will be compared. The simplified models thus developed could be used in the early stages of planning by professionals to test or plan pedestrian traffic infrastructure or traffic signals in corridors that are regularly used by children on the basis of easily definable parameters - considering the infrastructure elements and basic data on children.

Application of distributed and decentralized technologies in the management of intelligent transport systems

Shifting focus from the field of distributed and decentralized technologies in the management of intelligent transportation systems (ITS), we now delve into the specific application of blockchain in transportation management. Blockchain is a fundamental component of distributed and decentralized technologies. The research paper discusses the utilization of blockchain technology in managing transportation systems through multi-agent systems. Specifically, the use of blockchain technology is examined in the context of the quick road system (QRS) in ITS to provide a service for obtaining a special fare status. This service aims to establish a decentralized network that facilitates real-time road lane sharing. The study indicates that depending on traffic situations, drivers can share their lane space with other vehicles traveling on the same route by exchanging incentives via blockchain with other private car owners, thereby allowing for faster travel for individuals in a hurry or those requiring priority access to fast lanes. The paper also addresses the increasing number of connected devices in ITS due to the development of the internet of things (IoT) technology. It highlights the importance of efficiently utilizing large datasets and identifies the internet of vehicles (IoV) as a crucial area of integration for existing IoT technologies to address smart traffic within multi-agent systems.

РАЗРАБОТКА МАТЕМАТИЧЕСКОЙ МОДЕЛИ УПРАВЛЕНИЯ ДВИЖЕНИЕМ ТРАНСПОРТНОГО ПОТОКА

The paper analyzes the statistics of traffic accidents in the urban agglomeration. Sections of the road network with repeated types of traffic accidents have been identified. The issues of development of the theory of modeling of transport flows are considered. A fuzzy inference model based on the basic data of the traffic situation has been developed. A rule base has been formulated that allows determining the speed of traffic flow depending on the parameters of the traffic flow and the traffic situation.

Mechanical properties of cellulose-fibre reinforced bituminous mix under various loading rates

This research aims to observe the effect of applying cellulose fibre to the mechanical properties of bituminous mixtures when subjected to various loading speeds that simulate various traffic situations with fibre dosages of 0.3 – 0.5% of mix weight. The semi-circular bending (SCB) test was employed in this project, where the specimens were subjected to loading speeds of 0.25, 0.5, and 1 mm/min. The incorporation of cellulose was reported to enhance the tensile strength of the specimens up to 43.75% at the lowest loading rate and 13.75% at the highest rate, while the critical strain energy was elevated to 24.54% at the lowest rate and 38.2% at the highest rate, signifying better resistance against loading. Meanwhile, the flexibility index (FI) of modified specimens showed a mixed trend compared to the control mix, 30.43% higher at the lowest rate and 28.9% lower at the highest rate. To sum up, the incorporation of cellulose fibre into the bituminous mixture can enhance its strength and resistance against various loading rates, and the fibre dosage of 0.3% is recommended to give the best outcome.

Mixed-Integer Motion Planning on German Roads Within the Apollo Driving Stack

Traffic situations with interacting participants pose difficulties for today’s autonomous vehicles to interpret situations and eventually achieve their own mission goal. Interactive planning approaches are promising solutions for solving such situations. However, most approaches are only assessed in simulation, as researchers lack the resources to operate an autonomous vehicle. Likewise, open-source stacks for autonomous driving, such as Apollo, provide competitive and resource-efficient state-of-the-art planning algorithms. However, promising planning concepts from research are usually not included within a reasonable time, possibly due to resource restrictions or technical limitations. Without evaluating these novel algorithms in reality, the benefits and shortcomings of proposed approaches cannot be thoroughly assessed. This work aims to contribute methodology and implementation to integrate a novel mixed-integer optimization-based planning algorithm in Apollo’s planning component and assess its performance and real-time capability in theory and practice. It discusses the necessary modifications of Apollo for deployment on a different vehicle and presents three real-world driving experiments on a public road alongside a detailed experience report. The driving experiments show a smooth trajectory tracking performance operating robustly under varying perception data quality and the real-time capability of the closed-loop system.

Оцінка ергономічної стійкості транспортного потоку на дільницях дорожньої мережі. Ідентифікація математичної моделі

The paper discusses the structure of the mathematical model featuring assessment of the ergonomic stability of the traffic flow in various sections of the road network under the influence of external disturbances, which is the result of structural identification. The mathematical model differs from the known ones in that it takes into account the dynamics of the process development. In addition to the gradients of speed and density of traffic flows, the research takes into account the dynamic properties of vehicles and the multi-lane road network, as well as the time of delays at pedestrian crossings and traffic lights. The dynamic properties of the traffic flow are described with a third-order differential equation. The mathematical model is parametrically identified; expressions for determining the gains and time constants included in the differential equation are obtained. The input parameters impacting and affecting the stability of the traffic flow are substantiated – these are the gradients of the density and speed of the flow. The parameters characterizing the response of the traffic flow to disturbances are substantiated – these are time constants, the physical meaning of which is the inertia of all links included in the model. The study presents expressions for determining the gains and time constants included in the differential equation. The gain coefficient K1 characterizes the degree of influence of the density of the traffic flow on the reaction time of the driver. The gain coefficient K2 characterizes the influence of the degree of dynamism of the traffic flow on the time of delays during movement and loss of stability. The gain coefficient K3 characterizes the degree of influence of a change in the traffic situation on the delay time when moving in the stream and loss of stability. The value of the time constant T1 characterizes the inertia of the driver depending on the density and intensity of the traffic flow. The value of the time constant T2 characterizes the inertia of the car and is expressed in the ability to maneuver. The value of the time constant T3 characterizes the inertia of changing the traffic situation.

DynaTrack: Machine Learning Aided Variable Speed Limit System

In the past decade or so, we have seen traffic congestion become a ginormous issue in the major cities of the world, the main reasons for the above being exponential growth in vehicle purchases, constant unplanned maintenance of the road networks, and so on. And as far as the current situation is concerned, it can be safely said that even though steps have been taken to counter the issue at hand, the improvement made has been close to nothing as the problem persists. The solution to this is being suggested is based on the concept of Variable Speed Limits, which says that the speed limits for the road segments be dependent on the traffic volume and situations on the road segment rather than being constant. The system would use the feed from CCTV cameras to keep a track of the flow variable, which lies within the low traffic limits, the speed limits would be increased accordingly. This would ensure convenience for travelers, reduce travel time, fuel consumption, and greenhouse gas emission, and will also improve economic gains, among other advantages.

Longitudinal Vehicle Motion Prediction in Urban Settings With Traffic Light Interaction

Predictive cruise control functions designed to reduce the energy consumption of intelligent and automated vehicles require an accurate prediction of the upcoming traffic situation in general and the preceding vehicle in particular. This article presents the implementation of prediction models which do not rely on explicit information of the entire preceding vehicle queue. Using this assumption, prediction algorithms based on Conditional Linear Gauss (CLG) models and Deep Neural Network (DNNs) were trained using real-world measurements in an urban setting. The training was conducted with 6.6 hours of driving data specifically collected for this study. The results show that both approaches can provide accurate estimates of a preceding vehicle’s motion. For the CLG models, a queue-estimation logic was implemented to improve the prediction accuracy. The most accurate prediction could be obtained when DNNs are set up with Long Short-Term Memory (LSTM) layers, capable of modeling time dependencies. The results also show that accurate short and mid term predictions of a vehicle’s trajectory can be made based primarily on sensor data, even when no or only little data from vehicle-to-vehicle (V2V) communication is available.