Probability Of Traffic Accidents Research Articles

Analysis of the Relationship between Speed Consistency and Traffic Accident Probability (on the Mojokerto-Surabaya Toll Road)

Vehicle speed consistency plays a crucial role in maintaining traffic safety, with speed limits set to reduce accident fatalities. While the direct impact of speed limits on vehicle speed is minimal, their implementation significantly influences driver behavior, with about 85% of drivers complying with speed limits. However, variables such as traffic volume, weather conditions, and travel urgency often cause drivers to be inconsistent in adhering to these limits, increasing accident risk. On the Mojokerto-Surabaya toll road, data from IRSMS Korlantas Polri and Jasa Marga recorded 193 accidents with 101 fatalities from 2018 to August 2023, mostly caused by driver behavior such as excessive speed. This study analyzes the relationship between speed consistency and accident probability on this road, highlighting accident issues due to speed violations and inconsistency at ramp off/on locations. Results show the importance of toll road infrastructure considering vehicle volume, identifying accident blackspots, and implementing urgent speed management as key factors in improving safety on the Surabaya-Mojokerto toll road. The findings emphasize the need for better traffic management and speed regulation to reduce fatal accident risks.

Driver body condition monitoring system based on human-computer interaction

Tired driving is still a significant cause of traffic accidents, often causing huge economic losses to society.Therefore, how to avoid driver fatigue has become a meaningful problem. The purpose of this paper is to review and summarize various research methods of driver fatigue monitoring system at home and abroad, so as to reduce the probability of traffic accidents caused by fatigue driving. The main content of this paper is to review the existing fatigue driving monitoring technologies such as electrocardiogram (ECG), electroencephalogram (EEG), HRV, eye tracking system, CNN, facial state analysis, emotion recognition, 5G and brain wave recognition, and infer the fatigue level of drivers by using these technologies to extract various clues that usually represent the level of human vigilance. Assess cognitive load and identify emotional states to monitor tired driving. By discussing the advantages and disadvantages of various fatigue monitoring systems at home and abroad, this paper proposes an innovative method, which transforms the previous single-mode monitoring system into a multi-mode monitoring system, integrates multiple biosensors, extracts relevant features from the biosensor data and processes the data reasonably in order to understand the drivers physiological state more comprehensively. This method is expected to improve the accuracy of fatigue monitoring system.

Construction Technology of Warm Mix Asphalt Pavement in Cold High-Altitude Areas

With the continuous development of domestic highway construction, highway civil engineering and servicelevel quality have attracted much attention. Good pavement quality and high-quality service make people feel comfortable and smooth when traveling. High-quality pavement can significantly reduce the probability of traffic accidents. At present, there is a direct relationship between pavement quality and pavement construction operations. Carrying out pavement construction operations in cold high-altitude areas requires a reasonable selection of construction equipment and methods. The application of warm-mix asphalt pavement construction technology can ensure pavement quality. Therefore, this paper analyzes the advantages of warm-mix technology, the environmental characteristics of cold high-altitude areas, and construction preparations, and discusses the construction technology of warm-mix asphalt pavement in cold high-altitude areas in detail, to improve the overall road quality of cold high-altitude areas.

Beyond concept: The viability of exclusive lanes for zero emission vehicles on expressways

In response to climate change issues, various measures have been implemented to decarbonize transportation. One approach is to promote zero emission vehicles which can offer rapid and effective outcomes. Accordingly, exclusive lanes for zero emission vehicles on expressways have been pursued as a measure that can be implemented immediately with minimal detrimental impacts. To assess their economic viability, this study developed a methodological framework with a benefit-cost ratio for zero emission vehicle exclusive lanes, taking multiple traffic factors into account. Along with travel time savings, a safety performance function was proposed for traffic accident probability. A case study using diverse traffic datasets from the Republic of Korea revealed greater benefits from travel time savings than costs from congestion and accidents in all scenarios. The benefit-cost ratio ranged from 2.00 to 4.08, implying that operating these lanes can be an effective strategy for promoting zero emission vehicles, which address environmental issues.

Influence of Rubber Particles on Performance of Asphalt Mixture

The rubber material produced by waste tires greatly pollute the environment. Adding it to the asphalt mixture can improve its performance of the asphalt mixture. The elastic asphalt pavement paved with it has strong durability and can reduce the noise hazard to a certain extent. Adding large-diameter rubber particles into asphalt pavement materials can inhibit pavement icing and reduce the probability of traffic accidents caused by pavement skidding in winter. The results show that with the increase of the content of rubber particles, the ability of pavement to suppress ice formation is enhanced. However, when the temperature is below zero, with the decrease of temperature, the ice suppression effect of elastic material pavement is weakened; When the temperature is lower than -14 °C, the anti-icing performance of rubber particle asphalt pavement reaches the limit.

Intelligent interior atmosphere lamp system based on quantum dot LEDs for safe driving assistance.

A driver safety assisting system is essential to reduce the probability of traffic accidents. But most of the existing driver safety assisting systems are simple reminders that cannot improve the driver's driving status. This paper proposes a driver safety assisting system to reduce the driver's fatigue degree by the light with different wavelengths that affect people's moods. The system consists of a camera, an image processing chip, an algorithm processing chip, and an adjustment module based on quantum dot LEDs (QLEDs). Through this intelligent atmosphere lamp system, the experimental results show that blue light reduced the driver's fatigue degree when just turned on; but as time went on, the driver's fatigue degree rebounded rapidly. Meanwhile, red light prolonged the driver's awake time. Different from blue light alone, this effect can remain stable for a long time. Based on these observations, an algorith was designed to quantify the degree of fatigue and detect its rising trend. In the early stage, the red light is used to prolong the awake time and the blue light to suppress when the fatigue value increases, so as to maximize the awake driving time. The result showed that our device prolonged the awake driving time of the drivers by 1.95 times and reduced fatigue during driving: the quantitative value of fatigue degree generally decreased by about 0.2 times. In most experiments, the subjects were able to complete four hours of safe driving, which reached the maximum length of continuous driving at night allowed by China laws. In conclusion, our system changes the assisting system from a reminder to a helper, thus effectively reducing the driving risk.

Study on the mechanism of expanded graphite to improve the fading resistance of the non-asbestos organic composite braking materials

To improve the anti-fading performance and braking reliability of non-asbestos organic composite braking materials (NAOCBM) and to decrease vehicle driving risks due to braking failure, we studied the addition of expanded graphite (EG) to NAOCBM. The properties were optimized when the EG dose was 4 wt%. The reason for improvement was that the local flexible internal stress due to graphite expansion at high braking temperature partially offset the crack expansion, and thereby significantly decreased the damages of delamination wear to braking materials and braking performances. These results uncover the mechanism of EG addition to the performance optimization of braking materials, and provide an effective method to improve the reliability of braking materials and to reduce the probability of traffic accidents.

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.

Mental Health and Safety Assessment Methods of Bus Drivers

To explore the influence of the health psychology characteristics of bus driver on the probability of traffic accidents, such as the severity of unhealthy psychology and negative and impulsive personality. Combined with the demographic questionnaire, SCL-90 scale, and Y-G scale, the psychological factors of drivers causing traffic accidents were evaluated. The key factors selected by binary logistic regression analysis are used as node variables, and the Bayesian network structure was established by combining the K2 algorithm and expert knowledge. The EM algorithm was used for parameter learning. The work identified seven key factors that made bus drivers prone to accidents. The most likely factors were moderate depression, mild anxiety, and mild somatization. Bus drivers in the accident group were significantly more anxious, depressed, and more hypersensitive and emotionally unstable than drivers in the non-accident group. The psychological scale and a Bayesian network model were used to evaluate the mental health and traffic safety of bus drivers. It shows that different degrees of depression, anxiety, and different degrees of subjective and cyclical personality of bus drivers had different effects on traffic safety.

Development of Verification Device for Multitarget Radar Velocimeter Based on Echo Signal Simulation Technology

Speeding is one of the leading causes of traffic crashes worldwide. Radar velocimeter is widely used in the capture monitoring of road overspeed violations, which can effectively reduce the probability of traffic accidents and protect people’s life and property safety to the greatest extent. As a new type of radar velocimeter, multitarget radar velocimeter (MTRV) can monitor the speed of more than two vehicles at the same time. However, the verification method and device of MTRV’s performance need to be studied. In order to solve the problem of performance verification for MTRV, a verification device based on echo signal simulation technology is developed in this paper. The measurement mechanism of MTRV with different performance including velocity, distance, and angle is first introduced. Then, a verification method based on the echo signal simulation technology is proposed. The verification device can receive the emission signal of MTRV and process the signal by echo simulation technology, including target generation, Doppler frequency shift, time delay, and angel control, and targets are simulated with nominal velocity, distance, and angle value. The processed echo signal with simulated nominal parameter values is reflected to the MTRV. After the echo signal is received and processed by MTRV, the measurement values of simulated velocity, distance, and angle for targets are obtained. Comparing the measured values of the MTRV with the simulated nominal values of the verification device, the measurement error of MTRV is obtained. The verification device of MTRV is realized to verify the accuracy and reliability of the MTRV measurement results. The simulated velocity range of the verification device is up to (-300~300) km/h, and the simulated distance range of the verification device is up to (10~45) m when the simulated incident angle range was within the range of (-60~60)°. The simulation target generation for the two targets of the device is also verified. And the maximum permissible error (MPE) of the simulated velocity was ±0.05 km/h, the MPE of simulated distance is ±0.3 m, and the MPE of simulated angle is ±0.2°. Finally, the verification and uncertainty evaluation results of the MTRV sample validated the effectiveness and feasibility of the proposed verification method and the developed verification device of MTRV.

Vehicle Driving Risk Prediction Model by Reverse Artificial Intelligence Neural Network.

The popularity of private cars has brought great convenience to citizens' travel. However, the number of private cars in society is increasing yearly, and the traffic pressure on the road is also increasing. The number of traffic accidents is increasing yearly, and the vast majority are caused by small private cars. Therefore, it is necessary to improve the traffic safety awareness of drivers and help car manufacturers to design traffic risk prediction systems. The Backpropagation neural network (BPNN) algorithm is used as the technical basis, combined with the MATLAB operation program, to simulate the driving process of the car. Dynamic predictive models are built to predict and analyze vehicle safety risks. Multiple experiments found that: (1) in various simulations, the simulation driving process of MATLAB is more in line with the actual car driving process; (2) the error between BPNN and the actual driving prediction is within 0.4, which can meet the actual needs. Predictive models are optimized to deploy and predict in various traffic situations. The model can effectively prompt risk accidents, reduce the probability of traffic accidents, provide a certain degree of protection for the lives of drivers and passengers, and significantly improve the safety of traffic roads.

Recognising drivers’ mental fatigue based on EEG multi-dimensional feature selection and fusion

Detecting the mental state of a driver using electroencephalography (EEG) signals can reduce the probability of traffic accidents. However, EEG signals are unstable and nonlinear and fatigue detection based on one-dimensional features may provide insufficient information, resulting in low recognition efficiency. To resolve these challenges, this paper proposes an EEG-based multi-dimensional feature selection and fusion method to recognise mental fatigue in drivers. First, the EEG signals were decomposed into α, β and θ bands, and then the corresponding time domain, frequency domain and nonlinear features were generated respectively. Furthermore, a three-layer feature-selection method based on Logistic Regression, one-way Analysis of Variance and Recursive Feature Elimination (logistic-ARFE) was proposed to solve the feature redundancy. Logistic-ARFE is designed to automatically select the optimal subset of mental fatigue features. Principal component analysis was used to fuse the optimal feature subset from different dimensions to obtain the fusion feature at a cumulative contribution ratio of 90%, which was used as the final feature to express the recognition accuracy of eight conventional machine learning models. A publicly available EEG dataset for driver fatigue was used to validate the proposed method. The final results show that six of the eight models achieve high recognition accuracy, which indicates that the Logistic-ARFE feature selection algorithm has applicability widely. In particular, compared with other studies using the same dataset, the Gaussian SVM proposed in this study based on time–frequency-nonlinear fusion features achieves the highest recognition accuracy, which is improved by 6.32% and 6.11% respectively.

How People Perceive the Safety of Self-Driving Buses: A Quantitative Analysis Model of Perceived Safety

With the continuous development of automatic driving technology and advancements in related experimental research, the probability of traffic accidents caused by human factors has been greatly reduced. However, people are still cautious about the safety of automated driving technology. The purpose of this study was to investigate users’ perceived safety indicators and the psychological factors of their perceived safety judgment of self-driving buses. In this study, a structural model of the factors that influence self-driving buses, including behavioral intention of technology acceptance, trust theory, perceived risk, and perceived safety, was developed based on the technology acceptance model (TAM). Subsequently, a relevant survey of 215 respondents was conducted and analyzed using the partial least squares method. The results indicated that trust, perceived usefulness, and perceived ease of use were important factors for judging the perceived safety of self-driving buses. The structural model developed in this study can quantify and analyze user data to filter out the factors that influence the perceived safety of self-driving buses, which is conducive to improving people’s trust and acceptance of self-driving buses.

Effect of Periodic Technical Inspections of Vehicles on Traffic Accidents in the Slovak Republic

The article presents a search for links between the Periodic Technical Inspections of Vehicles (TI) and traffic accidents due to vehicle technical defects in the Slovak Republic. Based on these links, it is sought to assess the justification for TI regarding the road safety. For this purpose, statistical data on traffic accidents, caused by vehicle technical defects as well as data concerning TI carried out were examined. The first finding was that the approaching end of the validity of the TI, the probability of traffic accidents due to technical defects increases for vehicles. The second finding was that with the increasing number of vehicles assessed at the Periodical Technical Inspection Stations (PTI) as temporarily roadworthy and not roadworthy, the number of accidents due to vehicle technical defects was decreasing. The results formulated in this paper show that the TI have a measurable effect on traffic accidents caused by vehicle technical defects, thus positively affecting the road safety and thus having a demonstrable justification.

A grey convolutional neural network model for traffic flow prediction under traffic accidents

Accurate traffic flow prediction can effectively improve traffic efficiency and safety. This has become a trending topic in intelligent transportation systems. However, the occurrence of traffic accidents will cause great fluctuations of traffic flow in a short time, thus affecting the accuracy of flow prediction. This paper analyses the influence of traffic accident information on traffic flow, and proposes a grey convolutional neural network called G-CNN for traffic flow prediction. First, considering the small sample size of traffic accidents and the incomplete information description, the traffic flow and traffic speed change rate are defined to represent traffic accident grey information, and a grey fixed-weight clustering method for extracting the characteristics of traffic accident grey information is established. Second, the spatiotemporal characteristics of traffic flow are analysed, and a third-order tensor is developed to fuse them with the accident characteristics and serve as the input of the G-CNN. Then the G-CNN model is built to predict traffic flow in a traffic accident environment. The Canadian Whitemud Drive experiment on traffic flow without traffic accident information reveals that the developed G-CNN model has a better ability to predict future traffic flow with better accuracy and stability. Meanwhile, it is verified that the fluctuation of the traffic change rate will cause traffic accidents, and the probability of traffic accidents at a certain time point will be obtained through feature extraction of traffic accidents. Finally, the model is verified for the traffic flow of Hangzhou Viaduct in China, which demonstrates that the G-CNN model outperforms all of the compared models.

Reducing violation behaviors of pedestrians considering group interests of travelers at signalized crosswalk

Violating traffic signals behaviors of Pedestrians are common phenomenon. How to reduce or even eliminate these dangerous behaviors has attracted deep attention from researchers and governments. This paper proposes signal control algorithm considering dual-flow (SCACDF), which prioritizes the interests of travelers groups and aims to use real-time traffic data to predict future short-term traffic conditions, redistribute the green time for pedestrians and vehicles, timely meet the traffic demand of pedestrians and reduce the probability of traffic accidents. A typical signalized crosswalk is employed as a case study. Experiments, which are implemented in AnyLogic software, are conducted to investigate the impact of the proposed algorithm under different levels of traffic demand (pedestrian demand and vehicle demand) scenarios. The results show that compared with traditional timing signal control, the proposed algorithm could reduce pedestrian violation rates and improve traffic safety in multiple scenarios. Under low vehicle density, the pedestrian violation rate dropped respectively by 28.8% and 45.3% on average when the green splits ratio is 0.5 and 0.7. Especially when the green splits ratio is relatively large, the pedestrian violation rate drops from 22.6% to 14.3% under the proposed control algorithm. In most cases, the re-segmentation of the red-man phase under the proposed algorithm could have a certain negative impact on vehicle traffic per unit time. However, when the green splits ratio is 0.5, the algorithm even improves the total traffic volume per unit time including pedestrians and passengers. In addition, the factors that affect the proposed algorithm and the optimal scope of application are also briefly discussed.

Research on the Causes of Road Traffic Accidents Based on Cause and Effect Diagrams

To reduce the probability of traffic accidents and enhance the safety of traffic travel, this study aims to analyze the strength of the causal effect of the causes of traffic accidents. This study collected some road and social attribute feature data in California, used the Bayesian network to build a dependency graph, used the causal science to evaluate the causal effect between each attribute, then marked the weak correlation between attributes and ranked the importance. The final conclusion: Based on this data, the total local population has the strongest causal effect on causing traffic accidents, the Spanish account for the largest number of the local population, and the Principal Arterial added under MAP-21 has an impact on the average annual daily traffic (AADT) and annual average daily traffic of vehicles with 2 axles and 6 wheels and above (AADTT).

Development of a Method for Predicting the Probability of Traffic Accidents Using a Multimodal AI Model of Structured Data and Satellite Images

Development of a Method for Predicting the Probability of Traffic Accidents Using a Multimodal AI Model of Structured Data and Satellite Images

Design and implementation of intelligent monitoring system for public transport applications

With the rapid development of automobile technologies and great economic growth, cars have become more and more popular for their convenience. However, the increase in cars has caused serious air pollution and increased the probability of traffic accidents. This paper investigates the design and implementation of intelligent monitoring and early warning system, which can detect the traffic conditions and provide environmental monitoring data. To achieve a comprehensive monitor for both inside and outside the public transport, this system is based on the STC12C5A16S2 micro-controller and loaded with all kinds of sensors, such as alcohol sensors, temperature and humidity sensors, toxic gas sensors, photosensitive sensors and light sensors etc. If the system detects any abnormal situation, it will generate early warning signs in time to inform the driver and the traffic regulatory departments. Consequently, our intelligent monitoring system can protect the safety of passengers and reduce the incidence of traffic accidents.

Effectiveness evaluation method for traffic data acquisition based on vehicle-borne network

In order to reduce the probability of traffic accidents and enhance the safety of vehicle traffic, a method for evaluating the effectiveness of traffic data collection based on vehicle network is proposed. A virtual coil is set on the driving lane to detect vehicles through three features of texture change, foreground area and pixel movement within the coil. A traffic detector is introduced to collect traffic data for a long time. Based on the cognitive assessment theory, a comprehensive assessment index system for the effectiveness of traffic data collection is constructed to complete the assessment. The experimental results show that the evaluation time of this method is less than 18 s, and the evaluation energy consumption is lower than other methods by more than 20 J, which proves that the evaluation time of this method is shorter, the error is smaller and the energy consumption is lower.