Traffic Authorities Research Articles

VEHICLE BLACK BOX IMPLEMENTATION FOR INTERNET OF VEHICLES BASED LONG RANGE TECHNOLOGY

The vehicle Black Box system records variations in vehicles in order to minimize and analyze accidents; or records traffic rules violations automatically and reports them to the traffic authority system. In many countries (like Iraq) traffic violation recording implemented manually causing false traffic fine recordings, even when the vehicle is not at the same location at traffic fine recording, so it is necessary to develop such systems that confirm the availability of the vehicle in that location during violation occurrence. In this paper, a Vehicle Black Box system based 433MHz long-range wireless modulation technology developed for safe driving issues by utilizing gas and flames sensors, and for false traffic violation recording issues by proofing the availability of the vehicle at traffic violation location by utilizing a GPS module. The system-based Internet of Vehicles model transmits the acquired data, where two nodes representing vehicles used with one base station form Vehicle-to-Infrastructure communication mode. The system was tested in real-time and the sensed data was transmitted and stored in a database for future analyzing purposes where the acquired GPS data was used as proof of the vehicle’s correct location with date and time stamps. Two vehicle data where captured and stored indicating their correct location with date and time stamps and stored in a database at the base station. The measured Received Signal Strength indicator and Signal-to-Noise ratio values are stored for analyzing purposes. The performance of the system in such Internet of Vehicle environment is discussed according to the readings.

Determinants of self-efficacy of driving behavior among young adults in the UAE: Impact of gender, culture, and varying environmental conditions in a simulated environment

Research on traffic accidents have acknowledged that human error is the leading cause of road accidents around the world. In the UAE, those aged between 18 and 30 years are involved in the most accidents. As a result, this study examines the perception, attitude and driving behavior of young adults in the UAE. Virtual Reality (VR) was used to examine driving behavior because it offers alternatives to assess driving behavior with a high degree of immersive experience in a safe and replicable environment. Participants drove through a virtual environment that resembled the urban environment of Abu Dhabi in the UAE, which included six traffic events. A sample of 12 females and 27 males also completed a pre and post-simulation questionnaire to report and evaluate their personal driving experience in Abu Dhabi. The volunteer group represented young drivers with limited driving experience and diverse cultural backgrounds. Results indicated that male drivers were less adhering to safe driving behavior compared to females. Even though both males and females exceeded the designated speed limit, males traveled longer distances over the limit. Additionally, it was found that young drivers tend to overestimate their skills with factors like gender, cultural background, and driving experience being key contributors. The results indicate that traffic authorities should take into consideration different approaches in the formulation of policies related to young drivers with periodic reassessment of skills and training to enhance the safety of driving in the UAE and the region.

AUTOMATIC TRAFFIC SIGNAL CONTROLLER USING ARDUINO

Traffic congestion and accidents caused by more than speeding vehicles have been a major cause for anxiety in societies due to their negative effects such as stress to commuters, release of more toxic fumes into the atmosphere, accidents and loss of productive hours.The enhancement of Traffic Light Controller utilizing ultrasonic sensor and microcontroller. The Paper is designed for structuring a thickness based dynamic traffic signal framework where the planning of signal will change as a result on detecting the traffic density at any road junction. This paper offers an move toward for handling traffic congestion and over speeding offenders. This system uses infrared sensors as counters to measure the traffic density in each lane. The infrared sensors are also for speed detection with a Bluetooth module and Bluetooth serial monitor. These sensors are interfaced with the traffic light through a micro controller. The signal timing changes automatically on sensing the traffic density at the junction. The microcontroller used in this project is ARDUINO. In instance of any vehicle in emergency condition or on the other hand in emergency like VVIPs, a SMS is send to Traffic Control Authority, who has the control of microcontroller empowers microcontroller. The implementation of the prototype design show that traffic control and speed detection using infrared sensors and Arduino Atmega 2560 give a better performance and the time taken to clear traffic at an intersection reduced significantly with 60% time saved

Exploring spatiotemporal patterns and influencing factors of ridesourcing and traditional taxi usage using geographically and temporally weighted regression method

ABSTRACT The rivalry between ridesourcing and the traditional taxi has posed great challenges to traffic management authorities. Understanding the spatial patterns and influencing factors of their usage can help traffic authorities develop insightful policies and strategies to coordinate the operations of the two services better. This study develops a novel geographically and temporally weighted regression model (GTWR) to unravel the spatiotemporal patterns and influencing factors of the two services based on a high-resolution GPS dataset. The developed GTWR model achieves greater performance than other traditional methods. The results reveal that the spatiotemporal impacts of influencing factors on the usage of ridesourcing are quite different from that of traditional taxi. The spatiotemporal distribution and evolution of the coefficients are further discussed. The findings of the study could help traffic management authorities develop efficient regulatory policies to enhance the operations of the two services in specific areas and periods.

Primena Cube IQ softvera i modela višekriterijumske optimizacije za izbor vozila za transport robe u Vojsci Srbije

Introduction/purpose: An adequate selection of vehicles used for the transport of goods is a very important factor that affects the economical and rational use of vehicle fleets, as well as the quality and efficiency of carrying out transport activities in the Serbian Armed Forces. The goal of this work is to design a model that should be of help to the traffic service authorities to select the vehicle that is best for the performance of the assigned transport task based on the defined criteria. Methods: This paper therefore proposes a model for the selection of vehicles for the transport of goods using a fuzzy logic system, as a type of artificial intelligence system. In order to solve the problem of choosing a vehicle for the transport of goods, five criteria are defined in the work based on a survey of the commanders of the transport lines, which represent the input values in the fuzzy logic system.The vehicle is selected based on five criteria. The input variables are represented by three membership functions, while the output variable is defined by five membership functions. All the rules in the fuzzy logic system are determined using the rule premise weight aggregation method (ATPP), which enables the formation of a rule base based on experience. By applying this method and based on the number of input variables and the number of their membership functions, a base of 243 rules was defined. The values of the weighting coefficients of the membership functions were determined using the LMAW method. A user "interface" program was created for the developed fuzzy logic system, which enables the practical application of this model. Results: The model was tested on the example of choosing the optimal vehicle for goods transported to the IVP "Pasuljanske livade" in 2020. The selection of the optimal means of transport was made among the transport motor vehicles that are most used in the Serbian Army, namely: TAM 150 T11, FAP 2026 and FAP 1118. After packing all three vehicles with these goods in Cube IQ and after performing calculation and evaluation of individual vehicles in the user "interface" program, the values of the output variable for each vehicle were obtained. The obtained values for each vehicle were ranked and the optimal vehicle for the transport of defined goods was shown to be the FAP 1118. Conclusion: The significance of this study is that it is among the first ones to demonstrate the application of a model based on artificial intelligence that solves the problem of vehicle selection for the transportation of movable assets. The study provides considerable opportunity for further research.

A Quantitative Approach to Road Safety in Morocco: Reducing Accidents through Predictive Modeling

This paper uses machine learning to predict road accidents in Morocco, a country marked by high annual accident rates. Our model employs data such as weather, time of day, and road conditions, derived from historical accidents and environmental records. Findings suggest that such predictive modeling can enable traffic authorities to anticipate high-risk situations and enact pre-emptive safety measures, contributing to significant reductions in road accidents. This study provides a data-driven approach towards policy implementation for road safety, with insights applicable to global road safety initiatives.

Analysis of Agglomerate Fog Meteorological Characteristics in Anhui Province Based on Traffic Accident Data

Using the data of five agglomerate-fog-related traffic accidents during 2014–2017 and fog-related expressway accidents during 2013–2018 assessed by the Anhui provincial traffic authorities and corresponding meteorological observation data, this article analyzes the spatio-temporal characteristics, geographical environment and weather background of agglomerate-fog-related accidents in detail and identifies the characteristics of changes in terms of visibility, humidity, temperature and wind when agglomerate fog occurs. Furthermore, an agglomerate fog forecast index has been established in Anhui Province to provide a reference basis for the agglomerate fog forecast, early warning and prevention.

Automatic Recognition of Road Damage Based on Lightweight Attentional Convolutional Neural Network.

An efficient road damage detection system can reduce the risk of road defects to motorists and road maintenance costs to traffic management authorities, for which a lightweight end-to-end road damage detection network is proposed in this paper, aiming at fast and automatic accurate identification and classification of multiple types of road damage. The proposed technique consists of a backbone network based on a combination of lightweight feature detection modules constituted with a multi-scale feature fusion network, which is more beneficial for target identification and classification at different distances and angles than other studies. An embedded lightweight attention module was also developed that can enhance feature information by assigning weights to multi-scale convolutional kernels to improve detection accuracy with fewer parameters. The proposed model generally has higher performance and fewer parameters than other representative models. According to our practice tests, it can identify many types of road damage based on the images captured by vehicle cameras and meet the real-time detection required when piggybacking on mobile systems.

Analysis of the Accident Propensity of Chinese Bus Drivers: The Influence of Poor Driving Records and Demographic Factors

Previous studies have shown that bus drivers are a major contributing factor to bus accidents. The aim of this study is to explore the factors that contribute to the presence of accident propensity among bus drivers, as well as the relative importance of each influencing factor and the mechanism of influence. To this end, a C5.0 decision tree model was developed to determine the relative importance as well as rank the importance of the impact of poor driving records and demographic factors on accident propensity, and a binary logistic regression model was developed to analyze the relationship between accident propensity and the different values of each essential influencing factor. Based on our results, we found that: (1) the number of violations had the most significant effect on bus drivers’ accident propensity, followed by age, driving age, and number of alarms; (2) violations and alarms are positively related to bus driver accident propensity; age and driving age are inversely related to bus driver accident propensity; and (3) men have a higher accident risk probability than women. This study’s findings will help bus companies and traffic management authorities to implement more targeted improvements to their bus driver management programs.

Development of black ice prediction model using GIS-based multi-sensor model validation

Abstract. Fog, freezing rain, and snow (melt) quickly condense on road surfaces, forming black ice that is difficult to identify and causes major accidents on highways. As a countermeasure to prevent icing car accidents, it is necessary to predict the amount and location of black ice. This study advanced previous models through machine learning and multi-sensor-verified results. Using spatial (hill shade, river system, bridge, and highway) and meteorological (air temperature, cloudiness, vapour pressure, wind speed, precipitation, snow cover, specific heat, latent heat, and solar radiation energy) data from the study area (Suncheon–Wanju Highway in Gurye-gun, Jeollanam-do, South Korea), the amount and location of black ice were modelled based on system dynamics to predict black ice and then simulated with a geographic information system in units of square metres. The intermediate factors calculated as input factors were road temperature and road moisture, modelled using a deep neural network (DNN) and numerical methods. Considering the results of the DNN, the root mean square error was improved by 148.6 % and reliability by 11.43 % compared to a previous study (linear regression). Based on the model results, multiple sensors were buried at four selected points in the study area. The model was compared with sensor data and verified with the upper-tailed test (with a significance level of 0.05) and fast Fourier transform (freezing does not occur when frequency = 0.00001 Hz). Results of the verified simulation can provide valuable data for government agencies like road traffic authorities to prevent traffic accidents caused by black ice.

A Pilot Study on Impact of Mood State on Emergency Response Capacity for Young Novice Drivers

As young novice drivers are inclined to getting involved in traffic accidents due to their improper emergency response under sorts of gender affective state, namely, mood, widely generated in fast-paced urban life, it is of great necessary to study the impact of mood state on responsive capacity for young novice drivers. Fourteen college students were recruited to take part in complex reaction experiments for this pilot study. Each subject’s mood was collected through a simplified POMS scale, while their complex reaction time (CRT) and response error rate (RER) were acquired during the experiments. The study results showed that young novice drivers’ RER was significantly positively correlated (pc=0.323 ∗ ∗ , “pc” omitted next) with their score of total mood disturbance (TMD), and a logarithmic regression model was feasible to describe the correlations with a good fitting effect. Further, their RER was also significantly positively correlated with score of negative mood state components such as nervousness (0.290 ∗ ∗ ), anger (0.300 ∗ ∗ ), fatigue (0.278 ∗ ∗ ), depression (0.287 ∗ ∗ ), and fluster (0.261 ∗ ), and a quadratic or cubic regression model was suitable to describe the correlations. Additionally, the young novice drivers’ CRT was significantly positively correlated with score of nervousness (0.222 ∗ ∗ ), vigorousness (0.227 ∗ ), and fluster (0.273 ∗ ), and a quadratic or exponential regression model was suitable to describe the correlations. The results can provide theoretical support for developing targeted intervention to improve young novice drivers’ emergency response capacity for driving training or traffic management authorities.

Feasibility of Economic Forecasting Model Based on Intelligent Algorithm of Smart City

Smart cities make better use of space and have less traffic, cleaner air, and more efficient municipal services, improving people’s quality of life. The vast number of vehicles continually seeking to reach crowded spots in smart cities complicates acquiring a public parking space. It presents challenges for both traffic and residents. With such vast populations, road congestion is a serious challenge. It wastes vital resources such as fuel, money, and, most importantly, time. Finding a good location to park is one of the reasons for traffic congestion on the highway. This paper proposes a deep learning-based economic forecasting model (DL-EFM) for long-term economic growth in smart cities. Traffic management is vital for cities to guarantee that people and products can move freely across the city. Many automobiles attempting to reach crowded areas in smart cities make getting a public parking place difficult. It is inconvenient for both drivers and residents. Different traffic management authorities have implemented an artificial neural network (ANN) to resolve the issue, and modern vehicle systems have been coupled with intelligent parking solutions. The experimental outcome of the deep learning-based economic forecasting model improves traffic estimation, accuracy prediction in traffic flow, traffic management, and smart parking when compared to existing methods.

Network space analysis–based identification of road traffic accident hotspots: a case study

This study proposed a method for identifying traffic accident (TA) hotspots. The method combines kernel density estimation (KDE) with network kernel density estimation (NKDE). The hotspots identified through NKDE can be overlaid on the high-risk areas (hot zones) identified using planar KDE, resulting in accurate hotspot identification. The research site was Zhenjiang City in Jiangsu Province, China; data on 410 fatal traffic accidents from 2017 to 2020 were collected. An average nearest neighbour (ANN) ratio (0.563) was obtained using the global auto nearest neighbour distance method; thus, the fatal TAs had a cluster-type distribution in the city. Subsequently, the maximum clustering distance (7812.842591 m) in the research site was analysed using Ripley’s K-function, which yielded a reasonable KDE bandwidth and enabled identifying the distribution of hot zones for fatal TAs. Precise hotspot identification was achieved by overlaying the results of NKDE-based analysis in these hot zones. The proposed method was verified using data on 131 fatal TAs during January to July 2021. The results revealed that, in Zhenjiang City, the identification rate for hot zones and hotspots in hot zones was 71.75% and 38.29%, respectively, and the overall hotspot identification rate was 27.48%, demonstrating the method’s ability to relatively accurately identify hotspot locations, information that can help traffic authorities implement preventive measures.

Research on Application of Naive Bayes Algorithm Based on Attribute Correlation to Unmanned Driving Ethical Dilemma

At present, unmanned driving technology has made great progress, while those research on its related ethical issues, laws, and traffic regulations are relatively lagging. In particular, it is still a problem how unmanned vehicles make a decision when they encounter ethical dilemmas where traffic collision is inevitable. So it must hinder the application and development of unmanned driving technology. Firstly, 1048575 survey data collected by Moral Machine online experiment platform is analyzed to calculate the prior probability that the straight being protector or sacrificer in ethical dilemmas with single feature. Then, 116 multifeature ethical dilemmas are designed and surveyed. The collected survey data are analyzed to determine decision-making for these ethical dilemmas by adopting the majority principle and to calculate correlation coefficient between attributes, then an improved Naive Bayes algorithm based on attribute correlation (ACNB) is established to solve the problem of unmanned driving decision in multifeature ethical dilemmas. Furthermore, these ethical dilemmas are used to test and verify traditional NB, ADOE, WADOE, CFWNB, and ACNB, respectively. According to the posterior probability that the straight being protector or sacrificer in those ethical dilemmas, classification and decision are made in these ethical dilemmas. Then, the decisions based on these algorithms are compared with human decisions to judge whether these decisions are right. The test results show that ACNB and CFWNB are more consistent with human decisions than other algorithms, and ACNB is more conductive to improve unmanned vehicle’s decision robustness than NB. Therefore, applying ACNB to unmanned vehicles has a good role, which will provide a new research point for unmanned driving ethical decision and a few references for formulating and updating traffic laws and regulations related to unmanned driving technology for traffic regulation authorities.

Fast and Precise: Parallel Processing of Vehicle Traffic Videos Using Big Data Analytics

Cities worldwide use camera systems that collect and store large amounts of images, which are used to study vehicle traffic conditions, facilitating traffic management authorities’ decision-making. Typically, the inspection of those images is performed manually, which prevents extracting relevant information in a timely manner. There is a lack of platforms to collect and analyze key data from traffic videos in an automatic and speedy way. Computer vision can be used in combination with parallel distributed systems to provide city authorities tools for automatic and fast processing of stored videos to determine the most significant driving patterns that cause traffic accidents while allowing to measure the traffic density. We use a Convolutional Neural Network (CNN) to detect vehicles captured by traffic cameras, which are then tracked using an algorithm that we designed, based on multi-tracking Kalman filters. To speed up analysis, we propose a low-cost distributed infrastructure based on Hadoop and Spark frameworks for data processing: videos are equally divided and distributed to multicore CPU nodes for analysis. However, splitting up videos could generate inaccuracies in vehicle counting, which were avoided through the use of an algorithm that we present in this work. We found that it is possible to rapidly determine traffic densities, identify dangerous driving maneuvers, and detect accidents with high accuracy by using low-cost commodity cluster computing. There is a lack of computing platforms to collect and analyze key data from traffic videos in an automatic and speedy way. Computer vision can be used in combination with parallel distributed systems to provide city authorities tools for automatic and fast processing of stored videos to determine the most significant driving patterns that cause traffic accidents while allowing to measure the traffic density. This study explores the integration of different tools such as parallel data processing, deep learning, and probabilistic models. We present an approach based on Convolutional Neural Network (CNN) and Kalman filters to detect and track vehicles captured by traffic cameras. To speed up analysis, we propose and evaluate a low-cost distributed infrastructure based on Hadoop and Spark frameworks and comprised of multicore CPU nodes for data processing. Finally, we present an algorithm to allow vehicle counting while avoiding inaccuracies generated when videos are split to be distributed for analysis. We found that it is possible to rapidly determine traffic densities, identify dangerous driving maneuvers, and detect accidents with high accuracy by using low-cost commodity cluster computing.

Characterization of Pedestrian Crossing Spatial Violations and Safety Impact Analysis in Advance Right-Turn Lane.

In view of the pedestrian space violation in an advance right-turn lane, the pedestrian crossing paths are divided by collecting the temporal and spatial information of pedestrians and motor vehicles, and the characteristics of different pedestrian crossing behaviors are studied. Combined with the time and speed indicators of conflict severity, the K-means method is used to divide the level of conflict severity. A multivariate ordered logistic regression model of the severity of pedestrian–vehicle conflict was constructed to quantify the effects of different factors on the severity of the pedestrian–vehicle conflict. The study of 1388 pedestrians and the resulting pedestrian–vehicle conflicts found that the type of spatial violation has a significant impact on pedestrian crossing behavior and safety. The average crossing speed and acceleration variation values of spatially violated pedestrians were significantly higher than those of other pedestrians; there is a significant increase in the severity of pedestrian–vehicle conflicts in areas close to the oncoming traffic; the average percentage of pedestrian–vehicle conflicts due to spatial violations increased by 12%, and the percentage of serious conflicts due to each type of spatial violation increased from 18% to 87%, 74%, 30%, and 63%, respectively, compared with those of non-violated pedestrians. In addition, the decrease in the number of lanes and the increase in speed and vehicle reach all lead to an increase in the severity of pedestrian–vehicle conflicts. The results of the study will help traffic authorities to take measures to ensure pedestrian crossing safety.

Traffic costs of air pollution: the effect of PM2.5 on traffic violation.

Although emerging studies have investigated the effect of air pollution on traffic crashes, it is unclear to scholars whether air pollution affects another road safety problem-traffic violations. To address this gap, the current paper constructs a data set from 1,390,221 traffic violation records of 640,971 drivers from the Wuhan Traffic Management Bureau between January 2018 and December 2018. An ordered logistic regression was conducted to verify our hypotheses. The result shows that PM2.5 has no overall impact on the severity of traffic violations, but each 1% increase in the daily concentration of PM2.5 leads to a 1.02-fold increase in the odds of serious inexperience-related violations and a 0.99-fold decrease in the odds of serious overconfidence-related violations. This effect is the strongest in PM2.5, followed by NO2, and has not been observed in CO and O3. In addition, robustness tests indicate that the relationship between air pollution and traffic violations is consistent among the different subsets (e.g., clear weather, no rain and snow, and good visibility). We also provide valuable practical advice for drivers and traffic authorities.

Dynamic Lightweight Mechanism for Security and Performance in Internet of Things

Communication between machines has become commonplace in recent years, and the Internet of Things (IoT) is gaining notoriety. Congestion control is a prevalent issue in the transportation system, and GPS devices, which drivers and traffic authorities alike utilise, are well-suited to the job. Traffic density is expanding at an alarming rate, making it impossible for drivers to see and handle all of the possible scenarios in a traffic area they are ready to enter. It's also becoming increasingly difficult to maintain security and integrity due to a growing number of assaults by hackers that send harmful code or bogus packets. There are a few new technologies that have yet to be put into practise under the Internet of Things umbrella, including ubiquitous computing (IoT).

Analysis of Current E-Scooter Safety Regulation in a Large U.S. City Using Epidemiological Components as a Framework

E-scooters have been promoted as a solution to environmental and last-mile issues. However, physicians have reported an unusual increase in injured e-scooter riders, calling it an “epidemic.” An epidemiological investigation of e-scooter safety carried out in 2018 characterized injuries associated with e-scooter riders in Austin, TX for a period of 87 days. However, the effectiveness of the city’s current regulation on micromobility devices has not been discussed to determine whether it addresses the risk factors identified in this epidemiological investigation. To address this gap, this paper presents an analysis of the legal framework on micromobility devices from the components of the epidemiology triangle (agent, host, and environment) used by traffic authorities in the 1960s to curtail motor vehicle crashes. This analysis revealed that the current micromobility regulation does not offer practical provisions to tackle safety concerns efficiently. Furthermore, based on the number of motor vehicle crashes reported in Austin for 2018, it was found that if e-scooter riders were to travel the same distance in miles as motor vehicle drivers, there would be approximately 3,700,000 injuries compared with 15,133 motor vehicles injuries. Furthermore, this hypothetical figure does not consider the potential underreporting of e-scooter crashes, meaning that the number of injured people per 100,000 mi traveled on e-scooters could be even higher. At least 108 more injured riders were identified than was initially calculated in the 2018 epidemiological investigation. Overall, this study should encourage local authorities around the world to enact appropriate regulations to tackle safety issues surrounding micromobility devices.

In Situ Observations of Wind Turbines Wakes with Unmanned Aerial Vehicle BOREAL within the MOMEMTA Project

The MOMENTA project combines in situ and remote sensing observations, wind tunnel experiments, and numerical modeling to improve the knowledge of wake structure in wind farms in order to model its impact on the wind turbines and to optimize wind farm layout. In this context, we present the results of a first campaign conducted with a BOREAL unmanned aerial vehicle (UAV) designed to measure the three wind components with a horizontal resolution as fine as 3 m. The observations were performed at a wind farm where six turbines were installed. Despite the strong restrictions imposed by air traffic control authorities, we were able to document the wake area of two turbines during two flights in April 2021. The flight patterns consisted of horizontal racetracks with various orientations performed at different distances from the wind turbines; thus, horizontal wind speed fields were built in which the wind reduction area in the wake is clearly displayed. On a specific day, we observed an overspeed area between the individual wakes of two wind turbines, likely resulting from the cumulative effect of the wakes generated behind two successive rows of turbines. This study demonstrates the potential of BOREAL to document turbine wakes.