Speed Violations Research Articles

Estimation of Vehicle Speed at Night Based on Rear Lights

All Speed violations are common things. One way to prevent it is detecting the vehicle and estimating the speed of vehicle that is running. To detect the vehicle, we can use the vehicle lights. The rear lights of the vehicle could be detected easier than the front lights of the vehicle because the color of the rear lights was different than the common lights on the road at night. This paper focuses on estimating the speed of a vehicle that runs at night based on the rear lights of a vehicle. The first step is calibrating the camera used and extracting the video into frames. Then we tracked the vehicle position in each frame in the Region of Interest (ROI). The next step is bounding and labelling each detected vehicle. After that, the conversion from the camera angle and the camera height become the actual distance in each frame. The final step is to find the movement of the vehicle from one frame to another using Euclidean distance to determine the speed of the vehicle. This research results in high estimation accuracy when in the ROI II area for the actual vehicle speed between 20-40 km/h, and for vehicle with actual speed between 40-60 km/h, the accuracy was high if it is in the ROI III area.

Assessing the Effectiveness of an Integrated Speed Management Plan on Highways

Manned speed enforcement has long been used as a safety measure to improve drivers' compliance with posted speed limits on highways. The sustainable presence of police squads at high-risk locations is key to the successful implementation of an enforcement program and is usually supported by other measures, such as educational campaigns, messages, and warnings. The goal of this study is to evaluate the effectiveness of an integrated speed management plan that is focused on manned traffic enforcement at three highway locations near the City of Leduc, Canada. Baseline speed data was collected and used to develop an enforcement deployment schedule. Following a public educational and engagement program, the enforcement plan was implemented. A detailed analysis was conducted for the speed data before, during, and after manned enforcement operations. To account for potential confounding factors, the evaluation method utilized a control site to correct for trends and other effects. The results showed that there was a statistically significant reduction in the average speed of vehicles that ranged from 1.14 to 8.96 km/h while the number of speed violations dropped by up to 25.5% at enforcement locations. Overall, the results from this study demonstrated that implementing an integrated speed management program, with manned enforcement at its core, has a high potential to improve safety by improving compliance, reducing the number of violations, and decreasing the average speeds on highways. The sustained manned enforcement is expected to increase drivers’ compliance with speed limits, which should eventually reduce collisions and improve safety. Keywords: Vehicular Speed, Manned Enforcement, Speed Violations, Safety Impacts, Speed Limit Compliance DOI: 10.7176/CER/13-1-04 Publication date: January 31 st 2021

Scalable and Accessible Crash Hot Spot Detection for Traffic Law Enforcement

Law enforcement agencies have begun utilizing traffic and crash data to improve traffic law enforcement delivery. However, many agencies often do not have the resources or expertise to harness fully the benefits this data offers. A free to use, scalable traffic crash hot spot detection tool was developed to aid law enforcement agency decision makers, statewide to the local municipality level. The tool was developed to identify crash hot spots algorithmically with a range of customizable parameters based on location, date and time, and crash factors, enabling quick, dynamic queries. These capabilities provide the ability for law enforcement agencies to conduct “what if” analyses and make data-driven allocation decisions, placing officer resources where they are most needed. The two-step algorithm first identifies potential hot spots based on crash density and then ranks each hot spot using a standardized z-score measure of relative significance. To test the viability of the tool, a pilot was conducted identifying 27 hot spots across Wisconsin where targeted enforcement was then deployed. Despite officer skepticism, results from the pilot found officers at sites targeted for speeding and seatbelt violations were nearly twice as likely to initiate traffic stops compared to non-targeted hot spots. Empirical Bayes before-and-after crash analyses found fatal and injury crashes reduced significantly by nearly 11% during the months with targeted enforcement, while property damage crashes and total crashes were unchanged. Overall, the results show the algorithm can identify hotspots where, coupled with targeted enforcement, traffic safety improvements can be made.

Local linear negative binomial nonparametric regression for predicting the number of speed violations on toll road: a theoretical discussion

In this paper, we describe a theoretical discussion about local linear negative binomial regression for predicting the number of speed violations on toll road. Data on the number of speed violations on toll roads is a count data. Count data is a non-negative integer data generated from continuous calculation process. We usually use Poisson regression to analyze count data of a response variable. But, one of infractions on Poisson regression assumption is over-dispersion. To overcome that over-dispersion we should use negative binomial nonparametric regression model approach. The negative binomial nonparametric regression model is a development of the negative binomial parametric regression model. In this research, we theoretically discuss estimation of negative binomial nonparametric regression model based on local linear estimator which is applied to data of the number of speed violations on toll roads. The estimation results of the negative binomial nonparametric regression model that we have obtained then can be used to predict the number of speed violations on toll roads so that the Ministry of Transportation together with the police can use it to take preventive measures.

ECAS: an efficient and conditional privacy preserving collision warning system in fog-based vehicular ad hoc networks

VANET is a prominent way to provide road security and prevent vehicles from collision by using various methods, such as message dissemination, traffic management, etc. However, the traditional vehicular network faces some problems related to high latency, low bandwidth, and communication in open wireless environment. Thus, some researchers have attempted to combine fog computing with vehicular ad hoc networks to overcome these problems. In this paper, we design an efficient and conditional privacy preserving collision warning system for fog-based vehicular ad hoc networks without using bilinear pairing. The fog nodes collect the speed violation reports from the speed sensor of vehicles. This protocol achieves privacy protection, message authentication, and revocate malicious vehicles. We also provide strict security proof and illustrate how to reach the security requirements in the proposed protocol. Moreover, the experiment demonstrates that the proposed protocol provides better efficiency in computation overhead and communication overhead, and makes it more applicable for adoption in the VANET collision warning systems.

Detection of the triple riding and speed violation on two-wheelers using deep learning algorithms

To curb the accident rate and traffic levels, strict implementation of the rules and continuous monitoring of the traffic is mandatory. Traffic Rule Violation Monitoring System ensures that the rules are followed strictly and it reduces the human effort. The main objective of this work is to identify the Triple Riding. To detect the triple riders, the deep learning framework darknet is used, which in turn uses a type of convolutional neural networks i.e. Deconvolutional neural network-based YOLO (You Only Look Once) algorithm for detection of the number of persons riding a bike, the system classifies the vehicle as to the rule-breach vehicle or not. The junctions acting as the data collections center, collects the data. The image of the vehicle classified as the rule-breach is stored along with the data such as vehicle manufacturing ID and vehicle speed transferred at the particular frame. The transfer of the data is facilitated using the GSM module and the NodeMCU deployed on the vehicle. The vehicle number will be verified with the transport office. To survive the lack of internet connectivity or low internet connectivity, the system is being equipped with the GSM module; else, the data related to the vehicle can be pulled by the development boards deployed at the junctions, acting them as the central part of the public internetwork deployed. This public internetwork acting the medium to pull the data from the vehicle to the central system. This is carried out using the concept of dynamic network configuration in NodeMCU. The use of Node MCU and the public network system makes the system much more viable, available and reliable. Thereby making the riders follow the rules properly and reducing irresponsible driving.

Motorcycling performance and sleepiness during an extended ride on a dynamic simulator: relationship with stress biomarkers

Objective: Powered two-wheelers (PTW) make up a large proportion of fatal accidents. The aim of this study was to investigate the effects of time-of-day and total sleep deprivation (SD) on simulated motorcycling performance during extended riding sessions (60 min), while evaluating stress mechanisms. Approach: A total of 16 healthy males participated in four simulated motorcycling sessions at 07:00, 11:00, 15:00 and 19:00, including city (8 min), country (2 min) and highway pathways (40 min), after a normal night of sleep and after total SD (30 h), in a randomized counterbalanced order. The recorded motorcycle parameters included: variation of lateral position, number of inappropriate line crossings (ILC), falls, riding errors, speed and speed limit violations. Subject parameters included the number of microsleeps in each pathway, the number of lapses during the 3-min psychomotor vigilance task (PVT—Brief version), and the Karolinska sleepiness scale (KSS) score. Saliva samples were used to assess cortisol (sC), α-amylase (sAA), and chromogranin-A (sCgA). ANOVAs and Pearson’s correlation analysis were performed between these variables. Main results: Most parameters were influenced by an interaction effect between ‘Motorcycling pathways’ × ‘SD’ (speed (p < 0.05), legal speed violations (p < 0.01), variation of lateral position (p < 0.001), falls (p < 0.001), EEG-microsleeps (p < 005)). An interaction effect between ‘SD’ × ‘Time-of-day’ influenced the number of ILCs (p < 0.01), sC (p < 0.05) and sCgA (p < 0.05) levels. SD affected KSS scores (p < 0.001) and PVT lapses (p < 0.05). The highest disturbances were associated with highway motorcycling simulation. Significance: Sleepiness due to circadian or SD and fatigue effects significantly affect riding and increase the risks involved with PTWs. The activation of both stress systems seems not sufficient to alleviate these deleterious effects

Geographical patterns and effects of human and mechanical factors on road traffic crashes in Nigeria

Road traffic crashes (RTCs) remain an issue of global concern particularly in Nigeria where over 80 percent of these crashes are accounted by human and mechanical factors (HM). Many geographic studies on RTCs, fatality and injury have largely focused on the spatial clustering and associative analysis of risk factors; with little known about the relative contribution of HM factors within the spatial context. Hence, this study applied a spatial approach to the effect of HM factors on RTCs, fatality and injury across the regions of Nigeria. The data for this study were obtained from the National Bureau of Statistics for the year 2017-2019 and were analysed using spatial analysis techniques such as Global Moran’s I and spatial regression model. There was evidence of geographic clustering of RTCs, fatality and injury in Nigeria. On one hand, route violation and sleeping on steering were significant predictors in southern Nigeria while tyre burst was significant at the national level, in the north and rural areas. The use of mobile phone while driving was a significant predictor at the national level. On the other hand, overloading was associated with RTCs and injuries at the national level and northern Nigeria. Speed violation was a prominent risk factor across the country. The study, based on observed regional differentials, argues that extant laws and regulations on traffic safety should be strictly enforced to curtail RTCs in the country.

Prevention of road accidents by means of automatic photo-video recording of traffic violations

The article deals with the main reasons of injuries and deaths in traffic accidents is exceeding the safe speed. The problems and consequences of speeding by drivers, owners (co-owners) of the vehicle in the field of road traffic are analyzed. The advantages and disadvantages of the introduction of automatic fixation of traffic violations in Ukraine are highlighted. The principle of automatic fixing, consideration of an administrative case and issuance of a resolution is considered. The issue of payment of fines by owners (co-owners) and persons who imported a vehicle into the territory of Ukraine is singled out. Reviewed the statistics of speed violations since the introduction of automatic fixation on the roads of Ukraine. There is a link between speeding and the number of serious accidents. Reviewed other preventive measures to reduce speeding violations. Analyzed the experience of other countries on bringing the owner of a vehicle for administrative offenses in the field of road safety to administrative responsibility. The introduction of automatic recording of administrative offenses (speeding) in the field of road safety can be an effective preventive measure to reduce the number of road accidents. Video recording allows you to: significantly reduce the number of accidents and people injured in them; to exclude a corruption component at fixing of offenses and registration of the corresponding administrative materials; to improve relations in the field of road safety and bring them closer to European standards. Among the reasons that hinder the effective use of modern technologies in monitoring compliance with transport legislation is the ambiguous understanding of legal presumptions and legal liability of vehicle owners. It can be predicted that this may motivate dishonest drivers to resort to illegal actions in order to avoid liability.

Recognition Method for Speed Limit Signs and its Applicability in Recognition of Vehicle Entry Prohibition Signs at Night

Recently, cameras with sensing technology have been used in vehicles to prevent traffic accidents. Although vehicle drivers can visually notice road signs, a recent study developed a recognition system for road signs to prevent traffic accidents. However, this system does not always perform the intended operation and encounters issues if the vehicle is driven at night, the road environment is bad, or the sign is discolored due to aging. Furthermore, the death rate due to speed violations at night is higher compared with that due to other traffic violations. The drivers and existing systems may have difficulties recognizing these signs as they are susceptible to other lights on the roads, such as traffic lights, streetlights, and vehicle headlights at night. Therefore, we propose a recognition method for speed limit signs that are difficult for drivers to recognize at night. In addition, the proposed method is applied to recognize vehicle entry prohibition signs at night. © 2020 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

Investigating on-road crash risk and traffic offences in Vietnam using the motorcycle rider behaviour questionnaire (MRBQ)

The factor structure of the Motorcycle Rider Behaviour Questionnaire (MRBQ) was investigated in Vietnam, a developing country with an extensive motorcycling culture. In addition, we examined which of the MRBQ factors, riding information and demographic variables predict motorists’ crash risks and traffic violations. Exploratory factor analysis (EFA) of the MRBQ revealed a clear four-factor structure of 36 items (N = 2.254 riders). This study highlights some critical differences between motorists from Vietnam and other countries. Vietnamese riders without a driver’s licence had lower on-road crash/near-crash rates, and the use of safety equipment paradoxically increased the incidence of crash risks. Furthermore, crash/near-crash liability and offences of Vietnamese motorcyclists rose with riding years (in terms of years already riding a motorcycle). The 36-item version of the MRBQ provided in this paper may be applied to other motorcycling countries. Besides, based on the robust relationships between the MRBQ factors and accident risks, new effective on-road safety strategies can focus on minimizing the common aberrant riding behaviours such as traffic errors, control errors, speed and alcohol-related violations in Vietnam.

A SMART APPLICATION FOR VEHICLE AUTOMATION DEALING WITH TRAFFIC REGULATIONS AND AIR POLLUTION FOR ENVIRONMENT

Transportation has evolved greatly in recent years. With the modern Technologies, the automobile industry has obtained new heights with the respect to speed, efficiency and security. Despite growth and improvement in technology there has been rise in the rate of accidents. The technology used in the proposed design is embedded system. It is used to access, process, store and control the data. Now-a-day’s traffic rules are frequently violated by the drivers and over speeding occur due to bad driving behaviour. It may not be possible to view the regulatory (sign) boards placed by the Road safety Department to alert the drivers in such kind of places and there is a chance for accident. The main objective of the Project is to design and develop a new system that can effectively detect speed violations on the road, and support the driver to obey traffic rules while driving by maintaining the speed of vehicle in accordance to the speed limit prescribed for the particular zone. The proposed system gives an alert with the help of voice module and LCD. The main objective of the project is to overcome the challenges which include accidents that occur due to lack of concentration, conversing over phone while driving and the pollution emitted by the vehicles.

Motor-vehicle drivers’ behavioral response to increased bicycle traffic

Introduction: This paper investigates whether motor-vehicle driver behavior changes when there are more bicycles on the road. Method: Data on trips on a rapidly expanding public bike share scheme in Chicago are combined with speed violations captured by a network of 79 cameras. Using weekly data from July 2014 to December 2016, violations at 26 sites where there was a considerable increase in bicycle traffic are compared with a control group of 53 locations where rental bicycles are not available. Results: An increase in rental bicycle usage is statistically related to a reduction in the number of speeding violations, with an estimated elasticity of −0.04. Conclusion: The increased presence of bicyclists makes at least some motorists drive more cautiously. Practical Application: This research provides some insight into the mechanism behind the observed reduction in crash rates as bicyclists become more numerous. Some motorists moderate their speeds allowing more time to avoid collisions and a reduction in the severity of the vehicle-bicyclist collisions that still occur.

Stealthy Monitoring and Control of Vehicle Safety System

In recent times, traffic rules are more often violated by the drivers. And, also driving at high speed needs to be controlled. So, stealthy monitoring and control of the vehicle is required. Sometimes it is not possible to see the boards and traffic symbols kept alongside of the road. The main objective of this paper is to design and develop an efficient system that can effectively detect speed violations on the road using RF technology. The proposed system gives an alert to the driver through LCD and voice boards. In the proposed system, if over speeding vehicles don’t get controlled manually, then the system turns ON and will get controlled automatically and detect any vehicle opposite to, utilizing the vehicle on-board unit. The challenges to overcome include GSM-based Accident/Panic alert using ARDUINO mega.

Factors affecting the severity of traffic accident injuries; a cross-sectional study based on the Haddon matrix

Background: Based on the Haddon matrix, several factors such as environment, host, and agent can affect the severity of traffic-related injuries. Objectives: This study aimed to determine the factors affecting the severity of injuries due to traffic accidents using the Haddon matrix. Methods: In this cross-sectional study, all traffic accident victims (n=2015) referred to Imam Khomeini Hospital in Urmia during 2015-2016 were selected by the census method. Demographic data of the patients and data regarding Haddon risk factors was collected and then analyzed by descriptive statistics, chi-square test, and multivariate logistic regression using the SPSS 18 software. Results: The mean age of the participants was 33.63±18.53 years. Most of the victims of traffic accidents were male (age range, 17-30 years) with severe and critical injuries. The most common mechanisms of injury were car-pedestrian collisions (27.9%), car overturning (31.1%), and collisions between two cars (26.3%). Violation of speed limit (73.2%) and violation of right-of-way (17.9%) were the most common moving violations. Additionally, the results of the multivariate analysis regarding vehicle safety devices showed that there was a significant relationship between the time and location of the accident and the severity of the injury (p <0.001). Conclusion: The findings of this study reveal that many factors, including using vehicle safety tools and time and location of the accidents have been implicated in the severity of accident-related injuries. Hence, appropriate planning and training and national and international measures can be helpful in reducing injury severity.

Coordinated Fuzzy-Based Low-Voltage Ride-Through Control for PMSG Wind Turbines and Energy Storage Systems

Coordinated control methods involving a wind turbine (WT) and an energy storage system (ESS) have been proposed to meet several objectives, such as smoothing wind power (WP) fluctuations, shaving peaks, enabling power scheduling, and allowing low-voltage ride-through (LVRT). LVRT requirement is defined by grid operators, and it should be satisfied whenever grid faults occur. Several methodologies have been proposed for the LVRT both with or without the use of an ESS. Furthermore, using an ESS is more advantageous for several WP applications. By using an ESS, WTs can be operated in a more economic and reliable way. However, the installation cost of an ESS is high and it has operation range constraints for charging and discharging. Moreover, the WT operation condition and ESS state-of-charge (SoC) can be different when a grid fault occurs. Therefore, it is necessary to coordinate both units, WT and ESS, for reliable and economic operation during a grid fault. Thus, we propose a coordinated fuzzy-based LVRT method that considers the different operation conditions of a WT and an ESS. From the proposed method, the effective reference powers of a WT and an ESS are evaluated by considering the rotor speed and SoC in the fuzzy control algorithm. The effectiveness of the proposed method is validated by considering two case studies on ESS SoC and WT rotor speed violations.

Implementation of Datalogger for Live Tracking of Vehicle Parameters Using Gsm-Gps for Security

The state-of-art methods designed for object tracking are instant-based. No previous data will be stored in these methods. In current scenario, continuous tracking of the vehicle is important in providing more security, but the amount of data and power utilization will be high that affects the cost of the system. Designing and development of data logger tool will meet the demand. The track record for Trip, Ignition, Engine Temperature, Distance Travelled, Fuel consumption, Speed Violations, Engine Break Down Conditions, etc. are maintained by sensing the vehicle along with the Geographical locations and get stored in the data logger. The stored data provides more security for the rider as well as dependents and also for government organizations like RTO, Traffic department, etc., This work concentrated on data acquisition and vehicle automation using the Designed Server and Mobile Application for tracking and control. The current location of the vehicle, the speed of the vehicle, ignition on/off status, etc., can be tracked.

Exploring the influence of traffic enforcement on speeding behavior on low-speed limit roads

Speeding on low-speed limit roads is a common traffic offense in China, which could be due to the mild traffic safety enforcement. The article aims to explicit the impact of traffic enforcement measures on the speeding behavior on low-speed limit roads. First, field data were collected to demonstrate the severity of speeding by investigating speed distribution; second, a virtual traffic enforcement was designed by considering three factors related to traffic enforcement, and a stated preference survey questionnaire including six scenarios was designed and implemented; finally, a series of generalized regret random minimization models were established to study the relationship of speeding behavior and traffic enforcement as well as drivers’ personal characteristics. From the stated preference survey analysis, the research figures out that other vehicles’ average speed is the most important reference to choose speed rather than traffic penalties, and the model estimation results show that speeding violation grows severe if traffic enforcements are lenient. Therefore, increasing the violation costs is a powerful means of lowering the probability of speeding for individual, thus proceeding the drop of vehicles’ average speed, and the fall of average speed will contribute to decrease speeding subsequently.

Quantitative causal analysis of mainline passenger train accidents in the United States

The need for shared freight and passenger rail corridors in the United States is increasing due to the growing demand for regional and intercity passenger transport. Several researches have been conducted on reducing the risk of freight train accidents, but little research has been done on the risk of passenger train accidents. The accident rates of passenger trains have declined in the past two decades; however, faster and more frequent passenger train services require even higher safety standards, and therefore further reduction to the risk of passenger train accidents is needed. The research presented in this paper analyzed the passenger train accidents in the United States using the Federal Railroad Administration train accident database to understand the trend of passenger train accident rates, the frequency and severity of different types of accidents, and to explore the major factors that cause them. Derailments and collisions were identified as the most significant types of passenger train accidents, and track failures and human factors, respectively, were the primary causes of those accidents. Accidents caused due to human factors and train operations such as train speed violations and failure to obey signals are often high-consequence accidents and therefore pose the greatest risk. Higher risk infrastructure-related factors include track geometry defects and broken rails or welds. This study on passenger train accidents provides a solid foundation for further research on improving the safety of passenger rail and shared-use rail corridors.

Incorporating car owner preferences for the introduction of economic incentives for speed limit enforcement

Human error including driving misbehavior contributes to over 90 percent of road vehicle accidents, and speeding is considered to be risky. Smart technologies, such as Connected Vehicle System (CVS) are among the interesting technical options to improve driving behavior, and Pay-As-You-Speed (PAYS) is an effective economic incentive to reduce speed violations. We investigated the acceptability of CVS with and without the presence of economic incentives, such as PAYS, in the context of a middle-income country: Iran. We used a Zero-Inflated Ordered Probit model (ZIOP) to estimate drivers’ willingness to pay for a CVS, and a hazard-based model for predicting the incentive level needed for accepting CVS via a PAYS scheme. ZIOP model indicated that drivers with the following characteristics were more likely to pay more for CVS: having a comprehensive insurance coverage, being younger than 60 years, owning more than one car, and having older vehicles. The hazard-based model also confirmed that drivers that speed relatively often have a lower tendency to adopt CVS, and drivers who experienced an accident in the past were more inclined to adopt CVS via PAYS. Also, drivers' opinion about CVS, vehicle characteristics, demographics, and driving experience influenced the effect of PAYS characteristics on acceptability of CVS. Finally, we offer recommendations for how to effectively implement CVS, in order to significantly reduce the high fatality and accident rates in middle-income countries such as Iran.