Traffic Offences Research Articles

Did Immigrant Arrest Rates Change During the Trump Administration? Evidence From California and Texas

Despite substantial legal and policy debates, whether immigrant arrest rates changed during the Trump presidency remains surprisingly understudied. This is partially because immigration status is rarely available in crime data. We address this gap by applying difference-in-differences (DD) and difference-in-difference-in-differences (DDD) estimations to detailed arrest data from Texas and California from 2015 to 2018. We find little evidence, descriptive or otherwise, to suggest that the transition from the Obama to the Trump administration had a meaningful impact on immigrant arrests, whether measured as violence, property, drug, or traffic offenses. These results suggest that the immigration enforcement initiatives under President Trump did not deliver on their crime reduction pledges, but they also provide little evidence of over-policing of immigrants in discretionary actions such as traffic arrests.

Subject of prevention of road traffic offences: concept, features, classification, practical significance

The article is devoted to consideration of a number of fundamental issues related to the subject of prevention of motor vehicle offences, its main features, classifications, and the system of subjects. It is argued that the essence of preventing criminal manifestations is to eliminate or limit their determinants - causes and conditions. The study confirms the conclusion that the driving force, the dominant cause is the human factor, consciousness, will, emotions, interests of offenders, which requires activities, measures, and means which, in combination, can have a real impact on road traffic. In this regard, the author defines the subject of accident prevention, the main feature (among others) of which is the ability and capacity to eliminate or limit the criminogenic effect of "vulnerable" links in the deterministic chain of these offences using its respective resource potential. It is proposed to create a new or activate existing structures. Similar bodies have been created in many countries around the world, which have achieved significant positive results on the path of professionalisation and specialisation of actors, moving towards Vision Zero.
 The author classifies subjects according to the criterion of the functional purpose of their main activity. According to this criterion, 4 groups of entities are distinguished: entities for which preventive activity in the criminological sense is: the main, one of the main, function within other types of activity, informal public duty. It turned out that the first group of subjects of prevention is not represented in Ukraine on a permanent or long-term basis.
 The author makes a number of proposals regarding the creation of the first group of subjects, and critical remarks are made regarding the statement that the country has a system of crime prevention and its manifestation such as road traffic offences with grave consequences. Such a system has not been built in Ukraine.

Alcohol Use and Traffic Offenses

Alcohol Use and Traffic Offenses

Investigating the relationship between self-reported (near) crashes, fined traffic offences, and risky riding behaviours among Flemish motorcyclists using the motorcycle rider behaviour questionnaire

Little research on motorcycling safety and rider behaviour has been performed in Belgium. This study used the Motorcycle Rider Behaviour Questionnaire (MRBQ) as part of a larger questionnaire on motorcyclist safety. Data was collected in Flanders (i.e., the Dutch speaking part of Belgium) through 560 motorcycle riders by means of a questionnaire that consisted out of the MRBQ, demographics, personal background, riding experience and exposure, vehicle information, and self-reported (near) crashes and fined offences. Results showed a factor structure consistent with previous studies, comprising the factors ‘traffic errors’, ‘speed violations’, ‘stunts’, ‘protective gear’, and ‘control errors’. Due to the limited number of crashes, only an effect on near-crashes and violations was observed for some of the MRBQ factors, as well as for the age and exposure of riders. This research indicated that the MRBQ has its merits in terms of both construct and predictive validity. Nonetheless, the MRBQ was found to be subject to limitations. The need for more research was raised to examine the potential presence and impact of a social desirability bias and common method variance. Additionally, Implications for practitioners and policy makers have been discussed.

Criminalization in Islamic Penal Code: A Study of Principles, Criminalization Methods, and Declining Variations

This study explores the principles and methods used by the al-Qur'an in criminalizing. The method used in this research is a library method with primary data obtained from the al-Qur'an and secondary data obtained from the tafsīr books, hadith ahkām, jinayāt books, and literature on criminal law, criminology, victimology, and relevant previous research findings. The result found that the principles used by the al-Qur'an in criminalizing an act are the principles of benefit, prevention, material legality, and transcendental principles. Two methods of criminalization are used: (1) the texts of the al-Qur'an and the Hadith itself, which state that this action must be punished; (2) determined in one of three ways. (a) al-Qur'an and Hadith stipulate that this act is hazardous for society; (b) based on common sense considerations that the act could disturb public order; and (c) the act constitutes a preliminary offense for a more significant and more dangerous offense. Furthermore, it is found that the variant of the ta'zīr offense includes offenses that are related to morality, sexual crimes, crimes against religion, crimes against the body, crimes against property, crimes related to family/descent, disturbing public order, environmental crime, fraud and forgery, and traffic offense.

Forgotten but not gone: A multi-state analysis of modern-day debt imprisonment.

In almost every state, courts can jail those who fail to pay fines, fees, and other court debts-even those resulting from traffic or other non-criminal violations. While debtors' prisons for private debts have been widely illegal in the United States for more than 150 years, the effect of courts aggressively pursuing unpaid fines and fees is that many Americans are nevertheless jailed for unpaid debts. However, heterogeneous, incomplete, and siloed records have made it difficult to understand the scope of debt imprisonment practices. We culled data from millions of records collected through hundreds of public records requests to county jails to produce a first-of-its-kind dataset documenting imprisonment for court debts in three U.S. states. Using these data, we present novel order-of-magnitude estimates of the prevalence of debt imprisonment, finding that between 2005 and 2018, around 38,000 residents of Texas and around 8,000 residents of Wisconsin were jailed each year for failure to pay (FTP), with the median individual spending one day in jail in both Texas and Wisconsin. Drawing on additional data on FTP warrants from Oklahoma, we also find that unpaid fines and fees leading to debt imprisonment most commonly come from traffic offenses, for which a typical Oklahoma court debtor owes around $250, or $500 if a warrant was issued for their arrest.

Learning With a Supervisor Who has Traffic Offences and Young Driver Crashes: The DRIVE Study 13-Year Follow-Up

PurposeYoung learner drivers commonly must record substantial supervised practice driving before independent licensure. Supervisory driver requirements can be limited or highly regulated, yet research is lacking on the effectiveness of different approaches. The current objective was to explore whether young drivers who were mostly supervised by someone who they perceived had traffic offences versus no offences had different crash records over a period of 13 years postlicensing. MethodsDRIVE is an Australian prospective cohort study of more than 20,000 drivers who were aged 17–24 years and newly licensed during 2003–2004. They completed detailed baseline questionnaires, including whether the person they identified as supervising their learner driving the most had perceived traffic offences in the past 12 months. Responses were linked to their state crash, hospitalization, and death records to 2016. A parametric survival model was created to calculate hazard ratios of time to crash for those reporting that their supervisor had 0 versus 1 and 0 versus 2+ perceived offences, adjusting for the participants' prior crash history and other covariates. ResultsAfter adjusting for covariates, 369 participants reporting supervisory drivers with 2+ perceived offences, compared to 15,451 participants reporting no such offences, had up to 1.67 (95% confidence interval 1.10–2.53 at 6 months) times the rate of any crash for the first 2.5 years and up to 2.01 (95% confidence interval 1.26–3.19 at 3.5 years) times the rate of crashes resulting in injury for 5.5 years. DiscussionAlthough overall supervision by a driver with two or more perceived offences was low, further attention is needed to ensure improved supervised driving experiences, with mentoring programs and professional instructor partnerships worthy of exploration.

A procedurally just flyer reduces subsequent speeding offences: evidence from the Queensland Speeding Engagement Trial (QSET)

Abstract Objectives The purpose of this study was to test the impact of a procedural justice letter intervention that was delivered with traffic infringement notices on subsequent speeding offences. Method This study used the Queensland Speeding Engagement Trial (QSET) to experimentally test the impact of using the principles of procedural justice within a police issued letter, on individuals detected speeding by cameras. Participants in the control condition (n = 7946) received a speeding infringement notice as per normal while those in the experimental condition (n = 8209) received the procedurally just letter in addition to the speeding infringement notice. Administrative data for the 12 months following the intervention was used to identify if participants engaged in subsequent speeding and other traffic offending behaviours. Results The results indicated that the intervention reduced subsequent speeding offences for drivers 25 years and older in the experimental condition when compared with the control condition. No differences were found for other types of traffic offences. Conclusions Thus, this study demonstrates that the effects of procedural justice (a) can be achieved through purposeful written communication and (b) reduce subsequent offending behaviour.

Improved GBS-YOLOv5 algorithm based on YOLOv5 applied to UAV intelligent traffic

As the road traffic situation becomes complex, the task of traffic management takes on an increasingly heavy load. The air-to-ground traffic administration network of drones has become an important tool to promote the high quality of traffic police work in many places. Drones can be used instead of a large number of human beings to perform daily tasks, as: traffic offense detection, daily crowd detection, etc. Drones are aerial operations and shoot small targets. So the detection accuracy of drones is less. To address the problem of low accuracy of Unmanned Aerial Vehicles (UAVs) in detecting small targets, we designed a more suitable algorithm for UAV detection and called GBS-YOLOv5. It was an improvement on the original YOLOv5 model. Firstly, in the default model, there was a problem of serious loss of small target information and insufficient utilization of shallow feature information as the depth of the feature extraction network deepened. We designed the efficient spatio-temporal interaction module to replace the residual network structure in the original network. The role of this module was to increase the network depth for feature extraction. Then, we added the spatial pyramid convolution module on top of YOLOv5. Its function was to mine small target information and act as a detection head for small size targets. Finally, to better preserve the detailed information of small targets in the shallow features, we proposed the shallow bottleneck. And the introduction of recursive gated convolution in the feature fusion section enabled better interaction of higher-order spatial semantic information. The GBS-YOLOv5 algorithm conducted experiments showing that the value of mAP@0.5 was 35.3% and the mAP@0.5:0.95 was 20.0%. Compared to the default YOLOv5 algorithm was boosted by 4.0% and 3.5%, respectively.

APPLYING MACHINE LEARNING AND OBJECT DETECTION FORIDENTIFYING TRAFFIC RULE VIOLATION: OVER SPEEDING

We have observed that traffic offences are manually inspected by the traffic department in many developing nations, such as India. Such systems enable traffic management and traffic law enforcement. Following this, the present study focuses on Traffic violations as one of the key issues hindering the efficient operations and safety aspects in the urban traffic area. Currently, traffic violations are controlled using enforcement measure that demands a high manual workforce, and resource which entails high economic cost. Contrary, the use of automatic traffic violation detection systems in many of the urban cities of the developed and developing countries is being implemented for a higher degree of traffic enforcement and adherence to the traffic operation rules. Following this the present study focus on the applicationof machine learning for automatic traffic violation detection for weak- lane disciplined mixed traffic conditions. The resulting exhibit that the developed model can detect traffic violations of over-speeding at an accuracy of 95%. The traditional methods like speed camera use the basic principle of the Doppler Effect and RADAR technologies but at present,Due to their drawbacks which vary from the expense of purchasing equipment, increase in work and labour costs, and lack of monitoring, they are ineffective. As a result, it is a straightforward, affordable, and practical speed detecting method in Mixed traffic conditions. So here, we prefer automatic speed violation detection. The suggested solution will be highly accurate and cost-efficient.

A Novel Approach for Detecting Non-Helmet Drivers and License Plate Number of Two-Wheeler

Abstract: There are a number of problems with Indian traffic laws at the moment that can be fixed with different ideas. Riding a motorbike or pouted without a defensive cap is a traffic offense that has extended the repeat of setbacks and fatalities in India. Cops should investigate the casing where the petty criminal offense is happening and focus in on the tag on the off chance that the rider isn't wearing a protective cap under the ongoing framework, which generally utilizes CCTV (Closed Circuit Television) film. In any case, this requires a lot of work and time because of the recurrence of criminal traffic offenses and the everyday expansion in cruiser riders.

An Automatic Traffic Violation Ticketing System Using Radio Frequency Identification (RFID) Technology

Travel convenience is impacted by a number of factors, including the condition of the road, traffic, length of trip, accidents, speed, etc. The daily increase in accident rates poses the biggest concern. These events not only result in fatalities but also increase the nation's financial losses. Traffic congestion is brought on by road users' lack of discipline and sentiments, which may result in traffic offenses. The goals of this research are to curb any traffic infractions as well as reduce corruption and nepotism on the road. This system was developed using an Atmega328 microcontroller, which instructs the system to identify any driver who violates a traffic light and send an SMS message to the violator and the road management of the violation and the location where it was done. This identification is done with the help of a radio frequency identification (RFID) reader, which gets the unique ID of the vehicle and sends back the information to the microcontroller, which now instructs the GSM module to send the SMS message.

Traffic compliance effect of more women behind the wheel: Pride or prejudice?

Driving offences can cause road accidents with serious outcomes and are a topic where differences in attitudes and behaviour can be found between sexes. While men are more prone to taking risks and, statistically, are involved in a greater number of traffic accidents, even today, sex stereotypes persist with women regarded as worse drivers. To explore the truth behind the myth, this study analyses how the increase in the number of females in the driver census in Spain, could impact the effectiveness of traffic law enforcement in terms of traffic offences recorded by the traffic police (including the main violations such as passive safety feature use, speed limits and drunk driving) while considering a set of control variables related to the quality of infrastructure, legal reforms and other socio-economic attributes. The results point to Spanish women paying closer attention to traffic regulations, behaving more carefully, which should be considered in designing specific preventive policies and road safety awareness strategies.

Detection and Identification of Non-Helmet Riders and Their License Plate Numbers

Abstract: Due to the ever-increasing number of vehicles a variety of traffic regulations are imposed and are addressed using various approaches. Two-wheelers are the most preferred and commonly used vehicles among the youth due to their cost. Bike riders are highly supposed to use helmets, but wearing helmets is often neglected by bike riders leading to accidents and deaths. Riding a bike without a helmet is a traffic offense. While riding a motorcycle/bike, the rider and as well as the pillion rider should wear a helmet according to the traffic rules of road safety. In the current system, traffic offenses are largely monitored by the traffic police by investigating CCTV records. The traffic police zoom into every frame of the CCTV record and tries to identify the license plates of non-helmet riders. This will take a lot of labor and time. The proposed system uses deep learning algorithms, CNN (convolutional neural networks) to analyze real-time video footage from cameras installed in key locations, such as roads and intersections, to identify individuals riding without helmets. The proposed system is expected to significantly reduce the number of non-helmet riding incidents and, therefore, decrease the number of fatalities and serious injuries resulting from motorcycle accidents.

The Military Origins of Penal Liability for Causing a Road Accident in Polish Penal Law, according to the Penal Code of the Polish Army of 1944

The aim of the article is to present the impact of military criminal law on the development of the rules of criminal liability for causing a road accident in Poland in the second half of the 20th century. The fundamental question that guides the considerations undertaken here is whether and, if so, how the military criminal law influenced the development of the principles of criminal liability for traffic offences. The analysis of legal provisions and jurisprudence showed that the post-war provisions of Polish military law were a source of inspiration for developing common criminal law, which applies to road crimes. The offence of causing an accident from art. 144 The Penal Code of the Polish Army of 1944 was implemented for the ordinary judiciary only in the Penal Code of 1969. Until then, there was legal chaos in the ordinary judiciary.

The perceived effectiveness of penalties in traffic offender and non-offender groups: the role of the Dark Triad and Behavior Inhibition/Activation Systems

Although the number of studies about the perception of traffic enforcement has increased, the importance of personality characteristics on these perceptions remain under-researched. Thus, this study aimed to investigate the relationship between the Dark Triad personality trait and the perceived effectiveness of traffic penalties for traffic offenders and non-offenders and to test whether BIS and BAS mediate this relationship. A convenience sample of 694 Lithuanian drivers participated in the study (55.3 percent males; aged 18-73 years). More than half (57.9 percent) of the drivers were non-offenders, while 42.1 percent reported having been sanctioned for at least one traffic offence in the last year. A scale containing ten specific sanctions for traffic offences was developed to measure the perceived effectiveness of traffic penalties. In addition, the Dirty Dozen and BIS/BAS scales were included in the questionnaire, together with a number of demographic questions. There was a weak negative correlation between the Dark Triad personality trait and the perceived effectiveness of traffic penalties. Non-offenders scored lower than traffic offenders on the BIS scale, but the perceived effectiveness of traffic penalties, the Dark Triad personality trait and the BAS scores did not differ between traffic offenders and non-offenders. The mediation analysis showed that BAS mediated the positive relationship between the Dark Triad personality trait and the perceived effectiveness of traffic penalties. However, BIS did not mediate this relationship. Therefore, the present study revealed two opposite mechanisms regarding the relationship the Dark Triad personality trait has with the perception of penalty effectiveness: a direct negative path and an indirect positive path via higher BAS.

Criminal Behaviour After Diagnosis of a Neurocognitive Disorder: A Nationwide Finnish Register Study

ObjectiveTo explore criminal behavior of individuals with Alzheimer's disease (AD), frontotemporal dementia (FTD), or Lewy body dementias (LBD) after the diagnosis. DesignNationwide register study. SettingInformation on diagnoses and criminality was received from Finnish registers. Crime types and incidences were compared between disorders and the general population. ParticipantsAll Finnish individuals diagnosed with AD, LBD, or FTD (n = 92 189) during 1998–2015. MeasurementsTypes of crimes and incidences, the standardized criminality ratio (SCR, number of actual crimes per number of expected crimes), numbers of observed cases, and person-years at risk counted in 5-year age groups and for both sexes and yearly. ResultsAmong men, at least one crime was committed by 2.8% of AD, 7.2% of FTD, and 4.8% of LBD patients. Among women, the corresponding figures were 0.4%, 2.0%, and 2.1%. The most frequent type of crime was traffic offence, followed by property crime. After age adjustment, the relative number of crimes between groups did not differ, except that men with FTD and LBD committed more crimes than those with AD. The SCR (95% CI) among men were 0.40 (0.38–0.42) in AD, 0.45 (0.33–0.60) in FTD, and 0.52 (0.48–0.56) in LBD. Among women, these were 0.34 (0.30–0.38), 0.68 (0.39–1.09), and 0.59 (0.51–0.68). ConclusionsThe diagnosis of a neurocognitive disorder does not increase criminal behavior, but rather reduces it by up to 50%. Differences in crime activity are present between different neurocognitive disorders and between the sexes.

How psychological factors affect speeding behavior: Analysis based on an extended theory of planned behavior in a Chinese sample

Speeding, the most frequent traffic offense, is a key contributor to road crashes. This study explores the psychological factors affecting drivers’ speeding behavior (exceeding the posted speed limit by more than 10%) based on the extended theory of planned behavior (TPB). The scale incorporating TPB constructs and additional variables (Type A personality, risk perception, and driving habit) was developed to collect empirical data, and 945 valid samples were obtained. The reliability and validity of the developed scales were verified, and a structural equation model of drivers’ speeding behavior was established to explore the causal relationship between speeding behavior and psychological characteristics and the interrelationships between TPB variables (speeding intention, attitude, subjective norm, and perceived behavioral control), explicit cognition predictors (risk perception and Type A personality) and implicit habitual factors (driving habit). The results showed that speeding intention and driving habit positively correlated with self-reported speeding behavior. In addition, attitude and risk perception positively correlated with speeding intention. Although Type A personality trait had no significant effects on speeding behaviors, it had significant effects on attitude and risk perception. In addition, the results of the mediation analysis demonstrated that speeding intention, driving habit, and attitude toward speeding are the most influential factors. Attitude and risk perception have a significant indirect effect on speeding behavior mediated by speeding intention. Drivers’ attitudes, driving habits, and risk perception played a mediating role in the relationships between Type A personality traits and speeding behavior. The findings yield insights into useful information and directions for designing road safety interventions, developing driver education and training programs, and strengthening social messaging aimed at the general public.

One and ahalf years of e-scooters in Hamburg

Since electric scooters were introduced as an urban means of transportation in Hamburg in June 2019, ahigh number of violations of the current laws regarding alcohol consumption by e‑scooter riders have been recorded. The aim of this study is to obtain an overview of traffic offences committed by e‑scooter drivers under the influence of alcohol, to classify their relevance in relation to other road user groups, and to draw afirst interim balance with respect to their frequency after 1.5 years. The data of all e‑scooter drivers (n = 342) examined concerning their blood alcohol values analyzed at the Institute of Legal Medicine of the University Medical Centre Hamburg-Eppendorf between 15.06.2019 and 31.12.2020 were retrospectively evaluated with respect to their demographic information and the medical examination results. These were brought into context with the total number of offences against the road traffic regulations with subsequent blood alcohol measurement. 9.6% of the total number of offences against the road traffic regulations in connection with subsequent determination of the blood alcohol concentration were committed by e‑scooter drivers. 87.7% of those examined were male. The blood alcohol concentration was above the limit of 1.10 ‰ for absolute driving incapacity when using apassenger car in 76.9% of those examined. An accumulation of cases was particularly noticeable at night and at weekends.Due to imprecise records, acertain number of unreported e‑scooter incidents can be assumed among the unspecified motor vehicles. As e‑scooter drivers make up aconsiderable proportion of drunken road users and the accidents mostly occur at night and at weekends, increased education and, if necessary, adriving ban at these times would seem to make sense.

DEVELOPMENT OF A DEEP LEARNING MODEL FOR PROFILING AND PREDICTING TRAFFIC OFFENDERS

Traffic violation has been a big challenge to mankind as many of the accidents recorded over years is as a result of traffic violation. The traffic violation monitoring systems deployed in developing countries lack adequate authentication of road users, no efficient and effective profiling system of offenders and offences, as such, traffic agents cannot generate instant traffic history of an offender. The monitoring of traffic offenders in developing countries has a lot of challenges including; lack of proper authentication of vehicles and users, lack of substantive traffic system that suits the management of traffic offenders’ profile in both rural and urban areas, lack of predictable modules to forecast the tendency of an offender to cause accident in the future, poor means of communication between traffic agencies and vehicle users, poor traffic offence awareness for vehicle users and lack of a dependable traffic offenders profile database. The area of traffic offender identification using fingerprint and NIN and predicting the possibility of the traffic offender committing more traffic offence in the nearest future has not be researched on and this forms the research gap this paper is set to cover. Therefore, this thesis is providing a solution by development of a deep learning model for profiling and predicting traffic offender’sfocuses on developing a traffic offenders profiling and prediction system using deep learning algorithm to predict the likelihood of an offence to be committed by a road user.The proposed system developed a model that will profile traffic offenders in both urban and rural settings, create a traffic offender’s database that will interact with existing national databases to authenticate traffic offenders, provides a module that will predict the likelihood of a road user to commit severe traffic blunder in the future and provide intelligent information necessary for timely action by law enforcement agencies. The system also createdan SMS based traffic awareness module that handles traffic offences communication between traffic agents and offenders. These designs are implemented using a websystem developed with PHP, MySQL and JavaScript. The System Design followed the OODM methodology for componentization of the system modules giving room for coupling, decoupling, modification, encapsulation and reuse, as well as easy maintainability. Unified Modeling Language was extensively used to simplify the explanation of the system modules. The software performance was tested using accuracy of traffic offender prediction and Confusion Matrix was adopted for the thesis. For the purpose of this thesis, dataset was collected from data-world (https:// data. world/ health data ny/qutr-irdf) and the data is an excel sheet dataset for Traffic offenders. The result obtained from the new system developed shows 95% accuracy of the deep learning technique for predict the likelihood of a road user to commit traffic blunder in the future.