Speed Violations Research Articles

Forklift Design Integrated Speed Limiter and Position Tracking Through IoT-Based Website

Forklifts, as lifting and transportation equipment, play a crucial role in logistics. However, forklift operators often chase targets, pushing the forklift to perform at higher speeds. Operating a forklift at high speeds not only poses potential dangers to the working conditions but can also lead to a decrease in forklift performance. Operators do not ensure the forklift is running according to procedures due to a lack of supervision. In response to this issue, the author proposes an innovation in the form of a monitoring device capable of recording speed violations committed by operators. With the planned device, it is expected that forklift operators will exercise more caution and take greater responsibility when operating the forklift. The objective of this research is to reduce the likelihood of accidents and forklift damage due to improper use. The study produces a forklift design equipped with supporting components for the implementation of the device and a website as a monitoring platform for forklift operators and workers, providing real-time access to collected data. This system enhances operational safety in operating forklifts. The research significantly contributes to the development of safer and smarter forklift technology, aligning with strict demands for safety standards and risk management in industrial environments.

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

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

Exploring the potential of combining Mel spectrograms with neural networks optimized by the modified crayfish optimization algorithm for acoustic speed violation identification

Enforcing vehicle speed limits is paramount for road safety. This paper pioneers an innovative approach by synergizing signal processing and Convolutional Neural Networks (CNNs) to detect speeding violations, addressing a critical aspect of traffic management. While traditional methods have shown efficacy, the potential synergy of signal processing and AI techniques remains largely unexplored. We bridge this gap by harnessing Mel spectrograms extracted from vehicle recordings, representing intricate audio features. These spectrograms serve as inputs to a tailored CNN architecture, meticulously designed for pattern recognition in speeding-related audio cues. An altered variant of the crayfish optimization algorithm (COA) was employed to tune the CNN model. Our methodology aims to discriminate between normal driving sounds and instances of speed limit breaches. Notably absent from previous literature, our fusion method yields promising initial results, demonstrating its potential to accurately identify speeding violations. This contribution not only enhances traffic safety and management but also provides a pioneering framework for integrating signal processing and AI techniques in innovative ways, with implications extending to broader audio analysis domains.

Traffic crash prediction in Kano, Nigeria: multivariate long short-term memory method

Accurate traffic crash prediction is crucial for implementing effective road safety measures. In this study, the performance of long short-term memory (LSTM) and multivariate LSTM (MLSTM) models in forecasting total crash count data in Kano state, Nigeria were compared. Human and vehicle factors, including speed violations, tyre bursts, brake failures, sign/light violations and phone use while driving, were incorporated as covariates in the MLSTM model. An autoregressive integrated moving average with exogenous variables (Arimax) model was used to investigate the effects of the covariates. The MLSTM model outperformed both the basic LSTM model and individual covariate models, emphasising the synergistic effect of considering a broad range of factors. The Arimax model results revealed that speed violation is significantly positively correlated with total crashes; the other covariates showed positive correlations but did not reach the statistical significance. The findings underscore the importance of a multivariate approach in enhancing traffic crash prediction. The MLSTM model's superior performance highlights the value of considering a comprehensive range of factors that influence crash occurrence to achieve more accurate predictions. Practical applications of these models could involve leveraging them for proactive traffic safety measures, including increased enforcement of traffic rules, improvements to road infrastructure and targeted driver education and campaigns.

Which factors are related to crossing speed and road violations? A virtual reality study on young people’s crossing behavior.

In the literature, numerous studies examined pedestrian behavior within road environments, aiming to contribute to injury prevention. Nevertheless, significant gaps remain, particularly concerning the behavior of young individuals. The present Virtual Reality experiment aimed at two main objectives: (1) understanding the factors associated with the likelihood of young people violating traffic laws and (2) identifying factors influencing their crossing speed, who faced various pedestrian crossing scenarios designed to induce violations are administered to 63 participants aged 11-17. Three main factors are considered in the analysis: socio-economic characteristics, travel patterns, and behavioral tendencies while walking. The results reveal that students who commute on foot and those residing in larger cities are less inclined to violate traffic laws. Furthermore, individuals who report frequent real-life misconduct are more likely to disregard red lights when crossing a signalized crosswalk in the virtual environment. Differences in average crossing speeds are observed between middle school and high school students, as well as between one-lane and two-lane crossing speeds.

Analysis of crash severity in rear-end and angle collisions on an urban roundabout in heterogeneous non-lane-based traffic conditions

ABSTRACT This study examines crash risk factors and develops a severity model for rear-end and angle collisions in roundabouts and its vicinity in non-lane-based heterogeneous traffic. The rear-end severity is primarily influenced by posted speed limits, violation type, collision partner, single-vehicle collision, and vehicle age. Whereas type of violation, collision partner, and presence of road surface condition are the critical influencing factors for determining the severity outcomes of angle collision. The marginal effects of angle collision results indicated that the probability of fatal injury increased by 28% due to heavy-vehicle-vulnerable road user collisions. In the rear-end case, risk factors such as collision partners (heavy vehicles-vulnerable road users) increased by 18% and single-vehicle collisions increased by 25%, highly influencing the severity of fatal injury at roundabouts. Notably, rear-end collisions exhibit higher severity compared to angle collisions. This study provides valuable insights for developing strategies to mitigate roundabout collision severity.

Sayed`s Theory of Dark Energy and Dark Matter Forces Nature

Allah (God) is the Creator of the wonderful accelerated expansion of universe. The nature of Dark energy (DE) and matter (DM) is enigmatic mystery of the modern astrophysics. This article is a theory establishes the nature and laws of DE and DM forces. They are among the forces controlling the universe harmony and homogeneity. The derived equations were based on the published Sayed quantum gravity force formula. The Sayed`s Dark energy force (SDEF) formulas indicate that they are function in quantum gravity force (FQG), energy density(ρ), cosmological (Ʌ), Hubble (H), and Planck (h) constants. The formulas were verified and validated. For Planck and cosmos scales, the Dark energy force, energy density, and Ʌ were calculated and found to be 2.538x1084 N, 10-26 Kg/m3, and 10-52 m-2 respectively. These values are in high concordance with the declared values. The results obtained show that the DE force represents more than ¾ of the universe forces as the observations. The disputed Hubble constant was also calculated and found to be in the range; ~2.2 x10-18 s-1. It can also be stated and emphasized that the light speed violation (E≠mc2) is a must to solve the DE nature and many other universe mysteries.

IDENTIFICATION OF ACCIDENT LOCATION AND DEGREE OF VULNERABILITY ALONG OSOGBO-GBONGAN ROAD, OSUN STATE, NIGERIA

Road traffic accidents (RTAs) remain a significant global concern and leading cause of death of people within the economically active age-group in developing countries thereby mounting negative effect on people’s health, economy and the society at large. This research aimed at developing a prediction model for accident prone areas along Osogbo-Gbongan road as past studies revealed that the study area reported the highest RTAs in Osun State, Nigeria. Four years accident data obtained from FRSC, Osogbo Branch was characterized in terms of casualty, accident causes and location and affected age-groups (Adult/Children). Geometric parameters were obtained onsite by physical measurement to estimate the vulnerability of the road at blackspot locations. The identified accident blackspots were Ogo-oluwa (02+000), Ataoja (04+000), Abeere (06+000), Owode (10+000) and Akoda (15+000) with their vulnerability indices as 53, 67, 78, 66 and 70, respectively. The obtained total occurrence of accidents in 2018, 2019, 2020 and 2021 were 144, 128, 168 and 313, respectively with speed violation as the highest causative factor. Abeere Area has the highest accident occurrence of 31 and the highest T.SPWEG-A.V.I of 78 with three other locations showing similar pattern except Owode Area. Therefore, this model showed a perfect correlation of 80% thereby ensuring the validity of the model for predicting the accident vulnerability at accident locations. This study concluded that accident in this area can be characterized as minor, serious and fatal with the fatal causing deaths. The speed violation and poor road geometry were the primary factors contributing to the severity of accident and it will continue to increase if urgent intervention is not applied. The study recommended that speed law should be enforced, presence of safety personnel at the noon part of the day and those in charge of road design, supervision and construction should uphold good practice and rehabilitate the existing road geometry so as to curtail accident reoccurrence along the study area.

Effects of Behavior-Based Driver Feedback Systems on the Speeding Violations of Commercial Long-Haul Truck Drivers

Effects of Behavior-Based Driver Feedback Systems on the Speeding Violations of Commercial Long-Haul Truck Drivers

Behind the wheel: Probing into personality, skills, and driving behavior’s role in bus rapid transit crashes

Objective Personality traits and driving skills are significantly associated with driving behaviors and crashes. In the case of professional bus drivers, the relationships amongst these variables have not been sufficiently examined in terms of road crashes. Therefore, this study seeks to examine the relationship between personality traits, driving skills, driving behaviors, and crash involvement among Bus Rapid Transit (BRT) drivers. Methods The study employed a comprehensive data collection strategy involving self-reported questionnaires, including the driver behavior questionnaire, driver skill inventory, and Big Five inventory, alongside Global Positioning System (GPS)-extracted speeding data from a sample of 166 drivers. To explore the relationship between variables, the study utilized the Partial Least Squares Structural Equation Model (PLS-SEM) as the analytical method. Result The findings reveal that self-reported violations and actual speeding performed by drivers were positively associated with crash involvement, whereas positive driving behavior negatively influences violation, errors, speeding and crash involvement. The study also found that the safety skills were negatively associated with violations, errors, and speeding, while higher perceptual-motor skills were associated with higher instances of speeding violations, resulting to a higher possibility of getting involved in a crash. Finally, the study reveals that certain personality traits (extraversion and neuroticism) were positively associated with violations, errors, and speeding, leading to a higher risk of getting involved in crashes, whereas certain personality traits (conscientiousness and agreeableness) were associated with safe driving. Conclusion The study findings offer valuable insights into the predictors of crashes among professional BRT drivers, which can be used to enhance driving practices, ensuring the safety of the public. Moreover, these findings provide transportation agencies with better management and decision-making capabilities to implement effective interventions to improve road safety.

An Econometric Analysis to Explore the Temporal Variability of the Factors Affecting Crash Severity Due to COVID-19

This study utilizes multilevel ordinal logistic regression (M-OLR), an approach that accounts for spatial heterogeneity, to assess the dynamics of crash severity in Virginia, USA, over the years 2018 to 2023. This period was notably influenced by the COVID-19 pandemic and its associated stay-at-home orders, which significantly altered traffic behaviors and crash severity patterns. This study aims to evaluate the pandemic’s impact on crash severity and examine the consequent changes in driver behaviors. Despite a reduction in total crashes, a worrying increase in the proportion of severe injuries is observed, suggesting that less congested roads during the pandemic led to riskier driving behaviors, notably increased speed violations. This research also highlights heightened risks for vulnerable road users such as pedestrians, cyclists, and motorcyclists, with changes in transportation habits during the pandemic leading to more severe crashes involving these groups. Additionally, this study emphasizes the consistent influence of environmental and roadway features, like weather conditions and traffic signals, in determining crash outcomes. These findings offer vital insights for road safety policymakers and urban planners, indicating the necessity of adaptive road safety strategies in response to changing societal norms and behaviors. The research underscores the critical role of individual behaviors and mental states in traffic safety management and advocates for holistic approaches to ensure road safety in a rapidly evolving post-pandemic landscape.

Super Speeders: Mining Speed-Camera Data to Analyze Extreme Recidivism in New York City

New York City maintains the country’s largest network of automated speed cameras, though fines are set lower than if written by police, and are omitted from license-suspension calculations, regardless of the number incurred. What does such a program design entail in terms of speeding recidivism, particularly at the extreme end? Mining a decade of publicly available speed-camera data finds that beginning in 2020, individual automobiles crossed the threshold of 100 or more camera-based speeding violations per year, 25 times the number that would prompt license suspension if manually written by police. Cross-referencing these ‘super speeders’ against traffic-violation data finds they average 35 non-speeding violations (including driving through red lights), as well as roughly $11,000 in unpaid fines each. The emergence and growth of this extreme recidivism indicates the need to evaluate and potentially modify the penalty design and enforcement of New York City’s camera-based speed program.

Linking Deployment Outcomes to Program Impacts for Mobile Photo Enforcement

Previous research has delved into the effectiveness of Mobile Photo Enforcement (MPE) in curbing speed violations and enhancing road safety. This present study extends this investigation to explore the potential influence of MPE deployment efforts on subsequent collision occurrences. Specifically, the research team applied survival analysis techniques to examine the connection between MPE deployment variables and the duration between collisions. K-M survival estimates were employed to assess the survivability of classified groups, categorized based on deployment hours, visits, the ratio of hours to visits, and traffic count. The findings illuminated that the ratio of hours to visits emerged as the most impactful variable in prolonging the time interval between collisions and diminishing the likelihood of collisions. Notably, the anticipated reduction in collision hazards, signifying the occurrence of collisions, exhibited variations ranging from 22% to 52%. The most substantial decreases were observed when deployment occurred in high-traffic locations. These outcomes reinforce the positive impact of deployed MPE hours and visits in extending the duration between consecutive collisions, thus reducing the risk of collision occurrences.

Effect of Driving Simulator Training on Patients' Driving Performance and Self-Efficacy with Paraplegia due to Spinal Cord Injury

Objective: The purpose of this study was to investigate the effect of driving simulator training on the driving performance and self-efficacy of paraplegic patients with spinal cord injury.
 Method: Driving simulator training was provided to 12 participants with within subjects design, three sessions of basic driving, seven sessions of driving on the driving simulator. After training, performance time and operational performance scores, self-efficacy were analyzed.
 Results: This study found that the intervention by the driving simulator had statistically significant effects on driving time, performance error, ‘Sudden start’, ‘No neutral gear when stopping’, and ‘Speed violation’ and ‘Social self-efficacy’ of paraplegic spinal cord injury patients (p<.05).
 Conclusion: Driving simulator training can be a strategy to improve driving performance and social self-efficacy of paraplegic patients with spinal cord injury. Also, patients need sufficient sessions more than 10 times of driving simulator training.

Analysis and prediction of injury severity in single micromobility crashes with Random Forest

Urban micromobility represents a significant shift towards sustainable cities, underscoring the paramount importance of its safety. With the surge in micromobility adoption, collisions involving micromobility devices, such as bicycles and e-scooters, have surged in recent years. The second most common crash type involving these vehicles is one that only involves a micromobility vehicle (single micromobility crashes). This study analyzed 6030 single micromobility crashes that occurred in Spanish urban areas from 2016 to 2020. The Random Forest methodology was applied to create a classification model for the purpose of characterizing these crashes, predicting their injury severity, and identifying the primary influencing factors. To address the issue of imbalanced data, resulting from the relatively smaller dataset of fatal and seriously injured crashes compared to slightly injured ones, the Synthetic Minority Oversampling Technique (SMOTE) was applied.The results indicate that certain behaviors, such as not wearing a helmet, riding for leisure, and instances of speeding violations, have the potential to increase injury severity. Additionally, crashes occurring at intersections or at cycle lanes with bad pavement conditions are likely to result in more severe outcomes. Furthermore, the concurrent presence of various other factors also contributes to an escalation in crash injury severity.These findings have the potential to provide valuable insights to authorities, assisting them in the decision-making process to enhance micromobility safety and thereby promoting the creation of more equitable and sustainable urban environments.

Calculation of Dangerous Driving Index for Two-Wheeled Vehicles Using the Analytic Hierarchy Process

Given the high incidence of traffic accidents and fatalities on two-wheeled vehicles, there is a growing need for safety management. However, studies on evaluating two-wheeled vehicle driving in a quantitative and comprehensive form are insufficient. In this study, 11 items were defined for the first step to evaluate two-wheeled vehicle driving: signal violation, central line violation, helmet violation, pedestrian close driving, sidewalk driving, reverse lane driving, speed violation, rapid acceleration, rapid deceleration, rapid turn, and rapid lane change. The items were classified into three categories (traffic violation, pedestrian threat, and reckless driving), and their weights were derived using the AHP technique. For rapid acceleration, rapid deceleration, rapid turn, and rapid lane change, a high-performance driving simulator was used to establish risk criteria and calculate the weight based on the degree of risk. The calculated weight of each item indicates its importance in evaluating two-wheeled vehicle driving, with helmet violation (0.158), speed violation (0.124), and pedestrian close driving (0.122) having the highest weights. Finally, the dangerous driving index for two-wheeled vehicles was calculated by the weights of each evaluation item and applied to the driving trajectory data.

Utilizing MRBQ to investigate risky rider behavior in Chinese young riders: combining the effect of Big Five personality and sensation seeking

The motorcycle rider behavior questionnaire (MRBQ) is one of the most extensively used questionnaires to explore risky rider behavior worldwide. However, whether previous research adopted other scales or other versions of MRBQ, neither of them fully cover the typicality of the risky behavior in Chinese motorcyclists. Moreover, past research investigated the MRBQ while combining the joint effect of Big Five personality (BFP) and sensation seeking lacks. Our study aims to revise the Chinese version of MRBQ in young riders and explore the relationship among BFP, sensation seeking, MRBQ, and self-reported traffic violations. 278 online participants filled out the Big Five Inventory measuring BFP, the sensation seeking scale, MRBQ items selected from previous versions in other countries, and self-reported traffic violations from the traffic management system (crashes, traffic violation frequency, penalty points, and fines). Exploratory factor analysis suggested 7 factors (safety equipment, traffic errors, speed violations, control errors, stunts, traffic violations, and safety violations), and the internal consistency reliability ranged from 0.58–0.91. The hierarchical linear regression analysis showed that agreeableness and conscientiousness in BFP negatively predicted the total MRBQ score, while openness in BFP and sensation seeking positively predicted the total MRBQ score. In addition, the Poisson regression analysis suggested that all kinds of self-reported traffic violations could be positively predicted by the total MRBQ score. Path analysis suggested the fully mediating role of sensation seeking. In conclusion, the Chinese version of the MRBQ is useful for future studies and the sensation seeking plays a mediating role between the Big Five personality and MRBQ.

Prediction of Electric Load Neural Network Prediction Model for Big Data

The problem of traffic enforcement scheduling has become one of the key concerns of the government in recent years. How to carry out scientific and efficient speed measurement activities to ensure the safety of road traffic has therefore become the direction of the article's research. The paper selects accident-prone and speeding-prone locations as checkpoints, dividing them into three zones. The optimal speeding check time period is determined to be 18:00-23:00. To increase officers' efficiency, the problem is transformed into a TSP and solved using the Immunity Algorithm. The article defines the degree of safety, based on prior speeding violations, and evaluates the effectiveness of the inspection routes. The persistent effect of the dispatch scheme on local road safety is analyzed, taking an average of 3.67 days for a road to reach a higher risk state. Monte Carlo simulations are used to model vehicle speeding events and officer enforcement, with a final speeding violation detection probability of 0.871.

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.

Rollover Investigation of Two-Axle Heavy Vehicle Based on Load Transfer Ratio with Vehicle and Road Condition: A Simulation Approach

Commercial heavy vehicle is commonly used to transport goods and people efficiently and safely. A previous study has shown a number of gross vehicle weight (GVW) and speed violations recorded in selected areas in Malaysia, and two-axle single unit truck (SUT) is the most commercial heavy vehicle type that violated the weight and speed regulation. Moreover, accidents involving heavy vehicles result in severe traffic disruption and fatalities to other road users due to heavy vehicle size and capability to carry huge amounts of goods. Thus, the objective of this paper is to investigate the correlation and effect of the vehicle and road condition on the two-axle SUT rollover during cornering on the curved road using the simulation approach. The verified two-axle SUT model is simulated using IPG-TruckMaker® with different GVW, speed, and coefficient of friction values while the cornering radius, driver behaviour and load’s center of gravity remain constant. A correlation based on performance indices is established, and it is found that the heavy vehicle speed has a strong correlation to the lateral load transfer to cause a rollover followed by GVW and coefficient of friction, respectively.