Federal Road Safety Corps Research Articles

An AI-driven Approach to Traffic Congestion Prediction in Port Harcourt City Using Naïve Bayes

Four important intersections Location Junction, Rumuokoro Junction, GRA Junction, and Rumuokwuta Junction will be the focus of this study's investigation into the persistent problem of traffic congestion in Port Harcourt, Nigeria. In order to provide insights for improved traffic management, the project also aims to construct a predictive model that forecasts traffic congestion on certain routes using the Naïve Bayes algorithm. The study used both exploratory and descriptive research techniques to gather information about Port Harcourt's traffic congestion. Field surveys were carried out at the four intersections between 7 a.m. and 7 p.m. to collect traffic counts and vehicle footage. The investigation also included data from Rivers State Command, a division of the Federal Road Safety Corps (FRSC). Using this data to find patterns and trends in traffic flow, a Naïve Bayes algorithm was developed to classify and predict the degree of traffic congestion. With a 97% accuracy rate, the algorithm accurately classified 1164 out of 1200 traffic data points, predicting traffic congestion. The predictive algorithm used bar charts to show the traffic conditions; tall, moderate, and tiny bars, respectively, indicated high, medium, and low traffic congestion. The model's efficacy in real-time traffic forecasting was reinforced by the close match between the forecasts and observed traffic patterns. According to the study's findings, the Naïve Bayes algorithm can accurately forecast traffic congestion and provides road users and urban planners with useful information. The model can greatly enhance road efficiency by lowering travel times, relieving congestion, and improving traffic management on Port Harcourt's major arteries by accurately predicting traffic conditions.

Operational Stress, Coping Strategies and Psychological Well- Being Among Paramilitary Officers in the Bade Local Government Area of Yobe State

There have been reported cases of operational stress experienced by security and paramilitary officers in Nigeria ranging from high to moderate on a continuum of duty posts. Many officers tend to experience operational stress that affects their psychological well-being and productivity. Thus, a study on the influence of operational stress and coping strategies on psychological well -being among paramilitary officers in Bade LGA., Yobe State. The study objectives were: to examine independent influence of operational stress and coping strategies on the psychological well-being of paramilitary officers in Bade, LGA; investigate the joint influence of operational stress and coping strategies on the psychological well-being of paramilitary officers in Bade, LGA. A total of 220 participants, comprising Police (60), Civil Defense (50), Correctional Service (45), and Federal Road Safety Corps (65) were used with purposive sampling techniques. Three standardized instruments namely: Operational Stress; Psychological well-being, and Coping Strategies Scales, were used. Simple linear and multiple regression analyses were performed. The findings revealed that operational stress and coping strategies independently and jointly predicted psychological well-being among paramilitary officers in Bade LGA [F (1,218) =43.956; P<.01; F(1,218) = 51.423; P<.01]; [F (2,137) = 47.153; P<.01] . The study recommended, among others, that the Federal Government of Nigeria should increase the number of paramilitary formations and personnel in Northeast Nigeria with a view to reducing workloads for efficiency and psychological well-being. Additionally, resilience training for coping strategies should be provided by the Federal Government of Nigeria to improve psychological well-being.

ASSESSING FEDERAL ROAD SAFETY CORPS (FRSC) IMPACT ON EASING TRAFFIC CONGESTION ALONG THE ILESA-BENIN HIGHWAY AT AKURE, ONDO STATE, NIGERIA: EDUCATION, ENFORCEMENT, AND PATROL EFFICIENCY

This study evaluates the efficacy of the Federal Road Safety Corps (FRSC) in mitigating traffic congestion along the Ilesa-Benin Highway in Akure, Ondo State, Nigeria. It scrutinizes the efficiency of FRSC's education, patrol, and enforcement strategies to discern their impact on traffic congestion levels. The data were collected from primary sources via questionnaires and activity-based trip surveys, supplemented by secondary sources including satellite imagery and literature. The results revealed high internal consistency in the respondents' perceptions and no evidence of multicollinearity in the dataset. It also indicates the significant contributions of education and enforcement in reducing traffic congestion, contrary to the limited effectiveness of patrols. Among the recommendations include prioritizing improvements in education and enforcement efforts through enhanced training programs and community collaborations as well as re-evaluating and potentially restructuring patrol activities. These insights offer valuable guidance for policymakers and stakeholders in devising strategies to alleviate traffic congestion and enhance road safety on critical highway routes like the Ilesa-Benin Highway.

AN APPLICATION OF POISSON REGRESSION MODEL ON ROAD ACCIDENTS DATA IN KADUNA STATE, NIGERIA

The Poisson Regression model was used to analyze the data on road traffic crashes of Kaduna State from year 2014 to 2017. The data was collected from the Kaduna State Command of the Federal Road Safety Corps (FRSC). The analysis was carried out using the R (MASS Package) software. The variables considered are; Number of Persons Involved in accidents, Season (months of the year), Number of Crashes and Causes of Accidents. The results from the model were analyzed using the Akaike Information Criterion (AIC) and goodness of fit. Result shows that the Poisson Regression Model has an AIC value of 1185.7. The scatter-plots are clustered and it has few outliers from the predicted line. This is due to the good value of the deviance and the parameters of the model are very much estimated. The result of the Poisson Regression Model aids in overcoming the over-dispersion problem, resulting in a better fit. Thus, indicating the existence of significant dispersion.

Safety measures in the workplace: a case study of Federal Road Safety Corps (FRSC) vehicle plate manufacturing plant, Ojodu – Lagos, Nigeria

Introduction: Hazards associated with vehicle number plate manufacturing processes in developing countries have not been well elucidated. There is a need to investigate hazards in the vehicle number plate manufacturing plant. The study aimed to identify self-reported hazards in the vehicle plate manufacturing factory, explore their effect on workers’ health and recommend mitigation strategies. Methods: A descriptive cross-sectional study of workers in the vehicle plate manufacturing plant was done to identify hazards, protective measures and self-reported effects on workers’ health. Biochemical analysis of blood samples was also done and outliers were noted. ANOVA test was performed to determine differences in mean values of selected biochemical parameters. A p-value of less than 0.05 was considered statistically significant. Results: Exposure to toxic chemicals 38(69%), excessive noise 36 (65%) and heat 28 (51%) were the major hazards in the factory. Major health problems were stress and fatigue 41 (71%), skin disorders 28 (51%), respiratory problems 18 (33%) and hearing loss 16 (29%). More than one quarter, 17 (30 %) of the workers admitted not using personal protective equipment (PPE) regularly. Periodic medical tests were not done for 13 (24%) of the workers. Potassium (3%), Urea (10%), Creatinine (7%), Aspartate transaminase (18%) and Alanine transaminase (8%) were elevated among the workers but no significant association could be established between elevated levels and work post. Conclusion: Workers in the plate number manufacturing plant were mostly exposed to toxic chemicals and noise. Elevated electrolyte levels cut across departments. There is a need for better enforcement of safety rules, and periodic medical examinations should be conducted more consistently.

Transport fare and road traffic crashes in Nigeria: insights from a geographical analysis

Road traffic crashes (RTCs) are significantly high in Nigeria with serious social and health consequences. While existing studies on RTCs have mainly focused on the effect of socio-economic, environmental, human and mechanical factors to address the high rates, the relationship between road transport fares and RTCs has been glossed over in literature. Thus, this study examines the influence of road transport fares and other covariates on RTCs. Data on RTCs and the predictors between 2017 and 2022 were obtained from the records of the National Bureau of Statistics and the Federal Road Safety Corps. Spatial statistical techniques were used for the data analysis. RTCs vary across the country, and Northern Nigeria is the hot spot. Results from the spatial analysis show that road transport fares, population density, and illiteracy rate are significant predictors of RTCs. The study recommends striking a balance between fare affordability, the quality of service provided, and the implementation of effective transportation strategies.

Are Holidays or Festive Periods to blame for Seasonal Spikes in Road Traffic Accidents in Nigeria?

Over the years, citizens have characteristically viewed festive and holiday seasons in Nigeria as a period when there are spikes in traffic and road crashes. These spike in road crashes have been ascribed to some spiritual or mystical powers which often possess the road ways especially the intercity or highways within the country. The paper examined the impact of holiday and festive periods on the recorded road traffic accident cases in Nigeria for a period ten years (2012 – 2021) using weekly data obtained from the Federal Road Safety Corps Office, the research adopted exploratory data analysis conducted on the Road Traffic accidents data suggests that the data is not normally distributed (Shapiro-Wilk = 0.94, p = .002) hence Kruskal-Wallis test -a non-parametric statistics equivalent of Analysis of Variance (ANOVA) was adopted for test of difference. The aim was to isolate the actual period when road accidents occurred most between ordinary day, non-festive holiday and festive holiday periods. National holiday and festive dates were marked on the data sheet and a hypothesis was tested. A Kruskal-Wallis test was conducted to compare the median ranks of three groups: Festive Holiday, Non- holiday, and Non Festive Holiday. The test revealed a significant difference among the groups (χ2(2) = 11.02, p = .004.). Post hoc tests using the Dunn-Bonferroni method showed that Festive Holiday (FH) and Non-holiday (NH) groups had a significantly different median rank (KW = 58.38, Mdn_FH = 220, Mdn_NH = 180, adj.p = .007). Similarly, the Festive Holiday and Non Festive Holiday (NFH) groups had a significantly different median rank (KW = 84.16, Mdn_FH = 220, Mdn_NFH = 177.5, adj. p = .017). However, there was no significant difference in median rank (Mdn) between Non-holiday and Non Festive Holiday groups (KW = 25.77, Mdn_NH = 180, Mdn_NFH = 177.5, adj. p = .948). The study concluded that most road traffic accidents occur during festive holidays.

Motorcycle Road Traffic Injuries in a Tertiary Hospital in Nigeria: A Reflection of the Trauma Care Crisis.

Motorcycle is a popular and growing form of intracity transportation in many Nigerian cities owing mainly to poorly developed transport systems.It contributes significantly to road traffic injuries (RTIs), which are a leading cause of death and disabilities in low- and middle-income countries. There is a lack of information on the quality of care received and the treatment outcome in patients with motorcycle RTIs in Ibadan and many cities in Nigeria. This study evaluated the characteristics of motorcycle-related RTIs, the quality of care received, and the outcome of the patients managedin a trauma reference center in Ibadan, Nigeria. This is a prospective cohort study. All patients involved in motorcycle road traffic crashes who presented to the emergency department of the University College Hospital, Ibadan, between August 2020 and May 2021, were included in the study. Data on patients' demographics, history of the crash, injuries sustained, definitive care, and the outcome of in-hospital care were obtained from patients (and/or their carers)and the medical records. A total of 156patients were seen, out of which 74.4% were males. About 76.2% were less than 45 years with a mean age of 35.7 ± 16.3 years, and the peak age group was 18-44 years. About 37 (23.7%) patients were involved in motorcycle/motorcycle collisions, whereas 67 (42.9%) were involved in motorcycle/car collisions. Riders accounted for 59.6% (93), and although 62% (97) of the patients presented within six hours of the crash, only 10.9% (17) presented within one hour. About 48% received some form of prehospital care rendered by officials of the Federal Road Safety Corps, police officers, or passers-by, and none was attended by a dedicated emergency ambulance team. The head and the limbs were the most affected anatomical areas, while orthopedic and neurosurgical procedures were the most required emergency surgical interventions. About 66.7% were discharged home with only 21.2% of them fit to return to pre-trauma function at discharge, and the mortality rate was 17.3%. Patients who presented at 7-24 hours (AOR = 2.99; 95% CI = 1.04-8.62; p-value = 0.043) and >24 hours after the accident (AOR = 5.65; 95% CI = 1.64-19.53; p-value = 0.006) were 2.99 and 5.65 times, respectively, more likely to die from motorcycle-related accident compared to those who presented within the first six hours. This study identified the growing burden of disabilities and mortalities related to motorcycle RTIs. It highlights the lack of prehospital trauma care, which is a reflection of the deficiency of a national,regional, or jurisdictional trauma systemand the critical need to develop a functional trauma system.

Development of Adaptive Realtime Mobile Based Traffic Management System for Congested Urban Cities

congestion in Nigeria, focusing on Port Harcourt. The study analyzed four main routes: Location, Rumuokoro, Rumuokwuta, and GRA Junction. Field surveys collected traffic volume and video recordings of cars traveling the indicated routes between 7 am and 7 pm. The Rivers State Command of the Federal Road Safety Corps provided further data. A mobile-based Traffic Management System was designed and simulated using Proteus. The technology transmits real-time traffic data from congested areas to a local server. This data is easily accessible on mobile devices. Python software was used to classify traffic congestion as heavy (0-14km/hr), medium (15-28km/hr), and free (29km/hr and above). Background subtraction was used for vehicle image analysis. The system can detect road traffic, correctly determine car speeds, count the number of vehicles, and send this traffic information to consumers via mobile devices via online communications. After simulating 1200 traffic data instances, 36 had mistaken, while 1164 were classified. This represents good accuracy, with 97% accuracy and 3% inaccuracy. The traffic data will be maintained in a database to help researchers identify places with recurring traffic congestion. These findings suggest that authorities should build alternative routes. It can also help build traffic-congestion-reducing policy review, organization, and enforcement initiatives. Keywords: Background Subtraction Technique, Mobile devices, Proteus, Traffic Congestion, Python

Predictive Analysis of Road Traffic Accidents in Katsina State, Nigeria Using Machine Learning Algorithms: A Study on Factors and Mitigation Strategies

This study aims to explore the use of machine learning algorithms in predicting the likelihood of road traffic accidents in Katsina State, Nigeria, with the goal of reducing the high rate of road traffic accidents and associated loss of lives and properties. The study collects and analyzes data on road traffic accidents in Katsina State, including the number of accidents, location, time of day, and the type of vehicles involved. Machine learning algorithms such as Decision Trees, Random Forest, and K-Nearest Neighbors, are trained using the data to predict the likelihood of road traffic accidents. The study also identifies the factors that contribute to road traffic accidents in Katsina State by using techniques such as feature selection and correlation analysis, to identify the most important variables. The findings can be used by stakeholders, including the government, law enforcement agencies, and road safety organizations, to develop and implement effective strategies to reducing road traffic accidents in Katsina State. The study utilizes data collected from the Federal Road Safety Corps database in Katsina, Nigeria, and employs data cleaning and feature selection techniques to improve data quality. The Random Forest algorithm achieved the predictive value as 85% while K-Nearest Neighbors (KNN) and Decision Tree algorithms yielded 17% and 42% respectively.

The epidemic of texting and driving in Nigeria: A literature review of accidents on the highways

Driver distraction is an important risk factor for road traffic injuries. There are different types of driver distraction, usually divided into those where the source of distraction is internal to the vehicle – such as tuning a radio, or using a mobile phone, and those external to the vehicle – such as looking at billboard or watching people on the side of the road. This proper focuses on the use of mobile phones while driving, in response to concern among policy – makers that this potential risk to road safety is increasing rapidly as a result of the exponential growth in the use of mobile phones more generally in society. It aims to raise awareness about the risks of distracted driving associated with mobile phone use, and to present counter measures that are being used by Federal Road Safety Corps to tackle this growing problem in Nigeria.

Nexus Between Treasury Single Account, Revenue Generation, Transparency and Accountability: Evidence from Nigerian

This study assessed the impact of treasury single account (TSA) on revenue generations, transparency and accountability: evidence from Nigerian federal road safety corps. Correlational research design was adopted and one hundred and twenty-five copies of questionnaire were distributed to staff of federal road safety corps in Niger state, Nigeria. The study employed ordered logit regression to analyze the data obtained and the findings revealed that TSA improves revenue generation, promote transparency and accountability. It was recommended that federal government should conduct periodic checks on all MDAs so as to enforce the implementation of the TSA policy in all their revenue generation activities.

Assessing Work Ethics and Safety Standards among Roadside Mechanics in Adamawa State

The main aim of the study was to assess work ethics and safety standards among roadside mechanics in Adamawa State. The study was guided by three specific objectives as well as three research questions. The study used a descriptive survey research approach and was conducted in Adamawa State. The study's population comprised 21,597 individuals, including 755 roadside mechanics, 20,782 motorists, and 60 Federal Road Safety Corps officers. The sample size of 377 respondents was determined using on Krejcie and Morgan's (1970) sample size table and a simple random sampling technique was used to select respondents. A structured questionnaire with a reliability coefficient of 0.89 established through the Cronbach alpha designed by the researcher and validated by three experts was used for data collection. Mean statistics was used to answer the research questions while ANOVA was used to test the hypotheses. The study revealed that to a moderate extent, roadside mechanics adhere to ethical conduct and comply with safety standards. Based on the findings, it was recommended that efforts should be focused on improving access to education and training programs tailored to mechanics' needs, particularly emphasizing ethical work practices and safety standards, and the government should bolster infrastructure and provide necessary resources to mechanics.

Statistical Modelling of Road Traffic Accidents: Pattern and Trend in Kogi State, Nigeria

Road traffic accident (RTA) is defined as unplanned car crash that causes injuries, fatalities, and property damage. In order to better understand the pattern and trend of road traffic accident in Kogi state, Nigeria, we statistically modeled RTA data collected from Federal Road Safety Corps, Lokoja from January 2017 to December 2021. The data consisted of monthly RTA types and outcomes. The RTA types considered were fatal, serious and minor while the RTA outcomes were death, injury and no injury. Time series modeling was adopted for modeling and predicting the accident rates while Pearson correlation was used to determine the degree of relationship between RTA types and outcomes. Results showed that there were steady fluctuations in the patterns of RTA types and outcomes between February and October while there were upward trend in RTA from November to January. The augmented Dickey-Fuller test showed that RTA series was stationary and out of 10 candidate models obtained using ACF and PACF plots, the best model suitable for forecasting RTA rate was found to be ARIMA(1,0,1) using mean absolute deviation (MAD) and mean square error (MSE) selection criteria. In order to estimate the parameters of the model, the Shapiro-Wilk test was conducted on the RTA values and its residuals to confirm normality. Since p < 0.05 in both cases, they were both found to be non-normal, then the least absolute deviation (LAD) estimator was used for estimation. This gives rise to Yt = 29.0574 + 0.492151Xt-1 + 0.99994et-1 + εt as the best fitted model, which was found to be statistically significant at α=0.05. The estimated model was used to forecast RTA for 30months with 95percent confidence level and result showed that the forecast were good and there will continually be occurrence of RTA nearly every month and there will be higher RTA rates between November and January. The result of the Pearson correlation showed that fatal accident were 71percent more likely associated to death while serious accident were 61percent more likely associated to injury.

Urban Taxi Slogans: The People's Arts

Urban Taxi Slogans: The People's Arts

Human capacity development and Performance: A focus on the Abia sector command of Nigeria federal road safety corps, 2010-2020

This study examined the impact of human capacity development on staff performance in the Federal Road Safety Corps, Abia State Sector Command. Anchored on human capital theory and qualitative and quantitative date generated through documentary and survey methods, the study observed that the regular training and re-training FRSC periodically conducts for its Officers and Marshals enhanced the capacity of the Command to enforce road safety regulations as evident in increased compliance to road safety regulations by road users. The study thus posits that human capacity development enhanced staff performance in the Federal Road Safety Corps, Abia State Sector Command, between 2010 and 2020. Among others, the study recommends increased awareness campaign among commercial drivers to ensure continued compliance with road safety regulations. Keywords: Human capacity training, development, staff performance, organizational growth, road safety management

Computerization of FRSC (Federal Road Safety Corps) Database for Road Traffic Accidents and Infractions

This project aimed at designing a computerized web-based road traffic accident and infraction database system for the Federal Road Safety Corps (FRSC) which will enable FRSC to get summarized traffic accident and infraction information at various level easily and quickly. The collection, management and dissemination of road traffic accident and infraction related events have represented a serious issue to road safety officers and all stakeholders alike due to the absence of a central repository from which all accident and infraction data can be stored and managed. Where accessible, these data available in the paper-based form and this represents a bottleneck in updating the available data. The purpose of the system is to design a computerized system that will collect, record, disseminate and manage road traffic accident and infraction information for the FRSC. The system is achieved using structured system analysis and design methodology. The software that is used to design the system is Adobe Dreamweaver and XAMPP local server, the programming language that was used comprise of Hyper Text Mark-up Language (HTML), JavaScript, Cascading Style Sheet (CSS) and Hypertext Pre-Processor (PHP). The website was tested using on a local web server “XAMPP” and Structured Query Language is used for the Database design. The system has different subsystem to meet its core functionalities. An administration subsystem authenticates administrators and road safety officers to login to the system and executes their defined task. The accident and infraction report subsystem allow the officer to enter and submit road traffic accident and infraction details using a simple to use web-based interface.

Workers’ Participation in Decision Processes and the Performance of Public Sector: The Case of Federal Road Safety Corps Anambra State Command, 2011 - 202

Workers’ Participation in Decision Processes and the Performance of Public Sector: The Case of Federal Road Safety Corps Anambra State Command, 2011 - 202

Predicting road traffic crash severity in Kaduna Metropolis using some selected machine learning techniques

Road Traffic Crash (RTC) is among the leading causes of death in the world and has a significant impact on the socio–economic development in a society. Generally, RTC can be caused by one or a combination of the following factors: Human, environment and vehicle. This study utilized five data mining algorithm classifiers (Decision Tree (DT), K-Nearest Neighbor (KNN), J-Repeated Incremental Pruning to Produce Error Reduction (JRIP), Naïve Bayes (NB), and Multi-layer Perceptron (MLP)) to classify the severity of RTC and identify the significant causes of RTC in Kaduna State, Nigeria. The RTC data used in this study included 26 RTC attributes with 1580 instances from 2016 to 2018 that covered fatal, serious and minor cases obtained from the Federal Road Safety Corps, Kaduna sector command. Two sets of experiments were performed on the classifiers (without and with feature selection). The study results showed that among the five data mining algorithms used, K-NN had the best accuracies of 94.8% and 96.1% respectively for the without and with feature selection experiments.

Road Traffic Accidents in Port Harcourt, Rivers State: Pattern of Injuries That Caused Death, Risk Factors, Anatomical Sites Affected, and Autopsy Findings

Background: Road traffic accident (RTA) is one of the commonest causes of death among the healthy population and it’s among the leading cause of death due to human errors. Globally, it has contributed significantly to reducing the workforce as well as increasing the family expenditure on preventable health conditions. It is grossly underreported with no clear data on annual death reports, especially in most developing countries. Objectives: To determine the rate of death from a head injury due to road traffic accidents, risk factors; the commonly injured anatomical regions and immediate cause of death from autopsy examination of victims. Methods: This was a prospective longitudinal study in which 86 victims with CORONER FORM D were sampled recruited for the study. Primary data were obtained using the Crash Report Form (CRF) from the Federal Road Safety Corps, oral interviews from deceased relatives and security personnel who brought the victims, and subsequently, Coroner form D was used to obtain /record the findings of autopsy examination. Data was analysed using Statistical Product and Service Solutions (SPSS) version 25.0 (Armonk, NY). Results: The rate of death from a head injury due to RTA in Port Harcourt was 48.84%. The mean age of victims examined at autopsy was 28.61 ± 9.22 with a modal age group of 20-29. Majority of the victims were males [52(65.82%)], passengers [36(42%)], had superficial autopsy [32(37.20%)], multiple injuries [40 (46.51%)]. The commonest cause of immediate fatality was head injury (48.8%) followed by haemorrhage and shock 31(36.1%). Over speeding was the most common human risk factor (50%), followed by sleep/fatigue (10%). The commonest mechanical and environmental risk factors identified were brake failure and burst tyres (8%), and potholes (6%) respectively. Conclusion: The commonest cause of death among victims of RTA in Port Harcourt, Rivers State, Nigeria is head injury. Over speeding, sleep/fatigue, brake failure and burst tyres and pot holes are the risk factors for RTA in Port Harcourt. Measures targeted at controlling these risk factors would help reduce morbidity and mortalities associated with RTA.