Accident Hotspots Research Articles

IMPLEMENTATION OF PEDESTRIAN SAFETY SYSTEMS IN THE URBAN INFRASTRUCTURE OF KHARKIV

The article focuses on pedestrian safety issues within the urban infrastructure of Kharkiv City in Ukraine and the implementation of modern protection systems on roads. It examines approaches to organising pedestrian zones and safety islands, with particular emphasis on the RS PSS system, designed to enhance pedestrian safety on roads with high traffic intensity. Significant attention goes to the growing number of road traffic accidents (RTAs) at pedestrian crossings and main city highways. From 2018 to 2024, an analysis of accident hotspots took place based on statistical data. This analysis led to the creation of a map of RTAs, which helped identify the most dangerous areas for pedestrians and determine the priority locations for safety measures. One of the proposed solutions discussed is using bollards – protective posts that prevent vehicles from entering pedestrian areas. The article analyses the effectiveness of bollards in combination with other safety measures, such as safety islands and traffic management systems. In addition to bollards, landscape solutions that protect pedestrians and blend into the urban environment are also mentioned. The main focus is on the RS PSS system, which consists of pedestrian slabs and safety islands manufactured using specialised technology. The system has been tested in various locations throughout the city and has demonstrated a significant reduction in RTAs after its implementation. The article illustrates the process of installing the system using the example of the reconstruction of a safety island on Liudviha Svobody Avenue. The use of modern equipment and technology ensured the rapid and efficient completion of the project. According to official data, the number of RTAs in this area decreased from 21 to 4 after completing the project. In conclusion, the implementation of new safety systems has had a positive impact on reducing accidents and improving pedestrian protection. There is also a noted need for further development and advancement of such systems and the integration of advanced technologies to enhance urban infrastructure. Keywords: pedestrian safety, pedestrian safety systems, safety islands, bollard, refuge.

Optimal Allocation Model for Maritime Emergency Resource Considering the Spatial Correlation between Accident Hotspots

For effective maritime traffic emergency rescue (MTER) operations in the event of maritime traffic accidents (MTAs) and to improve rescue efficiency, it is necessary to analyze the MTER synergy problem and the cooperation between port states. First, the spatial information of accidents under the geographic information system data structure is clarified from the global integrated shipping information system of the International Maritime Organization, and the density-based spatial clustering of applications with noise algorithm is used to conduct hotspot mapping analysis of MTAs to establish the clustering and classification of accident characteristics in key areas. Second, the classification characteristics of accident samples are extracted based on spatial information, and the correlation attributes between MTA hotspots are analyzed. Furthermore, by introducing complex network measurement technology, a topological model of the MTER network is established considering the correlation of accident hotspots, and this model is combined with the sample data of MTAs in Southeast Asian waters from 1990 to 2022. Third, the MTER topological network model is quantitatively analyzed under the accident space of Southeast Asia, and the degree of correlation of traffic accidents in key areas is obtained to reveal the inevitable demand for MTER between regions. The results of the analysis show that there is a network correlation between inter-regional accident hotspots, and thus the degree of correlation between accident hotspots needs to be considered for MTER in key areas. Countries in densely connected regions would set up joint rescue exercises and consider rescue assistance between port country stakeholders, thus improving protection for accident emergency responses. The method of complex network topology based on spatial correlation between accident hotspots suggests a new approach for solving the MTER problem.

A Study on the Feng Shui Characteristics of “Pedestrian Accident Hotspots” in Seoul (Focusing on traffic accident analysis system data)

A Study on the Feng Shui Characteristics of “Pedestrian Accident Hotspots” in Seoul (Focusing on traffic accident analysis system data)

Identification of Road Traffic Accident Hotspots on Sargodha-Jhang Road (Punjab) Pakistan

Globally, road traffic accidents must be addressed even in an advanced transportation system. However, accident frequency can be reduced by the improvement of a road network and infrastructure. The main objective of this study was to explore the various tendencies and attributes of road traffic accidents (RTAs) frequency and intensity related to weak infrastructure and non-systemized traffic patterns on a second-class inter-district road (Sargodha-Jhang) in the region of central Punjab. Secondary data of RTAs collected from Rescue 1122 for the last 3 years (2021, 2022, and 2023) and used to detect hotspots in the study area. Data analysis was carried out in SPSS 17 software by applying the mode of time and space by the latest Tucky method corresponding to the analysis of variance (ANOVA) with a fitted mean. Accident rates were found to be fatal and statistically significant at a 99% significance level with a p-value (of 0.000). The base map of the road obtained from the Survey of Pakistan published the Road Network Map of Pakistan. A total of 14 locations were pointed out using the Global Positioning System (GPS), and mapping was done in the ArcGIS 10.5 environment using on-screen digitization and geo-referencing. Some conditional and critical attributes like 50% poor road conditions and weak infrastructure, the vacuum of safety measures, the rate of accidents, and total traffic exposure contributed to the frequency and intensity of RTAs.

Safety on Jordan's highways: A GIS-Based approach to identifying road accident hotspots

Safety on Jordan's highways: A GIS-Based approach to identifying road accident hotspots

Spatial and Temporal Analysis of Road Traffic Accidents in Major Californian Cities Using a Geographic Information System

Road traffic accidents have increased globally, which has led to significant challenges to urban safety and public health. This concerning trend is also evident in California, where major cities have seen a rise in accidents. This research conducts a spatio-temporal analysis of traffic accidents across the four major Californian cities—Los Angeles, Sacramento, San Diego, and San Jose—over five years. It achieves this through an integration of Geographic Information System (GIS) functionalities (space–time cube analysis) with non-parametric statistical and spatial techniques (DBSCAN, KDE, and the Getis-Ord Gi* method). Our findings from the temporal analysis showed that the most accidents occurred in Los Angeles over five years, while San Diego and San Jose had the least occurrences. The severity maps showed that the majority of accidents in all cities were level 2. Moreover, spatio-temporal dynamics, captured via the space–time cube analysis, visualized significant accident hotspot locations. The clustering of accidents using DBSCAN verified the temporal and hotspot analysis results by showing areas with high accident rates and different clustering patterns. Additionally, integrating KDE with the population density and the Getis-Ord Gi* method explained the relationship between high-density regions and accident occurrences. The utilization of GIS-based analytical techniques in this study shows the complex interplay between accident occurrences, severity, and demographic factors. The insight gained from this study can be further used to implement effective data-driven road safety strategies.

Fusion of satellite and street view data for urban traffic accident hotspot identification

As the number of vehicles and the volume of traffic swell in urban centers, cities have experienced a concomitant increase in traffic accidents. Proactively identifying accident-prone hotspots in urban environments holds the promise of preventing traffic mishaps, thereby curtailing the incidence of accidents and reducing property damage. This research introduces the Two-Branch Contextual Feature-Guided Converged Network (TCFGC-Net) utilizing multimodal satellite and street view data. Designed to extract global structural features from satellite imagery and dynamic continuous features from street view imagery, the model aims to improve the accuracy of detecting urban accident hotspots. For the satellite imagery branch, we propose the Contextual Feature Coupled Convolutional Neural Network (Trans-CFCCNN) designed to extract global spatial features and discern feature correlations across adjacent regions. For the street view imagery branch, we develop the Sequential Feature Recurrent Attention Network (SFRAN) to assimilate and integrate dynamic scene features captured from successive street view images. We designed the Multi-Branch Feature Adaptive Fusion Structure (MBFAF) to aggregate different branch features for accurate identification of accident hotspots. Experimental results show that the model performs well, with an overall accuracy of 93.7 %. Ablation studies confirm that relative to standalone street view and satellite branch analyses, implementing multimodal fusion enhances the model's accuracy by 12.05 % and 17.86 %, respectively. The innovative fusion structure proposed herein garners a 4.22 % increase in model accuracy, outpacing conventional feature concatenation techniques. Furthermore, the model outperforms existing deep learning models in terms of overall efficacy. Additionally, to showcase the efficacy of the proposed model structure, we utilize Class Activation Maps (CAM) to provide visual interpretability for the model. These results suggest that the dual-branch fusion model effectively decreases false alarm occurrences and directs the model's focus toward regions more pertinent to accident hotspots. Finally, the code and model used for identifying hotspots of urban traffic accidents in this study are available for access: https://github.com/gwt-ZJU/TCFGC-Net.

Analysis of road traffic accidents and casualties associated with electric bikes and bicycles in Guangzhou, China: A retrospective descriptive analysis

IntroductionElectric bicycles (e-bikes) and bicycles in large Chinese cities have recently witnessed substantial growth in ridership. According to related accident trends, this study analyzed characteristics and spatial distribution in the period when e-bike-related accidents rapidly increased to propose priority measures to reduce accident casualties. MethodsFor e-bike- and bicycle-related accident data from the Guangzhou Public Security Traffic Management Integrated System, linear regression was used to examine the trends in the number of accidents and age-adjusted road traffic casualties from 2011 to 2021. Then, for the period when e-bike-related accidents rapidly increased, descriptive statistics were computed regarding rider characteristics, illegal behaviors, road types, collision objects and their accident liability. One-way analysis of variance (ANOVA) followed by Bonferroni's multiple comparison test. P < 0.05 was considered statistically significant. Finally, the density distribution of accidents was presented, and Moran's I (MI) was used for assessing spatial autocorrelation. Hotspots were identified based on an optimized hotspot analysis tool. ResultsBetween 2011 and 2021, the number of accidents and casualty rate (per 100,000 population) increased for e-bikes but decreased for bicycles. After 2018, e-bike-related accidents increased rapidly, and bicycle-related accidents plateaued. Accident hotspots were concentrated in central city areas and suburban areas close to the former. Three-quarters of accidents occurred in motorized vehicle lanes. Most occurred on roads without physically segregated nonmotorized vehicle lanes. More than three-fifths of the accidents involved motor vehicles with at least four wheels. The prevalence (per 100 people) of casualties among e-bike rider victims and cyclist victims accounted for 92.0 % and 96.5 %, respectively. A total of 71.6 % of e-bike-related accidents involved migrant workers. Riding in motorized vehicle lanes was the most common illegal behavior. ConclusionsAlthough e-bike-related and bicycle-related accidents presented similar characteristics, the sharp increase in e-bike-related accidents requires attention. To improve e-bike safety, governments should develop appropriate countermeasures to prevent riders from riding on motorways, such as improving road infrastructure, adjusting the driver's license system and addressing priority control areas.

The Effect of the COVID-19 Pandemic on the Distribution of Traffic Accident Hotspots in New York City

The COVID-19 pandemic has had a substantial impact on the lives of city residents and has reshaped working patterns, with a concomitant impact on traffic accidents. We correlated data from multiple sources to explore the impact of residents’ mobility and residents’ travel behavior on the spatiotemporal distribution characteristics of urban traffic accident hotspots and its internal mechanism under the impact of the pandemic and subsequent policy measures. The results showed that the pandemic and policy measures inhibited the mobility of residents, had a significant impact on working patterns, and changed the composition structure of the purpose of residents’ travel behavior, which substantially impacted the spatiotemporal distribution characteristics of urban traffic accident hotspots. The quantity of traffic accidents decreased significantly, and the spatial distribution characteristics of urban traffic accident hotspots changed substantially, with accident hotspots changing from the single-center spatial distribution before the pandemic to the multi-center spatial distribution during the pandemic; urban accident-prone areas changed from being mainly distributed in the central business district before the pandemic to being more widely distributed in public service areas during the pandemic. The results of this study may be helpful to better understand the spatiotemporal distribution characteristics of urban traffic accident hotspots and their intrinsic mechanism.

Spatial-temporal and trend analysis of traffic accidents in AP Vojvodina (North Serbia)

Abstract The objective of this study is to analyse the spatial-temporal patterns of traffic accidents using data from 2011 to 2020 for the AP Vojvodina. The spatial-temporal patterns of traffic accident hotspots were identified at the municipality level using the Getis–Ord G i ⁎ {G}_{i}^{\ast } statistic in ArcGIS Pro software. Trends in traffic accidents were analysed using the Mann-Kendall (MK) statistic. A certain trend in the number of traffic accidents, fatalities, and injuries was detected in 15 out of 45 municipalities. In two municipalities, the trend in the number of traffic accidents is decreasing, while in seven, it is increasing. In three municipalities, a decreasing trend in the number of traffic fatalities was observed. A decreasing trend in traffic injuries was identified in five municipalities. Hotspot analysis on the number of traffic accidents and traffic injuries revealed hotspots in seven municipalities, while no hotspots were detected in the number of traffic fatalities. In the traffic casualties weighted number, persons in tractors, hotspots were identified in two municipalities. This research has the potential to enhance traffic safety by directing targeted safety measures towards identified hotspots. Such measures could ultimately lead to a reduction in traffic accidents, consequently curbing the number of both fatalities and injuries.

Spatiotemporal Analysis of Property Damage-only Accident Hotspots Using GIS in Sharjah, UAE

Recent research on road-traffic accidents (RTAs) has become a focal point for safety and transportation experts, focusing on understanding their rates, characteristics, causes and effects. A significant development in this field is using Geographic Information Systems (GISs) to analyze RTAs. The primary objective of this study is to introduce a GIS-based approach for the comprehensive analysis of the spatial and temporal distribution of RTAs, specifically Property Damage-only (PDO) accidents. This research also endeavors to pinpoint accident-prone areas, commonly called ‘hotspots,’ and high-density accident clusters. This was achieved through spatialautocorrelation analysis, incorporating techniques, including inverse distance weighting, Moran’s index and the Getis Ord Gi* statistic. By focusing on eight years of accident data (2015-2022) in the Sharjah Emirate of the United Arab Emirates, this study contributes to a deeper understanding of the distribution of traffic accidents. The analysis of temporal patterns revealed that the monthly distribution of PDO accidents showed the lowest frequencies of incidents in July, August and September throughout the study period. Furthermore, PDO accident frequencies peaked in 2015, followed by a decline until 2018, after which there was a slight increase until the conclusion of the analysis in 2022. Spatial analysis highlighted significant clustering of PDO-related RTAs. Hotspot analysis specifically identified downtown areas of Sharjah city as more prone to PDO accidents than other regions. The findings underscore the effectiveness of the analytical methods employed, which can be utilized for identifying and prioritizing accident hotspots. Keywords: Road-traffic accidents, Property Damage Only (PDO), Spatial analysis, Hotspot analysis, GIS.

Accident Severity Analysis of Traffic Accident Hot Spot Areas in Changsha City Considering Built Environment

Examining the impacts of accident characteristics and differentiated built environment factors on accident severity at inherent accident hotspots within cities can help managers to adjust traffic control measures through urban planning and design, thereby reducing accident casualties. In this study, time series clustering was used to identify traffic accident hotspots in Changsha City. Based on the hotspot identification results, Kruskal–Wallis tests were used to select differentiated built environment factors among different accident areas within the city. A severity analysis model for road crashes in Changsha’s hotspots, taking into account the built environment, was constructed using a Light gradient boosting machine (LightGBM). In addition, Shapley additive explanations (SHAP) were used to reveal the influences of accident characteristics and built environment factors on accident severity. The results showed that different accident characteristics and built environment factors affect accident severity in different urban accident areas. Factors such as type of accident, visibility, period of time, land use mixing degree, population density, density of commercial places, and density of industrial places showed varying degrees of importance in influencing accident severity, while the overall impact trends remained consistent. On the other hand, transportation accessibility, road network density, landform, and accident location showed significant differences in their impacts on accident severity between different accident areas within the city.

Traffic modeling and accidental data analysis using GIS: A Review

Nowadays, congestion and accidents are creating major risks to cities, including delays, higher fuel usage, and compromised safety. Effective traffic modelling and accident data analysis are critical for identifying high risk identifying accident-prone locations, understanding the causes of accidents and creating focused actions to enhance traffic flow and safety. GIS is an effective tool for integrating, analysing and visualizing different geographical data relevant to transportation networks such as, traffic flow, infrastructure, and safety. It enables geographical analysis and visualization of accident hotspots by integrating accident data, road conditions, traffic numbers, and environmental factors. The use of GIS in traffic modelling and accident data analysis provides considerable benefits in urban transportation planning and management. The aim of the paper is to provide an overview of the application of GIS in traffic modelling and accidental data analysis, highlighting the methodologies, advancements, and challenges in this field. The review shall provide a comprehensive assessment of the current state of traffic modelling and accidental data analysis using GIS. It will highlight the significant contributions of GIS technology, identify key research gaps, and offer insights into future directions for enhancing transportation planning and decision-making processes.

Effectiveness of educational interventions in minimizing reactance in traffic accident prevention

IntroductionClassical mechanics concepts are vital for understanding traffic safety, encompassing speed, acceleration, stopping distance, impact speed, deformation, kinetic energy, and their effects on vehicle occupants. Statistical physics principles are also employed to study multiple vehicles as a many-particle system. This study examines the effectiveness of using a damaged car and structured instruction in road safety initiatives.MethodsParticipants engaged with a damaged car and discussed deformation caused by excessive speed. They participated in role simulations, envisioning and solving risky traffic scenarios, while considering the potential boomerang effect resulting from excessive fear appeals within the campaign.ResultsCombining emotional appeals with solution-focused, action-oriented, and self-esteem-enhancing interventions is essential to mitigate the boomerang effect and encourage safety-compliant behavior. Educational institutions must implement tailored follow-ups to emotionally charged prevention campaigns. Using Design-Based Research, we developed effective methods for knowledge acquisition and transfer, drawing from cognitive behavioral therapy and established traffic safety programs.DiscussionThis publication explores non-standardized social training within focus groups, focusing on negative emotional states following an emotionalizing campaign similar to “Crash Kurs NRW,” targeting upper-level courses in North Rhine-Westphalian schools. Methods from cognitive behavioral therapy and social learning initiated, observed, and documented changes in participants’ norms, values, and attitudes, discussed herein. Additionally, we investigated whether the presence of police officers in prevention campaigns influences reactive behavior. Comparing pre- and post-surveys confirmed reactance after emotionalizing campaigns, and our structured post-survey effectively influenced stage-event-induced behavior. Surprisingly, police officers’ presence did not significantly impact reactive behavior. Furthermore, our follow-up tool extends its applicability to similar emotionalizing prevention campaigns, broadening its potential reach. Our future research will delve into students’ physics literacy during traffic space analysis of accident hotspots, advancing traffic safety initiatives.

Analisis Daerah Rawan Kecelakaan Lalu Lintas pada Jalan Tjilik Riwut Kota Kasongan Kabupaten Katingan Provisi Kalimantan Tengah

Katingan Regency is one of the areas known to be prone to traffic accidents in Central Kalimantan Province. As a result of these accidents, there are many casualties. Therefore, it is necessary to conduct research to determine the level of accidents that occur on the Tjilik Riwut Kasongan road section. The purpose of this study is to analyze accident data on the Tjilik Riwut Kasongan road, analyze the location of traffic accident hotspots on the road, analyze the main factors causing accidents and provide recommendations to relevant agencies to reduce the accident rate on the road. The results of the accident analysis on the Tjilik Riwut Kasongan road carried out for 5 years (2018-2022) accidents amounted to 51 events. Based on the Accident Equivalent Number (AEK) method and the Upper Control Limit (UCL) method, it shows that segment 1 (Km 0+000 - Km 5+000) is a traffic accident prone area. The main cause of accidents is due to human factors and the influence of road conditions. The alternative countermeasures are patching potholes, cleaning drainage, leveling asphalt patching, adding road medians, repainting faded signs, installing traffic signs, and socializing traffic safety and legal discipline in traffic.

Environmental effects from petroleum product transportation spillage in Nigeria: a critical review.

Nigeria has struggled to meet sustainable development goals (SDGs) on environmental sustainability, transportation, and petroleum product distribution for decades, endangering human and ecological health. Petroleum product spills contaminate soil, water, and air, harming humans, aquatic life, and biodiversity. The oil and gas industry contributes to environmental sustainability and scientific and technological advancement through its supply chain activities in the transport and logistics sectors. This paper reviewed the effects of petroleum product transportation at three accident hotspots on Nigeria highway, where traffic and accident records are alarming due to the road axis connecting the southern and northern regions of the country. The preliminary data was statistically analysed to optimise the review process and reduce risk factors through ongoing data monitoring. Studies on Nigeria's petroleum product transportation spills and environmental impacts between the years 2013 and 2023 were critically analysed to generate updated information. The searches include Scopus, PubMed, and Google Scholar. Five hundred and forty peer-reviewed studies were analysed, and recommendations were established through the conclusions. The findings show that petroleum product transport causes heavy metal deposition in the environment as heavy metals damage aquatic life and build up in the food chain, posing a health risk to humans. The study revealed that petroleum product spills have far-reaching environmental repercussions and, therefore, recommended that petroleum product spills must be mitigated immediately. Furthermore, the study revealed that better spill response and stricter legislation are needed to reduce spills, while remediation is necessary to lessen the effects of spills on environmental and human health.

Exploring the Factors Influencing Traffic Accidents: An Analysis of Black Spots and Decision Tree for Injury Severity

This research aimed to examine the spatial distribution of road traffic accidents in Budapest, Hungary. The primary objective was to identify the factors associated with traffic accidents on the city's transportation network and to determine the locations of the most frequent accidents during peak and off-peak hours. A quantitative methodology was employed in this study, utilizing a dataset of recent accidents that occurred between 2019 and 2021, classified into peak and off-peak incidents. The data was analyzed using Python software and Quantum Geographic Information System (QGIS) tools for big data analytics. These programs enabled the creation of spatial maps of the study area and the identification of accident spots based on latitude and longitude information. A decision tree classification approach was used in the machine-learning method implemented with Python software. Additionally, the dataset file was uploaded to QGIS, which applied the heatmap (Kernel Density Estimation) algorithm to identify accident hotspots. The study findings revealed that the city center was the most common location for accidents overall, with peak and off-peak times, lanes, and days of the week investigated as potential contributing factors. The target variable was the number of accidents involving serious and minor injuries, which were found to be significantly associated with the identified accidents in this study.

A GIS Approach for Analysis of Traffic Accident Hotspots in Abha and Bisha Cities, Saudi Arabia

Road traffic accidents present substantial global challenges, encompassing a wide range of consequences that have implications for the economy, public health, the environment, and society. The present study is focused on the phenomenon of rapid urbanization in Abha and Bisha, two cities located in the Kingdom of Saudi Arabia. These cities have witnessed a substantial growth in urbanization, with a notable increase of 225% over a span of 40 years. The expansion of urban areas has given rise to significant concerns regarding the density of the road infrastructure, which has been further exacerbated by an increase in the volume of vehicles. Consequently, this has led to a notable escalation in traffic-related issues and accidents. Analysis reveals that traffic accidents are concentrated in specific areas, with hotspots primarily located in the western regions of Abha and Bisha, while cold spots are concentrated in the northern areas. Furthermore, a strong positive correlation (r = 0.93) is observed between the number of traffic accidents and road type, with over half of the accidents occurring on highways. Notably, the highways in Abha and Bisha predominantly follow a direction from southwest to northeast at a 71.1° angle. In conclusion, this research offers significant findings regarding the prevalence and determinants of traffic accidents in Abha and Bisha, emphasizing the need for effective traffic management strategies to enhance road safety and mitigate the associated risks.

Sensing Accident-Prone Features in Urban Scenes for Proactive Driving and Accident Prevention

In urban cities, visual information on and along roadways is likely to distract drivers and lead to missing traffic signs and other accident-prone (AP) features. To avoid accidents due to missing these visual cues, this paper proposes a visual notification of AP-features to drivers based on real-time images obtained via dashcam. For this purpose, Google Street View images around accident hotspots (areas of dense accident occurrence) identified by a real-accident dataset are used to train a novel attention module to classify a given urban scene into an accident hotspot or a non-hotspot (area of sparse accident occurrence). The proposed module leverages channel, point, and spatial-wise attention learning on top of different CNN backbones. This leads to better classification results and more certain AP-features with better contextual knowledge when compared with CNN backbones alone. Our proposed module achieves up to 92% classification accuracy. The capability of detecting AP-features by the proposed model were analyzed by a comparative study of three different class activation map (CAM) methods, which are used to inspect specific AP-features causing the classification decision. The outputs of the CAM methods were processed by an image processing pipeline to extract only the AP-features that are explainable to drivers and notified using a visual notification system. Range of experiments was performed to prove the efficacy and AP-features of the system. Ablation of the AP-features taking 9.61%, on average, of the total area in each image sample increased the chance of a given area to be classified as a non-hotspot by up to 21.8%.

Examining Car Accident Prediction Techniques and Road Traffic Congestion: A Comparative Analysis of Road Safety and Prevention of World Challenges in Low-Income and High-Income Countries

Road accidents are a significant negative outcome of transportation systems, causing injuries, fatalities, traffic congestion, and economic losses. As cities expand and the number of vehicles on the road increases, traffic accidents (TAs) have become a significant problem. Studies have shown that urban development plays a more significant role in transportation safety than previously thought. Low-income countries have higher fatality rates than high-income countries, according to the Permanent International Association of Road Congress (PIARC) and the World Health Organization (WHO). Predicting and preventing the occurrence of accidents and congestion is necessary worldwide, especially in developing countries where fatality rates are higher. The objective of this study is to examine and make a comparative analysis in low-income and high-income countries of the existing literature on the global challenge of car accidents and use its prediction techniques to enhance road safety and reduce traffic congestion. The study evaluates various approaches such as logistic regression, decision tree, random forest, deep neural network, support vector machine, random forest, K-nearest neighbors, Naïve Bayes, empirical Bayes, geospatial analysis methods, and UIMA, NSGA-II, and MOPS algorithms. The research identifies current challenges, prevention ideas, and future directions for preventing accidents and congestion on the road network. Integrating GIS-based spatial statistical methods and temporal data and utilizing advanced optimization algorithms and machine learning methods can result in accurate prediction models that can help identify accident hotspots and reduce congestions and enhance traffic safety and mitigate their occurrence. Effectively preventing urban traffic congestion requires the integration of spatial data into precise accident prediction models. By employing spatial analysis, road safety planning can be enhanced, high-risk areas can be identified, interventions can be evaluated, and resources can be optimally allocated to facilitate effective road safety measures and decision-making, especially in settings with limited resources. Therefore, it is crucial to consider ML and spatial analysis techniques and advanced optimization algorithms to enhance traffic flow control, in road safety research and transport planning efforts.