Accident Hotspots Research Articles

Spatial-Temporal Evolution of Maritime Accident Hot Spots in the East China Sea: A Space-Time Cube Representation

As public concern for maritime safety grows, there is a pressing need to delve deeper into the root causes of maritime accidents and develop effective preventive strategies. Spatial-temporal analysis stands out as a powerful approach to pinpointing accident hot spots. While previous research has shed light on the spatial aspects of these incidents, a comprehensive understanding of their temporal dimensions remains elusive. This paper bridges this gap by leveraging the Space-Time Cube tool in conjunction with traditional Kernel Density analysis to chart the spatial-temporal dynamics of maritime accident hot spots. Focusing on the East China Sea, a region notorious for its high incidence of maritime accidents and home to numerous world-class ports, we present a case study that offers fresh insights. Data spanning from 1994 to 2020, sourced from the Lloyd’s List Intelligence (LLI) database, reveal the evolving landscape of maritime accidents in the area. Notably, since 2005, the Yangtze River Delta Region in China has emerged as a persistent hot spot for accidents, underscoring its significance in maritime safety discourse. Furthermore, our analysis from the 2010s detects a new hot spot expanding towards the southwest of Kaohsiung Port, China, signaling a burgeoning area of concern for maritime safety. While the Fujian coast of China has seen its share of accidents, it is not qualified as a hot spot zone. The Space-Time Cube proves to be an indispensable tool in unraveling the progression of maritime accidents, and our findings indicate that maritime accidents in certain areas may not be merely random occurrences but exhibit intricate patterns.

A novel analytical framework to identify and classify accident hotspots integrating gradient classifier and spatial clustering

A novel analytical framework to identify and classify accident hotspots integrating gradient classifier and spatial clustering

ASSESSMENT OF BLACK SPOTS IN URBAN BHOPAL WITH THE AID OF WEIGHTED SEVERITY INDEX AND KERNAL DENSITY ESTIMATION METHODS

One step toward lowering traffic accidents is identifying the locations of road accident hotspots and the appropriate assessment technique. This study evaluated the effectiveness of kernel density estimation (KDE) and the weighted severity index (WSI) in locating blackspots using the ArcGIS tool. Finding out accident severity levels is necessary to identify the accident-related blackspots. The formula for the WSI method was applied. This study examines five-year traffic accidents in the Bhopal, Madhya Pradesh, city intersection zone of the roads to envisage the appropriateness for the given technique on the basis of availability, consistency, and type of the data. This study designates five typical road network intersection zones as blackspot sites based on the criterion. This study's general conclusion is that, during the years 2017 to 2019, Govindpura Turning in Bhopal is a highly accident-prone area, and point density estimation is preferable to kernel density estimation for this purpose. Additionally, this study found that between 2015 and 2017, high-traffic accidents in Sukhisewaniya's Balampur Ghati were more likely to occur in intersection zones with a large number of legs. This study suggests that point density estimation be used to investigate high number of traffic accident areas by looking into blackspot locations at the macroscopic level of road networks. Furthermore, it is necessary to evaluate the effectiveness of the designated blackspot site and suggest corrective measures that reduce the number of accidents and their consequences.

Spatial and Temporal Analysis of Road Traffic Accidents Using GIS

Road traffic accidents are becoming more common, presenting serious public safety and urban development problems. This study employs Geographic Information Systems to conduct a comprehensive spatial and temporal analysis of road traffic accidents, aiming to identify accident hotspots and underlying patterns. By integrating and analyzing accident data from police reports, the distribution and frequency of accidents across different locations and periods were studied. The accident rate has increased by 1.27% in the fiscal year 2020/2021 than 2019/2020, 2.54% in 2021/2022 than 2020/2021, and by 6.03% in 2022/2023 than 2021/2022. About 12.7% of total accidents recorded were minor, 72.4% of total accidents were major and 14.9% of total accidents recorded were observed to be fatal. The findings reveal a high concentration of accidents at the central part along the Araniko highway. Banepa municipality has a high accident rate compared to Dhulikhel and Panauti due to overspeeding, overloading, overtaking, careless driving, mechanical failures, and loss of vehicle control. Minor accidents were frequent at high-traffic intersections, whereas the major and fatal were predominantly concentrated along the Araniko highway. 41% of medium size, 38% of heavy vehicles, and 39% of light vehicles encountered accidents. The study revealed that head-on collisions on the narrow section of Araniko Highway occur often. Intersection management and signalization in Banepa need to be addressed since rear-end collisions increased during periods of high traffic congestion. Two-wheel riders and pedestrians are the more vulnerable. The study utilizes spatial statistical tools such as Kernel Density Estimation (KDE) to identify accident-prone locations. Temporal analysis further explains peak times and seasons for accidents. The findings reveal distinct spatial clusters of accidents and temporal patterns during peak and non-peak hours. This spatial and temporal analysis of accidents showed the need for specific road safety interventions. Through the identification of accident causes and the implementation of recommended measures, these municipalities can significantly lower accident rates and improve overall road safety in their locality.

Identifying accident prone areas and factors influencing the severity of crashes using machine learning and spatial analyses

Identifying accident prone areas and the contributing environmental factors can save lives and enhance infrastructure durability. This research presents an innovative use of two hierarchical clustering methods (Agglomerative Hierarchical and BIRCH clustering algorithms) to detect accident hotspots with high accident rates on the Yazd-Kerman road, in Iran. These approaches identified clusters of accidents, highlighting significant clusters and categorizing their overlap as two accident prone areas. The high percentage of overlapping results from both methods indicates a high level of consistency in the findings. Through observations, field visits, police report analysis, and interviews with locals, the primary causes of accidents in accident prone areas were identified. In one of the accident prone areas, the most significant reasons for accidents included the presence of a resting area, insufficient lighting at curves, and poor road signage, which created a dilemma zone for drivers. In another accident prone area, the main contributing factor was reduced visibility during dust storms. Then, K-Nearest Neighbors and Random Forests machine learning algorithms were employed to predict the severity of accidents, using various input attributes such as lighting, climate, alignment slope, and road geometry. The K-Nearest Neighbor surpassed the Random Forest technique, achieving an overall accuracy of 71% in contrast to 60%. This study effectively evaluated the outcomes of clustering, uncovering the underlying causes of accidents to inform future practical interventions. Moreover, by predicting the severity of accidents along the road, a framework was developed to propose strategies for risk reduction.

An IoT Cloud System for Traffic Monitoring and Vehicular Accidents Prevention in Uttarakhand

The ongoing traffic challenges in Uttarakhand ne- cessitate innovative solutions. This paper proposes an Internet of Things (IoT) cloud-based system designed to tackle real-time traffic monitoring and accident prevention in the region. By utilizing mobile traffic sensors installed in private and public vehicles, the system collects real-time data. The OpenGTS and MongoDB are used to process this data, allowing for prompt detection of congestion, automated rerouting, and identification of potential accident hotspots. A systematic analysis, including pilot testing, indicates the effectiveness and efficiency of the proposed system, showcasing a significant enhancement in road safety and traffic management. The implementation of this technology represents a substantial step towards modernizing the region’s transportation infrastructure and could set a precedent for similar applications in other areas.

Machine learning and multisource data analysis approach towards traffic accident risk prediction

Traffic accidents are continually a concern across the world bringing with them huge economic costs and high rates of fatality and injuries each single year. Predictive modeling can lessen such risks if the accident hotspots and timing can easily be ascertained. This study collects various data including historical accident reports, water and meteorological conditions, road characteristics, and telematics data, to form traffic accident risk predictive models. To construct and analyze these predictive models, we use various machine learning techniques such as logistic regression, decision trees, and ensemble methods (Random Forest and Gradient Boosting). The results bear out as efficient and effective, ensemble models are streets ahead than regression-based techniques with Gradient Boosting achieving AUC-ROC scores of up to 0.89. The results of this research can also assist in applying effective safety measures in a more cost-effective manner as well as facilitate the development of efficient transport management systems and policies.

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.