Spatial and Temporal Distribution Analysis of Traffic Accidents Using GIS-Based Data in Harbin

This study applied GIS-based statistical analytic techniques to investigate the influence of accident Severity Index (SI) on temporal-spatial patterns of accident hotspots related to the specific time intervals of day and seasons. Road Traffic Accident (RTA) data in 3 years (2015 − 2017) in Hanoi, Vietnam were used to analyze and test this approach. Firstly, the RTA data were divided into four seasons in accordance with Hanoi’s weather conditions and the time intervals such as the daytime, nighttime, or peak hours. Then, the Kernel Density Estimation (KDE) method was applied to analyze hotspots according to the time intervals and seasons. Finally, the results were presented by using the comap technique. This study considered both analyses with and without SI. The accident SI measures the seriousness of an accident. The approach method is to give higher weights to the more serious accidents, but not with the extremely high values calculated on a direct rate to the accident expenditures. The results showed that both analyses determined the relatively similar hotspots, but the rankings of some hotspots were quite different due to the integration of SI. It is better to take into account SI in determining RTA hotspots because the gained results are more precise and the rankings of hotspots are more accurate. From there, the traffic authorities can easily understand the causes behind each accident and provide reasonable solutions to solve the most dangerous hotspots in case of limited budget and resources appropriately. This is also the first study about this issue in Vietnam, so the contribution of the article will help the traffic authorities easily solve this problem not only in Hanoi but also in other cities.

Objective To establish an autoregression moving average (ARMA) model for predicting general traffic accidents and analyzing distributional difference on time series and frequency of common traffic accident so as to provide certain basis for a prediction model with better stability and accuracy. Methods The data of road traffic accidents in one newly developed zone of Chongqing in 2000-2005 were collected. The monthly distribution regularity of road traffic accidents was analyzed with descriptive epidemiologic method. ARMA model was set up for retrospective and prospective prediction. The predicted data were compared. Results Based on the characteristics of monthly distribution, the frequency of general traffic accidents in this area showed a cyclic fashion. The frequency of general traffic accidents predicted by ARMA model had over 80% of coincidence with the actual value. Conclusion The ARMA model can be used to predict the frequency of general traffic accidents, with better accuracy of short-term prediction than the long-term prediction. Key words: Accidents, traffic; ARMA; Time series; Prediction model

BackgroundUnderstanding the spatial–temporal distribution characteristics of urban road traffic accidents is important for urban road traffic safety management. Based on the road traffic data of Wales in 2017, the spatial–temporal distribution of accidents is formed.MethodsThe density analysis method is used to identify the areas with high accident incidence and the areas with high accident severity. Then, two types of spatial clustering analysis models, outlier analysis and hot spot analysis are used to further identify the regions with high accident severity.ResultsThe results of density analysis and cluster analysis are compared. The results of density analysis show that, in terms of accident frequency and accident severity, Swansea, Neath Port Talbot, Bridgend, Merthyr Tydfil, Cardiff, Caerphilly, Newport, Denbighshire, Vale of Glamorgan, Rhondda Cynon Taff, Flintshire and Wrexham have high accident frequency and accident severity per unit area. Cluster analysis results are similar to the density analysis. Finally, the temporal distribution characteristics of traffic accidents are analyzed according to month, week, day and hour. Accidents are concentrated in July and August, frequently in the morning rush hour and at dusk, with the most accidents occurring on Saturday.ConclusionsBy comparing the two methods, it can be concluded that the density analysis is simple and easy to understand, which is conducive to understanding the spatial distribution characteristics of urban traffic accidents directly. Cluster analysis can be accurate to the accident point and obtain the clustering characteristics of road accidents.

The paper aims to provide insights of choosing suitable time series models and analysing road traffic accidents and injuries taking road traffic accident (RTA) and injuries (RTI) data in Oman as a case study as the country faces one of the highest numbers of road accidents per year. Data from January 2000 to June 2019 from several secondary sources were gathered. Time series decomposition, stationarity and seasonality checking were performed to identify the appropriate models for RTA and RTI. SARIMA (3, 1, 1)(2, 0, 0)(12) and SARIMA (0, 1, 1)(1, 0, 2)(12) models were found to be the best for the road traffic accident and injury data, respectively, comparing many different models. AIC, BIC and other error values were used to choose the best model. Model diagnostics were also performed to confirm the statistical assumptions, and 2-year forecasting was performed. The analyses in this paper would help many government departments, academic researchers and decision-makers to generate policies to reduce accidents and injuries.

Road traffic accidents (RTAs) impose substantial human, social and economic burdens. This study analysed 78,987 police-reported crashes that occurred in Auckland, New Zealand between 2015 and 2024 to examine how neighbourhood-level socioeconomic deprivation influences crash frequency and severity. Crash outcomes (fatal, serious, minor and non-injury) were assessed in relation to the IMD18, a composite index of deprivation across seven domains (Housing, Access, Crime, Health, Education, Income and Employment). Using regression analyses, strong associations between deprivation and crash frequency were found, with the Housing and Access domains being the most significant predictors of crash rates and related social costs (R 2 > 0.80). This reflects both environmental and systemic conditions – such as inadequate infrastructure, poor transport access and reliance on older vehicles. By linking deprivation domains to crash data, this study highlights the importance of targeted interventions to help reduce road traffic rates and their economic impact in the most affected areas.

In Nigeria, literature on the integration of traffic of pavement condition and traffic characteristics in predicting road traffic accident frequency on 2-lane highways are scanty, hence this article to fill the gap. A comparison of road traffic accident frequency prediction models on IIesha-Akure-Owo road based on the data observed between 2012 and 2014 is presented. Negative Binomial (NB), Ordered Logistic (OL) and Zero Inflated Negative Binomial (ZINB) models were used to model the frequency of road traffic accident occurrence using road traffic accident data from the Federal Road Safety Commission (FRSC) and pavement conditions parameters from pavement evaluation unit of the Federal Ministry of Works, Kaduna. The explanatory variables were: annual average daily traffic (aadt), shoulder factor (sf), rut depth (rd), pavement condition index (pci), and international roughness index (iri). The explanatory variables that were statistically significant for the three models are aadt, sf and iri with the estimated coefficients having the expected signs. The number of road traffic accident on the road increases with the traffic volume and the international roughness index while it decreases with shoulder factor. The systematic variation explained by the models amounts to 87.7, 78.1 and 74.4% for NB, ZINB and OL respectively. The research findings suggest the accident prediction models that should be integrated into pavement rehabilitation.
 
 Keywords: 

Road traffic safety: An analysis of the cross-effects of economic, road and population factors

Along with the development of the economy and society and the speedy increasing number of personal vehicles, especially motorcycles, many cities in developing countries like Hanoi (Vietnam) are facing many transportation problems, notably traffic accidents involving motorcyclists. Motorcycle accidents are caused by main human-error factors, and could be considered as the typical grey system with the features of complexity and imperfect information. In this study, a grey relational analysis (GRA) model of the human-error factors is presented to quickly explore the main human-error factors contributing to traffic accidents caused by motorcycles. In this model, the grey relational degree and the grey relational order of each human-error factor also are determined to rank the main contributing human-error factors. Taking the data of road traffic accidents caused by motorcycles in Hanoi, the capital of Vietnam between 2015 and 2017 as experimental data, the grey relational degrees of human-error factors have been analyzed quantitatively through the GRA model. The experimental results show the first three human-error factors include wrong lane shifting, poor road observation, and speeding, respectively. The results also help to conduct effective countermeasures for controlling human errors and reducing road traffic accidents.

Spatio-temporal correlation and interaction are significant features of traffic accident data. To account for the spatio-temporal correlation and interaction and the ordinal nature simultaneously, spatio-temporal generalized ordered logit models with different temporal treatments are advocated to examine accident injury severities. In the context of Bayesian inference, we estimate the advocated models and compare them with traditional generalized ordered logit model and spatial generalized ordered logit model using six-year (2014-2019) accident data from the Dongguang section of Guangzhou-Shenzhen Coastal Freeway in China. The results indicate that there are significant spatio-temporal effects and interaction in the accident severity data, and that the spatio-temporal generalized ordered logit models yield low values of deviation information criterion and more reasonable parameter estimates than the generalized ordered logit and spatial generalized ordered logit models do. The estimation results reveal that covariates related to vehicle type, accident type, season, time, horizontal curvature, and bridge have significant effects on accident injury severities. The findings above demonstrate the superiority of the proposed spatio-temporal generalized ordered logit models for predicting accident severities.

With the increase of truck ownership year by year, traffic accidents involving heavy trucks occur frequently and the consequence is more serious. The accident is the result of the maladjustment of various factors in the “man-vehicle-environment” system. In this context, using the data of 305 traffic accidents of heavy truck in a province of China in 2020, 11 influencing factors are selected from the three aspects of people, vehicle, and environment. The scientific analysis of the factors affecting the severity of heavy truck accidents is carried out by the Logistic regression model. Mining the influence of “man-machine-environment” system disharmony on the severity of the accident. The result shows that the main factors affecting the severity of heavy truck accidents are driving on the wrong side of the road, failing to yield as required, involving pedestrians, vehicle fault and without traffic control. Based on the above five factors, countermeasures to prevent heavy truck traffic accidents are put forward in order to reduce casualties and property damage caused by accidents. It is of great theoretical significance and practical application value to improve the level of road traffic safety in China.KeywordsMan-machine-environmentHeavy goods vehicleAccident severityLogistic regression models

As World Health Organization reports show more than 1.3 million people die each year from traffic accidents and more than twenty to fifty million people are harmed by non-fatal accidents. Understanding and identifying the major factors of road traffic accidents can help the stakeholders to take appropriate measures to minimize the accidents. Support Vector Machine (SVM), J48, and Naïve Bayes (NB) classification techniques are implemented to build a model that predicts accident severities and to identify road traffic accident factors. As experimental findings showed weather conditions, number of lanes, road lighting conditions, and speed limit are the determinant factors that cause more accident severity. Finally, the classification and prediction performance of the models are compared using various performance evaluation techniques. J48 classifier has better classification performance than Support Vector Machine and Naïve Bayes classifiers. Naïve Bayes is the classifier that showed the lowest performance than others, but it has good performance in classifying fatal accident severities rather than serious and slight accident severities.

The frequency of road traffic accidents (RTAs) is higher in China than in other countries, and traffic safety is a more complex issue. Our study of RTA severity differences among 31 provinces in China identifies the most important traffic management issues in each province. Provinces with the highest RTA risk were identified from RTA mortality data for 31 provinces during 2004–2013. Then provinces with the highest risk according to mortality/per capita GDP, mortality/10,000 vehicles, and mortality/100,000 persons were determined to eliminate the effect of differences in economic development, vehicle numbers, and population on mortality rates. The results show that Guangdong, Zhejiang, Jiangsu, Shandong, Sichuan, Anhui, Fujian, and Hebei have the highest RTA risk in China. Tibet, Anhui, Guangxi, Qinghai, Sinkiang, Gansu, Zhejiang, and Guangdong have the highest risk trend. Thus, Anhui, Zhejiang, and Guangdong have both the highest RTA risk and the highest risk trend. We discuss environmental, road, vehicle, and human causes of RTAs for the province groups. Improving traffic management, strengthening the deterrent effect of legal penalties, and establishing a road safety culture could reduce RTAs in China.

The occurrence of fatal traffic accidents often causes serious casualties and property losses, endangering travel safety. This work uses the statistical data of fatal road traffic accidents in Shenzhen from 2018 to 2022 as the basis to determine the characteristic patterns and the main influencing factors of the occurrence of fatal road traffic accidents. The accident description data are also analyzed using the analysis method based on Term Frequency-Inverse Document Frequency (TF-IDF) data mining to obtain the characteristics of accident fields, objects, and types. Furthermore, this work conducts a kernel density analysis combined with spatial autocorrelation to determine the hotspot areas of accident occurrence and analyze their spatial aggregation effects. A principal component analysis is performed to calculate the factors related to the accident subjects. Results showed that weak safety awareness of motorists and irregular driving operations are the main factors for the occurrence of accidents. Finally, targeted safety management strategies are proposed based on the analysis results. In the current data era, the research results of this paper can be used for the prevention and emergency of accidents to formulate corresponding measures, and provide a theoretical basis for decision making.

Accidents are unexpected events and expected to incur losses. The frequency and severity of accidents can be used as an evaluation in an effort to implement K3 and prevent accidents that have been applied to the plant. This research perform statistical calculations accidents that occur in the company of the year 2013-2016 CPO as an evaluation of the implementation of occupational health and safety techniques that already exist. The results showed that the highest severity rate in 2016 amounted to 103.2581 and the lowest in 2013 with a value of 0. Meanwhile, data on the number of workplace accidents shows that the highest frequency rate was in 2014 with a value of 15.1589 and the lowest was in 2016 with a value of 4.9763. Intervention programs and prevention of occupational accidents in the factory rated yet effective to decrease the frequency and severity of workplace accidents in the company due to increases in the frequency of workplace accidents in 2014 and the increase in the severity of accidents work in 2016.

In recent years, the number of studies on crashes involving large-trucks has increased due to its importance to the economy and the higher chance of fatalities. However, none of the previous studies has given attention to the spatial concentrations of large-truck crashes. Moreover, the literature lacks exploration of granular level land use and urban design factors. The current study used the DBSCAN (Density-Based Spatial Clustering of Application with Noise) method to identify the spatial concentrations of crashes involving large-trucks. Additionally, the study explored housing, population, employment, and road network density attributes along with the crash characteristics, roadway attributes, location type, traffic conditions, driver’s action and behavior, and environmental factors. The association rule analysis was employed to discover the contributory factors that lead to no injury, non-severe and severe injuries at the spatial concentrations of crashes involving large-trucks. The findings indicated that the rear-end collisions involving drunk drivers often lead to severe injuries in large-truck crashes. Non-interstate roads, speed limit from 40 to 80 kilometers per hour, high road network density, medium and high population density are frequent conditions of non-severe injuries. Lastly, collisions between large-trucks and fixed objects, sideswipe same direction collisions, snowy roads, clear weather, medium road network and employment density are likely to facilitate no injury crashes involving large-trucks. Road traffic authorities can use these insights to reduce the frequency and severity of crashes involving large-trucks at their spatial concentrations.