scholarly journals GIS-based analysis of spatial–temporal correlations of urban traffic accidents

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Qinglu Ma ◽  
Guanghao Huang ◽  
Xiaoyao Tang
Keyword(s):  
Cluster Analysis ◽  
Traffic Accidents ◽  
Road Traffic ◽  
Temporal Distribution ◽  
Urban Traffic ◽  
Distribution Characteristics ◽  
Urban Road ◽  
Accident Frequency ◽  
Accident Severity ◽  
Density Analysis

Abstract Background Understanding 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. Methods The 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. Results The 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. Conclusions By 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.

scholarly journals Analysis of Intra-Urban Traffic Accidents Using Spatiotemporal Visualization Techniques

2014 ◽  
Vol 15 (3) ◽  
pp. 227-232 ◽  
Author(s):  
Ali Soltani ◽  
Sajad Askari
Keyword(s):  
Traffic Congestion ◽  
Traffic Accidents ◽  
Road Traffic ◽  
Kernel Estimation ◽  
Temporal Distribution ◽  
Spatiotemporal Analysis ◽  
Urban Traffic ◽  
High Rate ◽  
Spatiotemporal Visualization ◽  
Visualization Techniques

Abstract Road traffic accidents (RTAs) rank in the top ten causes of the global burden of disease and injury, and Iran has one of the highest road traffic mortality rates in the world. This paper presents a spatiotemporal analysis of intra-urban traffic accidents data in metropolitan Shiraz, Iran during the period 2011-2012. It is tried to identify the accident prone zones and sensitive hours using Geographic Information Systems (GIS)-based spatio-temporal visualization techniques. The analysis aimed at the identification of high-rate accident locations and safety deficient area using Kernel Estimation Density (KED) method. The investigation indicates that the majority of occurrences of traffic accidents were on the main roads, which play a meta-region functional role and act as a linkage between main destinations with high trip generation rate. According to the temporal distribution of car crashes, the peak of traffic accidents incident is simultaneous with the traffic congestion peak hours on arterial roads. The accident-prone locations are mostly located in districts with higher speed and traffic volume, therefore, they should be considered as the priority investigation locations to safety promotion programs.

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

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Meina Wang ◽  
Jing Yi ◽  
Xirui Chen ◽  
Wenhui Zhang ◽  
Tiangang Qiang
Keyword(s):  
Traffic Safety ◽  
Road Network ◽  
Traffic Accidents ◽  
Road Traffic ◽  
Network Density ◽  
Road Traffic Accidents ◽  
Urban Centers ◽  
Accident Severity ◽  
Accident Data ◽  
Density Analysis

Road traffic safety is a social issue of widespread concern. It is important for traffic managers to understand the distribution patterns of road traffic accidents. To this end, this study examines the spatial and temporal patterns of road traffic accidents from both accident frequency and accident severity perspectives. Road traffic accident data from 2016 to 2018 in Harbin, China, were used for the analysis. First, the spatial localization of accidents was completed using geocoding, and the localized accident data were classified by season. Then, density analysis was performed both with and without considering road network density. The results of the density analysis showed that when road network density was considered, accidents were mainly distributed in urban centers, while accidents were more dispersed when road network density was not considered. Third, a cluster analysis considering accident severity found that low-severity accident clusters occurred mostly in urban centers. High-severity accident clusters were mostly present in suburban areas. Finally, the results of these two methods are shown by using the comap technique. Areas of the city with a high frequency and severity of crashes in each season were identified. This study will help traffic management to have a more visual and intuitive understanding of the urban traffic safety situation and to take targeted measures to improve it accordingly.

scholarly journals PATTERN RECOGNITION BASED SPEED FORECASTING METHODOLOGY FOR URBAN TRAFFIC NETWORK

Transport ◽  
2018 ◽  
Vol 33 (4) ◽  
pp. 959-970 ◽  
Author(s):  
Tamás Tettamanti ◽  
Alfréd Csikós ◽  
Krisztián Balázs Kis ◽  
Zsolt János Viharos ◽  
István Varga
Keyword(s):  
Neural Network ◽  
Input Data ◽  
Road Traffic ◽  
Urban Traffic ◽  
Traffic Network ◽  
Data Handling ◽  
Urban Road ◽  
Traffic Sensors ◽  
Network Training ◽  
Artificial Neural Network Ann

A full methodology of short-term traffic prediction is proposed for urban road traffic network via Artificial Neural Network (ANN). The goal of the forecasting is to provide speed estimation forward by 5, 15 and 30 min. Unlike similar research results in this field, the investigated method aims to predict traffic speed for signalized urban road links and not for highway or arterial roads. The methodology contains an efficient feature selection algorithm in order to determine the appropriate input parameters required for neural network training. As another contribution of the paper, a built-in incomplete data handling is provided as input data (originating from traffic sensors or Floating Car Data (FCD)) might be absent or biased in practice. Therefore, input data handling can assure a robust operation of speed forecasting also in case of missing data. The proposed algorithm is trained, tested and analysed in a test network built-up in a microscopic traffic simulator by using daily course of real-world traffic.

Evaluation and Optimization on Colors in Urban Traffic Based on PSO and GT-BPANN

2011 ◽  
Vol 97-98 ◽  
pp. 1162-1167
Author(s):  
Hong Wei Yuan ◽  
Wen Bo Zhang
Keyword(s):  
Quantitative Research ◽  
Traffic Accidents ◽  
Evaluation Model ◽  
Reliability And Validity ◽  
Urban Traffic ◽  
Evaluation Index System ◽  
Grey Theory ◽  
Urban Road ◽  
Modern Knowledge ◽  
Color Psychology

In order to reduce traffic accidents, achieving safety and harmony of traffic color, a quantitative research on traffic color of urban road were carried. Grounded on modern knowledge of color theory, color psychology, Grey Theory and Back-error Propagation Artificial Neural Network (GT-BPNN), Particle Swarm Optimization algorithm (PSO) and traffic questionnaires, the evaluation index system of traffic color in urban road, the evaluation model of transportation color and the model of color harmony and optimization in urban road were constructed. Assisted by MATLAB and other software, the reliability and validity of models were determined, taking a road in Xuzhou, Jiangsu as a test section. According to the results, some reasonable improvements on traffic safe color were recommended.

scholarly journals A new approach to solving the problem of road traffic injuries in agricultural areas

2001 ◽  
Vol 82 (5) ◽  
pp. 369-397
Author(s):  
I. G. Faizullin
Keyword(s):  
Traffic Accidents ◽  
Road Traffic ◽  
Severity Index ◽  
Road Traffic Accidents ◽  
New Approach ◽  
Accident Severity ◽  
Depth Study ◽  
The Russian Federation ◽  
The Republic ◽  
Agricultural Areas

In the Republic of Tatarstan from 1995 to the first half of 1999, there were 18,376 road traffic accidents (RTAs). They affected 23558 people, killed 3525 and injured 20033. The accident severity index averaged 14.8 during that period. Downward trends in the severity of accidents in 1995, 1996, 1997, 1998 were rather telling: 26.9; 17.6; 16.9; 13.7; 11.8. In spite of that, Tatarstan looks unfavorable against the background of other territories of the Russian Federation. In order to identify the cause-and-effect relations of the severity of traffic accidents, we conducted an in-depth study of them in the territory of cities and agricultural districts during this period.

scholarly journals Business Intelligence and Data Warehouse Technologies for Traffic Accident Data Analysis in Botswana

2021 ◽  
Author(s):  
Monkgogi Mudongo ◽  
Edwin Thuma ◽  
Nkwebi Peace Motlogelwa ◽  
Tebo Leburu-Dingalo ◽  
Pulafela Majoo
Keyword(s):  
Data Warehouse ◽  
Business Intelligence ◽  
Traffic Accidents ◽  
Road Traffic ◽  
Road Traffic Accidents ◽  
Managerial Decision ◽  
Vehicle Registration ◽  
Accident Severity ◽  
Accident Data ◽  
Causes Of Deaths

Road traffic accidents are a serious problem for the nation of Botswana. A large amount of money is used to compensate those who are affected by road accidents. Traffic accidents are one of the major causes of Deaths in Botswana. It is important for relevant organizations to have a reliable source of data for accurate evaluation of traffic accidents. Similarly, data on vehicle registration must be transformed and be readily available to assist managerial decision makers. In this article, we deploy a Business Intelligence (BI) and Data Warehouse (DW) solution in an attempt to assist the relevant departments in their road traffic accidents and vehicle registration evaluation. In Our evaluation of the traffic accidents our findings suggest that across accident severity, Damage Only accidents had the most interesting recent trend with a 11.93% decrease in the last 3 years on record. Count of Accident Severity for Damage Only accidents dropped from 13,491 to 11,881 between 2018 and 2020 whilst Minor accidents experienced the longest period of growth. Most accidents take place in rural locations and more accidents take place during the weekend. At 28,439, Sunday had the highest number of accidents and was 47.59% higher than Wednesday, which had the lowest count of accidents at 19,269. The results for vehicle registration reveal that the number of vehicle registration decreased for the last 3 years on record. The number of vehicles registered dropped from 65535 to 24457 during its steepest decline between 2019 and 2021.

scholarly journals Urban Wasteful Transport and Its Estimation Methods

2018 ◽  
Vol 10 (12) ◽  
pp. 4562 ◽  
Author(s):  
Xiangyang Cao ◽  
Bingzhong Zhou ◽  
Qiang Tang ◽  
Jiaqi Li ◽  
Donghui Shi
Keyword(s):  
Traffic Flow ◽  
Traffic Congestion ◽  
Transport Theory ◽  
Road Traffic ◽  
Urban Traffic ◽  
Flow Density ◽  
Urban Road ◽  
Road Capacity ◽  
Density Relationship

The paper studies urban road traffic problems from the perspective of resource science. The resource composition of urban road traffic system is analysed, and the road network is proved as a scarce resource in the system resource combination. According to the role of scarce resources, the decisive role of road capacity in urban traffic is inferred. Then the new academic viewpoint of “wasteful transport” was proposed. Through in-depth research, the paper defines the definition of wasteful transport and expounds its connotation. Through the flow-density relationship analysis of urban road traffic survey data, it is found that there is a clear boundary between normal and wasteful transport in urban traffic flow. On the basis of constructing the flow-density relationship model of road traffic, combined with investigation and analysis, the quantitative estimation method of wasteful transport is established. An empirical study on the traffic conditions of the Guoding section of Shanghai shows that there is wasteful transport and confirms the correctness of the wasteful transport theory and method. The research of urban wasteful transport also reveals that: (1) urban road traffic is not always effective; (2) traffic flow exceeding road capacity is wasteful transport, and traffic demand beyond the capacity of road capacity is an unreasonable demand for customers; (3) the explanation that the traffic congestion should apply the comprehensive theory of traffic engineering and resource economics; and (4) the wasteful transport theory and method may be one of the methods that can be applied to alleviate traffic congestion.

scholarly journals Accident Severity Prediction Using Data Mining Methods

2019 ◽  
pp. 528-536 ◽  
Author(s):  
S. Ramya ◽  
SK. Reshma ◽  
V. Dhatri Manogna ◽  
Y. Satya Saroja ◽  
G. Sanjay Gandhi
Keyword(s):  
Data Mining ◽  
Random Forest ◽  
Traffic Accidents ◽  
Road Traffic ◽  
Analytical Data ◽  
Developed Countries ◽  
Influential Factors ◽  
Living Environment ◽  
Severity Prediction ◽  
Accident Severity

The smart city concept provides opportunities to handle urban problems, and also to improve the citizens’ living environment. In recent years, road traffic accidents (RTAs) have become one of the largest national health issues in the world and it is leading cause for deaths. The burden of road accident casualties and damage is much higher in developing countries than in developed countries. Many factors (driver, environment, vehicle, etc.) are related to traffic accidents, some of those factors are more important in determining the accident severity than others. The analytical data mining solutions can significantly be employed to determine and predict such influential factors among human, vehicle and environmental factors. In this research, the classification technique i.e., Random forest algorithm is used to identify relevant patterns and for classifying the type of accident severity of various traffic accidents with the help of influential environmental features of RTAs that can be used to build the prediction model. This technique was tested using a real dataset. A decision system has been built using the model generated by the Random Forest technique that will help decision makers to enhance the decision making process by predicting the severity of the accident.

scholarly journals Appraisal and mapping the spatial-temporal distribution of urban road traffic noise

2009 ◽  
Vol 6 (2) ◽  
pp. 325-335 ◽  
Author(s):  
D. Banerjee ◽  
S. K. Chakraborty ◽  
S. Bhattacharyya ◽  
A. Gangopadhyay
Keyword(s):  
Road Traffic ◽  
Traffic Noise ◽  
Temporal Distribution ◽  
Road Traffic Noise ◽  
Urban Road
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