Statistical Analysis of Road Accident Data of UK Using Operational Intelligence Tool - Splunk

2020 ◽  
pp. 111-119
Author(s):  
Tapajyoti Deb ◽  
Niti Vishwas ◽  
Ashim Saha
Keyword(s):  
Statistical Analysis ◽  
Road Accident ◽  
Operational Intelligence ◽  
Accident Data

scholarly journals Visualization of Road Accident Data In Jabodetabek Area Year 2014-2017

2019 ◽  
Vol 1196 ◽  
pp. 012050
Author(s):  
Johan Setiawan ◽  
Sudecanto
Keyword(s):  
Road Accident ◽  
Accident Data

scholarly journals Representativeness of Czech In-Depth Accident Data

Safety ◽  
2021 ◽  
Vol 7 (2) ◽  
pp. 40
Author(s):  
Robert Zůvala ◽  
Kateřina Bucsuházy ◽  
Veronika Valentová ◽  
Jindřich Frič
Keyword(s):  
Road Safety ◽  
National Level ◽  
Road Accident ◽  
General Applicability ◽  
Weighting Method ◽  
The Czech Republic ◽  
Depth Data ◽  
Road Users ◽  
Depth Study ◽  
Accident Data

Road accident occurrence is often the result of driving system malfunctions, and road safety improvements need to focus on all basic driving components—the vehicle, road infrastructure, and road users. Only focusing on one type of improvement does not necessarily lead to increased road safety. Instead, improved road safety requires comprehensive measures that consider all factors using in-depth accident analysis. The proposed measures, based on the findings from in-depth data that have general applicability, are necessary to determine whether data gained from in-depth studies adequately represent national statistics. This article aims to verify the representativeness of the Czech In-Depth Accident Study at a national level. The main contribution of this article lies in the use of a weighting method (specifically, a raking procedure) to generalise research results and render them applicable to a whole population. The obtained results could be beneficial at the national level, in the Czech Republic, and also on the supranational level. The applicability of this method on accident data is verified; thus, the method can be applied also in other countries or can be used to verify the applicability of conclusions from the Czech in-depth study also on a European or worldwide level.

scholarly journals Statistical Analysis of Traffic Accidents in Shanghai River Crossing Tunnels and Safety Countermeasures

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Linjun Lu ◽  
Jian Lu ◽  
Yingying Xing ◽  
Chen Wang ◽  
Fuquan Pan
Keyword(s):  
Statistical Analysis ◽  
Traffic Accidents ◽  
Temporal And Spatial Distribution ◽  
Urban Road ◽  
Spatial Distribution Characteristics ◽  
Road Tunnels ◽  
Accident Data ◽  
Tunnel Safety ◽  
Temporal And Spatial ◽  
Safety Countermeasures

A large number of traffic tunnel accidents have been reported in China since the 21th century. However, few studies have been reported to analyze traffic accidents that have occurred in urban road tunnels. This study aims to examine the characteristics of the temporal, spatial, and modality distributions of traffic in Shanghai river crossing tunnels using statistical analysis and comparative analysis. Employing these techniques tunnel accident data obtained from Shanghai center 110 was analyzed to determine temporal and spatial distribution characteristics of traffic accidents in river crossing tunnels in Shanghai. The results of this analysis are discussed and summarized in this paper. Identification of the characteristics of tunnel traffic accidents can provide valuable information for development of effective countermeasures to improve tunnel safety in China.

scholarly journals Machine Learning Approaches to Traffic Accident Analysis and Hotspot Prediction

Computers ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 157
Author(s):  
Daniel Santos ◽  
José Saias ◽  
Paulo Quaresma ◽  
Vítor Beires Nogueira
Keyword(s):  
Machine Learning ◽  
Predictive Model ◽  
Road Accident ◽  
Influential Factors ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Economic Losses ◽  
Learning Approaches ◽  
Road Accidents ◽  
Accident Data

Traffic accidents are one of the most important concerns of the world, since they result in numerous casualties, injuries, and fatalities each year, as well as significant economic losses. There are many factors that are responsible for causing road accidents. If these factors can be better understood and predicted, it might be possible to take measures to mitigate the damages and its severity. The purpose of this work is to identify these factors using accident data from 2016 to 2019 from the district of Setúbal, Portugal. This work aims at developing models that can select a set of influential factors that may be used to classify the severity of an accident, supporting an analysis on the accident data. In addition, this study also proposes a predictive model for future road accidents based on past data. Various machine learning approaches are used to create these models. Supervised machine learning methods such as decision trees (DT), random forests (RF), logistic regression (LR), and naive Bayes (NB) are used, as well as unsupervised machine learning techniques including DBSCAN and hierarchical clustering. Results show that a rule-based model using the C5.0 algorithm is capable of accurately detecting the most relevant factors describing a road accident severity. Further, the results of the predictive model suggests the RF model could be a useful tool for forecasting accident hotspots.

Augmenting Classifiers Performance through Clustering

2018 ◽  
Vol 8 (1) ◽  
pp. 57-68 ◽  
Author(s):  
Sachin Kumar ◽  
Prayag Tiwari ◽  
Kalitin Vladimirovich Denis
Keyword(s):  
Traffic Accident ◽  
Latent Class ◽  
Safety Concern ◽  
Health And Safety ◽  
Road Accident ◽  
Support Vector ◽  
Road Accidents ◽  
Real World Data ◽  
The Public ◽  
Accident Data

Road and traffic accident data analysis are one of the prime interests in the present era. It does not only relate to the public health and safety concern but also associated with using latest techniques from different domains such as data mining, statistics, machine learning. Road and traffic accident data have different nature in comparison to other real-world data as road accidents are uncertain. In this article, the authors are comparing three different clustering techniques: latent class clustering (LCC), k-modes clustering and BIRCH clustering, on road accident data from an Indian district. Further, Naïve Bayes (NB), random forest (RF) and support vector machine (SVM) classification techniques are used to classify the data based on the severity of road accidents. The experiments validate that the LCC technique is more suitable to generate good clusters to achieve maximum classification accuracy.

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