This document proposes a system to provide a centralized database for road accident information to help with insurance claims. The system would collect data from police reports on accident victims, medical forms, and other documents. It would apply k-means clustering to analyze the data and identify high-risk locations, accident ratios in different areas, and common causes of accidents. The results would be made available to users and police authorities. Association rule learning using the Apriori algorithm would also be used to determine common factors associated with accidents. The goal is to help reduce accidents by 24% by predicting risks and notifying users.
Analysis of Machine Learning Algorithm with Road Accidents Data SetsDr. Amarjeet Singh
Beginning at now, street transport framework neglect to alter up to the exponential expansion in vehicular masses and to ascertaining the quickest driving courses and catastrophes inside observing differing traffic conditions is a critical issue right presently structures. To upset this issue is to explore the vehicle division dataset with bundle learning technique for finding the best street choice without calamity gauging by want aftereffects of best accuracy count by looking at oversaw AI figuring. In bits of information and AI, bundle strategies utilize diverse learning calculations to give indications of progress prudent execution. The assessment of dataset by facilitated AI technique (SMLT) to get two or three data takes after, factor perceiving proof, univariate evaluation, bivariate and multi-variate appraisal, missing worth medications and separate the information support, information cleaning/organizing and information perception will be done with everything taken into account given dataset. In addition, to look at and talk about the presentation of different AI figuring estimations from the given vehicle division dataset with assessment of GUI based street fiasco want by given attributes.
4Data Mining Approach of Accident Occurrences Identification with Effective M...IJECEIAES
Data mining is used in various domains of research to identify a new cause for tan effect in the society over the globe. This article includes the same reason for using the data mining to identify the Accident Occurrences in different regions and to identify the most valid reason for happening accidents over the globe. Data Mining and Advanced Machine Learning algorithms are used in this research approach and this article discusses about hyperline, classifications, pre-processing of the data, training the machine with the sample datasets which are collected from different regions in which we have structural and semi-structural data. We will dive into deep of machine learning and data mining classification algorithms to find or predict something novel about the accident occurrences over the globe. We majorly concentrate on two classification algorithms to minify the research and task and they are very basic and important classification algorithms. SVM (Support vector machine), CNB Classifier. This discussion will be quite interesting with WEKA tool for CNB classifier, Bag of Words Identification, Word Count and Frequency Calculation.
Nowadays, road crashes become a growing worldwide problem and result in around 1 million deaths now occurs in developing countries. Huge economic losses are now being incurred annually in the ASEAN countries as a direct result of road crashes and the most recent research suggests annual losses across the region are now in excess of US dollar 14 billion per year (around 2.1% of annual GDP of ASEAN region). In Myanmar, thousands of healthy lives are lost by road accidents comparing with other ASEAN countries. A research was conducted on a section of Pyay road with its high-accident locations to study and evaluate the cause of its frequent accidents. Initial study indicated that most of the accidents were attributed to human elements. This was included by the fact that a high percentage of accident was caused by the collision of moving vehicle and pedestrian. Identifying and removing hazardous spots to improve road safety will primarily requires well documented record on those roads with high-accident locations. These data base can inform to urban transport planner for road safety improvement. Kyaing"Road Accident Study on Some Areas in Yangon" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-5 , August 2018, URL: http://www.ijtsrd.com/papers/ijtsrd15944.pdf http://www.ijtsrd.com/engineering/civil-engineering/15944/road-accident-study-on-some-areas-in-yangon/kyaing
PREDICTING ROAD ACCIDENT RISK USING GOOGLE MAPS IMAGES AND ACONVOLUTIONAL NEU...ijaia
Location specific characteristics of a road segment such as road geometry as well as surrounding road features can contribute significantly to road accident risk. A Google Maps image of a road segment provides a comprehensive visual of its complex geometry and the surrounding features. This paper proposes a novel machine learning approach using Convolutional Neural Networks (CNN) to accident risk prediction by unlocking the precise interaction of these many small road features that work in combination to contribute to a greater accident risk. The model has worldwide applicability and a very low cost/time effort to implement for a new city since Google Maps are available in most places across the globe. It also significantly contributes to existing research on accident prevention by allowing for the inclusion of highly detailed road geometry to weigh in on the prediction as well as the new locationbased attributes like proximity to schools and businesses.
Analysis of Machine Learning Algorithm with Road Accidents Data SetsDr. Amarjeet Singh
Beginning at now, street transport framework neglect to alter up to the exponential expansion in vehicular masses and to ascertaining the quickest driving courses and catastrophes inside observing differing traffic conditions is a critical issue right presently structures. To upset this issue is to explore the vehicle division dataset with bundle learning technique for finding the best street choice without calamity gauging by want aftereffects of best accuracy count by looking at oversaw AI figuring. In bits of information and AI, bundle strategies utilize diverse learning calculations to give indications of progress prudent execution. The assessment of dataset by facilitated AI technique (SMLT) to get two or three data takes after, factor perceiving proof, univariate evaluation, bivariate and multi-variate appraisal, missing worth medications and separate the information support, information cleaning/organizing and information perception will be done with everything taken into account given dataset. In addition, to look at and talk about the presentation of different AI figuring estimations from the given vehicle division dataset with assessment of GUI based street fiasco want by given attributes.
4Data Mining Approach of Accident Occurrences Identification with Effective M...IJECEIAES
Data mining is used in various domains of research to identify a new cause for tan effect in the society over the globe. This article includes the same reason for using the data mining to identify the Accident Occurrences in different regions and to identify the most valid reason for happening accidents over the globe. Data Mining and Advanced Machine Learning algorithms are used in this research approach and this article discusses about hyperline, classifications, pre-processing of the data, training the machine with the sample datasets which are collected from different regions in which we have structural and semi-structural data. We will dive into deep of machine learning and data mining classification algorithms to find or predict something novel about the accident occurrences over the globe. We majorly concentrate on two classification algorithms to minify the research and task and they are very basic and important classification algorithms. SVM (Support vector machine), CNB Classifier. This discussion will be quite interesting with WEKA tool for CNB classifier, Bag of Words Identification, Word Count and Frequency Calculation.
Nowadays, road crashes become a growing worldwide problem and result in around 1 million deaths now occurs in developing countries. Huge economic losses are now being incurred annually in the ASEAN countries as a direct result of road crashes and the most recent research suggests annual losses across the region are now in excess of US dollar 14 billion per year (around 2.1% of annual GDP of ASEAN region). In Myanmar, thousands of healthy lives are lost by road accidents comparing with other ASEAN countries. A research was conducted on a section of Pyay road with its high-accident locations to study and evaluate the cause of its frequent accidents. Initial study indicated that most of the accidents were attributed to human elements. This was included by the fact that a high percentage of accident was caused by the collision of moving vehicle and pedestrian. Identifying and removing hazardous spots to improve road safety will primarily requires well documented record on those roads with high-accident locations. These data base can inform to urban transport planner for road safety improvement. Kyaing"Road Accident Study on Some Areas in Yangon" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-5 , August 2018, URL: http://www.ijtsrd.com/papers/ijtsrd15944.pdf http://www.ijtsrd.com/engineering/civil-engineering/15944/road-accident-study-on-some-areas-in-yangon/kyaing
PREDICTING ROAD ACCIDENT RISK USING GOOGLE MAPS IMAGES AND ACONVOLUTIONAL NEU...ijaia
Location specific characteristics of a road segment such as road geometry as well as surrounding road features can contribute significantly to road accident risk. A Google Maps image of a road segment provides a comprehensive visual of its complex geometry and the surrounding features. This paper proposes a novel machine learning approach using Convolutional Neural Networks (CNN) to accident risk prediction by unlocking the precise interaction of these many small road features that work in combination to contribute to a greater accident risk. The model has worldwide applicability and a very low cost/time effort to implement for a new city since Google Maps are available in most places across the globe. It also significantly contributes to existing research on accident prevention by allowing for the inclusion of highly detailed road geometry to weigh in on the prediction as well as the new locationbased attributes like proximity to schools and businesses.
Consequences of Road Traffic Accident in Nigeria: Time Series Approach Editor IJCATR
Road traffic accident in Nigeria is increasing at a worrying rate and has raised one of the country major concerns. We provided appropriate and suitable time series model for the consequences of road accident, the injured, killed and total casualty of the road accident in Nigeria. The most widely used conventional method, Autoregressive Integrated Moving Average (ARIMA) model of time series, also known as Box-Jenkins method is applied to yearly data on the consequences of road accident data in Nigeria from 1960-2013 to determine patterns of road traffic accident consequences; injured, killed and total casualty of the road accident along the Nigeria motorway. Appropriate models are developed for the accident consequences; injured, killed and total casualty. ARIMA (0; 2; 1) model is obtained for the injury and total casualty consequences, whilst ARIMA(1,2,2) model is obtained for the killed consequences, using the data from 1960-2011. The adequacy and the performance of the model are tested on the remaining data from 2012 to 2013. Seven years forecast are provided using the developed models and showed that road traffic accident consequences examined; injured, killed and total casualty would continue to increase on average.
PREDICTION OF ROAD ACCIDENT MODELLING FOR INDIAN NATIONAL HIGHWAYSIAEME Publication
The objective of this research article is to identify the most critical safety influencing variables of a section of four-lane National Highway-18(old)/40(New) through statistical models that explains the relationship between frequency of accident count and highway safety variables. The Highway traverses mainly through a plain terrain of mostly agricultural areas. The study is for newly constructing Four-Lane road between chainage 224.000 (Chagalamarri) to 359.9(Kurnool) to identify all safety deficiencies responsible for road accidents. The predictive ability using Multiple linear regression model is under two categories: First for the 2 lane sections and second for 4 lane sections separately. The validation tools were applied to examine the ability of models to predict accidents.
ANALYSIS OF ROADWAY FATAL ACCIDENTS USING ENSEMBLE-BASED META-CLASSIFIERSijaia
In the past decades, a lot of effort has been put into roadway traffic safety. With the help of data mining, the analysis of roadway traffic data is much needed to understand the factors related to fatal accidents. This paper analyses Fatality Analysis Reporting System (FARS) dataset using several data mining
algorithms. Here, we compare the performance of four meta-classifiers and four data-oriented techniques known for their ability to handle imbalanced datasets, entirely based on Random Forest classifier. Also, we study the effect of applying several feature selection algorithms including PSO, Cuckoo, Bat and Tabu on improving the accuracy and efficiency of classification. The empirical results show that the Threshold
selector meta-classifier combined with over-sampling techniques results were very satisfactory. In this regard, the proposed technique has gained a mean overall Accuracy of 91% and a Balanced Accuracy that varies between 96% to 99% using 7-15 features instead of 50 original features.
Quantifying modelingon risk of travel demand and measure to sustaining road s...eSAT Journals
Abstract In these days urbanization of road transportation facilities are more complexity to developing in the form of to improve road safety. With the increased usage of Vehicles has enhanced the need for developing the infrastructure where these motor vehicles can move safely. By developing safe roads which connect destinations and cities is a key foundation to infrastructural development in a safe connectivity of road transportation. Mainly in this study can approach the road safety by using principal component analysis(PCA) by using MAT LAB and geographical information system (GIS) Arc-GIS software to develop base maps and accident causing zones identify in the study area. In this study an attempt has been made to study the existing road network for Ongole, Pernamitta village road (Kurnool road state highway) area and propose the necessary improvements to be done. And this model presented in this paper discussing with a multi set of variables under the one dimensionality set to identifying and deriving the new data set for risk identify zones with raking by this analysis of principal component analysis. The safety audit is defined as the place or location which causes number of accidents. It may be curve or faulty infrastructure. Such accidents are taken as input from Ongole taluka Police Station at Ongole for further study. These accidents are registered from First Information Report (FIR) informed by people. The study areas taken into consideration are Ongole to Pernamitta village Road (Ongole to Kurnool UN divided two way line state highway). The aim of this study is to minimize the accidents and find out the risk identify zones on the particular road network. Key words: road safety,faulty infrastructure, PCA analysis, Arc-GIS,FIR and urbanization.
Utilizing GIS to Develop a Non-Signalized Intersection Data Inventory for Saf...IJERA Editor
Roadway data inventories are being used across the nation to aid state Departments of Transportation (DOTs) in decision making. The high number of intersection and intersection related crashes suggest the need for intersection-specific data inventories that can be associated to crash occurrences to help make better safety decisions. Currently, limited time and resources are the biggest difficulties for execution of comprehensive intersection data inventories, but online resources exist that DOTs can leverage to capture desired data. Researchers from The University of Alabama developed an online method to collect intersection characteristics for non-signalized intersections along state routes using Google Maps and Google Street View, which was tied to an Alabama DOT maintained geographic information systems (GIS) node-link linear referencing method. A GIS-Based Intersection Data Inventory Web Portal was created to collect and record non-signalized intersection parameters. Thirty intersections of nine different intersection types were randomly selected from across the state, totaling 270 intersections. For each intersection, up to 78 parameters were collected, compliant with the Model Inventory of Roadway Elements (MIRE) schema. Using the web portal, the data parameters corresponding to an average intersection can be collected and catalogued into a database in approximately 10 minutes. The collection methodology and web portal function independently of the linear referencing method; therefore, the tool can be tailored and used by any state with spatial roadway data. Preliminary single variable analysis was performed, showing that there are relationships between individual intersection characteristics and crash frequency. Future work will investigate multivariate analysis and develop safety performance functions and crash modification factors.
Vehicle accidents are by all accounts appalling and frightening occasions occurring which cause various deaths. As the number of accidents per year is increasing tremendously and so the lives affected by accidents. There are traditional ways to help the needy or the victim that is informing the right authority but needs assistance or help from others, but this tends to take ample of time and due to it could cost lives. So there is a need to develop an accident detection system that would detect and alert the proper authorities about the accident. The sudden assistance to the alert would in return lead to saving as many lives as possible. Many researchers have analyzed this technique using Convolutional neural network, HDNN, RCNN, etc. This paper will give us an overview of various techniques or methods that are used to detect accidents.
Predictive geospatial analytics using principal component regression IJECEIAES
Nowadays, exponential growth in geospatial or spatial data all over the globe, geospatial data analytics is absolutely deserved to pay attention in manipulating voluminous amount of geodata in various forms increasing with high velocity. In addition, dimensionality reduction has been playing a key role in high-dimensional big data sets including spatial data sets which are continuously growing not only in observations but also in features or dimensions. In this paper, predictive analytics on geospatial big data using Principal Component Regression (PCR), traditional Multiple Linear Regression (MLR) model improved with Principal Component Analysis (PCA), is implemented on distributed, parallel big data processing platform. The main objective of the system is to improve the predictive power of MLR model combined with PCA which reduces insignificant and irrelevant variables or dimensions of that model. Moreover, it is contributed to present how data mining and machine learning approaches can be efficiently utilized in predictive geospatial data analytics. For experimentation, OpenStreetMap (OSM) data is applied to develop a one-way road prediction for city Yangon, Myanmar. Experimental results show that hybrid approach of PCA and MLR can be efficiently utilized not only in road prediction using OSM data but also in improvement of traditional MLR model.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
International Refereed Journal of Engineering and Science (IRJES)irjes
International Refereed Journal of Engineering and Science (IRJES) is a leading international journal for publication of new ideas, the state of the art research results and fundamental advances in all aspects of Engineering and Science. IRJES is a open access, peer reviewed international journal with a primary objective to provide the academic community and industry for the submission of half of original research and applications
Study On Traffic Conlict At Unsignalized Intersection In Malaysia IOSR Journals
The research conducted is traffic conflict at unsignalized intersections . The purpose of this research
is to study accident data used as an identification of hazardous location leads to less accurate countermeasures.
It is because accidents are not always reported especially accident involving damage only and this situation can
reduce good comparative analysis. To overcome these lacks of accident data, many ways of employing nonaccident
data have been suggested. One of the ways using non-accident data is traffic conflicts, which is defined
as critical incidents not necessarily involving collisions. The traffic conflict technique was originally set up to
provide more reliable data and information of traffic problems at intersections which actually would replace the
unclear and incomplete recorded data accident. The conflict study was done at the selected unsignalized
intersection where types of traffic conflict can be identified and classified. Various road users involved in the
conflict at the unsignalized intersection were also observed. Then conflicts data captured were analyzed using
the computer program to observe for any conflicts at the intersections. The linear regression graph was used to
show the relationship between conflict and accident data where two different equations were derived from the
graph. This equation may be used to make a prediction for the relationship that might exist between those two
variables at another location.
Road traffic issues, moreover, has become the backbone for major injuries ,deaths in recent times.The problem lies between negligence and the false approach towards the better analysis of traffic events.
To avoid road mishaps, it is not enough to just improve the road conditions, but also needs to control the traffic accidents happening by analyzing the cause-and-effect regulations.
Business Intelligence Computational Intelligence in Vehicle and Transportatio...ijtsrd
The Traffic and Transportation system is big problem in the world. So business intelligence in vehicle and transportation system solve this problem and solution with the help of new technologies. In the computational intelligence in vehicle and transportation system used computer electrical and electronic conversion technology management. Akshay Shrikant Nehre "Business Intelligence (Computational Intelligence in Vehicle and Transportation System)" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-3 , April 2020, URL: https://www.ijtsrd.com/papers/ijtsrd30226.pdf Paper Url :https://www.ijtsrd.com/computer-science/artificial-intelligence/30226/business-intelligence-computational-intelligence-in-vehicle-and-transportation-system/akshay-shrikant-nehre
CREATING DATA OUTPUTS FROM MULTI AGENT TRAFFIC MICRO SIMULATION TO ASSIMILATI...csandit
The intensive development of traffic engineering and technologies that are integrated into
vehicles, roads and their surroundings, bring opportunities of real time transport mobility
modeling. Based on such model it is then possible to establish a predictive layer that is capable
of predicting short and long term traffic flow behavior. It is possible to create the real time
model of traffic mobility based on generated data. However, data may have different
geographical, temporal or other constraints, or failures. It is therefore appropriate to develop
tools that artificially create missing data, which can then be assimilated with real data. This
paper presents a mechanism describing strategies of generating artificial data using
microsimulations. It describes traffic microsimulation based on our solution of multiagent
framework over which a system for generating traffic data is built. The system generates data of
a structure corresponding to the data acquired in the real world.
Consequences of Road Traffic Accident in Nigeria: Time Series Approach Editor IJCATR
Road traffic accident in Nigeria is increasing at a worrying rate and has raised one of the country major concerns. We provided appropriate and suitable time series model for the consequences of road accident, the injured, killed and total casualty of the road accident in Nigeria. The most widely used conventional method, Autoregressive Integrated Moving Average (ARIMA) model of time series, also known as Box-Jenkins method is applied to yearly data on the consequences of road accident data in Nigeria from 1960-2013 to determine patterns of road traffic accident consequences; injured, killed and total casualty of the road accident along the Nigeria motorway. Appropriate models are developed for the accident consequences; injured, killed and total casualty. ARIMA (0; 2; 1) model is obtained for the injury and total casualty consequences, whilst ARIMA(1,2,2) model is obtained for the killed consequences, using the data from 1960-2011. The adequacy and the performance of the model are tested on the remaining data from 2012 to 2013. Seven years forecast are provided using the developed models and showed that road traffic accident consequences examined; injured, killed and total casualty would continue to increase on average.
PREDICTION OF ROAD ACCIDENT MODELLING FOR INDIAN NATIONAL HIGHWAYSIAEME Publication
The objective of this research article is to identify the most critical safety influencing variables of a section of four-lane National Highway-18(old)/40(New) through statistical models that explains the relationship between frequency of accident count and highway safety variables. The Highway traverses mainly through a plain terrain of mostly agricultural areas. The study is for newly constructing Four-Lane road between chainage 224.000 (Chagalamarri) to 359.9(Kurnool) to identify all safety deficiencies responsible for road accidents. The predictive ability using Multiple linear regression model is under two categories: First for the 2 lane sections and second for 4 lane sections separately. The validation tools were applied to examine the ability of models to predict accidents.
ANALYSIS OF ROADWAY FATAL ACCIDENTS USING ENSEMBLE-BASED META-CLASSIFIERSijaia
In the past decades, a lot of effort has been put into roadway traffic safety. With the help of data mining, the analysis of roadway traffic data is much needed to understand the factors related to fatal accidents. This paper analyses Fatality Analysis Reporting System (FARS) dataset using several data mining
algorithms. Here, we compare the performance of four meta-classifiers and four data-oriented techniques known for their ability to handle imbalanced datasets, entirely based on Random Forest classifier. Also, we study the effect of applying several feature selection algorithms including PSO, Cuckoo, Bat and Tabu on improving the accuracy and efficiency of classification. The empirical results show that the Threshold
selector meta-classifier combined with over-sampling techniques results were very satisfactory. In this regard, the proposed technique has gained a mean overall Accuracy of 91% and a Balanced Accuracy that varies between 96% to 99% using 7-15 features instead of 50 original features.
Quantifying modelingon risk of travel demand and measure to sustaining road s...eSAT Journals
Abstract In these days urbanization of road transportation facilities are more complexity to developing in the form of to improve road safety. With the increased usage of Vehicles has enhanced the need for developing the infrastructure where these motor vehicles can move safely. By developing safe roads which connect destinations and cities is a key foundation to infrastructural development in a safe connectivity of road transportation. Mainly in this study can approach the road safety by using principal component analysis(PCA) by using MAT LAB and geographical information system (GIS) Arc-GIS software to develop base maps and accident causing zones identify in the study area. In this study an attempt has been made to study the existing road network for Ongole, Pernamitta village road (Kurnool road state highway) area and propose the necessary improvements to be done. And this model presented in this paper discussing with a multi set of variables under the one dimensionality set to identifying and deriving the new data set for risk identify zones with raking by this analysis of principal component analysis. The safety audit is defined as the place or location which causes number of accidents. It may be curve or faulty infrastructure. Such accidents are taken as input from Ongole taluka Police Station at Ongole for further study. These accidents are registered from First Information Report (FIR) informed by people. The study areas taken into consideration are Ongole to Pernamitta village Road (Ongole to Kurnool UN divided two way line state highway). The aim of this study is to minimize the accidents and find out the risk identify zones on the particular road network. Key words: road safety,faulty infrastructure, PCA analysis, Arc-GIS,FIR and urbanization.
Utilizing GIS to Develop a Non-Signalized Intersection Data Inventory for Saf...IJERA Editor
Roadway data inventories are being used across the nation to aid state Departments of Transportation (DOTs) in decision making. The high number of intersection and intersection related crashes suggest the need for intersection-specific data inventories that can be associated to crash occurrences to help make better safety decisions. Currently, limited time and resources are the biggest difficulties for execution of comprehensive intersection data inventories, but online resources exist that DOTs can leverage to capture desired data. Researchers from The University of Alabama developed an online method to collect intersection characteristics for non-signalized intersections along state routes using Google Maps and Google Street View, which was tied to an Alabama DOT maintained geographic information systems (GIS) node-link linear referencing method. A GIS-Based Intersection Data Inventory Web Portal was created to collect and record non-signalized intersection parameters. Thirty intersections of nine different intersection types were randomly selected from across the state, totaling 270 intersections. For each intersection, up to 78 parameters were collected, compliant with the Model Inventory of Roadway Elements (MIRE) schema. Using the web portal, the data parameters corresponding to an average intersection can be collected and catalogued into a database in approximately 10 minutes. The collection methodology and web portal function independently of the linear referencing method; therefore, the tool can be tailored and used by any state with spatial roadway data. Preliminary single variable analysis was performed, showing that there are relationships between individual intersection characteristics and crash frequency. Future work will investigate multivariate analysis and develop safety performance functions and crash modification factors.
Vehicle accidents are by all accounts appalling and frightening occasions occurring which cause various deaths. As the number of accidents per year is increasing tremendously and so the lives affected by accidents. There are traditional ways to help the needy or the victim that is informing the right authority but needs assistance or help from others, but this tends to take ample of time and due to it could cost lives. So there is a need to develop an accident detection system that would detect and alert the proper authorities about the accident. The sudden assistance to the alert would in return lead to saving as many lives as possible. Many researchers have analyzed this technique using Convolutional neural network, HDNN, RCNN, etc. This paper will give us an overview of various techniques or methods that are used to detect accidents.
Predictive geospatial analytics using principal component regression IJECEIAES
Nowadays, exponential growth in geospatial or spatial data all over the globe, geospatial data analytics is absolutely deserved to pay attention in manipulating voluminous amount of geodata in various forms increasing with high velocity. In addition, dimensionality reduction has been playing a key role in high-dimensional big data sets including spatial data sets which are continuously growing not only in observations but also in features or dimensions. In this paper, predictive analytics on geospatial big data using Principal Component Regression (PCR), traditional Multiple Linear Regression (MLR) model improved with Principal Component Analysis (PCA), is implemented on distributed, parallel big data processing platform. The main objective of the system is to improve the predictive power of MLR model combined with PCA which reduces insignificant and irrelevant variables or dimensions of that model. Moreover, it is contributed to present how data mining and machine learning approaches can be efficiently utilized in predictive geospatial data analytics. For experimentation, OpenStreetMap (OSM) data is applied to develop a one-way road prediction for city Yangon, Myanmar. Experimental results show that hybrid approach of PCA and MLR can be efficiently utilized not only in road prediction using OSM data but also in improvement of traditional MLR model.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
International Refereed Journal of Engineering and Science (IRJES)irjes
International Refereed Journal of Engineering and Science (IRJES) is a leading international journal for publication of new ideas, the state of the art research results and fundamental advances in all aspects of Engineering and Science. IRJES is a open access, peer reviewed international journal with a primary objective to provide the academic community and industry for the submission of half of original research and applications
Study On Traffic Conlict At Unsignalized Intersection In Malaysia IOSR Journals
The research conducted is traffic conflict at unsignalized intersections . The purpose of this research
is to study accident data used as an identification of hazardous location leads to less accurate countermeasures.
It is because accidents are not always reported especially accident involving damage only and this situation can
reduce good comparative analysis. To overcome these lacks of accident data, many ways of employing nonaccident
data have been suggested. One of the ways using non-accident data is traffic conflicts, which is defined
as critical incidents not necessarily involving collisions. The traffic conflict technique was originally set up to
provide more reliable data and information of traffic problems at intersections which actually would replace the
unclear and incomplete recorded data accident. The conflict study was done at the selected unsignalized
intersection where types of traffic conflict can be identified and classified. Various road users involved in the
conflict at the unsignalized intersection were also observed. Then conflicts data captured were analyzed using
the computer program to observe for any conflicts at the intersections. The linear regression graph was used to
show the relationship between conflict and accident data where two different equations were derived from the
graph. This equation may be used to make a prediction for the relationship that might exist between those two
variables at another location.
Road traffic issues, moreover, has become the backbone for major injuries ,deaths in recent times.The problem lies between negligence and the false approach towards the better analysis of traffic events.
To avoid road mishaps, it is not enough to just improve the road conditions, but also needs to control the traffic accidents happening by analyzing the cause-and-effect regulations.
Business Intelligence Computational Intelligence in Vehicle and Transportatio...ijtsrd
The Traffic and Transportation system is big problem in the world. So business intelligence in vehicle and transportation system solve this problem and solution with the help of new technologies. In the computational intelligence in vehicle and transportation system used computer electrical and electronic conversion technology management. Akshay Shrikant Nehre "Business Intelligence (Computational Intelligence in Vehicle and Transportation System)" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-3 , April 2020, URL: https://www.ijtsrd.com/papers/ijtsrd30226.pdf Paper Url :https://www.ijtsrd.com/computer-science/artificial-intelligence/30226/business-intelligence-computational-intelligence-in-vehicle-and-transportation-system/akshay-shrikant-nehre
CREATING DATA OUTPUTS FROM MULTI AGENT TRAFFIC MICRO SIMULATION TO ASSIMILATI...csandit
The intensive development of traffic engineering and technologies that are integrated into
vehicles, roads and their surroundings, bring opportunities of real time transport mobility
modeling. Based on such model it is then possible to establish a predictive layer that is capable
of predicting short and long term traffic flow behavior. It is possible to create the real time
model of traffic mobility based on generated data. However, data may have different
geographical, temporal or other constraints, or failures. It is therefore appropriate to develop
tools that artificially create missing data, which can then be assimilated with real data. This
paper presents a mechanism describing strategies of generating artificial data using
microsimulations. It describes traffic microsimulation based on our solution of multiagent
framework over which a system for generating traffic data is built. The system generates data of
a structure corresponding to the data acquired in the real world.
IRJET-An Arrangement for Automatic Notification and Severity Estimation of A...IRJET Journal
Parthiban.p ,Vasanthkumar.ss ,Mohana.J "An Arrangement for Automatic Noti?cation and Severity Estimation of Automotive Accidents", International Research Journal of Engineering and Technology (IRJET), Volume2,issue-01 April 2015.e-ISSN:2395-0056, p-ISSN:2395-0072. www.irjet.net
Abstract
New contact technologies consolidated into present vehicles proposal an opportunity for larger assistance to people injured in traf?c accidents. Current studies display how contact skills ought to be upheld by arti?cial intellect arrangements capable of automating countless of the decisions to be seized by emergency services, thereby adapting the save time to the severity of the mishap and cutting assistance time. To improve the completed save procedure, a fast and precise estimation of the severity of the mishap embody a key point to aid emergency services larger guesstimate the needed resources. This paper proposes a novel intelligent arrangement that is able to automatically notice road accidents, notify them across vehicular webs, and guesstimate their severity established on the believed of data excavating and vision inference. Our arrangement considers the most relevant variables that can describe the severity of the accidents . Aftermath display that a finished Vision Creation in Databases (KDD) procedure, alongside an adequate selection of relevant features, permits producing estimation models that can forecast the severity of new accidents. We develop a prototype of our arrangement established on off-the-shelf mechanisms and validate it at the Applus+ IDIADA Automotive Scutiny Firm abiilities, showing that our planning can particularly cut the period demanded to alert and use emergency services afterward an mishap seizes place.
Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...Dr.Costas Sachpazis
Terzaghi's soil bearing capacity theory, developed by Karl Terzaghi, is a fundamental principle in geotechnical engineering used to determine the bearing capacity of shallow foundations. This theory provides a method to calculate the ultimate bearing capacity of soil, which is the maximum load per unit area that the soil can support without undergoing shear failure. The Calculation HTML Code included.
Water scarcity is the lack of fresh water resources to meet the standard water demand. There are two type of water scarcity. One is physical. The other is economic water scarcity.
Student information management system project report ii.pdfKamal Acharya
Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.
Welcome to WIPAC Monthly the magazine brought to you by the LinkedIn Group Water Industry Process Automation & Control.
In this month's edition, along with this month's industry news to celebrate the 13 years since the group was created we have articles including
A case study of the used of Advanced Process Control at the Wastewater Treatment works at Lleida in Spain
A look back on an article on smart wastewater networks in order to see how the industry has measured up in the interim around the adoption of Digital Transformation in the Water Industry.
CFD Simulation of By-pass Flow in a HRSG module by R&R Consult.pptxR&R Consult
CFD analysis is incredibly effective at solving mysteries and improving the performance of complex systems!
Here's a great example: At a large natural gas-fired power plant, where they use waste heat to generate steam and energy, they were puzzled that their boiler wasn't producing as much steam as expected.
R&R and Tetra Engineering Group Inc. were asked to solve the issue with reduced steam production.
An inspection had shown that a significant amount of hot flue gas was bypassing the boiler tubes, where the heat was supposed to be transferred.
R&R Consult conducted a CFD analysis, which revealed that 6.3% of the flue gas was bypassing the boiler tubes without transferring heat. The analysis also showed that the flue gas was instead being directed along the sides of the boiler and between the modules that were supposed to capture the heat. This was the cause of the reduced performance.
Based on our results, Tetra Engineering installed covering plates to reduce the bypass flow. This improved the boiler's performance and increased electricity production.
It is always satisfying when we can help solve complex challenges like this. Do your systems also need a check-up or optimization? Give us a call!
Work done in cooperation with James Malloy and David Moelling from Tetra Engineering.
More examples of our work https://www.r-r-consult.dk/en/cases-en/
Hierarchical Digital Twin of a Naval Power SystemKerry Sado
A hierarchical digital twin of a Naval DC power system has been developed and experimentally verified. Similar to other state-of-the-art digital twins, this technology creates a digital replica of the physical system executed in real-time or faster, which can modify hardware controls. However, its advantage stems from distributing computational efforts by utilizing a hierarchical structure composed of lower-level digital twin blocks and a higher-level system digital twin. Each digital twin block is associated with a physical subsystem of the hardware and communicates with a singular system digital twin, which creates a system-level response. By extracting information from each level of the hierarchy, power system controls of the hardware were reconfigured autonomously. This hierarchical digital twin development offers several advantages over other digital twins, particularly in the field of naval power systems. The hierarchical structure allows for greater computational efficiency and scalability while the ability to autonomously reconfigure hardware controls offers increased flexibility and responsiveness. The hierarchical decomposition and models utilized were well aligned with the physical twin, as indicated by the maximum deviations between the developed digital twin hierarchy and the hardware.