Extracting Hidden Patterns Within Road Accident Data Using Machine Learning Techniques

Vasavi, S.

doi:10.1007/978-981-10-5508-9_2

Cited by 16 publications

(6 citation statements)

References 3 publications

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“…Similarly, another study [86] developed a Stack Denoise Autoencoder Simulation model to predict the risk level of traffic accidents. In another study [87], the authors discovered that machine learning techniques (k-Clusters and priori algorithm) can identify valuable hidden patterns from a vehicle crash dataset of historical accidents. For example, according to [88], the most important factors associated to the fatal severity of an accident in United Arab Emirates were: gender (male with highest level of accidents), age (between 18-30 years old mostly involved in accidents), and collision type (car to pedestrian collision is the common one in accidents) and location of an accident (right angles).…”

Section: Predictive Modelsmentioning

confidence: 99%

Applications of Artificial Intelligence in Transport: An Overview

et al. 2019

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The rapid pace of developments in Artificial Intelligence (AI) is providing unprecedented opportunities to enhance the performance of different industries and businesses, including the transport sector. The innovations introduced by AI include highly advanced computational methods that mimic the way the human brain works. The application of AI in the transport field is aimed at overcoming the challenges of an increasing travel demand, CO2 emissions, safety concerns, and environmental degradation. In light of the availability of a huge amount of quantitative and qualitative data and AI in this digital age, addressing these concerns in a more efficient and effective fashion has become more plausible. Examples of AI methods that are finding their way to the transport field include Artificial Neural Networks (ANN), Genetic algorithms (GA), Simulated Annealing (SA), Artificial Immune system (AIS), Ant Colony Optimiser (ACO) and Bee Colony Optimization (BCO) and Fuzzy Logic Model (FLM) The successful application of AI requires a good understanding of the relationships between AI and data on one hand, and transportation system characteristics and variables on the other hand. Moreover, it is promising for transport authorities to determine the way to use these technologies to create a rapid improvement in relieving congestion, making travel time more reliable to their customers and improve the economics and productivity of their vital assets. This paper provides an overview of the AI techniques applied worldwide to address transportation problems mainly in traffic management, traffic safety, public transportation, and urban mobility. The overview concludes by addressing the challenges and limitations of AI applications in transport.

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Section: Predictive Modelsmentioning

confidence: 99%

Applications of Artificial Intelligence in Transport: An Overview

et al. 2019

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“…Unos de los temas más tratados por los investigadores son las causas externas [16] [20][21][22][23][24], estas son analizadas en países como India, China, Estados Unidos y otros. Se destacan dos artículos de la India que analizan accidentes donde estuvieron involucrados los vehículos más comunes en ese país conocidos como "auto-rickshaws" o "tuc-tuc" [25] y las motocicletas [26].…”

Section: Eje Tematico 2: Países Que Han Realizado Trabajos Donse unclassified

Los accidentes de tránsito desde la perspectiva de la minería de datos. Una revisión de la literatura.

Henao-Pereira

Tovar-León

Castillo-Landinez

et al. 2020

AiBi Revista de Investigación, Administración e Ingeniería

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Se habla de un accidente de tránsito (AT) como un hecho inesperado que se presenta sobre las vías, condicionado por factores de naturaleza humana (imprudencia, descuido, problemas de salud) o también mecánica, involucrando por lo menos un vehículo en movimiento que puede ser automóvil, motocicleta o bicicleta; estos eventos provocan pérdidas de vidas o lesiones. Las cifras de los entes gubernamentales señalan que los accidentes de tránsito son la segunda causa de muerte violenta en Colombia, por lo que se indaga, cómo a través de técnicas de minería de datos es posible analizar los accidentes de tránsito, desde otra perspectiva. Para tal fin, fueron recopilados trabajos de diferentes bases de datos digitales como Science Direct, IEEE, Google Academico, Scielo, Redib y SpringerOpen, los cuales se clasificaron en tres ejes temáticos. Como resultado, se encontraron 90 artículos que cumplían con los criterios de inclusión y de estos fueron considerados definitivos 70 para fundamentar esta investigación, identificando el país con más trabajos de minería de datos para analizar accidentes viales, así como las técnicas más usadas y el origen de los datos usados en las diferentes publicaciones.

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“…According this research [6] road accidents and injuries occur because of human fault or vehicle fault or infrastructure fault or sometimes combinations of these factors. Each of these factors individually or in combination may cause an accident.…”

Section: Review Of Related Literaturementioning

confidence: 99%

Predicting Road Accident Risk in the City of San Pablo, Laguna: A Predictive Model Using Time Series Forecasting Analysis with Multiplicative Model

Manaloto¹,

Cruz²

2020

csit

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This research used the mechanisms in time series analysis such as secular trend, irregular fluctuation, cyclical and seasonal patterns to conclude forecast using the multiplicative model. In the conduct of this study, time, day and location are the main focuses in predicting road accident patterns. By analyzing road accident patterns based on time series forecasting using a multiplicative model, this research will provide suggestions for the government to take effective measures to reduce accident impacts and improve traffic safety. This research analyzed the road accident data in San Pablo City, Laguna Philippines from the year 2014-2016 and forecast the possible prevalence of road accidents and its pattern. A total of 1229 road accidents were included in this study. As recorded based on the cumulative frequency, Barangay San Francisco has the greatest number of road accidents with an average of 101 cases per year. It can be attributed to the fact that the road is considered the busiest because it is the only gateway of provincial travelers from Metro Manila to Southern Provinces. While for the monthly pattern prediction, April is the most risky in road accidents with possible 33 cases in a year with 13.08% mean absolute error or 86.92% accuracy, probably because most of the community is in summer vacation. And in terms of the daily pattern, Sunday is the crucial day in terms of road accidents with 44 possible cases with 10.29% mean absolute error or 89.71% accuracy, and the majority of the possible road accident arises between 6:00 pm to 9:00 pm with 45 possible cases with 13.21% mean absolute error or 86.79% accuracy.

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Extracting Hidden Patterns Within Road Accident Data Using Machine Learning Techniques

Cited by 16 publications

References 3 publications

Applications of Artificial Intelligence in Transport: An Overview

Applications of Artificial Intelligence in Transport: An Overview

Los accidentes de tránsito desde la perspectiva de la minería de datos. Una revisión de la literatura.

Predicting Road Accident Risk in the City of San Pablo, Laguna: A Predictive Model Using Time Series Forecasting Analysis with Multiplicative Model

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