Review on big data applications in safety research of intelligent transportation systems and connected/automated vehicles

Lian, Yanqi; Zhang, Guoqing; Lee, Jae Young; Huang, Helai

doi:10.1016/j.aap.2020.105711

Cited by 69 publications

(18 citation statements)

References 58 publications

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“…Many researchers have paid attention to the rapid growth of data by applying BD to investigate and explore knowledge that could not be achieved in the past. In addition, the outcomes of the research conducted using specific data are more convincing 12 . Besides that, BD has led to the revolution of statistical analysis.…”

Section: Introduction To Industry Revolution 40 (Ir 40)mentioning

confidence: 99%

“…BD was explained as a vast quantity of structured, semi‐structured, or unstructured data continuously generated from diversified sources and impacted decision‐making through insightful mining information from rambling data 11 . According to Lian et al, 12 BD is the data set with more than 50,000 variables or observations and preprocessing to be conducted by computation of multiple data types. However, this is not always the case; BD is not only about data in Giga or Terabytes.…”

Section: Introduction To Industry Revolution 40 (Ir 40)mentioning

confidence: 99%

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A review on supervised machine learning for accident risk analysis: Challenges in Malaysia

Chong

Razak

Biak

et al. 2022

Process Safety Progress

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The new Fourth Industrial Revolution (IR 4.0) trend is driven by the concept of automation and artificial intelligence (AI). However, Malaysia is slightly behind Singapore in terms of adopting AI innovation among ASEAN countries. This paper aims to conduct a literature review of machine learning to overcome subjectivity and bias in risk ranking decision‐making. An introduction to machine learning concerning accident risk analysis is presented, and the challenges of its application in Malaysia are discussed. Existing machine learning features were evaluated to identify the feasible application in industrial accident analysis and ensure safety decision‐making consistency. This review observed how the IR 4.0 approaches were used in the risk analysis, especially on supervised machine learning. This study also highlights the finding from the previous works on challenges in utilizing supervised machine learning, which is the need to have publicly accessible large database from industries and agencies such as the Department of Occupational Safety and Health (DOSH) Malaysia for the development of algorithms, which can potentially improve accident risk analysis and safety, especially for Malaysian industries.

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Section: Introduction To Industry Revolution 40 (Ir 40)mentioning

confidence: 99%

Section: Introduction To Industry Revolution 40 (Ir 40)mentioning

confidence: 99%

A review on supervised machine learning for accident risk analysis: Challenges in Malaysia

Chong

Razak

Biak

et al. 2022

Process Safety Progress

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“…As a result, there is a dire need to improve the quality of safe driving and make a critical decision to respond accurately in emergencies. Predicting a driver's behavior [8] is a crucial part and shows a key role in the design of intelligent transport systems. Those systems helped to increase the efficiency and safety of drivers [9].…”

Section: Introductionmentioning

confidence: 99%

Hypo-Driver: A Multiview Driver Fatigue and Distraction Level Detection System

Riquelme¹,

Olivares²,

Mu駉z³

et al. 2022

Computers, Materials &Amp; Continua

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Traffic accidents are caused by driver fatigue or distraction in many cases. To prevent accidents, several low-cost hypovigilance (hypo-V) systems were developed in the past based on a multimodal-hybrid (physiological and behavioral) feature set. Similarly in this paper, real-time driver inattention and fatigue (Hypo-Driver) detection system is proposed through multi-view cameras and biosignal sensors to extract hybrid features. The considered features are derived from non-intrusive sensors that are related to the changes in driving behavior and visual facial expressions. To get enhanced visual facial features in uncontrolled environment, three cameras are deployed on multiview points (0 • , 45 • , and 90 • ) of the drivers. To develop a Hypo-Driver system, the physiological signals (electroencephalography (EEG), electrocardiography (ECG), electro-myography (sEMG), and electrooculography (EOG)) and behavioral information (PERCLOS70-80-90%, mouth aspect ratio (MAR), eye aspect ratio (EAR), blinking frequency (BF), head-titled ratio (HT-R)) are collected and pre-processed, then followed by feature selection and fusion techniques. The driver behaviors are classified into five stages such as normal, fatigue, visual inattention, cognitive inattention, and drowsy. This improved hypo-Driver system utilized trained behavioral features by a convolutional neural network (CNNs), recurrent neural network and long short-term memory (RNN-LSTM) model is used to extract physiological features. After fusion of these features, the Hypo-Driver system is classified hypo-V into five stages based on trained layers and dropout-layer in the deep-residual neural network (DRNN) model. To test the performance of a hypo-Driver system, data from 20 drivers are acquired. The results of Hypo-Driver compared to state-of-theart methods are presented. Compared to the state-of-the-art Hypo-V system, on average, the Hypo-Driver system achieved a detection accuracy (AC) of 96.5%. The obtained results indicate that the Hypo-Driver system based on multimodal and multiview features outperforms other state-of-the-art driver Hypo-V systems by handling many anomalies.

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“…With the extensive application of open source big data in relevant studies on urban cities and geography, data that can represent population mobility and interaction, including Point of Interest (POI) data [21], Location Based Services (LBS) data [22], mobile signalling data [23], transportation card data [24], Global Positioning System (GPS) data [25], and social media punch dates [26], all provide reliable sources for the quantitative analysis of urban vitality [27]. The merits and demerits of these data are mainly embodied in the following expects: POI data, as a geographic virtual representation of an urban entity, are of significant geographic reliability, but are more inclined to represent spatial forms [28]; for mobile signalling data, although a large number of locator data can be obtained in a relatively shorter period using a signal station, mobile signalling data still have certain shortcomings, such as lower reliability and inaccurate positioning information [29]; for GPS data, the positioning information obtained by GPS has a high accuracy and interference immunity, but its elevated operating costs make its use inappropriate for refined studies in small-scale regions [30]; as for social media punch data, using these types of data to represent the spatial activity of the elderly and children is rather difficult due to significant age differences, despite a relatively higher number of sample data and better accessibility to it [31]; for transportation card data, this type of data is restricted to the areas among transport lines, although it can represent living spaces and population movement [32]; and finally, as one of the LBS data types that derives from the Amap positioning service, Tencent-Yichucing data, can always generate well-positioned movement data for population positions, as long as the apps (including instant messaging software, such as WeChat and QQ) that belong to Tencent are used. This sample data has a wider coverage and higher accuracy, which can be used to represent the daytime vitality of an urban city [33,34].…”

Section: Introductionmentioning

confidence: 99%

Urban Food Takeaway Vitality: A New Technique to Assess Urban Vitality

Zikirya

et al. 2021

IJERPH

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As one of the most important criteria for measuring the quality of urban life and the environment, urban vitality has become the focus of urban-related research and related disciplines with an increasing number of advocates for the rapid and harmonious development of urban cities. Urban takeaway can represent urban vitality, but studies have not investigated this in a quantitative manner. Furthermore, current studies rarely focus on or even mention the urban food takeaway vitality generated by the spatial distribution of urban takeaway. This study first calculated the vitality of urban takeaways based on the urban takeaway distribution, building footprint, Open Street Map (OSM) data, and the Rapidly Exploring Random Tree (RRT). Then, the urban vitality was obtained using Tencent-Yichuxing data and night-time light data, followed by a spatial correlation analysis between the urban takeaway vitality and urban vitality. Finally, the results for Beijing, Shanghai, and Guangzhou were compared, and the following conclusions were drawn: (1) there is a significant spatial correlation between the urban takeaway vitality and urban vitality, but the correlation varies in different cities at different times; and (2) even in the same city, different road and building densities have an impact on the correlation. The urban takeaway vitality proposed in this study can be used as a new index to evaluate the urban vitality, which has important theoretical and practical significance for the sustainable development of future urban cities.

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Review on big data applications in safety research of intelligent transportation systems and connected/automated vehicles

Cited by 69 publications

References 58 publications

A review on supervised machine learning for accident risk analysis: Challenges in Malaysia

A review on supervised machine learning for accident risk analysis: Challenges in Malaysia

Hypo-Driver: A Multiview Driver Fatigue and Distraction Level Detection System

Urban Food Takeaway Vitality: A New Technique to Assess Urban Vitality

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