Supervised association rules mining on pedestrian crashes in urban areas: identifying patterns for appropriate countermeasures

Das, Subasish; Dutta, Anandi; Avelar, Raul; Dixon, Karen; Sun, Xiaoduan; Jalayer, Mohammad

doi:10.1080/12265934.2018.1431146

Cited by 62 publications

(25 citation statements)

References 45 publications

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“…In contradiction, algorithmic modeling focuses on the process of understanding the unknown by minimizing the error rate through a black box algorithmic model. Although deep learning has not been widely used in past studies, many studies have used machine learning and data mining algorithms in transportation safety analysis (28)(29)(30)(31)(32)(33)(34)(35)(36)(37)(38)(39)(40). Deep learning is a branch of artificial intelligence that attempts to model complex information through a series of processing layers.…”

Section: Overview Of Deep Learning Methods Analysismentioning

confidence: 99%

Using Deep Learning in Severity Analysis of At-Fault Motorcycle Rider Crashes

Das

Dutta

Dixon

et al. 2018

Transportation Research Record

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Motorcyclists are vulnerable highway users. Unlike passenger vehicle occupants, motorcycle riders do not have either protective structural surrounding or the advanced restraints that are mandatory safety features in cars and light trucks. Per vehicle mile traveled, motorcyclist fatalities occurred 27 times more frequently than passenger car occupant fatalities in traffic crashes. In addition, there were 4,976 motorcycle crash-related fatalities in the U.S. in 2014—more than twice the number of motorcycle rider fatalities that occurred in 1997. It shows that, in addition to current efforts, research needs to be conducted with additional resources and in newer directions. This paper investigated five years (2010–2014) of Louisiana at-fault motorcycle rider-involved crashes by using deep learning, which is a competent tool for mapping a high-multidimensional input into a smaller multidimensional output. The current study contributes to the existing injury severity modeling literature by developing a deep learning framework, named as DeepScooter, to predict motorcycle-involved crash severities. The final deep learning model can predict severity types with 100% accuracy with training data, and with 94% accuracy with test data, which is not attainable by using a statistical method or machine learning algorithm. The intensity of severities was found to be more likely associated with rider ejection, two-way roadways with no physical separation, curved aligned roadways, and weekends. It is anticipated that the DeepScooter framework and the findings will provide significant contributions to the area of motorcycle safety.

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Section: Overview Of Deep Learning Methods Analysismentioning

confidence: 99%

Using Deep Learning in Severity Analysis of At-Fault Motorcycle Rider Crashes

Das

Dutta

Dixon

et al. 2018

Transportation Research Record

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“…Furthermore, work zone crashes (Weng et al, 2016), vehicle-pedestrian crashes in Louisiana (Das et al, 2018), and crashes occurring during rainy weather (Das & Sun, 2014) were explicitly focused. Other researchers tried to discover meaningful relationships, patterns, and trends for railway accidents in China (Chen et al, 2017) and in Iran (Mirabadi & Sharifian, 2010).…”

Section: Analytical Models For the Construction Safetymentioning

confidence: 99%

An Association Rule Mining Model for the Assessment of the Correlations Between the Attributes of Severe Accidents

Ayhan

Doğan

Tokdemir

2020

Journal of Civil Engineering and Management

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Identifying the correlations between the attributes of severe accidents could be vital to preventing them. If such relationships were known dynamically, it would be possible to take preventative actions against accidents. The paper aims to develop an analytical model that is adaptable for each type of data to create preventative measures that will be suitable for any computational systems. The present model collectively shows the relationships between the attributes in a coherent manner to avoid severe accidents. In this respect, Association Rule Mining (ARM) is used as the technique to identify the correlations between the attributes. The research adopts a positivist approach to adhere to the factual knowledge concerning nine different accident types through case studies and quantitative measurements in an objective nature. ARM was exemplified with nine different types of construction accidents to validate the adaptability of the proposed model. The results show that each accident type has different characteristics with varying combinations of the attribute, and analytical model accomplished to accommodate variation through the dataset. Ultimately, professionals can identify the cause-effect relationships effectively and set up preventative measures to break the link between the accident causing factors.

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“…Setting the minimum support and confidence value too low might produce many meaningless rules, whereas, setting these values too high might eliminate some interesting/meaningful rules and the inherent association among different item sets. Therefore, this study used trial and error processes to find the best possible set of confidence and support values, as suggested by previous studies (15,32). Using these threshold values, the number of rules with lift values greater than 1 was found to be 118.…”

Section: Association Rules Miningmentioning

confidence: 99%

“…B, where A, B I and A \ B = [ (31). Where A is called the antecedent or left-hand-side (LHS), and B is called the consequent or right-hand-side (RHS) (32). To select interesting rules and to determine the strength of the association, various measures, including support, confidence, and lift, are used.…”

Section: Association Rules Miningmentioning

confidence: 99%

“…Since there were four levels of speed selection, four different sets of rules were generated for each of the levels. To get the optimum values of support and confidence, the trial and error process was used, as suggested by a previous study (32). In addition, the number of item sets is another factor to be considered for proper interpretation of the results.…”

Section: Association Rules Miningmentioning

confidence: 99%

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Non-Parametric Association Rules Mining and Parametric Ordinal Logistic Regression for an In-Depth Investigation of Driver Speed Selection Behavior in Adverse Weather using SHRP2 Naturalistic Driving Study Data

Khan

Das

Ahmed

2020

Transportation Research Record

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Human error is considered to be one of the major causes of crashes, especially in inclement weather. Although many studies have investigated the effect of adverse weather on traffic safety and operations, there is a lack of research into the differences in driving behavior and performance during adverse weather, particularly at a trajectory level. With this research gap in mind, this study presents a novel approach for an in-depth investigation of driver speed selection behavior in adverse weather utilizing trajectory-level data acquired from the SHRP2 Naturalistic Driving Study using a promising association rules data mining technique. The preliminary analysis revealed that drivers reduced their speeds by 3.9% in the presence of light rain, by 10.2% in heavy rain, 15.2% in light snow, 29.8% in heavy snow, 1.8% with distant fog, and 7.4% with near fog. The findings from the association rules mining approach indicated that driving more than 5 mph above the speed limit was closely associated with clear weather as well as young and inexperienced drivers; whereas a reduction in speed to more than 5 mph below the speed limit was closely associated with snowy road surfaces combined with affected visibility. These findings are also in line with the results from the ordered logistic regression, which revealed that drivers were 1.4 times more likely to reduce their speeds in light rain, 1.7 times in heavy rain, 4.3 in light snow, 12.2 in heavy snow, 1.7 with distant fog, and 2.0 with near fog. The findings from this study provide an unprecedented opportunity to develop a Human-in-the-Loop Variable Speed Limit algorithm.

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Supervised association rules mining on pedestrian crashes in urban areas: identifying patterns for appropriate countermeasures

Cited by 62 publications

References 45 publications

Using Deep Learning in Severity Analysis of At-Fault Motorcycle Rider Crashes

Using Deep Learning in Severity Analysis of At-Fault Motorcycle Rider Crashes

An Association Rule Mining Model for the Assessment of the Correlations Between the Attributes of Severe Accidents

Non-Parametric Association Rules Mining and Parametric Ordinal Logistic Regression for an In-Depth Investigation of Driver Speed Selection Behavior in Adverse Weather using SHRP2 Naturalistic Driving Study Data

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