Using conditional inference forests to identify the factors affecting crash severity on arterial corridors

Das, Abhishek; Abdel-Aty, Mohamed; Pande, Anurag

doi:10.1016/j.jsr.2009.05.003

Cited by 89 publications

(41 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Lower daily average traffic volume leads, in general, to higher severity injuries. This finding is consistent with a number of previous studies that have shown that crash severity is higher on low-traffic volume roads, and significantly decreases with increasing volumes, presumably due to better safety design on highways with high traffic volumes (see Das et al, 2009, Christoforou et al, 2010, and Chen and Chen, 2011. Lower traffic volume could be related with higher speeds that more often lead to severe crashes.…”

Section: Environmental Factorssupporting

confidence: 91%

A spatial generalized ordered response model to examine highway crash injury severity

Castro

Paleti

Bhat

2013

Accident Analysis & Prevention

112

View full text Add to dashboard Cite

This paper proposes a flexible econometric structure for injury severity analysis at the level of individual crashes that recognizes the ordinal nature of injury severity categories, allows unobserved heterogeneity in the effects of contributing factors, as well as accommodates spatial dependencies in the injury severity levels experienced in crashes that occur close to one another in space. The modeling framework is applied to analyze the injury severity sustained in crashes occurring on highway road segments in Austin, Texas. The sample is drawn from the Texas Department of Transportation (TxDOT) crash incident files from 2009 and includes a variety of crash characteristics, highway design attributes, driver and vehicle characteristics, and environmental factors. The results from our analysis underscore the value of our proposed model for data fit purposes as well as to accurately estimate variable effects. The most important determinants of injury severity on highways, according to our results, are (1) whether any vehicle occupant is ejected, (2) whether collision type is head-on, (3) whether any vehicle involved in the crash overturned, (4) whether any vehicle occupant is unrestrained by a seat-belt, and (5) whether a commercial truck is involved.

show abstract

Section: Environmental Factorssupporting

confidence: 91%

A spatial generalized ordered response model to examine highway crash injury severity

Castro

Paleti

Bhat

2013

Accident Analysis & Prevention

112

View full text Add to dashboard Cite

show abstract

“…Machine learning techniques have become popular in many disciplines [44–46] to analyze non-experimental/observational data, as they often have lower requirements with respect to input data quality. Several algorithms used in earlier studies in similar contexts [27,44,45,47], as well as others with potentially desirable characteristics, were evaluated in relation to the main characteristics of our problem: (i) observational error and noisy data in both predictors and response variables, (ii) high likelihood of non-linear relationships between predictors and yield, (iii) high potential for correlated predictors, and (iv) the need to retrieve the relevant variables that actually explain the yield variability.…”

Section: Methodsmentioning

confidence: 99%

Assessing Weather-Yield Relationships in Rice at Local Scale Using Data Mining Approaches

et al. 2016

View full text Add to dashboard Cite

Seasonal and inter-annual climate variability have become important issues for farmers, and climate change has been shown to increase them. Simultaneously farmers and agricultural organizations are increasingly collecting observational data about in situ crop performance. Agriculture thus needs new tools to cope with changing environmental conditions and to take advantage of these data. Data mining techniques make it possible to extract embedded knowledge associated with farmer experiences from these large observational datasets in order to identify best practices for adapting to climate variability. We introduce new approaches through a case study on irrigated and rainfed rice in Colombia. Preexisting observational datasets of commercial harvest records were combined with in situ daily weather series. Using Conditional Inference Forest and clustering techniques, we assessed the relationships between climatic factors and crop yield variability at the local scale for specific cultivars and growth stages. The analysis showed clear relationships in the various location-cultivar combinations, with climatic factors explaining 6 to 46% of spatiotemporal variability in yield, and with crop responses to weather being non-linear and cultivar-specific. Climatic factors affected cultivars differently during each stage of development. For instance, one cultivar was affected by high nighttime temperatures in the reproductive stage but responded positively to accumulated solar radiation during the ripening stage. Another was affected by high nighttime temperatures during both the vegetative and reproductive stages. Clustering of the weather patterns corresponding to individual cropping events revealed different groups of weather patterns for irrigated and rainfed systems with contrasting yield levels. Best-suited cultivars were identified for some weather patterns, making weather-site-specific recommendations possible. This study illustrates the potential of data mining for adding value to existing observational data in agriculture by allowing embedded knowledge to be quickly leveraged. It generates site-specific information on cultivar response to climatic factors and supports on-farm management decisions for adaptation to climate variability.

show abstract

“…The ensemble approach that combines outputs from a collection of trees reduces prediction error and is usually more stable (Das et al, 2009;De'ath, 2007;Elith et al, 2008). There are two ensemble approaches based on decision trees: random forests and boosted regression trees (BRT).…”

Section: Literature Reviewmentioning

confidence: 99%

Prioritizing Highway Safety Manual’s crash prediction variables using boosted regression trees

Saha

Alluri

Gan

2015

Accident Analysis & Prevention

View full text Add to dashboard Cite

Using conditional inference forests to identify the factors affecting crash severity on arterial corridors

Abstract: The authors were able to identify roadway locations where severe crashes tend to occur. For example, segments and access points were found to be riskier for single vehicle crashes. Higher skid resistance and k-factor also contributed toward increased severity of injuries in crashes.

Cited by 89 publications

References 28 publications

A spatial generalized ordered response model to examine highway crash injury severity

A spatial generalized ordered response model to examine highway crash injury severity

Assessing Weather-Yield Relationships in Rice at Local Scale Using Data Mining Approaches

Prioritizing Highway Safety Manual’s crash prediction variables using boosted regression trees

Contact Info

Product

Resources

About