Aggregate nonparametric safety analysis of traffic zones

Siddiqui, Chowdhury; Abdel-Aty, Mohamed; Huang, Hongwei

doi:10.1016/j.aap.2011.07.019

Cited by 68 publications

(22 citation statements)

References 27 publications

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“…Siddiqui et al . adopted random forests approach to identify and rank macro‐level crash and planning variables so as to incorporate proactive safety measures in transportation planning. Hossain and Muromachi developed random forests of logit models to identify the influential factors associated with traffic crashes on basic freeway segments and ramp areas.…”

Section: Literature Reviewmentioning

confidence: 99%

“…In transportation, Harb et al [34] used random forests technique to rank the variables of angle, headon, and rear-end crashes associated with drivers' crash avoidance maneuvers. Siddiqui et al [35] adopted random forests approach to identify and rank macro-level crash and planning variables so as to incorporate proactive safety measures in transportation planning. Hossain and Muromachi [36] developed random forests of logit models to identify the influential factors associated with traffic crashes on basic freeway segments and ramp areas.…”

Section: Literature Reviewmentioning

confidence: 99%

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A random forests approach to prioritize Highway Safety Manual (HSM) variables for data collection

Saha

Alluri

Gan

2015

J of Advced Transportation

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SUMMARYThe Highway Safety Manual (HSM) recommends using the empirical Bayes method with locally derived calibration factors to predict an agency's safety performance. The data needs for deriving these local calibration factors are significant, requiring very detailed roadway characteristics information. Many of these data variables are currently unavailable in most of the agencies' databases. Furthermore, it is not economically feasible to collect and maintain all the HSM data variables. This study aims to prioritize the HSM calibration variables based on their impact on crash predictions. Prioritization would help to identify influential variables for which data could be collected and maintained for continued updates, and thereby reduce intensive data collection efforts. Data were first collected for all the HSM variables from over 2400 miles of urban and suburban arterial road networks in Florida. Using 5 years (2008)(2009)(2010)(2011)(2012) of crash data, a random forests data mining approach was then applied to measure the importance of each variable in crash frequency predictions for five different urban and suburban arterial facilities including two-lane undivided, three-lane with a two-way left-turn lane, four-lane undivided, four-lane divided, and five-lane with a twoway left-turn lane. Two heuristic approaches were adopted to prioritize the variables: (i) simple ranking based on individual relative influence of variables; and (ii) clustering based on relative influence of variables within a specific range. Traffic volume was found as the most influential variable. Roadside object density, minor commercial driveway density, and minor residential driveway density variables were the other variables with significant influence on crash predictions.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

A random forests approach to prioritize Highway Safety Manual (HSM) variables for data collection

Saha

Alluri

Gan

2015

J of Advced Transportation

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“…11,[14][15][16][17][18][19][20][21][22][23] Moreover, due to the importance of knowledge acquisition from large data set of crash records, data mining methods have become an essential component in many road safety studies. Numerous data mining 12,13,[24][25][26][27][28][29][30][31] related studies have been undertaken to analyze crash records locally and globally with frequently varying results depending on the study area and the methods used.…”

Section: Literature Reviewmentioning

confidence: 99%

“…There are many algorithms that are widely used in the literature for decision tree construction; however, the CART model (Classification and Regression Trees which developed by Breiman and Friedman 49 ) is the most commonly used in the field of motor vehicle crashes. 27,[29][30][31]50,51 Taking into consideration the CART performance for crash-rule induction, the indicators such as: kappa statistic, RMSE, precision, recall, F-measure, and ROC area are used to evaluate the goodness of CART decision tree method in crash severity prediction. An open source framework that is used in this study for the construction of CART model is Weka.…”

Section: Ontology-based Data Mining Crash Rule Inductionmentioning

confidence: 99%

A semantic‐based classification and regression tree approach for modelling complex spatial rules in motor vehicle crashes domain

Effati

Sadeghi‐Niaraki

2015

WIREs Data Min & Knowl

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Innovative data mining and knowledge discovery approaches that take advantage of geospatial analysis can be useful for analysis of motor vehicle crashes on regional highway corridors. This study presents an Ontology-based Classification and Regression Tree (OCART) approach to induct crash rules on regional highway corridors by adding ontological reasoning to Classification and Regression Tree (CART) decision rules. It develops an ontology-driven geospatial framework to predict the motor vehicles crash severity through the proposed OCART approach. A system prototype has been developed and implemented on a regional highway corridor in order to illustrate and evaluate the proposed method. The results demonstrate the application of ontological reasoning and spatial computation significantly improves the performance of CART crash rules. The proposed method reveals new relationships among crash severities and contributing factors that have been kept implicit in CART decision rules.

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“…The area-level safety analyses are associated with traffic analysis zones (TAZs) which are typical units in transportation planning process. Since a TAZ is a geographic unit for inventorying socioeconomic data and estimating trip generation, the area-level crash analysis usually focuses on examining the relationship between crashes and both socioeconomic factors and network variables [1,2]. The road-level safety analyses can be further categorized into segment level and intersection level.…”

Section: Introductionmentioning

confidence: 99%

Spatial Influence Analysis of Traffic Safety in Diverging Areas between Freeway Segments and Off Ramps

Zhang

Yan

An³

et al. 2015

Discrete Dynamics in Nature and Society

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There tend to be more crashes occurring in freeway diverging segments due to increasing traffic conflicts between diverging vehicles and nondiverging vehicles. The diverging segments have a safety impact on the precedent basic segments and the following off ramps. It is always a challenge to accurately define the safety influential area of freeway diverging segments. In previous studies, fixed buffer in size is pregiven for crash frequency analysis in diverging segments, which lacks theoretical and practical support. In this study, the safety influential area was investigated from the statistical point of view. Data from a geocoded GIS crash database for Colorado Springs metropolitan area was used; the statistically significant factors associated with crash frequency were examined for the spatial influence of freeway diverging segments. Also, the generalized linear models with negative binomial link function were applied to predict the crash frequency for freeway diverging segments and off ramps based on the influential area. The results may give some insights into the causation of crashes on diverging segments and off-ramp intersections. Hindawi Publishing Corporation

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Aggregate nonparametric safety analysis of traffic zones

Cited by 68 publications

References 27 publications

A random forests approach to prioritize Highway Safety Manual (HSM) variables for data collection

A random forests approach to prioritize Highway Safety Manual (HSM) variables for data collection

A semantic‐based classification and regression tree approach for modelling complex spatial rules in motor vehicle crashes domain

Spatial Influence Analysis of Traffic Safety in Diverging Areas between Freeway Segments and Off Ramps

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