Analyses of Crash Occurrence and Injury Severities on Multi Lane Highways using Machine Learning Algorithms

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@PhdThesis{Das:thesis,
  author =       "Abhishek Das",
  title =        "Analyses of Crash Occurrence and Injury Severities on
                 Multi Lane Highways using Machine Learning Algorithms",
  school =       "Department of Civil, Environmental, and Construction
                 Engineering (CECE) of the University of Central
                 Florida",
  year =         "2009",
  address =      "Orlando, USA",
  month =        "13 " # oct,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.cecs.ucf.edu/graddefense/pdf/10",
  URL =          "http://purl.fcla.edu/fcla/etd/CFE0002928",
  size =         "212 pages",
  abstract =     "Reduction of crash occurrence on the various roadway
                 locations (mid-block segments; signalized
                 intersections; un-signalized intersections) and the
                 mitigation of injury severity in the event of a crash
                 are the major concerns of transportation safety
                 engineers. Multi lane arterial roadways (excluding
                 freeways and expressways) account for forty-three
                 percent of fatal crashes in the state of Florida.
                 Significant contributing causes fall under the broad
                 categories of aggressive driver behavior; unforgiving
                 weather and environmental conditions; and roadway
                 geometric and traffic factors. The objective of this
                 research was the implementation of innovative,
                 state-of-the-art analytical methods to identify the
                 contributory factors for crashes and injury severity.
                 Advances in computational methods render the use of
                 modern statistical and machine learning algorithms.
                 Even though most of the contributing factors are known
                 a-priori, advanced methods unearth changing trends.
                 Heuristic evolutionary processes such as linear genetic
                 programming; sophisticated data mining methods like
                 conditional inference tree; and mathematical treatments
                 in the form of sensitivity analyses outline the major
                 contributions in this research. Application of
                 traditional statistical methods like simultaneous
                 ordered probit models, identification and resolution of
                 crash data problems are also key aspects of this study.
                 In order to eliminate the use of unrealistic uniform
                 intersection influence radius of 250 ft, heuristic
                 rules were developed for assigning crashes to roadway
                 segments, junctions with traffic lights intersection
                 and access points using parameters, such as 'site
                 location' and 'traffic control'. Use of Conditional
                 Inference Forest instead of Classification and
                 Regression Tree to identify variables of significance
                 for injury severity analysis removed the bias towards
                 the selection of continuous variable or variables with
                 large number of categories. Concepts of evolutionary
                 biology like crossover and mutation were implemented to
                 develop models for classification and regression
                 analyses based on the highest hit rate and minimum
                 error rate, respectively. Annual daily traffic;
                 friction coefficient of pavements; on-street parking;
                 curbed medians; surface and shoulder widths; alcohol /
                 drug usage are some of the significant factors that
                 played a role in both the crash occurrence and injury
                 severities. Relative sensitivity analyses were used to
                 identify the effect of continuous variables on the
                 variation of crash counts. This study improved the
                 understanding of the significant factors that could
                 play an important role in designing better safety
                 countermeasures on multi lane highways, and hence
                 enhance their safety by reducing the frequency of
                 crashes and severity of injuries.",
  notes =        "Supervisor Mohamed A. Abdel-Aty",
}

Genetic Programming entries for Abhishek Das

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