Dynamic travel time prediction using data clustering and genetic programming

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@Article{Elhenawy:2014:TRPCET,
  author =       "Mohammed Elhenawy and Hao Chen2 and Hesham A. Rakha",
  title =        "Dynamic travel time prediction using data clustering
                 and genetic programming",
  journal =      "Transportation Research Part C: Emerging
                 Technologies",
  volume =       "42",
  pages =        "82--98",
  year =         "2014",
  month =        may,
  ISSN =         "0968-090X",
  DOI =          "doi:10.1016/j.trc.2014.02.016",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0968090X14000588",
  keywords =     "genetic algorithms, genetic programming, Travel time
                 prediction, Clustering, Sampling with replacement,
                 Probe data",
  abstract =     "The current state-of-practice for predicting travel
                 times assumes that the speeds along the various roadway
                 segments remain constant over the duration of the trip.
                 This approach produces large prediction errors,
                 especially when the segment speeds vary temporally. In
                 this paper, we develop a data clustering and genetic
                 programming approach for modelling and predicting the
                 expected, lower, and upper bounds of dynamic travel
                 times along motorways. The models obtained from the
                 genetic programming approach are algebraic expressions
                 that provide insights into the spatio-temporal
                 interactions. The use of an algebraic equation also
                 means that the approach is computationally efficient
                 and suitable for real-time applications. Our algorithm
                 is tested on a 37-mile freeway section encompassing
                 several bottlenecks. The prediction error is
                 demonstrated to be significantly lower than that
                 produced by the instantaneous algorithm and the
                 historical average averaged over seven weekdays
                 (p-value <0.0001). Specifically, the proposed algorithm
                 achieves more than a 25percent and 76percent reduction
                 in the prediction error over the instantaneous and
                 historical average, respectively on congested days.
                 When bagging is used in addition to the genetic
                 programming, the results show that the mean width of
                 the travel time interval is less than 5 minutes for the
                 60-80 min trip.",
}

Genetic Programming entries for Mohammed Elhenawy Hao Chen2 Hesham A Rakha

Citations