- author = "Abhishek Das and Mohamed Abdel-Aty and Anurag Pande",
- title = "Genetic Programming to Investigate Design Parameters Contributing to Crash Occurrence on Urban Arterials",
- journal = "Transportation Research Record: Journal of the Transportation Research Board",
- year = "2010",
- volume = "2147",
- pages = "25--32",
- publisher = "Transportation Research Board of the National Academies",
- keywords = "genetic algorithms, genetic programming",
- ISSN = "0361-1981",
-
URL = "
http://trb.metapress.com/content/52881hl17685547l/fulltext.pdf",
-
DOI = "
doi:10.3141/2147-04",
- size = "8 pages",
- abstract = "Nonlinear models were developed to estimate crash frequency on urban arterials with partial access control. These multilane arterials consist of midblock segments joined by signalised and signalised intersections (or access points). Crashes included in the analysis are of three major types: rear-end, angle, and head-on. Each crash type is further sorted into mutually exclusive categories on the basis of the roadway element responsible for the crashes: midblock segment, signalised intersection, and access point. Genetic programming (GP) is adopted for predicting crash frequency. GP, which is primarily based on genetic algorithms, uses the concept of evolution to develop models through the processes of crossover and mutation. The GP modelling approach gives independence for model development without restrictions on distribution of data. The models developed were compared to the basic negative binomial models. Morning and afternoon peak periods are observed to have fewer occurrences of rear-end crashes at all roadway elements. Higher traffic volume results in an increased number of angle crashes. Instances of angle crashes have increased at signalised intersections, even at lower maximum posted speeds. A higher average truck factor increases the instances of head-on crashes on midblock segments and at signalised intersections.",
-
notes = "Online Date Friday, October 01, 2010
p31 'GP outperforms NB [negative binomial] in lower MSE values (for validation data set only) in six of the nine models discussed in the study.'",
