Transport Choice Modeling for the Evaluation of New Transport Policies

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@Article{su10041230,

• author = "Ander Pijoan and Oihane Kamara-Esteban and Ainhoa Alonso-Vicario and Cruz E. Borges",
• title = "Transport Choice Modeling for the Evaluation of New Transport Policies",
• journal = "Sustainability",
• volume = "10",
• year = "2018",
• number = "4",
• article_number = "1230",
• keywords = "genetic algorithms, genetic programming",
• ISSN = "2071-1050",
• URL = "http://www.mdpi.com/2071-1050/10/4/1230",
• DOI = "doi:10.3390/su10041230",
• abstract = "Quantifying the impact of the application of sustainable transport policies is essential in order to mitigate effects of greenhouse gas emissions produced by the transport sector. One of the most common approaches used for this purpose is that of traffic modelling and simulation, which consists of emulating the operation of an entire road network. This article presents the results of fitting 8 well known data science methods for transport choice modelling, the area in which more research is needed. The models have been trained with information from Biscay province in Spain in order to match as many of its commuters as possible. Results show that the best models correctly forecast more than 51percent of the trips recorded. Finally, the results have been validated with a second data set from the Silesian Voivodeship in Poland, showing that all models indeed maintain their forecasting ability.",

}

Genetic Programming entries for Ander Pijoan Oihane Kamara-Esteban Ainhoa Alonso-Vicario Cruz Enrique Borges

Citations