期刊
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
卷 77, 期 -, 页码 16-32出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.trc.2017.01.009
关键词
CHAID; CART; Trip distribution modeling; Gravity models; Disaggregated analysis
资金
- National Counsel of Technological and Scientific Development (CNPq, Brazil)
- Coordination for the Improvement of Higher Education Personnel (CAPES, Brazil)
- State of Sao Paulo Research Foundation, Brazil [FAPESP-2013/25035-1]
- Fundacao de Amparo a Pesquisa do Estado de Sao Paulo (FAPESP) [13/25035-1] Funding Source: FAPESP
Traditional trip distribution models usually ignore the fact that destination choices are made individually in addition to aggregated factors, such as employment and average travel costs. This paper proposes a disaggregated analysis of destination choices for intercity trips, taking into account aggregated characteristics of the origin city, an impedance measurement and disaggregated variables related to the individual, by applying nonparametric Decision Tree (DT) algorithms. Furthermore, each algorithm's performance is compared with traditional gravity models estimated from a stepwise procedure (1) and a doubly constrained procedure (2). The analysis was based on a dataset from the 2012 Origin Destination Survey carried out in Bahia, Brazil. The final selected variables to describe the destination choices were population of the origin city, GDP of the origin city and travel distances at an aggregated level, as well as the variables: age, occupation, level of education, income (monthly), number of cars per household and gender at a disaggregated one. The comparison of the DT models with gravity models demonstrated that the former models provided better accuracy when predicting the destination choices (trip length distribution, goodness-of-fit measures and qualitative perspective). The main conclusion is that Decision Tree algorithms can be applied to distribution modeling to improve traditional trip distribution approaches by assimilating the effect of disaggregated variables. (C) 2017 Elsevier Ltd. All rights reserved.
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