Resident travel mode prediction model in Beijing metropolitan area
With the development of economic integration, Beijing has become more closely connected with surrounding areas, which gradually formed the Beijing metropolitan area (BMA). The authors define the scope of BMA from two dimensions of space and time. BMA is determined to be the built-up area of Beijing...
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| Autores principales: | , , , , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Public Library of Science (PLoS)
2021
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/6d2de6154c634a07a565c575a7d4d763 |
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| Sumario: | With the development of economic integration, Beijing has become more closely
connected with surrounding areas, which gradually formed the Beijing
metropolitan area (BMA). The authors define the scope of BMA from two dimensions
of space and time. BMA is determined to be the built-up area of Beijing and its
surrounding 10 districts. Designed questionnaire survey the personal
characteristics, family characteristics, and travel characteristics of residents
from 10 districts in the surrounding BMA. The statistical analysis of
questionnaires shows that the supply of public transportation is insufficient
and cannot meet traffic demand. Further, the travel mode prediction model of
Softmax regression machine learning algorithm for BMA (SRBM) is established. To
further verify the prediction performance of the proposed model, the Multinomial
Logit Model (MNL) and Support Vector Machine (SVM), model are introduced to
compare the prediction accuracy. The results show that the constructed SRBM
model exhibits high prediction accuracy, with an average accuracy of 88.35%,
which is 2.83% and 18.11% higher than the SVM and MNL models, respectively. This
article provides new ideas for the prediction of travel modes in the Beijing
metropolitan area. |
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