Pengjun ZHAO, Yushu CAO. Identifying Metro Trip Purpose using Multi-source Geographic Big Data and Machine Learning Approach [J]. Journal of Geo-information Science, 2020, 22(9): 1753
- Journal of Geo-information Science
- Vol. 22, Issue 9, 1753 (2020)
Fig. 1. Spatial distribution of land-use related variables
Fig. 2. Spatial distribution of metro trip records in a week
Fig. 3. Schematic diagram of estimating trip purpose of the smart card transactions
Fig. 4. MDA values of different feature importance in the RF classifier
Fig. 5. The OOB accuracy of the RF classifier changes with the number of features
Fig. 6. Convergence of training of random forest classifiers and judgment of the optimal number of trees
Fig. 7. Temporal distribution of metro trip departures and arrivals for different travel purposes
Fig. 8. Spatial distribution of metro trip departures and arrivals for different travel purposes
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Table 1. Data sources and brief description
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Table 2. Number and proportion of metro trips by purpose intraffic survey data
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Table 3. Variables included in the random forest classifier
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Table 4. Random forest classifier confusion matrix results
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Table 5. Comparison of confusion matrix betweenrandom forest classifier with or without travel-related variables
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