A Two-Stage Trip Inference Model of Purposes and Socio-Economic Attributes of Regular Public Transit Users
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866910808722636800 |
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| author | Chen, Yitong Dong, Wentao Yu, Chengcheng Yuan, Quan Yang, Chao |
| author_facet | Chen, Yitong Dong, Wentao Yu, Chengcheng Yuan, Quan Yang, Chao |
| contents | Data-driven research is becoming a new paradigm in transportation, but the natural lack of individual socio-economic attributes in transportation data makes research such as activity purpose inference and mobility pattern identification lack convincingness and verifiability. In this paper, a two-stage trip purpose and socio-economic attributes inference model is proposed based on travel resident survey and smart card data. In the first stage, the trip purpose of each trip is inferred by a combination of rule-based and XGBoost models. In the second stage, based on the trip purpose, a machine-learning model is built to inference the socio-economic attributes of individuals. A teacher-student model based on self-training is then applied on the models above to transfer them to smart card data. The impact of independent variables of socio-economic attributes inference model is also investigated. The results show that models for inferring trip purposes and socio-economic attributes have overall accuracies of 92.7% and 76.3%, respectively. Travel time, arrival time, departure time and purpose of the first two trips are most important factors on age and job status, while the land price of jobs-housing are significant to the inference of individual incomes. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_00644 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Two-Stage Trip Inference Model of Purposes and Socio-Economic Attributes of Regular Public Transit Users Chen, Yitong Dong, Wentao Yu, Chengcheng Yuan, Quan Yang, Chao Applications Data-driven research is becoming a new paradigm in transportation, but the natural lack of individual socio-economic attributes in transportation data makes research such as activity purpose inference and mobility pattern identification lack convincingness and verifiability. In this paper, a two-stage trip purpose and socio-economic attributes inference model is proposed based on travel resident survey and smart card data. In the first stage, the trip purpose of each trip is inferred by a combination of rule-based and XGBoost models. In the second stage, based on the trip purpose, a machine-learning model is built to inference the socio-economic attributes of individuals. A teacher-student model based on self-training is then applied on the models above to transfer them to smart card data. The impact of independent variables of socio-economic attributes inference model is also investigated. The results show that models for inferring trip purposes and socio-economic attributes have overall accuracies of 92.7% and 76.3%, respectively. Travel time, arrival time, departure time and purpose of the first two trips are most important factors on age and job status, while the land price of jobs-housing are significant to the inference of individual incomes. |
| title | A Two-Stage Trip Inference Model of Purposes and Socio-Economic Attributes of Regular Public Transit Users |
| topic | Applications |
| url | https://arxiv.org/abs/2502.00644 |