A Two-Stage Trip Inference Model of Purposes and Socio-Economic Attributes of Regular Public Transit Users

TL;DR

This paper presents a two-stage machine learning model to infer trip purposes and socio-economic attributes of public transit users using survey and smart card data, achieving high accuracy and revealing key influencing factors.

Contribution

It introduces a novel two-stage inference framework combining rule-based, XGBoost, and self-training models for socio-economic attribute estimation from transit data.

Findings

Trip purpose inference accuracy: 92.7%

Socio-economic attribute inference accuracy: 76.3%

Key factors include travel times, trip purpose, and land prices.

Abstract

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.