An Integrated Rule-Based and Machine Learning Method for Trip Purpose Identification

KE Rihong; XIAO Liang; ZHAO Dazhi; LI Shengnan; WANG Pu

doi:10.13203/j.whugis20240329

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An Integrated Rule-Based and Machine Learning Method for Trip Purpose Identification

Cartography and GIS | 更新时间：2026-11-05

- An Integrated Rule-Based and Machine Learning Method for Trip Purpose Identification
- Geomatics and Information Science of Wuhan University Vol. 51, Issue 6, Pages: 1273-1282(2026)
- 作者机构：
  
  中南大学交通运输工程学院，湖南长沙，410075
- 作者简介：
- 基金信息：
- DOI：10.13203/j.whugis20240329
  CLC： TP208
- Received：05 April 2025，
  
  Published：05 June 2026
- 稿件说明：
移动端阅览
柯日宏,肖亮,赵大郅，等.融合规则方法和机器学习的居民出行目的识别方法[J].武汉大学学报（信息科学版）,2026,51(6):1273-1282.

KE Rihong,XIAO Liang,ZHAO Dazhi,et al.An Integrated Rule-Based and Machine Learning Method for Trip Purpose Identification[J].Geomatics and Information Science of Wuhan University,2026,51(6):1273-1282.
柯日宏,肖亮,赵大郅，等.融合规则方法和机器学习的居民出行目的识别方法[J].武汉大学学报（信息科学版）,2026,51(6):1273-1282. DOI：10.13203/j.whugis20240329

KE Rihong,XIAO Liang,ZHAO Dazhi,et al.An Integrated Rule-Based and Machine Learning Method for Trip Purpose Identification[J].Geomatics and Information Science of Wuhan University,2026,51(6):1273-1282. DOI：10.13203/j.whugis20240329

摘要

针对手机定位数据缺乏语义信息的问题，探索手机用户出行目的识别方法，有助突破手机定位数据应用的局限性。当前出行目的识别的研究中普遍存在数据量不足、数据类型单一和非通勤出行目的识别效果不理想等问题。基于大规模、长周期的手机定位数据和多源时空数据，提出一种融合规则方法和机器学习的居民出行目的识别框架。首先，基于规则方法提取用户职住信息，进而识别出通勤出行目的；然后，使用兴趣面数据识别部分非通勤出行目的；最后，构建

-Prototypes-XGBoost模型识别剩余非通勤出行目的。模型综合建成环境、用户属性、活动属性和用户历史活动等多源时空数据，构建特征变量，能够有效地捕捉个体出行习惯与行为模式。研究结果表明，所提方法能够较为准确地识别手机用户出行目的；与5种基准方法进行对比，

-Prototypes-XGBoost模型预测非通勤出行目的效果最好。利用

-Prototypes模型提取相似出行行为的人群再进行预测，能够进一步提高模型性能和可解释性。

Abstract

Objectives

Aiming at the lack of semantic information in mobile phone data

the method for identifying the trip purposes of mobile phone users is explored to break through the limitations of mobile phone data application.

Methods

This paper proposes a framework for identifying the trip purposes of residents by combining a rule-based approach and machine learning utilizing large-scale and long-term mobile phone data and multi-source spa

tiotemporal data. The home and work locations of users are first analyzed using the rule-based approach to identify commuting trips. The area of interest data are then used to identify the purposes of some non-commuting trips. Finally

The

-Prototypes-XGBoost model is used to identify the purposes of other non-commuting trips. The proposed model constructs feature vectors based on multi-source spatiotemporal data

including built environment

user attributes

activity attributes and historical activity of user

to capture the travel behavior habits and patterns of individuals.

Results

Comparative experiments on known trip-purpose activity data from May 1st to 31st

2023 show that

without introducing spatiotemporal features of historical activity of user and clustering information

extreme gradient boosting （XGBoost） outperforms adaptive boosting， random forest， gradient boosting decision tree

and artificial neural network

achieving accuracy of 73.2%

precision of 74.3%

recall of 73.2%

and

1-score of 74.3%. After introducing spatiotemporal features of historical activity of user

the prediction performance of all five models is significantly improved

with the average accuracy

precision

recall

and

1-score increasing by 24.2%

21.2%

24.1%

and 23.0%

respectively. On this basis

the proposed

-Prototypes-XGBoost model further improves the prediction performance by incorporating user clustering information. Compared with the conventional XGBoost model

its accuracy

precision

recall

and

1-score increase by 2.7%

1.5%

2.7%

and 2.8%

respectively

and all four evaluation metrics reach 91.5%

indicating the best overall performance in identifying non-commuting trip purposes from large-scale

long-term mobile phone data. The confusion matrix further reveals that the proposed model performs best in identifying medical activities

with an accuracy of 94.7%

whereas dining activities exhibit

the lowest recognition accuracy of 88.4%. The main misclassifications occur among leisure and entertainment

dining

and shopping activities

because these activities often share highly overlapping spatiotemporal characteristics and are frequently located within the same commercial complexes or adjacent areas.

Conclusions

By integrating rule-based inference with the

-Prototypes-XGBoost model

this paper provides an effective solution for recovering the semantic information of trip purposes from mobile phone data. The proposed framework enables a more complete reconstruction of trip purpose chains and promotes the transformation of mobile phone data applications from mobility pattern description to semantic understanding of travel behavior

thereby supporting finer-grained urban transport planning and human activity analysis.

关键词

Keywords

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