Multi-step prediction of individual activity sequence based on multi-source data

doi:10.3785/j.issn.1008-973X.2025.11.011

Journal of ZheJiang University (Engineering Science)

2025, Vol. 59

Issue (11): 2317-2325 DOI: 10.3785/j.issn.1008-973X.2025.11.011

Multi-step prediction of individual activity sequence based on multi-source data

Yang SHU1(

),Yilin SUN1,2,*(

),Zhenyu MEI1,Yimin ZHANG2,Yifang HUANG2

1. College of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, China
2. Polytechnic Institute, Zhejiang University, Hangzhou 310015, China

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Abstract

A multi-step prediction method for individual activity sequences based on multi-source data was proposed by considering the limitations of traditional research on individual activity sequence prediction based on aggregated data or sampled data. Mobile phone signaling data and point of interest (POI) data were used. Activity types of residents were inferred by using the rule-based and latent Dirichlet allocation (LDA) topic modeling methods, and multi-day activity sequences were constructed. The nTreeClus clustering framework was introduced to identify six typical multi-day activity patterns of residents in Hangzhou City. Meteorological and calendar data in the traffic environment were integrated, and the temporal fusion transformer (TFT) model was applied to conduct multi-step prediction of activity sequences. The micro-average F1 score was 87%, which increased by 11%, 6% and 3% respectively compared with traditional statistical models and mainstream deep learning models. The F1 scores for "entertainment" and "personal maintenance" activities increased by 31% and 27% respectively compared with the sequence-to-sequence gated recurrent unit (S2SGRU) model, significantly improving the accuracy of multi-step prediction of individual activity sequences.

Key words： urban transportation traffic demand forecasting time series prediction activity type mining multi-source data

Received: 22 October 2024 Published: 30 October 2025

CLC:

U 491

Fund: 浙江省“尖兵”“领雁”研发攻关计划资助项目（2023C01240）；国家自然科学基金资助项目（52131202）.

Corresponding Authors: Yilin SUN E-mail: shuyang@zju.edu.cn;yilinsun@zju.edu.cn

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Cite this article:

Yang SHU,Yilin SUN,Zhenyu MEI,Yimin ZHANG,Yifang HUANG. Multi-step prediction of individual activity sequence based on multi-source data. Journal of ZheJiang University (Engineering Science), 2025, 59(11): 2317-2325.

URL:

https://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2025.11.011 OR https://www.zjujournals.com/eng/Y2025/V59/I11/2317

基于多源数据的个体活动序列多步预测

针对传统的以集计数据或抽样数据在个体活动序列预测研究中的局限性，提出基于多源数据的个体活动序列多步预测方法. 基于手机信令数据和兴趣点 (POI)数据，使用考虑规则和潜在狄利克雷分配（LDA）主题建模方法推断居民活动类型，构建多日活动序列. 引入nTreeClus聚类框架，识别出杭州市居民6种典型的多日活动模式，融合交通环境中的气象、日历数据，应用时间融合Transformer (TFT) 模型进行活动序列的多步预测，微平均F1分数为87%. 与传统统计模型及主流深度学习模型进行比较，分别提升了11%、6%、3%，F1分数在“娱乐”、“个人维护”活动上较序列到序列门控循环单元（S2SGRU）模型分别提升了31%、27%，显著提高了个体活动序列多步预测的准确性.

关键词： 城市交通, 交通需求预测, 时间序列预测, 活动类型挖掘, 多源数据

Tab.1 Table of meaning of user basic attribute

Tab.2 Sample of processed mobile phone signaling data

Tab.3 Example of some POI data in Hangzhou City

Fig.1 Comparison of process of resident activity record and document generation

Tab.4 Example of residents' activity chain

Fig.2 Reconstruction of residents' multi-day activity sequence

Fig.3 Average silhouette coefficient

Fig.4 Clustering result of activity sequence

Fig.5 Schematic diagram of multi-step prediction of activity sequence with multi-source data

Fig.6 Functional diagram of multi-step activity prediction

Tab.5 Comparison of prediction performance in precision index between TFT and other methods %

Tab.6 Comparison of prediction performance in recall index between TFT and other methods %

Tab.7 Comparison of prediction performance in F₁ between TFT and other methods %

Fig.7 Relative importance of variable


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