Activity‐based model based on multi‐day cellular data: Considering the lack of personal attributes and activity type

Autor: Yudong Guo, Fei Yang, Haomin Yan, Siyuan Xie, Haode Liu, Zhuang Dai
Jazyk: angličtina
Rok vydání: 2023
Předmět:
Zdroj: IET Intelligent Transport Systems, Vol 17, Iss 12, Pp 2474-2492 (2023)
Druh dokumentu: article
ISSN: 1751-9578
1751-956X
DOI: 10.1049/itr2.12425
Popis: Abstract Cellular data is a sequence of base station‐interaction data that records user ID, timestamp, location area code (LAC), and cell identity (CID). With long observation periods, the data allows traffic planners to analyze coarse‐granularity user travel behaviours at low costs. However, utilizing cellular data for urban planning is not an easy task as the data lacks user socioeconomic attributes due to privacy issues. The data is also challenging to recognize user activity types. This paper proposed an activity‐based model (ABM) with skeleton schedule constraints for multi‐day cellular data. The model first infers the activity pattern and home location. Then it predicts start time, duration, and locations separately for primary and secondary activities. Next, the model infers the travel mode and path considering user multi‐day travel behaviour, path non‐linear coefficient, and transfers. Finally, a time adjustment module is proposed to avoid time conflicts in consecutive activities. The proposed activity‐based model is validated at activity, travel, and path levels. Results show that the proposed model can effectively predict activities and has much higher stability than existing ABMs based on travel surveys.
Databáze: Directory of Open Access Journals
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