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

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.

Urban Day-to-Day Travel and Its Development in an Information Environment: A Review

“…Guo et al have proposed an activity-based model (ABM) with skeleton scheduling constraints to address the issue of MSD lacking individual socio-economic attributes. In addition to perceiving specific travel activities, the model also considers the attributes of activity locations, achieving more accurate identification of activity objectives [196]. Huang et al have also proposed an accurate map-matching method tailored for MSD based on the incremental hidden Markov model (HMM) algorithm.…”