Smart City Gnosys
Smart city article details
| Title | Online Trajectory Prediction For Metropolitan Scale Mobility Digital Twin |
|---|---|
| ID_Doc | 40085 |
| Authors | Fan Z.; Yang X.; Yuan W.; Jiang R.; Chen Q.; Song X.; Shibasaki R. |
| Year | 2022 |
| Published | GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems |
| DOI | http://dx.doi.org/10.1145/3557915.3561040 |
| Abstract | Knowing "what is happening"and "what will happen"of the mobility in a city is the building block of a data-driven smart city system. In recent years, mobility digital twin that makes a virtual replication of human mobility and predicting or simulating the fine-grained movements of the subjects in a virtual space at a metropolitan scale in near real-time has shown its great potential in modern urban intelligent systems. However, few studies have provided practical solutions. The main difficulties are four-folds: 1) the daily variation of human mobility is hard to model and predict; 2) the transportation network enforces a complex constraints on human mobility; 3) generating a rational fine-grained human trajectory is challenging for existing machine learning models; and 4) making a fine-grained prediction incurs high computational costs, which is challenging for an online system. Bearing these difficulties in mind, in this paper we propose a two-stage human mobility predictor that stratifies the coarse and fine-grained level predictions. In the first stage, to encode the daily variation of human mobility at a metropolitan level, we automatically extract citywide mobility trends as crowd contexts and predict long-term and long-distance movements at a coarse level. In the second stage, the coarse predictions are resolved to a fine-grained level via a probabilistic trajectory retrieval method, which offloads most of the heavy computations to the offline phase. We tested our method using a real-world mobile phone GPS dataset in the Kanto area in Japan, and achieved good prediction accuracy and a time efficiency of about 2 min in predicting future 1h movements of about 220K mobile phone users on a single machine to support more higher-level analysis of mobility prediction. © 2022 ACM. |
| Author Keywords | human mobility prediction; mobility digital twin; traffic intelligence |
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