What is the Human Mobility in a New City: Transfer Mobility Knowledge Across Cities

He, Tianfu; Bao, Jie; Li, Ruiyuan; Ruan, Sijie; Li, Yanhua; Li, Song; He, Hui; Zheng, Yu

doi:10.1145/3366423.3380210

Cited by 42 publications

(25 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…CityCoupling [14] first utilized massive human mobility data to discover the corresponding locations between the source city and the target city, then generated the mapped human trajectories in the target city under event situations like a big earthquake. Reference [17] also generated the human mobility in a new city by generating the Origin-Destination (OD) pairs and paths from a learned mobility intention model. CityCoupling [14] generates trajectories for a special situation of the target city, and Reference [17] generates trajectories for a newly built city.…”

Section: Related Workmentioning

confidence: 99%

“…Reference [17] also generated the human mobility in a new city by generating the Origin-Destination (OD) pairs and paths from a learned mobility intention model. CityCoupling [14] generates trajectories for a special situation of the target city, and Reference [17] generates trajectories for a newly built city. These two studies fall into the category of trajectory simulation and generation, while ours is still a problem of trajectory prediction, with specific constraints, namely, limited training data.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Transfer Urban Human Mobility via POI Embedding over Multiple Cities

Jiang

Song

Fan

et al. 2021

ACM/IMS Trans. Data Sci.

View full text Add to dashboard Cite

Rapidly developing location acquisition technologies provide a powerful tool for understanding and predicting human mobility in cities, which is very significant for urban planning, traffic regulation, and emergency management. However, with the existing methodologies, it is still difficult to accurately predict millions of peoples’ mobility in a large urban area such as Tokyo, Shanghai, and Hong Kong, especially when collected data used for model training are often limited to a small portion of the total population. Obviously, human activities in city are closely linked with point-of-interest (POI) information, which can reflect the semantic meaning of human mobility. This motivates us to fuse human mobility data and city POI data to improve the prediction performance with limited training data, but current fusion technologies can hardly handle these two heterogeneous data. Therefore, we propose a unique POI-embedding mechanism, that aggregates the regional POIs by categories to generate an artificial POI-image for each urban grid and enriches each trajectory snippet to a four-dimensional tensor in an analogous manner to a short video. Then, we design a deep learning architecture combining CNN with LSTM to simultaneously capture both the spatiotemporal and geographical information from the enriched trajectories. Furthermore, transfer learning is employed to transfer mobility knowledge from one city to another, so that we can fully utilize other cities’ data to train a stronger model for the target city with only limited data available. Finally, we achieve satisfactory performance of human mobility prediction at the citywide level using a limited amount of trajectories as training data, which has been validated over five urban areas of different types and scales.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Transfer Urban Human Mobility via POI Embedding over Multiple Cities

Jiang

Song

Fan

et al. 2021

ACM/IMS Trans. Data Sci.

View full text Add to dashboard Cite

show abstract

“…To reduce the feature drift impact between different cities, we project context features from different cities to a shared latent space in which the reward function can be applied. Borrowing from [6], we employ the domain generation algorithm based on transfer component analysis (TCA) [10]. The algorithm transfers components C to the Reproducing Kernel Hilbert Space (RKHS) by minimizing the MMD between different feature spaces so that the distributions of features in different cities get close to each other.…”

Section: Mapping Locations To Feature Spacementioning

confidence: 99%

“…However, these methods can only transfer the observed trajectories, which require the population and distribution are the same in the source area to the target area. He et al (2020) [6]'s work is relevant to our task, which is to learn the spatial features from the data-rich area and then generates trips in the target area with a unified mobility knowledge model. Nevertheless, the above-mentioned methods cannot be applied to our problem, which is to reproduce a daily movements of people rather than to reproduce short-range trips for a specific purpose.…”

Section: Introductionmentioning

confidence: 99%

Intercity Simulation of Human Mobility at Rare Events via Reinforcement Learning

Pang

Tsubouchi²,

Yabe³

et al. 2020

Proceedings of the 28th International Conference on Advances in Geographic Information Systems

View full text Add to dashboard Cite

Agent-based simulations, combined with large scale mobility data, have been an effective method for understanding urban scale human dynamics. However, collecting such large scale human mobility datasets are especially difficult during rare events (e.g., natural disasters), reducing the performance of agent-based simulations. To tackle this problem, we develop an agent-based model that can simulate urban dynamics during rare events by learning from other cities using inverse reinforcement learning. More specifically, in our framework, agents imitate real human-beings' travel behavior from areas where rare events have occurred in the past (source area) and produce synthetic people movement in different cities where such rare events have never occurred (target area). Our framework contains three main stages: 1) recovering the reward function, where the people's travel patterns and preferences are learned from the source areas; 2) transferring the model of the source area to the target areas; 3) simulating the people movement based on learned model in the target area. We apply our approach in various cities for both normal and rare situations using real-world GPS data collected from more than 1 million people in Japan, and show higher simulation performance than previous models. CCS CONCEPTS • Information systems → Location based services; • Computing methodologies → Modeling methodologies; Agent / discrete models; • Networks → Location based services.

show abstract

“…This problem is receiving a lot of attention in urban computing [21], not only for retailing but also from a more general perspective. Previous works in this line have explored knowledge transfer between cities for air quality prediction [22] and for human mobility patterns [23,24], among other applications.…”

Section: Introductionmentioning

confidence: 99%

Knowledge Transfer in Commercial Feature Extraction for the Retail Store Location Problem

2021

View full text Add to dashboard Cite

Location is the most important strategic decision in retailing. The location problem is markedly complex and multicriteria. One of the key factors to consider is the so-called balanced tenancy -i.e., the degree to which neighboring businesses complement each other. There are several network-based methodologies that formalize the notion of balanced tenancy by capturing the spatial interactions between different commercial sectors in cities. Some of these methodologies provide indices that have been successfully used as input features in location recommendation systems. However, from a predictive perspective, it is still unknown which of the indices provides best results. In this work, we analyze the performance of six of these indices on a set of nine Spanish cities. Our results show that the combined use of all of them in an ensemble model such as random forest significantly improves predictive accuracy. In addition, we explore the effect of knowledge transfer between cities from two different perspectives: (i) we quantify how much the quality of solutions degrades when the balanced tenancy of a city is explored through the indices obtained from another city, and (ii) we investigate the interest of network consensus approaches for knowledge transfer in retailing.

show abstract

What is the Human Mobility in a New City: Transfer Mobility Knowledge Across Cities

Cited by 42 publications

References 30 publications

Transfer Urban Human Mobility via POI Embedding over Multiple Cities

Transfer Urban Human Mobility via POI Embedding over Multiple Cities

Intercity Simulation of Human Mobility at Rare Events via Reinforcement Learning

Knowledge Transfer in Commercial Feature Extraction for the Retail Store Location Problem

Contact Info

Product

Resources

About