Discovering Urban Functional Zones Using Latent Activity Trajectories
By Nicholas Jing Yuan, Yu Zheng, Xing Xie, Yingzi Wang, Kai Zheng, Hui Xiong. IEEE Transactions on Knowledge and Data Engineering, 2015
409 citations recorded by the source corpus.
The step of urbanization and modern civilization fosters different functional zones in a city, such as residential areas, business districts, and educational areas. In a metropolis, people commute between these functional zones every day to engage in different socioeconomic activities, e.g., working, shopping, and entertaining. In this paper, we propose a data-driven framework to discover functional zones in a city. Specifically, we introduce the concept of latent activity trajectory (LAT), which captures socioeconomic activities conducted by citizens at different locations in a chronological order. Later, we segment an urban area into disjointed regions according to major roads, such as highways and urban expressways. We have developed a topic-modeling-based approach to cluster the segmented regions into functional zones leveraging mobility and location semantics mined from LAT. Furthermore, we identify the intensity of each functional zone using Kernel Density Estimation. Extensive experiments are conducted with several urban scale datasets to show that the proposed framework offers a powerful ability to capture city dynamics and provides valuable calibrations to urban planners in terms of functional zones.
Subfields: Geography, Planning and Development, Transportation, Signal Processing
View Paper: https://www.semanticscholar.org/paper/710b724423fa8a2df15067e1f188e97e4eda76fb
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Citations in the corpus
- Trajectory Data Mining (2015, methodology): “The full dataset was used to suggest the practically fastest driving directions to normal drivers [124][125], recommend passenger-pickup location for taxi drivers [127][130], enable dynamic taxi ridesharing [65][66], glean the problematic design in a city’s transportation network [151], and identify”
- Methodologies for Cross-Domain Data Fusion: An Overview (2015, methodology): “[74], [76] infer the functional regions in a city using road network data, points of interests, and human mobility learned from a large number of taxi trips.”