Understanding Urban Dynamics via Context-Aware Tensor Factorization with Neighboring Regularization

Abstract

Recent years have witnessed the world-wide emergence of mega-metropolises with incredibly huge populations. Understanding residents mobility patterns, or urban dynamics, thus becomes crucial for building modern smart cities. In this paper, we propose a Neighbor-Regularized and context-aware Non-negative Tensor Factorization model (NR-cNTF) to discover interpretable urban dynamics from urban heterogeneous data. Different from many existing studies concerned with prediction tasks via tensor completion, NR-cNTF focuses on gaining urban managerial insights from spatial, temporal, and spatio-temporal patterns. This is enabled by high-quality Tucker factorizations regularized by both POI-based urban contexts and geographically neighboring relations. NR-cNTF is also capable of unveiling long-term evolutions of urban dynamics via a pipeline initialization approach. We apply NR-cNTF to a real-life data set containing rich taxi GPS trajectories and POI records of Beijing. The results indicate: 1) NR-cNTF accurately captures four kinds of city rhythms and seventeen spatial communities; 2) the rapid development of Beijing, epitomized by the CBD area, indeed intensifies the job-housing imbalance; 3) the southern areas with recent government investments have shown more healthy development tendency. Finally, NR-cNTF is compared with some baselines on traffic prediction, which further justifies the importance of urban contexts awareness and neighboring regulations.

Empowering A* Search Algorithms with Neural Networks for Personalized Route Recommendation

“…With the popularization of GPS-enabled mobile devices, a huge volume of trajectory data from users has become available in a variety of domains [27][28][29]. Personalized Route Recommendation (PRR) is one of the core functions in many online location-based applications, e.g., online map.…”

Personalized Route Recommendation With Neural Network Enhanced Search Algorithm

“…With the availability of user-generated trajectory information, route recommendation has received much attention from the research community [3], [5], [6], [23], [24], which aims to generate reachable paths between the source and destination locations. The task can be defined as either personalized [5], [6], [25] or nonpersonalized [8], [10], [23], [26], and constructed based on different types of trajectory data, e.g., GPS data [26], [27] or POI check-in data [28], [29].…”

“…N OWADAYS, GPS-enabled mobile devices have been widely used by a large number of users, and their trajectory data has been accumulated in a dramatic rate [1], [2], [3], [4]. In the literature, various studies have been proposed to utilize large-volume trajectory data for improving realworld applications.…”

Scalable Data Model for Traffic Congestion Avoidance in a Vehicle to Cloud Infrastructure

“…The work in [ 41 ] approaches various urban traffic indicators (e.g., flow, speed, accident risk) for prediction based on deep learning. In [ 42 ] is described a neighbor-regularized and context-aware non-negative tensor factorization model (NR-cNTF) to discover and interpret urban dynamics based on urban heterogeneous data. In this work, a large amount of historical data was processed (six million trips from 20 thousand taxis and 400 thousand POI records in Beijing) with the risk of becoming irrelevant due to fast changes of the traffic context in time.…”