Archives

Our paper on Understanding the interplay between bus, metro and cab ridership dynamics in Shenzhen, China has been accepted by Transactions in GIS.

Abstract

The most common mass transit modes in metropolitan cities include buses, subways and taxicabs, each of which contribute to an interconnected complex network that delivers urban dwellers to their destinations. Understanding the intertwined usages of these three transit modes at different places and time allows for better sensing of urban mobility and the built environment. In this article, we leverage a comprehensive data collection of bus, metro and taxi ridership from Shenzhen, China to unveil the spatio-temporal interplay between different mass transit modes. To achieve this goal, we develop a novel spectral clustering framework that imposes spatio-temporal similarities between mass transit mode usage in urban space and differentiates urban spaces associated with distinct ridership patterns of mass transit modes. Five resulting categories of urban spaces are identified and interpreted with auxiliary knowledge of the city’s metro network and land-use functionality. In general, different mass transit modes cooperate or compete based on demographic and socioeconomic attributes of the underlying urban environments. Our proposed analytical framework provides a novel and effective way for exploring the mass transit system and the functional heterogeneity in cities. It demonstrates great potential for assisting policymaker and municipal manager in optimizing public transportation facility allocation and city-wide daily commuting distribution.

[read full article] [download pdf]

Archives

The team attended The 14th Conference on Location Based Services at Zurich, Switzerland.

Abstract

The 14th International Conference on Location Based Services was successfully held by ETH Zurich and the ICA Commission on Location Based Services in Zurich, Switzerland on 15-17 January 2018.

placeholder

[read full article] [download pdf]

Archives

Our paper on Quantifying tourist behavior patterns by travel motifs and geo-tagged photos from Flickr has been published on ISPRS International Journal of Geo-Information.

Abstract

With millions of people traveling to unfamiliar cities to spend holidays, travel recommendation becomes necessary to assist tourists in planning their trips more efficiently. Serving as a prerequisite to travel recommender systems, understanding tourist behavior patterns is therefore of great importance. Recently, geo-tagged photos on social media platform like Flickr provide a rich data source that captures location histories of tourists and reflects their preferences. This article utilizes geo-tagged photos from Flickr to extract trajectories of tourists, and then extends the concept of motifs from topological spaces, to temporal spaces, and to semantic spaces, for detecting tourist mobility patterns. By representing trajectories in terms of three distinct types of travel motif and further using them to measure user similarity, typical tourist travel behavior patterns associated with distinct sightseeing tastes/preferences are identified and analyzed for tourism recommendation. Our empirical results confirm that the proposed analytical framework is effective to uncover meaningful tourist behavior patterns.

[read full article] [download pdf]