Work

Dynamic Trip Attraction Estimation With Location Based Social Network Data Balancing Between Time of Day Variations and Zonal Differences

Year: 2015

Type: article

Abstract: . The emergence of location based social network (LBSN) services make it accessible and affordable to study individuals’ mobility patterns in a fine-grained level. Via mobile devices, LBSN enables the availability of large-scale location-sensitive data with spatial and temporal context dimensions, which is capable of the potential to provide traffic patterns with significantly higher spatial and temporal resolution at a much lower cost than can be achieved by traditional methods. In this paper, the Foursquare LBSN data was applied to analyze the trip attraction for the urban area in Austin, Texas, USA. We explore one time-dependent function to validate the LBSN’s data with the origin-destination matrix regarded as the ground truth data. The objective of this paper is to investigate one new validation method for trip distribution. The results illustrate the promising potential of studying the dynamic trip attraction estimation with LBSN data for urban trip pattern analysis and monitoring.

Source: ISPRS annals of the photogrammetry, remote sensing and spatial information sciences

DOI: 10.5194/isprsannals-ii-4-w2-193-2015

Authors: N. W. Hu, P. J. Jin

Institution: Rutgers, The State University of New Jersey

Language: English

Topic: Human Mobility and Location-Based Analysis

Subfield: Transportation

Field: Social Sciences

Domain: Social Sciences

SDG: Sustainable cities and communities

Open Access status: diamond

OpenAlex ID: W2102048812