The Application of Venue-Side Location-Based Social Networking (VS-LBSN) Data in Dynamic Origin-Destination Estimation

The Application of Venue-Side Location-Based Social Networking (VS-LBSN) Data in Dynamic Origin-Destination Estimation

Fan Yang (University of Wisconsin – Madison, USA), Peter J. Jin (Rutgers University, USA), Meredith Cebelak (University of Texas – Austin, USA), Bin Ran (Southeast University, China & University of Wisconsin – Madison, USA) and C. Michael Walton (University of Texas – Austin, USA)
DOI: 10.4018/978-1-4666-6170-7.ch015
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Location-Based Social Networking (LBSN) allows users to confirm their current locations and trip purposes by “checking in” with places of interests (“venues”) registered at the LBSN Websites. Such individual activity data provides the potential to collect dynamic travel demand data in a temporal and spatial resolution that cannot be achieved using traditional survey-based methods. In this chapter, the authors investigate and propose LBSN data-based urban travel demand estimation methods—specifically, the dynamic Origin-Destination (OD) demand estimation. This chapter investigates the feasibility of using VS-LBSN data to estimate dynamic Origin-Destination (OD) travel demand for general trips. A combined non-parametric cluster and regression model is used to establish the relationship between VS-LBSN data and the trip production and attraction. A modified gravity model-based trip distribution method with three friction function variations is proposed to estimate the OD matrix. The proposed methods are calibrated and evaluated against the ground truth OD data from CMAP (Chicago Metropolitan Agency for Planning). The results demonstrate the promising potential of using VS-LBSN data for dynamic OD estimation.
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In this study, we propose a dynamic (time-dependent) Origin-Destination(OD) travel demand monitoring system based on venue-side location-based social network (VS-LBSN) data for the operations and management of an urban transportation network. The Origin-Destination (OD) matrix describes the number of trip exchanges between the origins and destinations in a transportation network during a specified time period which is a crucial input in prevailing transportation planning. With the recent development in Active Traffic and Demand Management (ATDM) technologies in the US, the practical needs for collecting dynamic demand information such as dynamic OD matrix increase significantly. Dynamic OD matrix can be directly adopted by transportation agencies to monitor, manage, and respond to dynamic travel demand changes within urban networks and provide reliable inputs for proactive solutions to address mobility, reliability, and sustainability issues. In this study, we propose the use of venue-side location-based social network (VS-LBSN) data to generate dynamic OD information. The study is based upon the existing work by the research team on static OD demand estimation based on VS-LBSN data (Jin, Yang, Cebelak, Ran, & Walton, 2013; Yang, Jin, Cheng, & Ran, 2013).

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