An adaptive hawkes process formulation for estimating time-of-day zonal trip arrivals with location-based social networking check-in data

• Published in: Transportation research. Part C, Emerging technologies Vol. 79; pp. 136 - 155
• Main Authors: Hu, Wangsu, Jin, Peter J.
• Format: Journal Article
• Language: English
• Published: Elsevier India Pvt Ltd 01.06.2017
• Subjects: Dynamic trip arrival estimation; Hawkes process; Human activity patterns; Location-based social networking; State space model; Hawkes process; State space model; Human activity patterns; Location-based social networking; Dynamic trip arrival estimation
• ISSN: 0968-090X; 1879-2359
• DOI: 10.1016/j.trc.2017.02.002

•LBSN data is applied to estimate time-of-day trip arrival patterns.•A stochastic point-process model and state-space modeling is proposed.•The model captures trip arrival patterns for the work, retail and recreation trips.•A small number of parameters is needed for the calibration.•The generated bi-hourly patterns consistent with those reflected in MPO data.•The sampling bias is reduced through a state-space modeling framework with feedbacks. Location-Based Social Networking (LBSN) services, such as Foursquare, Facebook check-ins, and Geo-tagged Twitter tweets, have emerged as new secondary data sources for studying individual travel mobility patterns at a fine-grained level. However, the differences between human social behavioral and travel patterns can cause significant sampling bias for travel demand estimation. This paper presents a dynamic model to estimate time-of-day zonal trip arrival patterns. In the proposed model, the state propagation is formulated by the Hawkes process; the observation model implements LBSN sampling. The proposed model is applied to Foursquare check-in data collected from Austin, Texas in 2010 and calibrated with Origin-Destination (OD) data and time of day factor from Capital Area Metropolitan Planning Organization (CAMPO). The proposed model is compared with a simple aggregation model of trip purposes and time of day based on a prior daily OD estimation model using the LBSN data. The results illustrate the promising benefits of applying stochastic point process models and state-space modeling in time-of-day zonal arrival pattern estimation with the LBSN data. The proposed model can significantly reduce the number of parameters to calibrate in order to reduce the sampling bias of LBSN data for trip arrival estimation.