ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-4/W2, 207-214, 2017
https://doi.org/10.5194/isprs-annals-IV-4-W2-207-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.
 
20 Oct 2017
MODELING A SPATIO-TEMPORAL INDIVIDUAL TRAVEL BEHAVIOR USING GEOTAGGED SOCIAL NETWORK DATA: A CASE STUDY OF GREATER CINCINNATI
M. Saeedimoghaddam and C. Kim Department of Geography & GIS, University of Cincinnati, Cincinnati, OH 45221, USA
Keywords: Spatiotemporal modeling, Location-based social network, Machine learning, Travel behavior Abstract. Understanding individual travel behavior is vital in travel demand management as well as in urban and transportation planning. New data sources including mobile phone data and location-based social media (LBSM) data allow us to understand mobility behavior on an unprecedented level of details. Recent studies of trip purpose prediction tend to use machine learning (ML) methods, since they generally produce high levels of predictive accuracy. Few studies used LSBM as a large data source to extend its potential in predicting individual travel destination using ML techniques. In the presented research, we created a spatio-temporal probabilistic model based on an ensemble ML framework named “Random Forests” utilizing the travel extracted from geotagged Tweets in 419 census tracts of Greater Cincinnati area for predicting the tract ID of an individual’s travel destination at any time using the information of its origin. We evaluated the model accuracy using the travels extracted from the Tweets themselves as well as the travels from household travel survey. The Tweets and survey based travels that start from same tract in the south western parts of the study area is more likely to select same destination compare to the other parts. Also, both Tweets and survey based travels were affected by the attraction points in the downtown of Cincinnati and the tracts in the north eastern part of the area. Finally, both evaluations show that the model predictions are acceptable, but it cannot predict destination using inputs from other data sources as precise as the Tweets based data.
Conference paper (PDF, 1312 KB)


Citation: Saeedimoghaddam, M. and Kim, C.: MODELING A SPATIO-TEMPORAL INDIVIDUAL TRAVEL BEHAVIOR USING GEOTAGGED SOCIAL NETWORK DATA: A CASE STUDY OF GREATER CINCINNATI, ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-4/W2, 207-214, https://doi.org/10.5194/isprs-annals-IV-4-W2-207-2017, 2017.

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