Enhancing Trip Distribution Prediction with Twitter Data

Pourebrahim, Nastaran; Sultana, Selima; Thill, Jean‐Claude; Mohanty, Somya D.

doi:10.1145/3281548.3281555

Cited by 20 publications

(2 citation statements)

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“…In one instance, GSND in combination with a set of points of interest were used to develop a gravitation model for the city of Chicago [24]. A study conducted in New York City describes training a neural network model based on GSND for augmentation of a gravity model [25]. The aforementioned gravity model [26] also defines the principles based on which we derive flow data from GSND in this study.…”

Section: Related Workmentioning

confidence: 99%

Commuter Mobility Patterns in Social Media: Correlating Twitter and LODES Data

Petutschnig

Albrecht

Resch

et al. 2021

IJGI

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The Longitudinal Employer-Household Dynamics Origin-Destination Employment Statistics (LODES) are an important city planning resource in the USA. However, curating these statistics is resource-intensive, and their accuracy deteriorates when changes in population and urban structures lead to shifts in commuter patterns. Our study area is the San Francisco Bay area, and it has seen rapid population growth over the past years, which makes frequent updates to LODES or the availability of an appropriate substitute desirable. In this paper, we derive mobility flows from a set of over 40 million georeferenced tweets of the study area and compare them with LODES data. These tweets are publicly available and offer fine spatial and temporal resolution. Based on an exploratory analysis of the Twitter data, we pose research questions addressing different aspects of the integration of LODES and Twitter data. Furthermore, we develop methods for their comparative analysis on different spatial scales: at the county, census tract, census block, and individual street segment level. We thereby show that Twitter data can be used to approximate LODES on the county level and on the street segment level, but it also contains information about non-commuting-related regular travel. Leveraging Twitter’s high temporal resolution, we also show how factors like rush hour times and weekends impact mobility. We discuss the merits and shortcomings of the different methods for use in urban planning and close with directions for future research avenues.

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Section: Related Workmentioning

confidence: 99%

Commuter Mobility Patterns in Social Media: Correlating Twitter and LODES Data

Petutschnig

Albrecht

Resch

et al. 2021

IJGI

View full text Add to dashboard Cite

show abstract

“…The gravity model is the typical traditional model originating from physics, which describes mobility fluxes. Despite its extensive use to predict mobility patterns at different spatial scales [18] [19], the gravity model relies on specific parameters fitted from systematic collections of traffic data. The formula is as follows:…”

Section: B Vehicle Mobility Modelsmentioning

confidence: 99%