Utilising Location Based Social Media in Travel Survey Methods

Abbasi, Alireza; Rashidi, Taha Hossein; Maghrebi, Mojtaba; Waller, S. Travis

doi:10.1145/2830657.2830660

Cited by 51 publications

(36 citation statements)

References 18 publications

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“…The temporal bins have been defined as follows: before: June 27-July 13, 2012;during: July 27-August 12, 2012;after: August 27-September 12, 2012. Spatiotemporal subsetting (hypothesizing residents and visitors): The self-reported geolocation data from tweets and the frequency of their presence in the temporal subsets were used to identify presumable "residents" and "visitors" in London. Our approach is based on the work of Abbasi et al (2015), who identified these user types in Sydney for city trip analysis. The rationale for identifying the two groups was the following: A person who tweeted at least once in each of the temporal subsets was considered a "resident", whereas a person who tweeted in just one of them was considered a "visitor" (non-resident).…”

Section: Pre-processingmentioning

confidence: 99%

“…Also, Abbasi et al (2015) stated that dividing social media users into residents and visitors is not an easy task. They categorized these types of people in Sydney for city trips supporting urban planning.…”

Section: Identifying Residents Vs Visitorsmentioning

confidence: 99%

“…This is where the advantages of the datadriven era become relevant, most concretely with respect to the real-time availability of social media data. This data provides unseen contextual insights into spatiotemporal phenomena on a finer scale in cities through users' digital traces on different online platforms such as Twitter, Foursquare/Swarm and Flickr (Abbasi, Rashidi, Maghrebi, & Waller, 2015;Aubrecht, Ungar, & Freire, 2011;Crooks et al, 2015;Girardin, Vaccari, Gerber, Biderman, & Ratti, 2009). This is of central importance to urban planning dealing with the optimization of the abovementioned networks.…”

Section: Introductionmentioning

confidence: 99%

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#London2012: Towards Citizen-Contributed Urban Planning Through Sentiment Analysis of Twitter Data

Kovács-Györi

Ristea

Havas

et al. 2018

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The dynamic nature of cities, understood as complex systems with a variety of concurring factors, poses significant challenges to urban analysis for supporting planning processes. This particularly applies to large urban events because their characteristics often contradict daily planning routines. Due to the availability of large amounts of data, social media offer the possibility for fine-scale spatial and temporal analysis in this context, especially regarding public emotions related to varied topics. Thus, this article proposes a combined approach for analyzing large sports events considering event days vs comparison days (before or after the event) and different user groups (residents vs visitors), as well as integrating sentiment analysis and topic extraction. Our results based on various analyses of tweets demonstrate that different spatial and temporal patterns can be identified, clearly distinguishing both residents and visitors, along with positive or negative sentiment. Furthermore, we could assign tweets to specific urban events or extract topics related to the transportation infrastructure. Although the results are potentially able to support urban planning processes of large events, the approach still shows some limitations including well-known biases in social media or shortcomings in identifying the user groups and in the topic modeling approach.

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Section: Pre-processingmentioning

confidence: 99%

Section: Identifying Residents Vs Visitorsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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#London2012: Towards Citizen-Contributed Urban Planning Through Sentiment Analysis of Twitter Data

Kovács-Györi

Ristea

Havas

et al. 2018

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“…Instant messaging has five fundamentals aspects. First, it guarantees that your correspondent is available (Abbasi, Rashidi, Maghrebi, & Waller, 2015). Second, it has multitasking capabilities.…”

Section: Introductionmentioning

confidence: 99%

The Role of Instant Messanging on the Effectiveness of Lecturer's Performance at the Universitas Abdurrab

Hartati

Arisandi

2017

pijss

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“…Among the travel characteristics, trip destination and activity pattern received significant attention in recent studies (Ermagun et al, 2017). Traditionally, household travel survey sources are used to analysis human mobility pattern and travel behavior and create predictive models (Abbasi et al, 2015). The more recent studies tend to use machine learning (ML) methods since they generally produce higher levels of predictive accuracy than probabilistic and rule-based methods (Ermagun et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Modeling a Spatio-Temporal Individual Travel Behavior Using Geotagged Social Network Data: A Case Study of Greater Cincinnati

Saeedimoghaddam

Kim

2017

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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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.

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Utilising Location Based Social Media in Travel Survey Methods

Cited by 51 publications

References 18 publications

#London2012: Towards Citizen-Contributed Urban Planning Through Sentiment Analysis of Twitter Data

#London2012: Towards Citizen-Contributed Urban Planning Through Sentiment Analysis of Twitter Data

The Role of Instant Messanging on the Effectiveness of Lecturer's Performance at the Universitas Abdurrab

Modeling a Spatio-Temporal Individual Travel Behavior Using Geotagged Social Network Data: A Case Study of Greater Cincinnati

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