Tweeting Transit: An examination of social media strategies for transport information management during a large event

Cottrill, Caitlin; Gault, Paul; Yeboah, Godwin; Nelson, John D.; Anable, Jillian; Budd, Thomas

doi:10.1016/j.trc.2017.02.008

Cited by 79 publications

(26 citation statements)

References 21 publications

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“…It has been shown that existing travel information systems in the UK, Sweden, and Germany tend to be biased towards planning in the pre-travel stage of the journey, leading to limited functionalities on the on-trip/in-trip as well as post-trip stages of the journey (Kramers 2014). There is also evidence that public transport service providers are increasingly considering dissemination of travel information to travellers via social media platforms where there is the possibility for information exchange in a trusted, accurate, transparent and open manner (Cottrill et al 2017).…”

Section: Introductionmentioning

confidence: 99%

Understanding factors influencing public transport passengers’ pre-travel information-seeking behaviour

et al. 2019

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This paper investigates factors influencing public transport passengers' pre-travel information-seeking behaviours in a British urban environment. Public transport traveller surveys were conducted to better understand the journey stages at which information was sought and the information sources used. A multivariate explanatory model of pre-travel information-seeking behaviour was developed using binomial logistic regression. Explanatory factors considered include socio-demographics, trip context, frequency of public transport use, information sources used, and smartphone ownership and use. Findings suggest that travel behaviour (5 + trips weekly, and < 1 trip weekly), socio-demographics (unemployment/unknown employment), trip context (journey planning stages, mode of transport), and preferred information sources (Internet site, word-of-mouth, visits to travel shop/centre/library) were significant predictors of pre-travel information-seeking behaviours among surveyed travellers. While the final model found that bus users are significantly associated with the use of Internet sites as a source of pre-travel information, rail users rely significantly on a multiplicity of sources comprising Internet sites, word-of-mouth, and visits to a travel shop/centre/library. The final model suggests that metro (light rail) users tend not to seek pre-travel information. The odds of seeking pre-travel public transport information are 2.512 times greater for respondents who reported < 1 trip per week as opposed to those who reported 5 + trips per week. These findings are relevant for passenger information strategies deployed by operators and authorities and can be used to caution against a "one size fits all" strategy for travel information service provision. Implications and suggestions for future research are discussed.

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Section: Introductionmentioning

confidence: 99%

Understanding factors influencing public transport passengers’ pre-travel information-seeking behaviour

et al. 2019

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“…The newly emerged data source, social media data, has proved its capability in recent traffic studies including activity pattern identification (Hasan and Ukkusuri, 2014), special traffic-related events (Ni et al, 2014;Shirky, 2011), traffic flow prediction (Cottrill et al, 2017;Lin et al, 2015;Ni et al, 2017), transport information management (Cottrill et al, 2017), travel mode detection (Maghrebi et al, 2016), destination or route choice (Huang et al, 2017), etc. According to Rashidi et al (2017), as social media data encompasses information that is revealed by users in realistic situations, such data is free from sampling, surveying or laboratory biases.…”

Section: Review Of Social Media In Traffic-related Studiesmentioning

confidence: 99%

A deep learning approach for detecting traffic accidents from social media data

Zhang¹,

Gao

et al. 2018

Transportation Research Part C: Emerging Technologies

295

134

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This paper employs deep learning in detecting the traffic accident from social media data. First, we thoroughly investigate the 1-year over 3 million tweet contents related to traffic accidents in two metropolitan areas: Northern Virginia and New York City. Our results show that paired tokens can capture the association rules inherent in the accident-related tweets and further increase the accuracy of the traffic accident detection. Second, two deep learning methods: Deep Belief Network (DBN) and Long Short-Term Memory (LSTM) are investigated and implemented in extracted tokens. Results show that DBN can obtain an overall accuracy of 85% with about 44 individual token features and 17 paired token features. The classification results from DBN outperform those of Support Vector Machines (SVMs) and supervised Latent Dirichlet allocation (sLDA). Finally, to validate this study, we compare the accident-related tweets with both the traffic accident log on freeways and traffic data on local roads from 15,000 loop detectors. It is found that nearly 66% of the accident-related tweets can be located by the accident log and more than 80% of them can be tied to nearby abnormal traffic data. Several important issues of using Twitter to detect traffic accidents have been brought up by the comparison including the location and time bias, as well as the characteristics of influential users and hashtags.

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“…GIS can manage tweets coordinates to represent them as geographic points, or to apply over them geospatial functions; there are open source GIS such as QGIS, Grass, gvSIG, SAGA, and open source databases to manage geographic data such as PostGIS, SpatialLite and MySQLSpatial [28]. Some GIS-Twitter researches are focused on analyzing people's reports of weather, rain, snow storms, floods [4,6]; some others model the activities of people in urban environments, such as traffic, pollution, public demonstrations, taxi routes [18,42,44]. In addition, there exists some researches interested in studying and managing critical situations [56,53,16].…”

Section: Where Twitter Meets Gismentioning

confidence: 99%

When Twitter Becomes a Data Source for Geospatial Analysis

Saldaña-Pérez¹,

Cavalière²,

Torres-Ruiz³

et al. 2019

RCS

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Social media has become a useful data source for processes and researches interested in improving people's life. Publications done by social media users provide details about the people perceptions of their environment, and updated observations about what happens in real world. In this approach the relevance of Twitter as a data source for scientific purposes is analyzed, as well as its use in geospatial researches. Tweets have two main characteristics, a text where user describes its ideas, and metadata, where features such as the coordinates of the place where the tweet was posted are stored. Different computing procedures are applied over tweets in order to make them useful for different tasks; commonly, text mining, classification, and regression algorithms are used to process tweets. The coordinates of tweets make possible to link events described in tweets with the geographical area where they occur. The analysis of tweets and coordinates provides updated data, useful in natural disasters control, decision taking processes and urban studies. The present approach studies what motivates users to tweet, and analyzing the messages produced by Twitter users, classifies the tweets into three groups according to the information expressed in their text: self-centered, social information and collective information. Additionally, some methods to extract information from tweets are studied, common problems presented when working with tweets, and some researches that use them in a geospatial domain are presented.

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Tweeting Transit: An examination of social media strategies for transport information management during a large event

Cited by 79 publications

References 21 publications

Understanding factors influencing public transport passengers’ pre-travel information-seeking behaviour

Understanding factors influencing public transport passengers’ pre-travel information-seeking behaviour

A deep learning approach for detecting traffic accidents from social media data

When Twitter Becomes a Data Source for Geospatial Analysis

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