Sharing Real-Time Traffic Information With Travelers Using Twitter: An Analysis of Effectiveness and Information Content

Rahman, Rezaur; Roy, Kamol Chandra; Abdel-Aty, Mohamed; Hasan, Samiul

doi:10.3389/fbuil.2019.00083

Cited by 6 publications

(4 citation statements)

References 32 publications

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“…In contrast to these supervised classification models, a number of works have used unsupervised models to explore the semantics behind traffic related tweets. For example, [7], [8], [14], [23] proposed a Latent Dirichlet Allocation (LDA) to extract different traffic related topics from tweets.…”

Section: State-of-the-artmentioning

confidence: 99%

Exploring the Potential of Twitter to Understand Traffic Events and Their Locations in Greater Mumbai, India

Das

Purves

2020

IEEE Trans. Intell. Transport. Syst.

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Detecting traffic events and their locations is important for an effective transportation management system and better urban policy making. Traffic events are related to traffic accidents, congestion, parking issues, to name a few. Currently, traffic events are detected through static sensors e.g., CCTV camera, loop detectors. However they have limited spatial coverage and high maintenance cost, especially in developing regions. On the other hand, with Web 2.0 and ubiquitous mobile platforms, people can act as social sensors sharing different traffic events along with their locations. We investigated whether Twitter-a social media platform can be useful to understand urban traffic events from tweets in India. However, such tweets are informal and noisy and containing vernacular geographical information making the location retrieval task challenging. So far most authors have used geotagged tweets to identify traffic events which accounted for only 0.1%-3% or sometimes less than that. Recently Twitter has removed precise geotagging, further decreasing the utility of such approaches. To address these issues, this research explored how ungeotagged tweets could be used to understand traffic events in India. We developed a novel framework that does not only categorize traffic related tweets but also extracts the locations of the traffic events from the tweet content in Greater Mumbai. The results show that an SVM based model performs best detecting traffic related tweets. While extracting location information, a hybrid georeferencing model consists of a supervised learning algorithm and a number of spatial rules outperforms other models. The results suggest people in India, especially in Greater Mumbai often share traffic information along with location mentions, which can be used to complement existing physical transport infrastructure in a cost-effective manner to manage transport services in the urban environment.

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Section: State-of-the-artmentioning

confidence: 99%

Exploring the Potential of Twitter to Understand Traffic Events and Their Locations in Greater Mumbai, India

Das

Purves

2020

IEEE Trans. Intell. Transport. Syst.

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“…Researchers rely on this method to better understand social phenomena, pinpoint noteworthy individuals or groups, analyze information flow, and forecast behaviors within social networks. When applied to Twitter, this method can unveil users' connectivity and highlight influential users and their connections by measuring their centrality [20,81,82].…”

Section: Social Media Influence Analysismentioning

confidence: 99%

Assessing the EU Climate and Energy Policy Priorities for Transport and Mobility through the Analysis of User-Generated Social Media Content Based on Text-Mining Techniques

Nikolaidou,

Kopsacheilis,

Gavanas

et al. 2024

Sustainability

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For over three decades, the European Union’s (EU) transport policy has aimed at fostering environmental sustainability and energy efficiency. Since 2015, European policymakers have focused more on three key sustainable development goals: decarbonizing the transport system, promoting low-emission mobility solutions, and transitioning to renewable and alternative fuels. To effectively communicate priorities and engage stakeholders, EU policymakers regularly use social media platforms like Twitter (now known as X). This active discourse involves policymakers, industrial stakeholders, the media, and the public, offering insights into the role of transport policy in addressing climate change and energy transition challenges. The current research endeavors to track and analyze the evolution of user-generated content related to climate change, energy transition, and smart mobility on Twitter from 2011 to 2021. This research uses text-mining and social network analysis techniques to quantitatively and qualitatively assess the dynamics of relevant EU policies and their effects. The study’s findings can be used to establish a robust monitoring and evaluation framework at the EU and national levels. This framework will assess the effectiveness of communicating strategic priorities for sustainable transport development. It also holds potential for application in other sectors, broadening its impact.

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“…Rahman et al, in their seminal work, evaluated the effectiveness and efficiency of real-time traffic information dissemination mechanisms through social media platforms. Their study, backed by empirical data, proposed a novel framework, SMART-Feed, designed to enhance the efficiency and reach of traffic updates shared on platforms like Twitter [8].…”

Section: Related Researchmentioning

confidence: 99%

The Sharing Economy Increases Social Network Activity and thus the Ability to Realize Traffic

Zhou

2023

AEMPS

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The digital transformation of the 21st century has catalyzed the emergence of two dominant phenomena: the sharing economy and the expansive growth of social networks. This paper meticulously delves into the intricate relationship between these two transformative domains, offering a comprehensive exploration of their convergence and the profound impact this has on consumer behavior, evolving business models, and overarching societal norms. Presently, the landscape is distinctly marked by the democratization of resources, enabling greater access and utilization, coupled with a significant shift in communication paradigms. This shift is predominantly driven by innovative platforms such as Airbnb, Uber, and Facebook, which have redefined user engagement and economic transactions. Peering into the future, it becomes evident that technological advancements, stand at the forefront of refining this convergence. These technologies promise to address and offer robust solutions to prevailing challenges, especially those related to trust, transparency, and tailored personalization. Yet, this promising horizon is interspersed with challenges. Pressing issues such as regulatory frameworks, the imperative of data privacy, and the consistent demand for quality assurance necessitate astute navigation by all stakeholders involved. In summation, the dynamic interplay between the sharing economy and social networks heralds a transformative paradigm shift in the digital age. This shift places a spotlight on sustainability, fosters community collaboration, and champions the ethos of shared value. Stakeholders, ranging from businesses to end consumers, are emphatically encouraged to navigate this evolving digital terrain with adaptability, strategic foresight, and informed prudence.

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Sharing Real-Time Traffic Information With Travelers Using Twitter: An Analysis of Effectiveness and Information Content

Cited by 6 publications

References 32 publications

Exploring the Potential of Twitter to Understand Traffic Events and Their Locations in Greater Mumbai, India

Exploring the Potential of Twitter to Understand Traffic Events and Their Locations in Greater Mumbai, India

Assessing the EU Climate and Energy Policy Priorities for Transport and Mobility through the Analysis of User-Generated Social Media Content Based on Text-Mining Techniques

The Sharing Economy Increases Social Network Activity and thus the Ability to Realize Traffic

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