Big Data Analytics: A Case Study of Public Opinion Towards the Adoption of Driverless Cars

Buch, Rasmus Brødsgaard; Beheshti-Kashi, Samaneh; Nielsen, Thomas Alexander Sick; Kinra, Aseem

doi:10.1007/978-3-319-74225-0_47

Cited by 3 publications

(2 citation statements)

References 7 publications

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“…It uses Dirichlet distribution to represent the text data to find the most important topics. It was used by 13.5% of the studies (Alamsyah et al, 2018;Buch et al, 2018;Kovács-Győri et al, 2018;Kulkarni et al, 2018;Pournarakis et al, 2017;D. Wang et al, 2017;Wayasti et al, 2018).…”

Section: S6-rq1: What Are the Datasets Used By Researchers?mentioning

confidence: 99%

Investigating transportation research based on social media analysis: a systematic mapping review

et al. 2021

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Social media is a pool of users' thoughts, opinions, surrounding environment, situation and others. This pool can be used as a real-time and feedback data source for many domains such as transportation. It can be used to get instant feedback from commuters; their opinions toward the transportation network and their complaints, in addition to the traffic situation, road conditions, events detection and many others. The problem is in how to utilize social media data to achieve one or more of these targets. A systematic review was conducted in the field of transportation-related research based on social media analysis (TRR-SMA) from the years between 2008 and 2018; 74 papers were identified from an initial set of 703 papers extracted from 4 digital libraries. This review will structure the field and give an overview based on the following grounds: activity, keywords, approaches, social media data and platforms and focus of the researches. It will show the trend in the research subjects by countries, in addition to the activity trends, platforms usage trend and others. Further analysis of the most employed approach (Lexicons) and data (text) will be also shown. Finally, challenges and future works are drawn and proposed.

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Section: S6-rq1: What Are the Datasets Used By Researchers?mentioning

confidence: 99%

Investigating transportation research based on social media analysis: a systematic mapping review

et al. 2021

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“…Additionally, some studies have used newspaper articles and Reddit (Bakalos et al, 2020;Buch et al, 2018;Kinra et al, 2020). Second, opinion on AVs has been examined using topic modeling, and sentiment analysis.…”

Section: Autonomous Vehicle Centered Big Data Analysis Approachesmentioning

confidence: 99%

Understanding acceptance of Autonomous Mobility Services using statistical and deep learning approaches

Bala¹

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The emergence of vehicle automation and its subsequent growth has led to new transport service offerings, generally known as Autonomous Mobility Services (AMS), that have the potential to facilitate or even replace human-operated vehicles. AMS contains different forms of potential mobility options which may contradict current transport modal concepts in terms of functionalities. For example, an autonomous shuttle bus which is a form of autonomous transit may serve similarly as an autonomous taxi/robo-taxi in terms of functionalities, coinciding with the concept of Shared Autonomous Mobility Services (SAMS). Even if the functionalities or operational principles are different, peoples' perceptions of sharing rides in any of these services may be alike. Apart from these confusions in functionalities mentioned above, peoples' attitudes and acceptance of AMS, once it's implemented in any form in the public road environment, remains a significant research aspect. To address these issues, this thesis tried to first clearly distinguish different types of AMS. Second, it tried to assemble the progress till now in acceptance-related research of AMS while reviewing the previous study approaches, outcomes, policy implications, and future research directions. Third, this study attempted to understand the acceptance of AMS using statistical and deep learning approaches leveraging both survey and social media data. Fourth, this study tried to present the consequent applicabilities and limitations of using both types of data sources for autonomous vehicle acceptance research. Eventually, this thesis intends to present an overall idea of the AMS acceptance research with future directions leveraging both data sources in an individualistic or combined manner.

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Public Perspectives on Automated Vehicles in Online Discussion Forums: A Social Constructionist Perspective

Waltermann

Henkel

2022

SSRN Journal

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Big Data Analytics: A Case Study of Public Opinion Towards the Adoption of Driverless Cars

Cited by 3 publications

References 7 publications

Investigating transportation research based on social media analysis: a systematic mapping review

Investigating transportation research based on social media analysis: a systematic mapping review

Understanding acceptance of Autonomous Mobility Services using statistical and deep learning approaches

Public Perspectives on Automated Vehicles in Online Discussion Forums: A Social Constructionist Perspective

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