Utilizing Social Media in Transport Planning and Public Transit Quality: Survey of Literature

Nikolaidou, Anastasia; Papaioannou, Panagiotis

doi:10.1061/jtepbs.0000128

Cited by 32 publications

(22 citation statements)

References 35 publications

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“…However, by comparing Figure 3a,b, it can be observed that the city center has a relatively dense check-in distribution. Generally, it is considered that more and more people prefer to live near the communities where they have easy access to services, i.e., transport, healthcare, and entertainment [163,164]. Even from the current study, high check-in density is found nearby subways and highways.…”

Section: Density Variations and Distribution Of Check-inscontrasting

confidence: 61%

Spatiotemporal Analysis to Observe Gender Based Check-In Behavior by Using Social Media Big Data: A Case Study of Guangzhou, China

2019

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In a location-based social network, users socialize with each other by sharing their current location in the form of “check-in,” which allows users to reveal the current places they visit as part of their social interaction. Understanding this human check-in phenomenon in space and time on location based social network (LBSN) datasets, which is also called “check-in behavior,” can archive the day-to-day activity patterns, usage behaviors toward social media, and presents spatiotemporal evidence of users’ daily routines. It also provides a wide range of opportunities to observe (i.e., mobility, urban activities, defining city boundary, and community problems in a city). In representing human check-in behavior, these LBSN datasets do not reflect the real-world events due to certain statistical biases (i.e., gender prejudice, a low frequency in sampling, and location type prejudice). However, LBSN data is primarily considered a supplement to traditional data sources (i.e., survey, census) and can be used to observe human check-in behavior within a city. Different interpretations are used elusively for the term “check-in behavior,” which makes it difficult to identify studies on human check-in behavior based on LBSN using the Weibo dataset. The primary objective of this research is to explore human check-in behavior by male and female users in Guangzhou, China toward using Chinese microblog Sina Weibo (referred to as “Weibo”), which is missing in the existing literature. Kernel density estimation (KDE) is utilized to explore the spatiotemporal distribution geographically and weighted regression (GWR) method was applied to observe the relationship between check-in and districts with a focus on gender during weekdays and weekend. Lastly, the standard deviational ellipse (SDE) analysis is used to systematically analyze the orientation, direction, spatiotemporal expansion trends and the differences in check-in distribution in Guangzhou, China. The results of this study show that LBSN is a reliable source of data to observe human check-in behavior in space and time within a specified geographic area. Furthermore, it shows that female users are more likely to use social media as compared to male users. The human check-in behavior patterns for social media network usage by gender seems to be slightly different during weekdays and weekend.

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Section: Density Variations and Distribution Of Check-inscontrasting

confidence: 61%

Spatiotemporal Analysis to Observe Gender Based Check-In Behavior by Using Social Media Big Data: A Case Study of Guangzhou, China

2019

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“…Attempts have also been made to apply findings from urban informatics and social media analytics to domain-specific cases like policymaking, city-planning [ 11 , 29 , 54 ], computational epidemiology [ 68 ] and automatic geolocation detection [ 10 , 23 , 62 , 74 ]. For example, Chenge et al [ 10 ] and Wing et al [ 74 ] utilize word frequency to model spatial variations and predict geolocations of tweets.…”

Section: Related Workmentioning

confidence: 99%

Measuring spatio-textual affinities in twitter between two urban metropolises

Kejriwal

2021

J Comput Soc Sc

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With increasing growth of both social media and urbanization, studying urban life through the empirical lens of social media has led to some interesting research opportunities and questions. It is well-recognized that as a ‘social animal’, most humans are deeply embedded both in their cultural milieu and in broader society that extends well beyond close family, including neighborhoods, communities and workplaces. In this article, we study this embeddedness by leveraging urban dwellers’ social media footprint. Specifically, we define and empirically study the issue of spatio-textual affinity by collecting many millions of geotagged tweets collected from two diverse metropolises within the United States: the Boroughs of New York City, and the County of Los Angeles. Spatio-textual affinity is the intuitive hypothesis that tweets coming from similar locations (spatial affinity) will tend to be topically similar (textual affinity). This simple definition of the problem belies the complexity of measuring it, since (re-tweets notwithstanding) two tweets are never truly identical either spatially or textually. Workable definitions of affinity along both dimensions are required, as are appropriate experimental designs, visualizations and measurements. In addition to providing such definitions and a viable framework for conducting spatio-textual affinity experiments on Twitter data, we provide detailed results illustrating how our framework can be used to compare and contrast two important metropolitan areas from multiple perspectives and granularities.

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“…[25] Presents the challenges faced in large scale transportation operations in Indian railways. [26] Presented a survey using social media on collecting transportation information, creating transportation lexicon, transportation planning, operations, opportunities and challenges in transportation domain.…”

Section: Related Researchmentioning

confidence: 99%

Opinion Mining on Social Media Transit Tweets using Text Pre-Processing and Machine Learning Techniques

Rani¹,

Sumathy²

2019

IJITEE

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Capturing public insights related to transit systems in social media has gained huge popularity presently. The regional transportation agencies use social media as a tool to provide information to the public and seek their inputs and ideas for meaningful decision making in transportation activities. This exploratory study attempts to gauge the impact of social media use in transportation planning that in turn would help transportation administration in identifying the day-to-day challenges faced by the customers and to suggest a suitable solution. This paper presents the effect of pre-processing techniques on transit opinion analysis to improve the performance. Performance of different pre-processing methods namely stop word removal, stemming, lemmatization, negation handling and URL removal using feature representation models namely TF-IDF with unigram, TF-IDF with bigram on three feature selection techniques including information gain, standard deviation and chi-square on social media transit rider’s opinion is carried out. The experimental results are evaluated using four different classifiers such as Support vector machine, Naïve Bayes, Decision Tree, K-Nearest Neighborhood in terms of accuracy, precision, recall, and f-measure. On analyzing the social media related transit opinion data, it is observed that pre-processing with bigram technique performs better than the other approaches specifically with Support Vector Machine and Naïve Bayes.

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Utilizing Social Media in Transport Planning and Public Transit Quality: Survey of Literature

Cited by 32 publications

References 35 publications

Spatiotemporal Analysis to Observe Gender Based Check-In Behavior by Using Social Media Big Data: A Case Study of Guangzhou, China

Spatiotemporal Analysis to Observe Gender Based Check-In Behavior by Using Social Media Big Data: A Case Study of Guangzhou, China

Measuring spatio-textual affinities in twitter between two urban metropolises

Opinion Mining on Social Media Transit Tweets using Text Pre-Processing and Machine Learning Techniques

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