How do new transit stations affect people's sentiment and activity? A case study based on social media data in Hong Kong

Chang, Haoliang; Huang, Jianxiang; Yao, Weiran; Zhao, Weizun; Li, Lishuai

doi:10.1016/j.tranpol.2022.03.011

Cited by 9 publications

(7 citation statements)

References 51 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There is only one recent study that has investigated specific changes made to the transit network with respect to social media sentiment. Chang et al ( 17 ) studied the causal effect of adding new transit stations in Hong Kong on sentiment and user activity on Twitter using the difference-in-differences quasi-experimental method. The authors concluded that the introduction of these transit stations had a positive influence on Twitter activity, which became significant in the long run.…”

Section: Literature Reviewmentioning

confidence: 99%

Using Twitter to Gauge Customer Satisfaction Response to a Major Transit Service Change in Calgary, Canada

Al-Sahar,

Klumpenhouwer,

Shalaby

et al. 2023

Transportation Research Record: Journal of the Transportation R

View full text Add to dashboard Cite

Measuring public opinion about the quality of transit services is a key factor in understanding and addressing customer dissatisfaction and increasing customer loyalty and ridership. Sentiment analysis using social media—in particular Twitter—is a relatively cheap and potentially powerful complement to traditional survey methods, which are expensive and limited in sample size. This study aims to evaluate customer response to the introduction of Calgary Transit’s MAX routes. We compared customer satisfaction expressed on Twitter with measured service reliability in the form of on-time performance. We also employed a qualitative research approach using content analysis from Twitter to gauge rider satisfaction over several service attributes before and after the service change. A transit-specific sentiment lexicon was developed to support this study using a hybrid approach. This lexicon outperformed generic sentiment lexicons traditionally used in transit studies with regard to both accuracy (18.4%) and F1-score (7.1%). We found that the overall perception of on-time performance from riders using Twitter was similar to the actual performance in the field. This was also observed for one individual route on which stops with poor schedule adherence were linked with negative feedback. This study concludes that combining customer-oriented measures from Twitter with operational-oriented ones would enable transit agencies to make better-informed decisions for planning and operational purposes.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Using Twitter to Gauge Customer Satisfaction Response to a Major Transit Service Change in Calgary, Canada

Al-Sahar,

Klumpenhouwer,

Shalaby

et al. 2023

Transportation Research Record: Journal of the Transportation R

View full text Add to dashboard Cite

show abstract

“…Social-media analytics has been used in rail services for disruption management ( 15, 24, 25, 27, 28, 57 ), communication strategy ( 58 ), demand estimation ( 59 ), service-quality analysis ( 16–19, 36–38, 60, 61 ), and user opinion capturing ( 62 , 63 ) purposes. In line with the purpose and scope of this research, we will focus on the papers examining service quality and user opinion.…”

Section: Social-media Analytics In Rail Service Qualitymentioning

confidence: 99%

“…Similar to the previous research ( 16 , 18 , 26 ), the authors found significant temporal (hourly and daily) and spatial variations among different service dimensions. More recently, Chang et al ( 63 ) used geotagged tweets to track the footprints of citizens and examine the impact of new transit stations on people’s activity and sentiments.…”

Section: Social-media Analytics In Rail Service Qualitymentioning

confidence: 99%

Service Quality in Rail Systems: Listen to the Voice of Social Media

Güner,

Taşkın,

Cebeci

et al. 2023

Transportation Research Record: Journal of the Transportation R

View full text Add to dashboard Cite

Service quality is essential to increase and maintain user loyalty to the railway system. In the literature, surveys have been used to measure user satisfaction, and mathematical methods have been applied to quantify the survey results. In recent years, user-generated content, including comments and complaints shared via social media, has been used to measure the quality of rail services. This content may provide important insights into the quality of the service provided with its dynamic structure. In this study, a SERVQUAL-based social-media analytics approach is used to measure railway service quality, placing special emphasis on the temporal variations in a national rail system. Topic modeling was used to assign each content item to the relevant service dimension and sentiment analysis was applied to measure the level of satisfaction. Importance–performance analysis was employed at the final stage to generate policy suggestions. Gathering more than 2.3 million social-media messages posted from 2011 to 2021, we examined the temporal evolution of service quality of the Turkish rail system. The results reveal the most and least important services and the satisfaction level of each dimension. The differences between the priorities of conventional and high-speed rail passengers are defined, and policy recommendations are presented.

show abstract

“…Social media data from other cities worldwide were also used in order to gain knowledge about the transportation systems particularly in megacities from Brazil as São Paulo [30], Rio de Janeiro [30], United Kingdon such as London [13], [25] and Manchester [34], and China such as Shanghai [35], Nanjing [36] and Shenzhen [27].…”

Section: Literature Reviewmentioning

confidence: 99%

“…These text mining studies are usually ranging from a few days [34], [37] or weeks [15], [21], to several months [17], [30] or years [20], [26], [31], [38], and the number of messages can range from a few hundred [14], [21], [37] to millions [26], [30], [31], [35].…”

Section: Literature Reviewmentioning

confidence: 99%

Leveraging Social Media as a Source of Mobility Intelligence: An NLP-Based Approach

Fontes,

Murços,

Carneiro

et al. 2023

IEEE Open J. Intell. Transp. Syst.

View full text Add to dashboard Cite

This work presents a deep learning framework for analyzing urban mobility by extracting knowledge from messages collected from Twitter. The framework, which is designed to handle largescale data and adapt automatically to new contexts, comprises three main modules: data collection and system configuration, data analytics, and aggregation and visualization. The text data is pre-processed using NLP techniques to remove informal words, slang, and misspellings. A pre-trained, unsupervised word embedding model, BERT, is used to classify travel-related tweets using a unigram approach with three dictionaries of travel-related target words: small, medium, and big. Public opinion is evaluated using VADER to classify travel-related tweets according to their sentiments. The mobility of three major cities was assessed: London, Melbourne, and New York. The framework demonstrates consistently high average performance, with a Precision of 0.80 for text classification and 0.77 for sentiment analysis. The framework can aggregate sparse information from social media and provide updated information in near real-time with high spatial resolution, enabling easy identification of traffic-related events. The framework is helpful for transportation decision-makers in operational control, tactical-strategic planning, and policy evaluation. For example, it can be used to improve the management of resources during traffic congestion or emergencies.INDEX TERMS Social media, urban mobility, text classification, sentiment analysis, framework.

show abstract

How do new transit stations affect people's sentiment and activity? A case study based on social media data in Hong Kong

Cited by 9 publications

References 51 publications

Using Twitter to Gauge Customer Satisfaction Response to a Major Transit Service Change in Calgary, Canada

Using Twitter to Gauge Customer Satisfaction Response to a Major Transit Service Change in Calgary, Canada

Service Quality in Rail Systems: Listen to the Voice of Social Media

Leveraging Social Media as a Source of Mobility Intelligence: An NLP-Based Approach

Contact Info

Product

Resources

About