National Academies Press: OpenBook

Uses of Social Media in Public Transportation (2022)

Chapter:Chapter 2 - Literature Review

« Previous: Chapter 1 - Introduction
Page 8
Suggested Citation:"Chapter 2 - Literature Review." National Academies of Sciences, Engineering, and Medicine. 2022. Uses of Social Media in Public Transportation. Washington, DC: The National Academies Press. doi: 10.17226/26451.
×
Page8
Page 9
Suggested Citation:"Chapter 2 - Literature Review." National Academies of Sciences, Engineering, and Medicine. 2022. Uses of Social Media in Public Transportation. Washington, DC: The National Academies Press. doi: 10.17226/26451.
×
Page9
Page 10
Suggested Citation:"Chapter 2 - Literature Review." National Academies of Sciences, Engineering, and Medicine. 2022. Uses of Social Media in Public Transportation. Washington, DC: The National Academies Press. doi: 10.17226/26451.
×
Page10
Page 11
Suggested Citation:"Chapter 2 - Literature Review." National Academies of Sciences, Engineering, and Medicine. 2022. Uses of Social Media in Public Transportation. Washington, DC: The National Academies Press. doi: 10.17226/26451.
×
Page11
Page 12
Suggested Citation:"Chapter 2 - Literature Review." National Academies of Sciences, Engineering, and Medicine. 2022. Uses of Social Media in Public Transportation. Washington, DC: The National Academies Press. doi: 10.17226/26451.
×
Page12
Page 13
Suggested Citation:"Chapter 2 - Literature Review." National Academies of Sciences, Engineering, and Medicine. 2022. Uses of Social Media in Public Transportation. Washington, DC: The National Academies Press. doi: 10.17226/26451.
×
Page13
Page 14
Suggested Citation:"Chapter 2 - Literature Review." National Academies of Sciences, Engineering, and Medicine. 2022. Uses of Social Media in Public Transportation. Washington, DC: The National Academies Press. doi: 10.17226/26451.
×
Page14
Page 15
Suggested Citation:"Chapter 2 - Literature Review." National Academies of Sciences, Engineering, and Medicine. 2022. Uses of Social Media in Public Transportation. Washington, DC: The National Academies Press. doi: 10.17226/26451.
×
Page15
Page 16
Suggested Citation:"Chapter 2 - Literature Review." National Academies of Sciences, Engineering, and Medicine. 2022. Uses of Social Media in Public Transportation. Washington, DC: The National Academies Press. doi: 10.17226/26451.
×
Page16
Page 17
Suggested Citation:"Chapter 2 - Literature Review." National Academies of Sciences, Engineering, and Medicine. 2022. Uses of Social Media in Public Transportation. Washington, DC: The National Academies Press. doi: 10.17226/26451.
×
Page17

Below is the uncorrected machine-read text of this chapter, intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text of each book. Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.

8 Introduction In recent years, transportation agencies have used social media for a variety of reasons, such as communicating with present and potential riders, collecting passenger data, distributing infor- mation, gathering feedback, and improving the agency’s image (Bregman 2012). The current study aims to review the recent literature about social media use by transit agencies, especially focusing on many advanced studies that have been conducted in recent years to address the issue. The current review focuses on a wide range of scientific articles and reports on the best practices and lessons learned by transit agencies while using social media. The literature review section considers both U.S. and international studies on this issue. This chapter contains the following sections: • Practices to provide information such as service alerts, ongoing promotions, and general guidance to the riders. • Various techniques adopted by transit agencies and recommended by several studies for capturing public attention, reaching out to current and potential riders, and improving the interaction between transit agencies and the public. This section also presents a review of the extraction and analysis of sentiments and opinions about transit agencies from social media data. • Studies about collecting location, crash, traffic, ridership, and other passenger information from social media. The use of information to find characteristics like mobility patterns, mode preference, and crash-prone zones is also included in this section. • Studies on transit promotion via social media. • How to use social media for meeting organization goals. • A review of studies that have surveyed transit agencies regarding their techniques and prac- tices related to social media use. This section suggests some best practices and explains cautions and challenges for transit agencies while using social media. • A review of papers that explore the link and effect of the transportation sector on other sec- tors (e.g., economic and social sectors) and vice versa. This section also includes discussion of studies containing several methodologies and strategies for deploying social media tools for a variety of purposes. • Key findings. Information Sharing and Feedback Collection Providing information and collecting feedback are the primary reasons that transit agencies use social media (Liu and Ban 2017). This chapter reviews the literature that discusses strategies for effectively deploying social media to pass information to the riders and collect feedback such as complaints, comments, and service requests. C H A P T E R 2 Literature Review

Literature Review 9   Crawford (2013) surveyed people in nine locations around the world about their preferred method of receiving information from transit agencies. The study found that a vast majority of people preferred receiving information such as promotions and fares through social media. Respondents also preferred a paperless ticketing system and were hugely in favor of increased technology use in public transit. Several researchers have proposed methods for using the ability to provide real-time infor- mation on web-based platforms, including social media. Providing information to riders in real time is vital to the successful operation of the daily activities of transportation agencies, and therefore solutions such as developing apps and websites, providing text alerts, posting information, and answering queries on social media accounts have been suggested. Ma et al. (2017) proposed a framework named MOBility ANAlyzer (MOBANA) for providing real-time integrated information from different sources, including social media. The authors demonstrated the capability of the developed platform to classify texts, provide the real-time position of vehicles, and filter information from tweets. Mahmood et al. (2017) looked at the problem of transit disruption in Pakistan resulting from violent activities in Karachi. The authors designed an algorithm that could detect locations affected by violence and inform bus riders in real time via Twitter. In a case study by Chan and Schofer (2014), communications by transit agencies in New York during Hurricane Sandy were analyzed. The study found that users received extremely important information through Twitter. Pender (2014) and Pender et al. (2014a, 2014b) high- lighted the efficiency of social media networks in relaying information during service dis- ruptions. Douglass et al. (2018) also measured social media’s effectiveness for establishing firm and fast communication between transit agencies and riders. However, Tomasic et al. (2016) highlighted the problems associated with providing real- time information through Really Simple Syndication (RSS) feeds or Twitter. The authors dem- onstrated that riders could easily miss information on such platforms because it requires a lot of effort to navigate to the appropriate source of information. The authors suggested using more consistently structured messages for better communication of real-time alerts to riders. Moreover, data from information and communications technology (ICT) are susceptible to various security challenges. Beecroft (2019) reviewed several studies and identified several challenges using several ICT sources, including social media. The authors advised caution to transit agencies in adopting a practical approach against such threats. The collection of web-based feedback from both current and potential riders has several benefits and challenges. Some of the benefits include the ability to collect feedback in real time, participation of a larger audience, reduced workload on call centers, increased cost-effectiveness, and the ability to collect information in a safe and secure way (Watkins et al. 2015). The authors provided a tool kit to transit agencies of all sizes for collecting customer feedback through several social media platforms, surveys, and other applications. Choudhury (2013) further discusses the use of technology to gather better rider information. The author suggested that transit agencies should make an integrated platform that assists riders by connecting their social media accounts to their contact centers and payment platforms. A similar solution was discussed by Ferreira et al. (2017), who presented a platform called OneRide that integrated social media with payment and route-planning sites. The effectiveness of the app was dem- onstrated in Porto, Portugal. Watkins et al. (2015) also highlighted the various challenges of using social media. The chal- lenges mentioned by the authors include providing accessibility to all demographic groups, protecting users’ private information, dealing with a lack of resources and staff, and under- standing how to manage negative feedback. Table 2 lists the aims and key findings of some of the mentioned studies in tabular format.

10 Uses of Social Media in Public Transportation Study Location/Agency Social Media Platform Aim Key Findings Choudhury (2013) — — Explore ways to improve passenger information systems and communication with riders Technology including social media can be used Crawford (2013) South Korea, Brazil, Germany, France, Spain, United Kingdom, and United States — Understand passengers’ attitudes regarding technology in nine major cities 90 percent of people say they would prefer to receive information through social media Chan and Schofer (2014) New York Twitter Examine communication by transit agencies regarding transit disruptions Showed that Twitter is a firm source of information for riders during natural disasters Pender et al. (2014b) Melbourne, Victoria, Australia Twitter and Facebook Examine the management of unplanned passenger rail disruptions Showed significance of social media usage Watkins et al. (2015) — — Seek best practices among transit agencies for improving the collection of feedback from riders Developed a tool kit for transit agencies Tomasic et al. (2016) Port Authority of Allegheny County, Metropolitan Transportation Authority (MTA) Tiramisu Transit App and Twitter Provide real-time information to riders Improved the structure of message alerts Ferreira et al. (2017) Porto, Portugal OneRide Provide information to riders with ease Developed an app to integrate mobile payment, social media, and route planning Ma et al. (2017) Pavia, Italy Twitter Provide real-time public transit information Developed the platform MOBANA Mahmood et al. (2017) Karachi, Pakistan Twitter Provide real-time information about recent violent activities affecting transit The developed framework could detect locations hit by violence to inform riders and then suggest alternate routes Douglass et al. (2018) Tyne and Wear Metro system in Tyne and Wear, United Kingdom — Measure the effectiveness of a transit agency’s social media engagement with travelers Only 40 percent of surveyed people checked the transit agency’s social media page at least three times a week Beecroft (2019) — — Identify security-related issues in public transportation Data collected from ICTs are susceptible to several issues NOTE: A dash means not applicable. Table 2. Studies focusing on social media usage for information sharing and feedback collection.

Literature Review 11   Public Engagement and Sentiment Analysis Transit agencies over the years have expressed their desire to analyze rider satisfaction and attitudes toward traveling via their service. Traditionally, various methods of interaction have been used to engage with riders. However, with the rising popularity of social media, it is now easier than ever to reach out to current and potential riders. This section reviews studies that have used data from social media and performed sentiment analysis to analyze rider satisfac- tion and opinions toward transit agencies (see Table 3 for some of the key studies discussed as follows). Schweitzer (2014) inspected social media practices of transit agencies and their effect on rider sentiment. The author recommended interacting with users directly on social media rather than merely announcing information on agencies’ feed to produce a relatively more positive sentiment. Vlk and Hauger (2015) advised transit agencies to use status messages of users on popular social networking platforms such as Twitter and Facebook to analyze personal sentiment. Casas and Delmelle (2017) mined text from Twitter by searching for keywords Study Location/Agency Social Media Aim Key Findings Schweitzer (2014) — Twitter Perform sentiment analysis Interactive conversations have positive effects Vlk and Hauger (2015) — Facebook and Twitter Analyze personal sentiment Status messages can contain information about personal sentiments Casas and Delmelle (2017) Cali, Colombia Twitter Capture public perceptions regarding transit Leading concerns of riders regarding public transportation were behavior of fellow passengers, transit infrastructure, and safety Haghighi et al. (2018) — Twitter Assess transit performance Use topic modeling instead of a keyword search to better extract the information from tweets Kim et al. (2019) New York metropolitan area Twitter Analyze feedback Identified the area of feedback and the sentiment of the message Qi and Costin (2019) Miami-Dade County Twitter Perform sentiment analysis User habits greatly influence sentiment value Zhang et al. (2019) China — Perform content analysis of public social media data Opinions and problems were analyzed using LDA Osorio- Arjona et al. (2021) Madrid Metro, Spain Twitter Explore social media semantic perceptions The main issue was punctuality in key stations, especially in early weekday mornings Das and Zubaidi (2021) New York and California Twitter Perform sentiment analysis Created a framework by developing multilevel sentiment analysis/emotion mining and politeness measures NOTE: A dash means not applicable; LDA = latent Dirichlet allocation. Table 3. Studies focusing on social media usage for public engagement and sentiment analysis.

12 Uses of Social Media in Public Transportation and then applied contentment analysis. The study showed that the leading concerns of riders regarding public transportation in Cali, Columbia, were the behavior of fellow passengers, transit infrastructure, and safety. Haghighi et al. (2018) suggested using topic modeling instead of a keyword search to better extract the information from tweets. The study then performed tweet-per-topic index-based sentiment analysis to understand rider feedback and causes of dissatisfaction. Kim et al. (2019) collected user feedback from Twitter and developed a deep learning framework to classify the feedback based on the area of transit services such as clean- liness, mobility, or timeliness. Furthermore, the sentiment from each tweet was classified as positive or negative. Zhang et al. (2019) collected information about public opinions via a web crawler and used latent Dirichlet allocation (LDA) to categorize them based on opinions and traffic problems. Osorio-Arjona et al. (2021) extracted topics and sentiments from Twitter via text mining and artificial intelligence algorithms. The extracted information was then mapped based on location and time to inspect any underlying patterns. Qi and Costin (2019) stressed the need to perform such analysis because the authors demonstrated that user habits greatly influence sentiment value. Delays and breakdowns infuriate people, and often riders express this in their social media posts. Das and Zubaidi (2021) collected transit-related tweets from New York and California to perform sentiment analysis and measure politeness. Gathering Passenger Information The collection of passenger information such as mobility patterns, rider demographics, mode preference, and rider behavior is important to transit agencies because the information can help agencies tailor their strategies and campaigns to the interests of their riders. For this purpose, many researchers have tried to come up with ways to collect rider information and characteris- tics. This section reviews such studies with a focus on using social media data to gather infor- mation (see Table 4 for some of the key studies discussed as follows). Gkiotsalitis and Stathopoulos (2016) collected the mobility data of riders traveling to joint leisure activities from social media in order to optimize transit vehicle timings and routes. Cottrill et al. (2017) examined the information-sharing practices on social media by transit agencies during the Commonwealth Games in 2014 hosted by Scotland. The study highlighted the leading role played by social media in communicating efficiently with riders to successfully execute transportation throughout the games. Similarly, Imran et al. (2015) collected informa- tion about Chinese residents living in New Zealand through social media to understand their travel mode choices. Lantz et al. (2015) demonstrated the use of data collected via social media and location-tracking mobile applications in developing countries for various purposes. Zhang et al. (2016) mined data from social media to extract rider information in special cases. Both unplanned (e.g., a road crash) and planned (e.g., a sports game) events were taken into con- sideration in this study. The authors predicted passenger flow and detected a road crash using social media data. In addition, the developed algorithm was able to establish a relationship between increased traffic flow and social media data. Ni et al. (2017) also forecasted ridership using data from social media (particularly Twitter). The authors first proposed an algorithm to detect events based on hashtags and then, using several models, predicted an increase in pas- senger flow resulting from large public gatherings. Blumenberg and Taylor (2018) analyzed the changes in the travel behavior of Millennials as a result of technological and social changes. Chandra et al. (2020) displayed a successful strategy to change the travel mode preference of college students. Using the crowd-based perception of students posting on Twitter, the authors observed that there was an increase in walking and biking, while the use of other modes (e.g., a car) decreased.

Literature Review 13   Transit Promotion One of the main goals of transit agencies is to effectively reach their users and provide quality services to them. This section reviews studies that have discussed social media deployment strategies for effectively reaching out to riders and subsequently increasing agencies’ rider base (see Table 5 for details of some key studies). Several studies have reported that a relatively younger class of people (e.g., Millennials and individuals who are under 24 years old) use public transportation rather than personal vehicles (e.g., Yang and Cherry 2017). Many studies have focused on observing the behavior of teens and young adults and devising strategies to attract them. For example, Delbosc and Currie (2015) identified that increased use of social media among young adults leads to more fre- quent traveling. Shafer and Macary (2018) looked at effective ways to communicate with young people. The authors found that young people were unlikely to participate via text messages and disliked targeted messaging. Instead, the authors recommended that transit agencies use social media. The study also indicated that most young people in the Portland, OR, region held Study Location/ Agency Social Media Platform Aim Key Findings Imran et al. (2015) Auckland — Explore the travel behavior of Chinese residents living in New Zealand Identified the communication, expectations, and perception gaps between institutional practices and Chinese users of the public transportation systems Lantz et al. (2015) Nairobi, Kenya; Istanbul, Turkey; and Dhaka, Bangladesh — Integrate online media with location-based social media posts and mobile-phone location tracking Ethnicity is a critical factor in explaining travel behavior Gkiotsalitis and Stathopoulos (2016) Stockholm, Sweden Twitter Improve the operations of demand- responsive public transportation Social media has great potential to inform and manage the planning and operation of transit networks in developing countries Zhang et al. (2016) MTA Twitter Mine Twitter data Developed effective and efficient techniques to extract social media data Ni et al. (2017) — Twitter Predict subway passenger flow with social media data Developed robust models to improve the demand- responsiveness of public transportation systems Cottrill et al. (2017) Glasgow, Scotland Twitter Mine Twitter data Data from social media can be used in producing an effective network of communication between public transportation agencies and passengers Blumenberg and Taylor (2018) __ __ Travel behavior Explored travel behavior of Millennials as a result of technological and social changes Chandra et al. (2020) California State University, Long Beach, CA Twitter Mine information from social media to estimate any changes in mode-shift behavior of college students Developed a crowdsourcing- based perception framework NOTE: A dash means not applicable. Table 4. Studies focusing on social media usage for gathering passenger information in order to understand customer characteristics and needs.

14 Uses of Social Media in Public Transportation a positive attitude toward public transit and preferred to avoid the use of personal cars until they were old enough to afford driving one. While certain research focuses on targeting specific audiences, Pinkett and Wylie (2013) focused on devising strategies to market to all riders and matching social media platforms with the types of information delivered. Abellera and Panangadan (2016) examined the spread of influence on Twitter and identified individuals who exerted the greatest influence on social media. These individuals could have been used by transit agencies to effectively reach the public and could be the potential face of future campaigns. Dau-Ngo et al. (2013) stressed the impor- tance of public outreach and participation during the planning stages of transit projects and suggested using several platforms, including social media, for this purpose. While many studies recommended the use of social media for reaching out to riders, some advised cautionary measures to ensure inclusiveness and reach out to all riders, including vulnerable population groups. For instance, Bjerkan and Øvstedal (2020) reviewed studies about the barriers that people with disabilities face when accessing transport. The authors stressed the need for screen-readable websites and high-contrast texts for better readability and comprehension. Nisar and Prabhakar (2018) suggested framing messages concerning particular objects. In many consumer markets, such as train journeys, firms frame messages in both positive and negative lights to persuade individuals to make purchase decisions (i.e., take intended journeys). The authors emphasized the important role that marketer-generated content plays in shaping social-media-based consumer-relationship management strategies. Support of and Influence on Organizational Goals This section reviews studies that have recommended some best practices as well as some precautions about using social media for meeting the organizational goals of transit agencies (see Table 6). Study Location/Agency Social Media Platform Aim Key Findings Pinkett and Wylie (2013) Australia and the United Kingdom Twitter, Facebook, YouTube, Instagram, and so forth Find social media platforms’ functions Each social media platform has a different function Abellera and Panangadan (2016) California high- speed rail Twitter Analyze the spread of influence in social media Developed a tool to identify most popular influencers Shafer and Macary (2018) Portland, OR — Engage youths in use of public transportation Young people dislike targeted advertising, such as for transportation service providers, and prefer to use public transportation Groth (2019) Rhine, Germany — Explore multimodal behavior Fear of breach of privacy leads to social exclusion Bjerkan and Øvstedal (2020) — — Examine the inclusion of people with difficulties in transport Websites need to be more readable and comprehensible NOTE: A dash means not applicable. Table 5. Studies focusing on social media usage in transit promotion.

Literature Review 15   Nikolaidou and Papaioannou (2018) reviewed multiple studies to inspect the appropriate use of social media platforms by transit agencies. The authors found that social media could be used for a variety of purposes, including analyzing mobility patterns, detecting traffic incidents, and determining public opinion. The most effective social media platforms for each purpose were identified. For example, Foursquare could be used to collect information regarding mobility patterns and trip purpose, and Facebook could be useful for increasing public participation and engagement. Kinawy et al. (2017) focused on pitching transit projects to the local community by extracting topics of interest from Twitter. Through case examples, the authors reported that there was a mismatch in the interests of the local community and ideas pitched by transit authorities. Liu et al. (2019) made a toolbox for integrating General Transit Feed Specification (GTFS) data and social media data. The authors suggested that by using this platform, transit agencies could achieve multiple objectives, such as assessing the agency’s performance, identifying areas needing an infrastructure boost, evaluating the efficiency of a particular network, and many others. Weisenford et al. (2018) identified several tools to train transit authorities. The authors found several benefits and challenges of using social media for this purpose. Some of the ben- efits included quick access to information, the ability to create a more interactive and dynamic community of learners, and the potential to reach a mass audience. Some challenges identi- fied were the requirement of facilitators to be knowledgeable about social media tools and the requirement for active engagement of the learners. Kaufman and Moss (2014) highlighted the need for co-monitoring systems in order to establish a firm and fast line of communication between transit agencies and riders. Furthermore, the authors highlighted some benefits, chal- lenges, and policy change recommendations associated with using such systems. Study Location/ Agency Social Media Platform Aim Key Findings Pinkett and Wylie (2013) Australia and the United Kingdom Several Discuss the benefits and concerns of using social media Identified what works best for a specific type of interaction with an individual or community in social media Kaufman and Moss (2014) — — Perform transit management using web-based rider input Provided strategies to address the concerns presented Kinawy et al. (2017) __ Twitter To pitch transit projects to the local community by extracting topics of interest There was a mismatch in the interests of the local community and ideas pitched by transit authorities Nikolaidou and Papaioannou (2018) — Several Mine and analyze social media data Provided direction to tailor information to the platform Weisenford et al. (2018) Several agencies, including the Chicago Transit Authority YouTube and Facebook Document the best models of technical training programs in various media, including social media Provided directions on using each social media platform according to its different type of function Liu et al. (2019) Several agencies, including the Utah Transit Authority Twitter Improve communication with riders Built a novel and open-source socio-transportation analytic toolbox to integrate social media and GTFS data NOTE: A dash means not applicable. Table 6. Studies focusing on social media usage for understanding best practices to meet organization goals.

16 Uses of Social Media in Public Transportation Surveys on Transit Agency Practices This section focuses on studies that have surveyed transit agencies regarding their techniques and practices related to social media use (see Table 7). Stewart and Cochrane (2018) looked at the digital practices of Novo Rail in Sydney, Australia. The authors reported that the agency used social media to answer customer queries and promote their main app, NovoView. Through interviews with the social media staff of train-operating companies, Howard (2019) found that Twitter was the main platform used to provide efficient customer service. The author also recommended several policy changes that could help improve communication with riders and ultimately enhance the reputation of the agency. Other Issues This section explores studies of the effect of transportation on other sectors and vice versa (see Table 8 for details of the key studies). The reviewed studies have proposed several methodologies and strategies for deploying social media tools for a variety of purposes. Meyer and Shaheen (2017) inspected the eco- nomic impact of transportation changes, such as the increasing ride-share services and the growing influence of social media and technology. Jiang and Mondschein (2019) estimated Study Location/ Agency Social Media Platform Aim Key Findings Jiang and Mondschein (2019) Seven cities across North America and Europe Yelp Measure the willingness to travel by rail for nonwork- related purpose Using a location-based social media platform, researchers showed that land use around a rail station impacts the willingness to travel by rail Klingen (2019) Paris, France Twitter Explore the effect of a Twitter announcement of metro disruption on public bicycle use Public bicycle use increased Chen and Yang (2020) China WeChat Improve the design of city areas Researchers used data from social media to identify key problem locations and rectify them Li et al. (2020) Guangzhou, China Tencent social media (TikTok) Establish a relationship between rail transit and built environment Based on social media exploration, researchers made suggestions to increase rail transit ridership Table 8. Studies on social media usage in different transportation sectors. Study Location/ Agency Social Media Platform Aim Key Findings Stewart and Cochrane (2018) Sydney, Australia — Explore the usage of digital innovation in the mass transit system environment Social media is used for customer support and promotion of the app Howard (2019) United Kingdom Twitter Measure Twitter- based customer engagement Twitter is instrumental in providing customer service, and several policy recommendations are made NOTE: A dash means not applicable. Table 7. Studies focusing on survey studies of transit agency promotions.

Literature Review 17   the willingness of a person to take public transit to travel to nonwork locations and the effect of distance on the person’s decision. The study used location-based information from Yelp for this purpose. The study found a high correlation between land usage around rail stations and the choice of riders to take a train to commute. A similar study, conducted by Li et al. (2020), collected geospatial information from the China-based social networking platform Tencent (TikTok) to extract built-environment data in order to analyze Tencent’s effect on rail passenger flow. The study further identified areas in cities that require an upgrade in infrastructure to meet citizens’ needs. The proposed methodology could directly impact transit agencies by increasing their ridership, and the methodology may facilitate coordina- tion between land use and public transportation. Klingen (2019) studied the effect of Twitter announcements about metro disruption on the usage of rental bicycles. The study found that the usage of rental bicycles increased substantially, and the probability of finding an empty bike station increased by 15 percent during a metro disruption. Chen and Yang (2020) used data from WeChat in China to identify traffic and facility shortage problems resulting from an increase in tourist activities. The data were then used to redesign the neighborhood and subsequently decreased conflicts between tourists and the host community. Key Findings The key findings from the literature reviewed are as follows: • Several researchers have deployed social media platforms to reach out to riders (especially people under the age of 25 and Millennials). However, it is important to match social media platforms with the appropriate type of content to maximize outreach. Precautions could be taken to be inclusive and reach out to all (potential) riders. • Riders prefer to receive information through social media. Social media has an important role in increasing rider participation and establishing a direct line of communication with riders. Integrated platforms could greatly benefit transit agencies in better managing com- munication with riders and providing services more efficiently. • Social media can be deployed to collect real-time feedback from riders in the form of com- ments, complaints, or service requests. • Data from social media can be used to mine key information about mobility, traffic, safety, transport mode preference, and so forth, and can aid transit agencies in making well-informed decisions. • Data from social media (especially Twitter) could be used to analyze the opinions and senti- ments of riders about transit agencies. Strategies such as conducting interactive conversa- tions with riders have a positive effect on sentiment. • Many surveys to date indicated that a majority of transit agencies are using social media. However, most of them do not have a well-defined goal or metrics to assess the performance of their social media ventures. • Transit agencies can use passenger data, riders’ feedback, and sentiment analysis to achieve multiple organizational goals such as improving the agency’s performance, developing a posi- tive image, facilitating better communication with existing and potential riders, modifying infrastructures, evaluating the network, and meeting other important milestones. • Few studies highlighted the problems associated with data collection and information distri- bution via social media. Some solutions include using structured and consistent messages, adopting a practical security approach, and using the transit agency’s own app for commu- nication with riders.

Next: Chapter 3 - Survey »
Uses of Social Media in Public Transportation Get This Book
×
MyNAP members save 10% online.
Login or Register to save!
Download Free PDF

A high percentage of transit agencies believe social media is important for increasing ridership, improving customer satisfaction, and improving agency image.

The TRB Transit Cooperative Research Program's TCRP Synthesis 156: Uses of Social Media in Public Transportation updates 2012's TCRP Synthesis 99: Uses of Social Media in Public Transportation and again explores the use of social media among transit agencies. It documents innovative and effective practices in the United States and Canada.

  1. ×

    Welcome to OpenBook!

    You're looking at OpenBook, NAP.edu's online reading room since 1999. Based on feedback from you, our users, we've made some improvements that make it easier than ever to read thousands of publications on our website.

    Do you want to take a quick tour of the OpenBook's features?

    No Thanks Take a Tour »
  2. ×

    Show this book's table of contents, where you can jump to any chapter by name.

    « Back Next »
  3. ×

    ...or use these buttons to go back to the previous chapter or skip to the next one.

    « Back Next »
  4. ×

    Jump up to the previous page or down to the next one. Also, you can type in a page number and press Enter to go directly to that page in the book.

    « Back Next »
  5. ×

    To search the entire text of this book, type in your search term here and press Enter.

    « Back Next »
  6. ×

    Share a link to this book page on your preferred social network or via email.

    « Back Next »
  7. ×

    View our suggested citation for this chapter.

    « Back Next »
  8. ×

    Ready to take your reading offline? Click here to buy this book in print or download it as a free PDF, if available.

    « Back Next »
Stay Connected!