During the outbreak of COVID-19, people’s reliance on social media for pandemic-related information exchange, daily communications, and online professional interactions increased because of self-isolation and lockdown implementation. Most of the published research addresses the performance of nonpharmaceutical interventions (NPIs) and measures on the issues impacted by COVID-19, such as health, education, and public safety; however, not much is known about the interplay between social media use and travel behaviors. This study aims to determine the effect of social media on human mobility before and after the COVID-19 outbreak, and its impact on personal vehicle and public transit use in New York City (NYC). Apple mobility trends and Twitter data are used as two data sources. The results indicate that Twitter volume and mobility trend correlations are negative for both driving and transit categories in general, especially at the beginning of the COVID-19 outbreak in NYC. A significant time lag (13 days) between the online communication rise and mobility drop can be observed, thereby providing evidence of social networks taking quicker reactions to the pandemic than the transportation system. In addition, social media and government policies had different impacts on vehicular traffic and public transit ridership during the pandemic with varied performance. This study provides insights on the complex influence of both anti-pandemic measures and user-generated content, namely social media, on people’s travel decisions during pandemics. The empirical evidence can help decision-makers formulate timely emergency responses, prepare targeted traffic intervention policies, and conduct risk management in similar outbreaks in the future.

Shared mobility has gained widespread popularity as a result of its innovative business models. However, due to incomplete background checks of drivers, not only government authorities (i.e., transport authorities) but also the public has become concerned about service security. Currently, social networks such as Sina Weibo, a Chinese version of Twitter, provide an informative data source for capturing people’s attitudes toward and perceptions of these mobility services. This dataset has the potential to help service platforms and regulators respond better to public demands and realize co-governance of the travel market. In this study, one month of Weibo data before and after a murder committed by a Didi ride-hailing driver was crawled. A hybrid approach integrating Latent Dirichlet Allocation (LDA) model and dictionary-based sentiment analysis was applied to extract topics receiving lots of attention and sentiment fractions, as well as to detect abnormal events. The results demonstrated that top-4 concerns of the public are service of ride-hailing, examination and responsibility definition of platforms, governmental regulations for market entry and capitalization, and case related description. People had negative attitudes toward ride-hailing platforms when talking about their lack of efficient management and crisis response strategies, while they were more positive about the business rivalries which are expected to enhance the quality of service in the travel market. Additionally, both events in the real world and interactions within social networks (i.e., #Boycott events) contributed to the public’s sentiment. These findings can not only be useful for service platforms to detect public concerns about their services in real-time and to improve their business operations but also can be applied as a reference for government authorities to recognize multiple agents’ interests for the co-governance of the urban mobility market.

The Chinese government has launched a digital health code system to detect people potentially exposed to the coronavirus 2019 (COVID-19) disease and to curb its spread. Citizens are required to show the health code on their smartphones when using public transport. However, many seniors are not allowed to use public transport due to their difficulties in obtaining health codes, leading to widespread debates about these unfair events. Traditionally, public perceptions and attitudes toward such unfair events are investigated using analytical methods based on interviews or questionnaires. This study crawled seven-month messages from Sina Weibo, the Chinese version of Twitter, and developed a hybrid approach integrating term-frequency–inverse-document-frequency, latent Dirichlet allocation, and sentiment classification. Results indicate that a rumor about the unfair treatment of elderly travelers triggered public concerns. Primary subjects of concern were the status quo of elderly travelers, the provision of transport services, and unfair event descriptions. Following the government’s responses, people still had negative attitudes toward transport services, while they became more positive about the status quo of elderly travelers. These findings will guide government authorities to explore new forms of automated social control and to improve transport policies in terms of equity and fairness in future pandemics.

Social media has become a valuable platform that enables public and private stakeholders to participate and interact in various transport policies. Using a network-based perspective and a case study of bike-sharing pricing strategies in China, this paper aims to quantitatively characterize the pattern and structure of multi-stakeholders engagement networks. Furthermore, this paper also empirically examines the confirmation bias that might exist among participants. Dataset on retweets from the Chinese Twitter-Sina Weibo is collected. Results reveal two types of important actors with unequal roles in terms of information diffusion: the “network root” and the “network bridge.” The former is mainly comprised of organizations and influential individuals who dominate message sharing, whereas the latter is comprised of the general public with various occupational backgrounds who control the efficiency and the scope of information spreading. The result also reveals a hierarchical structure in both networks and a community gathering like-minded individuals. The empirical result also demonstrates the existence of echo chambers in the transport participation network of governments and enterprises. Most echo chambers operate such that organizations or influential individuals amplify the views of the general public with more critical viewpoints. These findings of this study can assist transport stakeholders in crafting more sustainable strategies based on the understanding of uneven patterns in online public participation. Furthermore, this study sheds insights on how social media could be used to facilitate the collection of diverse people’s opinions and the evaluation of multi-stakeholder engagement for major transport issues.

The electric vehicle (EV) market in China has expanded substantially thanks to a series of incentive policies. The operational characteristics and usage patterns of EVs are different from those of traditional fuel vehicles (TFVs), especially in terms of energy savings and cruising duration of battery. With required energy management devices, EVs inadvertently generate fine-grained and continuous trajectory data, which provides an unprecedented opportunity to study the multi-person and multi-day usage patterns of EVs. The data set used in this study includes GPS and on-board diagnostics (OBD) information of 8,000 EVs including both plug-in hybrid electric vehicle (PHEVs) and battery electric vehicles (BEVs) within one-week in Shanghai. A multi-day activity data set is created for each user. The concept of entropy is introduced and modified to describe and distinguish the usage stability of each vehicle through the multi-day activity data set. Based on indicators including usage duration, travel mileage, and spatial parking location, the usage patterns of EVs could be classified into three modes: individual, commercial, and semi-commercial. Among all activities, over 4.5% is used fully for commercial, and 20% is used partly for e-hailing services. Furthermore, results reveal that PHEVs’ average travel distance of individual groups is significantly higher than that of BEVs. The findings have a profound long-term implication on urban traffic demand with the growth of EVs. The research expands the understanding of the changes brought by transport technological evolutions to human activity patterns, and provides quantitative support for the adjustment of electric vehicle supporting policies.