SOCIAL NETWORK INFLUENCE ON RIDESHARING, DISASTER COMMUNICATIONS, AND COMMUNITY INTERACTIONS

The complex topology of real networks allows network agents to change their functional behavior. Conceptual and methodological developments in network analysis have furthered our understanding of the effects of interpersonal environment on normative social influence and social engagement. Social influence occurs when network agents change behavior being influenced by others in the social network and this takes place in a multitude of varying disciplines. The overarching goal of this thesis is to provide a holistic understanding and develop novel techniques to explore how individuals are socially influenced, both on-line and off-line, while making shared-trips, communicating risk during extreme weather, and interacting in respective communities. The notion of influence is captured by quantifying the network effects on such decision-making and characterizing how information is exchanged between network agents. The methodologies and findings presented in this thesis will benefit different stakeholders and practitioners to determine and implement targeted policies for various user groups in regular, special, and extreme events based on their social network characteristics, properties, activities, and interactions

Crisis Communication Patterns in Social Media during Hurricane Sandy

Hurricane Sandy was one of the deadliest and costliest of hurricanes over the past few decades. Many states experienced significant power outage, however many people used social media to communicate while having limited or no access to traditional information sources. In this study, we explored the evolution of various communication patterns using machine learning techniques and determined user concerns that emerged over the course of Hurricane Sandy. The original data included ~52M tweets coming from ~13M users between October 14, 2012 and November 12, 2012. We run topic model on ~763K tweets from top 4,029 most frequent users who tweeted about Sandy at least 100 times. We identified 250 well-defined communication patterns based on perplexity. Conversations of most frequent and relevant users indicate the evolution of numerous storm-phase (warning, response, and recovery) specific topics. People were also concerned about storm location and time, media coverage, and activities of political leaders and celebrities. We also present each relevant keyword that contributed to one particular pattern of user concerns. Such keywords would be particularly meaningful in targeted information spreading and effective crisis communication in similar major disasters. Each of these words can also be helpful for efficient hash-tagging to reach target audience as needed via social media. The pattern recognition approach of this study can be used in identifying real time user needs in future crises

Identifying Diversity, Equity, Inclusion, and Accessibility (DEIA) Indicators for Transportation Systems using Social Media Data: The Case of New York City during Covid-19 Pandemic

The adoption of transportation policies that prioritized highway expansion over public transportation has disproportionately impacted minorities and low-income people by restricting their access to social and economic opportunities and thus resulting in residential segregation. Policymakers, transportation researchers, planners, and practitioners have started acknowledging the need to build a diverse, equitable, inclusive, and accessible (DEIA) transportation system. Traditionally, this has been done through survey-based approaches that are time-consuming and expensive. While there is recent attention on leveraging social media data in transportation, the literature is inconclusive regarding the use of social media data as a viable alternative to traditional sources to identify the latent DEIA indicators based on public reactions and perspectives on social media. This study utilized large-scale Twitter data covering eight counties around the New York City (NYC) area during the initial phase of the Covid-19 lockdown to address this research gap. Natural language processing techniques were used to identify transportation-related major DEIA issues for residents living around NYC by analyzing their relevant tweet conversations. The study revealed that citizens, who had negative sentiments toward the DEIA of their local transportation system, broadly discussed racism, income, unemployment, gender, ride dependency, transportation modes, and dependent groups. Analyzing the socio-demographic information based on census tracts, the study also observed that areas with a higher percentage of low-income, female, Hispanic, and Latino populations share more concerns about transportation DEIA on Twitter

Identifying Crisis Response Communities in Online Social Networks for Compound Disasters: The Case of Hurricane Laura and Covid-19

Online social networks allow different agencies and the public to interact and share the underlying risks and protective actions during major disasters. This study revealed such crisis communication patterns during hurricane Laura compounded by the COVID-19 pandemic. Laura was one of the strongest (Category 4) hurricanes on record to make landfall in Cameron, Louisiana. Using the Application Programming Interface (API), this study utilizes large-scale social media data obtained from Twitter through the recently released academic track that provides complete and unbiased observations. The data captured publicly available tweets shared by active Twitter users from the vulnerable areas threatened by Laura. Online social networks were based on user influence feature ( mentions or tags) that allows notifying other users while posting a tweet. Using network science theories and advanced community detection algorithms, the study split these networks into twenty-one components of various sizes, the largest of which contained eight well-defined communities. Several natural language processing techniques (i.e., word clouds, bigrams, topic modeling) were applied to the tweets shared by the users in these communities to observe their risk-taking or risk-averse behavior during a major compounding crisis. Social media accounts of local news media, radio, universities, and popular sports pages were among those who involved heavily and interacted closely with local residents. In contrast, emergency management and planning units in the area engaged less with the public. The findings of this study provide novel insights into the design of efficient social media communication guidelines to respond better in future disasters

Best Practices for Maximizing Driver Attention to Work Zone Warning Signs

Studies have shown that rear-end crashes in the advance warning area for a work zone are the most common type of work zone crashes. Driver inattention (or distraction) is reported as the most common issue and a major contributing factor to those types of crashes. As such, there is a need to identify the technologies that are successful in alerting drivers when approaching work zones

Behavioral models to understand routing considerations and evacuation preparation time in hurricanes

Due to the vulnerability to hurricanes in the United States and its territories, comprehensive evacuation plans and strategies need to integrate transportation theory with evacuation behavior from a household level. Public agencies and emergency officials need to understand different dimensions of the overall evacuation process in order to mitigate devastating impacts of frequently occurring hurricanes. Route choice during evacuation is a complex process, because evacuees may prefer to take the usual or familiar route on the way to the destination or they might follow the routes recommended by the emergency officials. Depending on the condition of the traffic stream, sometimes they might switch to a different route to obtain better travel time from the one initially attempted. In this thesis, we explain a modeling approach which offers better understanding of the routing strategies taken by the evacuees to reach a safe destination during hurricane evacuation. By using data from Hurricane Ivan, a mixed (random parameters) logit model is estimated which captures the decision making process on what type of route to select while accounting for the existence of unobserved heterogeneity across households. In addition, an ordered probit model with random parameters has been developed to capture the underlying unobserved characteristics in the timing behavior of the evacuees that elapses in between their evacuation decision and actual evacuation. Estimation findings indicate that the choices of evacuation routing strategy and the timing behavior involve a complex interaction of variables related to household location, evacuation characteristics, socio-economic characteristics and some other important characteristics. The findings of this research are useful to determine different fractions of people in selecting a type of route and evacuees evacuating early or delaying for some time for a given socio-demographic profile once they actually decide to evacuate during a hurricane evacuation