Transportation systems are central to all cities because of the various functions they provide. Public transit in particular encourages economic activity by enabling the mobility of consumers and lower income workers. Public ridership can also reduce environmental externalities such as congestion and pollution by offsetting personal vehicle usage. In addition, vehicular emissions are increasingly costly to cities due to congestion and a range of health-related impacts. For instance, transportation makes up about 75 percent of all CO2
emissions and around 28 percent of total greenhouse gas emissions found in US cities [1
]. Public transportation, therefore, is critical for the long-term viability of any large urban area, and policy makers take special interest in assuring these systems are efficient, functional, sustainable, and, increasingly, that they have a positive impact on human health. Subsequently, a range of work has emerged on policy determinants and incentives to encourage public ridership. One determinant of increasing interest is the role that media can play in both promoting and deterring ridership. We intend to contribute to this discussion by exploring whether media attention to local air quality and meteorological conditions have any impacts on transit ridership.
To explore this relationship, we study media key term usage across a range of meteorological and air quality conditions and their association with ridership on the Utah Transit Authority (UTA) service area in Utah (USA) from 1 January 2014 to 31 December 2016. We also aim to understand if ridership is further associated with the Utah Division of Air Quality’s (UDAQ) “air quality (AQ) days” systems that rate local air quality by red (worst), orange, yellow, or green air quality each day. Based on this analysis, we hope to understand if strong media attention on an upcoming meteorological event (e.g., winter storm) has a relationship with UTA ridership and whether this effect varies across green, yellow, orange, and red AQ days.
UTA provides public transportation to a seven-county area, primarily within Utah’s Wasatch Front (Figure 1
). This area faces significant air quality challenges both in winter, due to elevated fine particulate matter (PM2.5
], and summer due to elevated ozone levels [5
]. Other pollutants, including carbon monoxide (CO), sulfur dioxide (SO2
), and coarse particulate matter (PM10
), have also been found to be in excess of national ambient air quality standards (NAAQS) by the Utah Division of Air Quality (DAQ) (Figure 1
]. Among the mitigation strategies, the Utah Department of Transportation (UDOT) has implemented the “Clear the Air Challenge” during periods of elevated pollution [8
] to encourage a reduction of personal vehicle use and increase in transit use or telecommuting. Efforts to incentivize ridership further during poor air quality events is of great importance to the effective management of the local transportation system.
1.2. Previous Work
The relationship between public transit ridership, air quality, and human behavior is becoming progressively more connected. For example, the type and design of transportation systems are increasingly studied due to the insights they provide for greater ridership. Sun et al. [10
] used city-level data from China to show how increased public ridership is an efficient policy intervention and also a cost-effective way to reduce urban air pollution. This causal influence may be circular in some circumstances, as shown by the impact of strict driving restrictions on acceptance of public transport [11
]. However, the authors found that attitudes toward ridership, and public transit more generally, have a large impact on policy acceptance. Inefficiencies in the transportation system, such as longer commutes and extensive connections, can also result in greater policy pushback and can lead to policy underperformance. Finally, public transit vehicles are increasingly used as a platform for on-board mobile sensing networks. Such networks can produce real-time environmental and road traffic information along with guidance for passengers and fleet managers, which drastically improves overall system efficiency [12
]. The additional information provided to customers, therefore, can have significant impacts on ridership—for better or worse.
The wider literature on transit ridership has also focused in depth on the factors that can influence the level of transit ridership (e.g., physical environment, weather conditions, type of transit and stations, and various seasonality issues—season, year, month, day of week, specific holidays). Population density, levels of private vehicle ownership, topography, freeway network extent, parking availability and cost, transit network extent and service frequency, transit fares, and transit system safety and cleanliness all play a role. However, the relative importance of these various factors and the interaction between them are not well understood. In a meta study of ridership factors, Taylor and Fink [13
] found that due to the extensive heterogeneity of cities worldwide, the list of factors that impact ridership were extensive, but not uniformly applied to each case. Other authors have focused on single factors such as population density, levels of private vehicle ownership, topography, freeway network capacity, parking availability and cost, transit network infrastructure and service frequency, transit fares, and transit system safety and cleanliness. For instance, Gutiérrez et al. [14
] studied transit ridership at the station level to calculate a measure for “distance-decay”, which illustrates how poorly managed time tables, excessive connections, and distance to a local station can diminish ridership. Similarly, Mendoza et al. [9
] explored the impact of air quality on ridership in urban settings.
Another important factor in explaining ridership outcomes is media effects or influence. Media influence occurs when mediated communication implicitly or explicitly shapes social and behavioral outcomes. The role of media influence on society is well established across a range of disciplines and topics [15
]. Wakefield et al. [17
], for instance, reviewed the outcomes of mass media campaigns in the context of various health-risk behaviors (e.g., use of tobacco, alcohol, and other drugs, heart disease risk factors, sex-related behaviors, road safety, cancer screening and prevention, child survival, and organ or blood donation) and concluded that mass media campaigns can produce positive changes or prevent negative changes in health-related behaviors across large populations. Through their work, Wakefield et al. [17
] highlighted the direct and indirect mechanisms by which media can be used to change individual behavior. Directly, media influences individuals by providing information on the new norm of desirable behavior [18
]. This information can be descriptive or prescriptive [19
]. By doing so, the media shape individuals’ attitude and beliefs about certain actions and therefore creates societal norms [20
]. Additionally, the media provide individuals with the tools to properly conform to the newly advised behavior [23
Indirectly, media impact human behavior through two pathways. First, the media have an agenda-setting influence on institutions, which then directly influences the public [24
]. Institutions such as legislative bodies (e.g., Congress), enforcement agents (e.g., the police), or religious institutions influence behavior by disseminating the message to social circles and ensuring compliance by placing material constraints and incentives. Second, the media promote social diffusion of the norms on correct action [27
]. This occurs explicitly, when the media promulgate individuals to spread the important message to their social circles [23
]. It also occurs tacitly as the norm naturally diffuses through interpersonal communication within families and social networks [28
Media impacts on ridership specifically can be both positive and negative. For instance, Liu et al. [30
] found that positive social media campaigns can greatly increase ridership, while Boisjoly et al. [31
] found that transit messaging in the media and unreliable transportation scheduling websites could both lead to reduced ridership in some cases. Media influence, as a two-part framework with information selectivity, can also be used to explain mob mentality in online media platforms [32
]. Media impacts on ridership and other forms of human behavior are most effective when the desired action is a “one-off” demand rather than a habitual, life-style change [17
]. There is debate in the literature on whether media as a direct mechanism of behavioral change or media as an agenda setting tool on the institutions are more effective [25
]. Outside of ridership, the scholarship on health-related behavioral change has focused on issues such as drug use, smoking, and preventable diseases [33
]. Additionally, there is specific research that focuses on the media’s ability to mitigate the spread of pandemics [35
]. Overall, media can be an effective, inexpensive tool to influence human behavior in a majority of the population, although sustained efforts are required for the intervention to be lasting.
As described further below, to measure media influence on ridership in this research, we use daily media counts as a percentage of total media, which are drawn from regional media sources for key terms related to air quality and meteorology. To better understand ridership behaviors, we use data drawn from the UTA system. In addition to cash and voucher payments, UTA services use an on/off electronic fare collection system where a user “taps on” with their transport card when boarding and “taps off” when alighting. These data are collected for each passenger trip and provide necessary information, such as temporal and spatial demand, to inform UTA for service improvement decisions and have been used in a previous study to estimate the impact of UTA vehicles on air quality in its service area [9