A STUDY ON THE MEDIATING EFFECT OF FoMO ON SOCIAL MEDIA (INSTAGRAM ) INDUCED TRAVEL ADDICTION AND RISK TAKING TRAVEL BEHAVIORAL INTENTION IN YOUTH

The effect of social media on youth is immense. Fear of missing out (FoMO) is a common repercussion found in youth due to extreme usage of social media platforms. They suffer from FoMO in the context of travel and tour. This behavior leads to impulsive travel decisions plus risk-taking travel attitude. The present study examines the impact of Social Media (Instagram) induced Travel Addiction on youth's Risk-Taking Travel Behavioral Intention with FoMO as a mediating variable. An online survey conducted amidst 420 South Indian students revealed that Social Media (Instagram) induced Travel Addiction (SMTA) had a direct positive impact (β = 0.29, p <0.05) on Risk Taking Travel Behavioural Intention (RTTBI). It was also observed that Social Media (Instagram) induced Travel Addiction had a direct positive impact on FoMO (β = 0.58, p <0.05), and FoMO had a direct positive impact (β = 0.19, p <0.05) on Risk-Taking Travel Behaviour Intention. Further, FoMO was also observed to mediate the connection between Social Media (Instagram) induced Travel Addiction and Risk-Taking Travel Behavioural Intention. It is proposed that parents, teachers, and educational institutes regulate risk-taking travel behavior in youth. Furthermore, it is suggested that the youths be provided with coping/resilience strategies to deal with social media addiction. The limitations, implications for future research, and recommendations are also discussed in the paper.

Urban Traffic Network Sustainability of One-Way and Two-Way Streets: A Case Study in Downtown Brickfields, Kuala Lumpur

The once-held wisdom of the supreme efficiency of one-way streets has been gradually sup-planted by the perceived sustainability of two-way streets in the design of livable cities that prioritizes the safety of pedestrians and thriving of local businesses. However, it is rarely dis-cussed on whether one-way street conversions have truly improved the long-term traffic effi-ciencies on urban street networks, as conflating socioeconomic factors such as vehicular popula-tion growth and induced travel demand may render empirical analysis inconclusive. In this study, microscopic traffic simulations implemented on SUMO platform was performed to ana-lyze the effect of street conversion in Downtown Brickfields, Kuala Lumpur. This approach can control and standardize travel demand in both one-way and two-way street networks, and would therefore give a fairer evaluation by precluding all socioeconomic factors. It was found that one-way streets do not necessarily improve the traffic efficiency of the network, as it is very dependent on the traffic scenario evolution over time. One-way streets perform better at the on-set of traffic congestion due to its higher capacity, but on average, the 4-fold longer travel times that made it harder to clear traffic by getting vehicles to their destinations compared to two-way streets. As time progresses, congestion in one-way streets may become twice as worse compared to two-way streets. This study may contribute to a more holistic assessment of traffic circulation plan designed for smart and livable cities

White-box flight simulator built with system dynamics to support urban transportation decision-making and address induced travel demand

Induced Travel Demand is a phenomenon (ITD) wherein building new road infrastructure increases private car use. ITD has been measured and corroborated through econometric models that give an account of how much travel demand can be induced after road construction. The latter without claims of causality in their inner structure (black-box approach). Beyond the contributions of black-box models, it is still needed to explain structurally ITD for understanding and identifying its causes. Thus, this approach allows policy-makers to design comprehensive policies to deal with ITD in urban context wherein new roads are still needed to guarantee connectivity. In this paper, we present a white-box flight simulator based on a System Dynamics model to support urban transportation decision-making and address ITD. Through the simulator developed, it is possible to improve the causal understanding of ITD. Besides, although the considered policies to intervene in this phenomenon have a conceptual connotation, the simulator is a means to acquire knowledge of structural complexity underlying the interaction between the policies and ITD.

Induced Vehicle Travel in the Environmental Review Process

If we expand roadway capacity, more drivers will come, or so economic theory suggests and a substantial body of empirical research now shows. Despite strong evidence, the “induced travel” effect is often ignored, underestimated, or misestimated in the planning process, particularly in the assessment of the environmental impacts of roadway capacity expansions. Underestimating induced travel will generally lead to overestimation of the traffic congestion relief benefits a highway expansion project might generate, along with underestimation of its environmental impacts. A major reason that induced travel tends to be underplayed in environmental analyses is that travel demand models do not typically include all of the feedback loops necessary to accurately predict the induced travel effect. We developed an online tool, based on elasticities reported in the literature, to facilitate the estimation of the induced vehicle travel impacts of roadway capacity expansion projects in California, with potential future expansion to other geographies. We describe the tool, apply it to five case study highway capacity expansion projects, and then compare the results with the induced travel estimates reported in the environmental impact analyses for those projects. Our results suggest that environmental analyses frequently fail to fully capture the induced vehicle travel effects of highway capacity expansion projects.

Impact of air pollution on short-term movements: evidence from air travels in China

While there is abundant evidence demonstrating that residents permanently migrate in search of locations with cleaner air, there are considerably fewer studies that investigate if travellers also take short-term trips to reduce their exposure to air pollution. In this study, we use a complete dataset of flights at Beijing International Airport to investigate if travel patterns are indeed correlated with air quality-differences across cities in China. Our identification strategy is aided by instrumenting air quality using thermal inversions. We find that a one-unit increase in the Air Pollution Index of origin over destination city would lead to a 0.36% increase in number of passengers on the flight. When considered separately by cabin-class, the number of first-class passengers increased about three-times faster than economy-class. Using lagged air quality information, we also find that averting-related travel decisions are most sensitive to destination’s air quality on day-of-travel. This indicates that flight passengers likely rely on air quality forecast information to make air pollution-induced travel decisions.