This chapter is written for the welfare of the society, questioning and enlightening the effects of the increment or decrement in the percentage of quality of air causing pollution due to the rise in the traffic post-lockdown due to COVID-19 in metro cities, specifically in Delhi. In this chapter, we address the question about people's preference in moving in the shared taxis to their workplaces or their reluctance and denial of the idea of moving in the shared vehicle because of the fear of getting infected. The sensitivity of the situation will compel the people to move in a single occupied vehicle (SOV). The rise in the number of vehicles on the roads will result in traffic jams and different kinds of pollution where people battling with the pandemic will inevitably get exposed to other health-related issues. We use a BPR (Bureau of Public Roads) model to combat this issue endangering the environment and public health. We exploit the BPR function to relate average travel time to the estimated number of commuters travelling by car. We collect mode share data from the NITI Ayog, the State Resource Centre and other authentic sources, which gives unique figures of the impact of shared mobility in India and how, in its absence, various sectors will get affected. Using the given data and the BPR, we evaluate increased vehicle volumes on the road if different portions of transit and carpool users switch to single-occupancy vehicles and its effect on multiple other factors. Based on the study of densely populated city, Delhi, we predict that cities with significant transit ridership are at risk for extreme traffic and pollution unless transit systems can resume safe with effective protocols.

Commuting is a vital travel channel for college students throughout their higher education career; nevertheless, excessive commute time negatively impacts students’ school satisfaction, academic achievement, health, and quality of life. Meanwhile, few studies have explored students’ commute time, covering a large population and scope. Therefore, this study analyzes the dwelling type that influences college and graduate students’ school commute times in the Seoul Metropolitan Area, using data from the 2016 Household Travel Diary Survey. This study uses spatial data on housing development to classify five dwelling types (i.e., new town, housing site development, Seoul, Incheon, and Gyeonggi-do). A multiple linear regression model empirically analyses students’ household and personal characteristics. The results reveal that dwelling type significantly affects students’ commute time. Specifically, Students living in Seoul have a shorter commute to school than those living in new towns. Socioeconomic characteristics have less effect on college and graduate students' school commute time. This study discusses the problem of excessive commuting of college and graduate students in new towns.