COVID-19 and Travel Behavior

The COVID-19 pandemic has caused negative impacts not only on the health and economic sectors but also on the transportation sector, both in the short and long run. It is necessary to maintain personal hygiene measures (e.g., using masks and gloves, maintaining social distance, and sanitizing the hands) to be safe from this highly infectious disease. However, the degree of difficulty to maintain these measures differs from one travel mode to another, and consequently, mode-wise perceived risk of COVID-19 transmission also varies. Therefore, people's mode choice behavior might be changed, and people might shift their travel mode to a safer one if they feel that viral transmission risk is higher in their pre-pandemic regular travel mode. The economic condition of the people has also deteriorated due to the lockdown resulting from the pandemic. Furthermore, people are using the virtual platform (e.g., work from home, online shopping, mobile banking, and online education) extensively than in the pre-COVID-19 situation, which might reduce travel demand and change travel behavior in the new normal situation. Deteriorating economic conditions and increasing virtual activities might also impact people's mode choice behavior. Therefore, it would be worthwhile to see how people perceive risk in different modes and their perceptions about their travel mode choice in the new normal situation. Therefore, this study investigated the impact of COVID-19 pandemic on travel behavior of the people using data of COVID-19 first wave. More specifically, this study:

• Estimated people’s perceived risk of COVID-19 transmission in different travel modes in Bangladesh and identified factors influencing mode-specific perceived risks using ordinal logistic regression.

• Explored the short-, medium-, and long-term effects of the pandemic on the expected change in active and public transportation usage through multinomial logistic regression.

• Explored how the pandemic influenced decisions regarding motorcycle purchases and identified the key driving factors using binary logistic regression.