Understanding walking behaviour during COVID-19: binary logit and ANN approach

The coronavirus disease 2019 (COVID-19) pandemic had severe impacts on society. It negatively affected many sectors, and transportation is one of those. Naturally, the walking trip behaviour of individuals was also altered. This study aims to investigate the changes in walking trips of individuals by using two models: binary logit (BL) and artificial neural network (ANN). An online survey was conducted with 387 individuals. BL model investigated if respondents’ walking trips would increase during and after the pandemic. On the other hand, ANN models were developed to determine the significant factors in changes in walking behaviour. Results indicate that ANN models capture more factors than BL models. Demographics and attitudes toward public transportation, taxi, and walking trips during the pandemic are found to be effective in walking behaviour changes. Policies can be made to increase public transit ridership, and infrastructure for walking can be improved. Future research suggestions are given.