Evaluating pre-travel decision making under snowy weather for commuting trips

Abstract Traveling in inclement winter weather is challenging due to reduced mobility and increased travel risks. It is essential to aid commuters in making adequate pre-travel decisions under such conditions by providing real-time information on road and weather conditions. This study investigates factors that affect the effectiveness of such information on commuting routes, using data collected via a stated-preference survey. Respondents were asked to make travel decisions (likelihood of canceling their trip) based on observable outdoor weather conditions in their neighborhoods and route-specific road and weather conditions conveyed through digital media platforms. The real-time location and short-term trajectory of snowplowing trucks were also presented to evaluate impacts of such information on travel decisions during winter weather. The random parameter multinomial logit model with means and variances heterogeneity model was applied for modeling travel decisions given different weather and information scenarios. The results demonstrate that such information can significantly influence pre-travel decision-making during winter weather, with the display of snowplow truck status notably increasing likelihood to commute. The study underscores the importance of customizing information delivery strategies, as their effectiveness varies based on respondent income, job requirements, commuting distances, driving experience, and vehicle performance. Furthermore, an equity issue was identified, indicating that commuters with in-person working requirements and low income are more likely to take travel risks regardless of road and weather conditions. This study provides valuable insights for technology developers, policymakers, and traffic operators regarding the potential impacts of delivering information of real-time road and weather conditions on the route to commuters.