Riders on the storm: Exploring weather and seasonality effects on commute mode choice in Chicago

An exploratory investigation of public perceptions towards key benefits and concerns from the future use of flying cars

“…In this manner, unbalanced panel effects stemming from possible systematic variations across the collector-specific survey responses are effectively captured. The grouped random parameters are formulated as (Washington et al, 2011;Fountas and Anastasopoulos, 2017;Sarwar et al, 2017a;Anastasopoulos et al 2017;Fountas et al, 2018aFountas et al, , 2018cMenon et al, 2019;Hyland et al, 2018):…”

Influence of Weather Conditions on the Intercity Travel Mode Choice: A Case of Xi’an

“…In addition, the number of students using car increased by 18.1%. Hyland et al [ 18 ] used the survey data of Chicago travelers by Standard & Poor's to show that most respondents were less likely to choose cars to travel in bad weather than in good weather. Furthermore, Chen and Wang [ 10 ] concluded that the OTP of HSR in southeast coastal areas was more likely to be affected by rainstorms and thunderstorms than other weather conditions, while snowstorms seemed to be more destructive to HSR in the central-eastern and northern regions.…”

“…Previous studies have revealed the impact of weather conditions on the choice of urban transportation modes, including the season [10][11][12]18], humidity [10][11][12][13][14][15], temperature [11][12][13][14][15][16][17], wind speed [10,[13][14][15][16][17], snow [10,11,16,17], fog [10,16], rain [15][16][17], the air quality index (AQI) [14], and extreme weather [18,19]. For example, Böcker et al [12] found car use seemed to be less attractive in spring, and travel by walking and cycling was less common in winter than in spring during 2004-2009 in the Randstad Holland.…”

“…Ton et al [19] suggested that weather conditions had limited influence on cycling or walking by using census data from the Netherlands Mobility Panel. e most widely used models in the intercity travel mode choice affected by weather conditions are still based on the utility theory of discrete selection models, including the multinomial logit (MNL) model [12,14], multinomial probit (MNP) model [14], mixed multinomial logit model [19], and panel mixed logit model [18]. Other models have also been used, such as machine learning [13], moving average method [11], regression analysis [10,15,16], agent-based model [20,21], and structural equation models (SEM) [17].…”

“…Table 1 presents a literature summary on the travel mode choice. Existing models typically used maximum likelihood estimation as a parameter estimation method [12,14,18,19,22]. e discrete choice modeling technology brings the development to the Bayesian parameter estimation method in the traffic safety field [23][24][25].…”