Abstract
This paper investigates the outdoor non-work activity allocation behaviors of commuters in Xiaoshan District of Hangzhou, China, as well as the underlying relationship among different types of outdoor non-work activities. As per their commute and work schedules, commuters’ outdoor non-work activities are classified into six categories and considered as binary dependent variables for modeling analysis, including from home before work, on commute way from home to work, going home during work, going out (not going home) during work, on commute way from work back home, and from home after work. Independent variables include commute attributes, work schedules, sociodemographic attributes, and built-environmental attributes. A multivariate probit model is developed to explore the effects of explanatory variables and capture correlations among unobserved influential factors. The model estimation results show that daily work time, education years, and traffic zone have substantial impacts on commuters’ non-work activity allocations. As for the underlying relationship among unobserved factors, a positive correlation is found between the outdoor non-work activities on commute way to and from work, indicating a mutually promotive relationship. All other correlations are negative, indicating other types of non-work activities are mutually substitutive. These findings will help to better understand commuters’ behaviors of outdoor activity arrangement subject to the time-space constraint from fixed work schedules, and shed some light on the mechanism of complex work tour formation, so as to guide the development of activity-based travel demand models for commuters.
Highlights
As a developing country with rapid economic development, China has experienced constant population growth and spatial expansion in many large cities
The estimation results of the multivariate probit model are shown in Table 4, in which six types of outdoor non-work activities are represented by Act1–Act6
The elasticity of a continuous variable is calculated as the percentage of the probability change in each type caused by a 1% increase in the variable, while the semi-elasticity is calculated for a discrete variable
Summary
As a developing country with rapid economic development, China has experienced constant population growth and spatial expansion in many large cities. People may undertake some non-work activities (such as picking up children and spouses, shopping, etc.) during a commute trip to make an efficient use of time [2,3,4,5,6], which results in the increase of stops in trip chains. In this case, the departure time and mode choice of commuters can be significantly affected by the complexity of the activity-travel pattern, which may change the midway destinations or routes of commute trips. In order to verify this conclusion, this paper takes the mode choice of commuters as one of the independent variables
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