Modeling the Commuting Travel Activities within Historic Districts in Chinese Cities

<table class="table-group" id="tab2"><tr><td><table class="table"><tr><td class="thead-hr" colspan="3"><hr/></td></tr><tr class="thead"><td align="left">Category</td><td align="center">Variables </td><td align="left">Description</td></tr><tr><td class="thead-hr" colspan="3"><hr/></td></tr><tr><td align="left" rowspan="3">Subsistence activity</td><td align="center">Commute time</td><td align="left">Time allocated to work or work-related trips</td></tr><tr><td align="center">Commute trip number</td><td align="left">Number of work or work-related trips</td></tr><tr><td align="center">Duration of the commuting</td><td align="left">Successive time of work or work-related activities in the workday</td></tr><tr><td align="center" colspan="3"><hr/></td></tr><tr><td align="left" rowspan="3">Travel behavior</td><td align="center">Number of trips</td><td align="left">The total number of trips in a whole day</td></tr><tr><td align="center">Number of trip chains</td><td align="left">Number of home-based trip chains</td></tr><tr><td align="center">Type of trip chain</td><td align="left">A sequence of trips that starts and ends at the household location in a whole day</td></tr><tr><td align="center" colspan="3"><hr/></td></tr><tr><td align="left">Travel mode</td><td align="center">Mode choice</td><td align="left">Nonmotorized mode; public transportation; automobile</td></tr><tr class="table-tr"><td colspan="3"><hr class="tbody-hr"/></td></tr></table></td></tr></table>

Description of endogenous variables in SEM.

Computational Intelligence and Neuroscience

tab2

Table 2

Table 2: Modeling the Commuting Travel Activities within Historic Districts in Chinese Cities 