Research Article

Multiagent-Based Simulation of Temporal-Spatial Characteristics of Activity-Travel Patterns Using Interactive Reinforcement Learning

Table 2

Activity-travel schedules.

Agent type Activity-travel schedule

hwhh (00:00–08:00, 05) w (08:30–16:45, 08)
h (17:30–24:00, 05)
hwshh (00:00–07:00, 12) w (07:30–17:00, 11)
s (17:15–18:15, 11) h (18:45–24:00, 12)
hwhshh (00:00–07:45, 01) w (08:15–15:30, 02)
h (15:45–19:00, 01) s (19:15–19:45, 03)
h (20:00–24:00, 01)
hwhwhh (00:00–06:30, 11) w (07:30–11:15, 07)
h (12:00–13:00, 11) w (13:45–17:30, 07)
h (18:30–24:00, 11)
hwswhh (00:00–07:15, 04) w (07:45–11:45, 06)
s (12:15–12:45, 05) w (13:15–16:30, 06)
h (17:15–24:00, 04)
hlhh (00:00–05:45, 03) l (06:00–07:00, 02)
h (07:30–24:00, 03)
hlhshh (00:00–04:45, 02) l (05:15–06:15, 08)
h (06:45–07:00, 02) s (07:30–08:00, 03)
h (08:30–24:00, 02)
hshh (00:00–06:30, 15) s (07:00–07:30, 05)
h (08:00–24:00, 15)
hshlhh (00:00–06:00, 04) s (06:30–06:45, 08)
h (07:15–07:30, 04) l (08:00–08:30, 08)
h (09:00–24:00, 04)
hshshh (00:00–05:15, 17) s (06:30–07:00, 03)
h (08:00–16:30, 17) s (17:15–17:45, 03)
h (18:30–24:00, 17)