  Understanding pedestrian route choice behavior in complex buildings is
important to ensure pedestrian safety. Previous studies have mostly used
traditional data collection methods and discrete choice modeling to understand
the influence of different factors on pedestrian route and exit choice,
particularly in simple indoor environments. However, research on pedestrian
route choice in complex buildings is still limited. This paper presents a
data-driven approach for understanding and predicting the pedestrian decision
point choice during normal and emergency wayfinding in a multi-story building.
For this, we first built an indoor network representation and proposed a data
mapping technique to map VR coordinates to the indoor representation. We then
used a well-established machine learning algorithm, namely the random forest
(RF) model to predict pedestrian decision point choice along a route during
four wayfinding tasks in a multi-story building. Pedestrian behavioral data in
a multi-story building was collected by a Virtual Reality experiment. The
results show a much higher prediction accuracy of decision points using the RF
model (i.e., 93% on average) compared to the logistic regression model. The
highest prediction accuracy was 96% for task 3. Additionally, we tested the
model performance combining personal characteristics and we found that personal
characteristics did not affect decision point choice. This paper demonstrates
the potential of applying a machine learning algorithm to study pedestrian
route choice behavior in complex indoor buildings.
