سال انتشار:
2020
عنوان انگلیسی مقاله:
Development of people mass movement simulation framework based on reinforcement learning
ترجمه فارسی عنوان مقاله:
توسعه چارچوب شبیه سازی حرکت جمعی مردم مبتنی بر یادگیری تقویتی
منبع:
Sciencedirect - Elsevier - Transportation Research Part C, 117 (2020) 102706. doi:10.1016/j.trc.2020.102706
نویسنده:
Yanbo Panga,⁎, Takehiro Kashiyamaa, Takahiro Yabea,b, Kota Tsubouchic, Yoshihide Sekimotoa
چکیده انگلیسی:
Understanding individual and crowd dynamics in urban environments is critical for numerous
applications, such as urban planning, traffic forecasting, and location-based services. However,
researchers have developed travel demand models to accomplish this task with survey data that
are expensive and acquired at low frequencies. In contrast, emerging data collection methods
have enabled researchers to leverage machine learning techniques with a tremendous amount of
mobility data for analyzing and forecasting people’s behaviors. In this study, we developed a
reinforcement learning-based approach for modeling and simulation of people mass movement
using the global positioning system (GPS) data. Unlike traditional travel demand modeling approaches,
our method focuses on the problem of inferring the spatio-temporal preferences of
individuals from the observed trajectories, and is based on inverse reinforcement learning (IRL)
techniques. We applied the model to the data collected from a smartphone application and attempted
to replicate a large amount of the population’s daily movement by incorporating with
agent-based multi-modal traffic simulation technologies. The simulation results indicate that
agents can successfully learn and generate human-like travel activities. Furthermore, the proposed
model performance significantly outperforms the existing methods in synthetic urban
dynamics.
Keywords: Travel demand modeling | Reinforcement learning | Mobility data | Citywide people mass movement simulation
قیمت: رایگان
توضیحات اضافی:
تعداد نظرات : 0