{"title":"A micro-action-based decision-making framework for simulating overtaking behaviors of heterogeneous pedestrians","authors":"Jingxuan Peng, Zhonghua Wei, Yanyan Chen, Shaofan Wang, Yongxing Li, Liang Chen, Fujiyama Taku","doi":"10.1016/j.inffus.2024.102898","DOIUrl":null,"url":null,"abstract":"In many public places, the heterogeneity of pedestrians leads to diverse travel behaviors including overtaking behavior. However, according to the variety of factors such as the heterogeneous attributes of pedestrians and the alterations of surrounding environment, the previous models for simulating overtaking behavior exist the problems of behavior loss or decision imbalance. By observing that overtaking behavior can be regarded as a process consisting of multiple micro-actions, this paper proposes a micro-action-based macro-to-micro decision-making (M3DM) framework to simulate fine-grained overtaking behavior of heterogeneous pedestrians. The framework incorporates two modules: micro-action modeling (MM) and macro-to-micro decision-making (MMDM) modules. The former module constructs the mapping relationship between proposed micro-actions and multiple personality characterization, and builds the simulation model of each micro-action. While the latter module integrates the density based macro and energy consumption based micro decision into framework, which achieves a more realistic simulation of overtaking behavior. Extensive real experiments are conducted to calibrate the parameters and verify the rationality of our framework. Moreover, two different simulation cases prove the authenticity of the proposed simulation model. The results indicate that the M3DM framework can significantly enhance the simulation accuracy of pedestrian behaviors, providing valuable insights for pedestrian flow management and safety in high-density environments.","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"74 1","pages":""},"PeriodicalIF":14.7000,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Information Fusion","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1016/j.inffus.2024.102898","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
Abstract
In many public places, the heterogeneity of pedestrians leads to diverse travel behaviors including overtaking behavior. However, according to the variety of factors such as the heterogeneous attributes of pedestrians and the alterations of surrounding environment, the previous models for simulating overtaking behavior exist the problems of behavior loss or decision imbalance. By observing that overtaking behavior can be regarded as a process consisting of multiple micro-actions, this paper proposes a micro-action-based macro-to-micro decision-making (M3DM) framework to simulate fine-grained overtaking behavior of heterogeneous pedestrians. The framework incorporates two modules: micro-action modeling (MM) and macro-to-micro decision-making (MMDM) modules. The former module constructs the mapping relationship between proposed micro-actions and multiple personality characterization, and builds the simulation model of each micro-action. While the latter module integrates the density based macro and energy consumption based micro decision into framework, which achieves a more realistic simulation of overtaking behavior. Extensive real experiments are conducted to calibrate the parameters and verify the rationality of our framework. Moreover, two different simulation cases prove the authenticity of the proposed simulation model. The results indicate that the M3DM framework can significantly enhance the simulation accuracy of pedestrian behaviors, providing valuable insights for pedestrian flow management and safety in high-density environments.
期刊介绍:
Information Fusion serves as a central platform for showcasing advancements in multi-sensor, multi-source, multi-process information fusion, fostering collaboration among diverse disciplines driving its progress. It is the leading outlet for sharing research and development in this field, focusing on architectures, algorithms, and applications. Papers dealing with fundamental theoretical analyses as well as those demonstrating their application to real-world problems will be welcome.