相关实验视频

Updated: Jul 4, 2025

Design and Analysis for Fall Detection System Simplification

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

通过使用机器学习算法,评估不同旅行目的的行人服务水平

Deborah Paul¹, Sara Moridpour², Srikanth Venkatesan²

¹Department of Civil and Infrastructure Engineering, RMIT University, Melbourne, Australia. s3764996@student.rmit.edu.au.

Scientific reports

|February 2, 2024

概括

根据旅行目的,行人舒适度 (PLOS) 不同. 机器学习模型确定了人际距离和交通噪音等关键因素,改进了可持续城市旅行的步道设计.

科学领域:

城市规划城市规划
运输工程运输工程
人类因素人类因素

背景情况:

步行者舒适度对选择可持续的旅行方式有很大的影响.
步行者服务水平 (PLOS) 是评估步行者设施质量的关键指标.
了解影响PLOS的因素对于保持和升级城市步行能力至关重要.

研究的目的:

分析基于工作,教育和休旅行目的的行人服务水平 (PLOS).
为了确定影响舒适度的重要路径和行人流动特征.
开发适合不同旅行目的的PLOS预测机器学习模型.

主要方法:

通过在墨尔本CBD的行人问卷调查和传感器收集数据.
利用相互信息获取来选择每个旅行目的的关键影响因素.
在Python中开发和比较随机森林和轻GBM机器学习模型.
使用SHAP (Shapely Additive解释) 来解释因素的解释性.

主要成果:

机器学习模型使用LightGBM.实现了0.74 (教育),0.80 (娱乐) 和0.70 (工作) 的预测准确度.
影响PLOS的关键因素因旅行目的而异.
人际距离,接近车辆,建筑工地,车辆数量,交通噪音和步行道表面被确定为主要影响因素.

结论:

旅行目的是行人舒适度和影响因素的关键决定因素.
机器学习模型提供了PLOS的准确预测,有助于有针对性的城市设计.
通过解决特定的影响变量,研究结果支持开发更舒适和可持续的行人环境.

更多相关视频

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Published on: February 25, 2013

Simulation of Human-induced Vibrations Based on the Characterized In-field Pedestrian Behavior

Simulation of Human-induced Vibrations Based on the Characterized In-field Pedestrian Behavior

Published on: April 13, 2016

相关概念视频

Levels of Use of a GIS

Levels of Use of a GIS

Geographic Information Systems (GIS) operate across three levels of application, each representing an increasing degree of complexity: data management, analysis, and prediction. These levels reflect the expanding functionality and versatility of GIS technology in handling spatial data for diverse purposes.Data ManagementAt its foundational level, GIS serves as a tool for data management, enabling the input, storage, retrieval, and organization of spatial data. This level is often employed in...

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...