Paul Deborah, Moridpour Sara, Venkatesan Srikanth, Withanagamage Nuwan
Department of Civil and Infrastructure Engineering, RMIT University, Melbourne, Australia.
Faculty of Information Technology, Monash University, Melbourne, Australia.
Sci Rep. 2024 Feb 2;14(1):2813. doi: 10.1038/s41598-024-53403-7.
The pedestrians' feeling of comfort while walking on footpaths varies according to the time of day, environment, and the purpose of the trip. The quality of service offered by pedestrian facilities such as walkways, intersections, and public places is evaluated by the Pedestrian level of service (PLOS) and has been measured from time to time, to upgrade and maintain the sustainable travel choice of people. This paper aims to focus on the level of service based on three main trip purposes such as work, education, and recreation, while considering various path characteristics and pedestrian flow characteristics that affect the pedestrian's feeling of comfort on the walkways. The data has been collected using pedestrian questionnaire surveys and pedestrian sensors in the Melbourne central business district and the significant factors that influence the PLOS for each trip purpose will be chosen using the Mutual Information gain, which is found to be different for each trip purpose. The major influencing factors that affect the PLOS will be used to develop machine learning models for three trip purposes separately using Random Forest and Light-GBM algorithm in Python. The accuracy of prediction using the light GBM model is 0.74 for education, 0.80 for recreation, and 0.70 for work trip purposes. It is found using SHAP which stands for Shapely Additive explanations that the factors such as interpersonal distance, distance from vehicles, construction sites, vehicle volume, traffic noise, and footpath surface are the most influencing variables that affect the PLOS based on three different trip purposes.
行人在人行道上行走时的舒适感会因一天中的时间、环境以及出行目的而有所不同。诸如人行道、十字路口和公共场所等行人设施所提供的服务质量是通过行人服务水平（PLOS）来评估的，并且会不时进行测量，以提升和维持人们可持续的出行选择。本文旨在聚焦于基于工作、教育和娱乐这三个主要出行目的的服务水平，同时考虑各种影响行人在人行道上舒适感的路径特征和行人流量特征。数据是通过在墨尔本中央商务区进行的行人问卷调查和行人传感器收集的，并且将使用互信息增益来选择影响每种出行目的PLOS的重要因素，结果发现每种出行目的的重要因素各不相同。影响PLOS的主要因素将被用于在Python中分别使用随机森林和Light - GBM算法为三个出行目的开发机器学习模型。使用Light GBM模型进行预测的准确率，教育出行目的为0.74，娱乐出行目的为0.80，工作出行目的为0.70。使用代表Shapely加法解释的SHAP方法发现，人际距离、与车辆的距离、建筑工地、车辆流量、交通噪音和人行道表面等因素是基于三种不同出行目的影响PLOS的最具影响力的变量。
