Estimating Dynamic Distribution Condition of Pedestrian Concentration on an Urban Scale

Abstract

The distribution of pedestrians in urban space reflects the status of urban spatial planning to some extent. The reasonable prediction of pedestrian concentration is of great significance to the evaluation of urban vitality, urban comfort, and urban spatial layout planning. In this paper, a method for predicting pedestrian concentration is proposed, which can estimate pedestrian concentration in a whole city without being limited to a specific intersection or city node. According to the characteristics of three kinds of transportation accessibility based on space syntax and commercial vitality index, a dynamic distribution estimation model of pedestrian concentration is proposed. Taking Xi’an city of China as a case study, through multiple linear regression (MLR), a support vector regression (SVR) algorithm, and random forest (RF) algorithm, the pedestrian concentration in five periods of a day was predicted and analyzed, and the spatial and temporal characteristics of crowd distribution are comprehensively described. The results show that the dynamic distribution model of pedestrian concentration constructed by RF is superior to the MLR and SVR, and its average prediction accuracy can reach 93.86%.