A Novel Approach for Improved Pedestrian Walking Speed Prediction: Exploiting Proximity Correlation

Accurately predicting pedestrian speed is crucial for analyzing pedestrian behavior and optimizing intelligent transportation systems. This paper investigates the feasibility of modeling pedestrian walking speed as a time series. Building upon previous research highlighting the spatio-temporal nearest neighbor correlation in pedestrian walking speed, we propose a deep learning method that leverages this correlation. Experimental results demonstrate the superiority of our approach over traditional methods in accurately predicting pedestrian walking speed and capturing temporal characteristics and trends. The findings of this study have significant implications for enhancing pedestrian traffic flow management, improving the pedestrian travel experience, and enhancing overall traffic safety. Future research can focus on exploring advanced time series methods and deep learning models to further enhance the accuracy and practicality of pedestrian walking speed prediction.