Fulu Wei¹, Xin Li¹, Yongqing Guo^1,*, Zhenyu Wang², Qingyin Li¹, Xueshi Ma³

Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 2001-2018, 2023, DOI:10.32604/iasc.2023.035799

Abstract Traffic flow prediction plays an important role in intelligent transportation systems and is of great significance in the applications of traffic control and urban planning. Due to the complexity of road traffic flow data, traffic flow prediction has been one of the challenging tasks to fully exploit the spatiotemporal characteristics of roads to improve prediction accuracy. In this study, a combined flow direction level traffic flow prediction graph convolutional network (GCN) and long short-term memory (LSTM) model based on spatiotemporal characteristics is proposed. First, a GCN model is employed to capture the topological structure of the data graph and extract… More >

Pengfei Gong^1,2, Qun Wang^2,*, Junjun Zhu³

Intelligent Automation & Soft Computing, Vol.29, No.1, pp. 31-42, 2021, DOI:10.32604/iasc.2021.017695

Abstract This study analyzed in-depth investigation reports for 418 traffic accidents with at least five deaths (TALFDs) in China from 2016 to 2019. Statistical analysis methods including hierarchical cluster analysis were employed to examine the distribution characteristics of these accidents. Accidents were found to be concentrated in July and August, and the distribution over the seven days of the week was relatively uniform; only Sunday had a higher number of accidents and deaths. In terms of 24-hour distribution, the one-hour periods with the most accidents and deaths were 8:00–9:00, 10:00–11:00, 14:00–15:00, and 18:00–19:00. Tibet, Qinghai, and Ningxia had the highest death… More >