Dynamic Study of Intelligent Traffic Behaviour Based on Multiple Traffic Modes

Hongwei Jing,Xiaoming Li,Mengli Zhu,Guangquan Xu,Fangyuan Liu,Haoyang Peng,Li Shen,Zhu Xiao

doi:10.1155/2021/7254907

Abstract

With the rapid development of society, the traffic problem has become increasingly severe, and the traditional methods can no longer effectively solve the current social traffic behaviour problems. Although studies on the dynamics of human traffic behaviour based on traffic modes can effectively reveal the anomalies in traffic behaviour, few studies integrate intelligent traffic behaviour with multiple traffic modes. Based on the numerous traffic data of bike-sharing and ride-hailing in a Chinese city, this paper reveals the dynamic characteristics of various traffic behaviours in the city by combining spatiotemporal characteristics index and urban spatial structure with human traffic behaviour patterns. The experimental results show that the traffic behaviour of the town presents a double logarithmic power-law distribution in time characteristics, and there is a close interdependent dynamic relationship with the city’s spatial structure. The research in this paper can reveal the relationship between bimodal power-law distribution and spatial characteristics in complex systems and help solve social traffic problems effectively in social reality. Further research results can provide practical planning guidance for the behavioural integration of multiple traffic in smart cities.

Highlights

Since 2005 and 2006, Barabasi [1] and Brockmann et al [2] have published two papers on human behavioural dynamics in Nature, which initiated a research boom in the emerging interdisciplinary field of human behavioural dynamics
Geohash is an address coding method that encodes two-dimensional spatial longitude and latitude data into a string
Before in the work of this paper, we read a lot of related research studies on the dynamics of human behaviour and found that human behaviour time interval presents a powerlaw distribution or with heavy tail phenomenon shows drab exponential distribution, and we did not see any research that is similar to ours; this piece of research results has two obvious peaks of a power-law distribution. erefore, we focus on this research. e study found that many major cities in China currently have one broken bike, and it is likely that there are several broken bikes at a parking spot

Summary

Introduction

Since 2005 and 2006, Barabasi [1] and Brockmann et al [2] have published two papers on human behavioural dynamics in Nature, which initiated a research boom in the emerging interdisciplinary field of human behavioural dynamics. Traffic engineers have long started to collect daily travel data of urban residents utilizing questionnaires and other methods and build models to predict traffic flow based on these data [18] These traditional traffic survey methods often have a high cost, making it difficult to observe and record human space movement behaviour on a large scale and for a long time. Ese data provide statistical methods to study space motion behaviour; based on the research of collective human mobility, Giannotti et al [19] developed a knowledge discovery method; using the original GPS trajectory data, it is converted into the overall movement mode. The temporal characteristics of traffic behaviour in human behavioural dynamics are emphasized to analyze the material attributes of traffic behaviour of different individuals and groups In this way, we can find out the regularity of traffic behaviour patterns to further serve intelligent transportation planning.

Related Work

Data Sets

Experimental Results

Conclusions