Predictability of Road Traffic and Congestion in Urban Areas

PLoS One

Yu Mao¹  Zhang Xiong²  Jing Li²  Jingyuan Wang²  Wen-Xu Wang² 

[1] Research Institute in Shenzhen, Beihang University, Shenzhen, China;School of Computer Science and Engineering, Beihang University, Beijing, China

关键词: Roads;    Entropy;    Human mobility;    Algorithms;    Urban areas;    Forecasting;    Information entropy;    Air pollution;   

DOI  :  10.1371/journal.pone.0121825

学科分类：医学（综合）

来源: Public Library of Science

【 摘 要 】

Mitigating traffic congestion on urban roads, with paramount importance in urban development and reduction of energy consumption and air pollution, depends on our ability to foresee road usage and traffic conditions pertaining to the collective behavior of drivers, raising a significant question: to what degree is road traffic predictable in urban areas? Here we rely on the precise records of daily vehicle mobility based on GPS positioning device installed in taxis to uncover the potential daily predictability of urban traffic patterns. Using the mapping from the degree of congestion on roads into a time series of symbols and measuring its entropy, we find a relatively high daily predictability of traffic conditions despite the absence of any priori knowledge of drivers' origins and destinations and quite different travel patterns between weekdays and weekends. Moreover, we find a counterintuitive dependence of the predictability on travel speed: the road segment associated with intermediate average travel speed is most difficult to be predicted. We also explore the possibility of recovering the traffic condition of an inaccessible segment from its adjacent segments with respect to limited observability. The highly predictable traffic patterns in spite of the heterogeneity of drivers' behaviors and the variability of their origins and destinations enables development of accurate predictive models for eventually devising practical strategies to mitigate urban road congestion.

【 授权许可】

CC BY