Abstract
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.
Highlights
The past decades have witnessed a rapid development of modern society accompanied with an increasing demand for mobility in metropolises [1,2,3,4], accounting for the conflict between the limits of road capacity and the increment of traffic demand reflected by severe traffic congestions [5,6,7]
Our main contribution is that we extend the tools of time series analysis to the collective dynamics of road traffic rather than at individual level, by mapping the vehicle records from Global Position System (GPS) into road usage so as to offer the predictability of traffic conditions at different locations
We explore the predictability of traffic conditions by using the GPS records of more than 12000 taxis in Beijing, China
Summary
The past decades have witnessed a rapid development of modern society accompanied with an increasing demand for mobility in metropolises [1,2,3,4], accounting for the conflict between the limits of road capacity and the increment of traffic demand reflected by severe traffic congestions [5,6,7] Induced by such problem, citizens suffer from the reduction of travel efficiency and the increase of both fuel consumption [8] and air pollution [9] related with vehicle emission. “big data” as the inevitable outcome in the information era opens new routes to reinvent urban traffic systems and offer solutions for increasingly serious traffic jams [22] In this light, mobile phone and LBS data have been employed to explore road usage patterns in urban areas [23,24,25,26]. To eventually implementing control on road traffic, predicting traffic conditions is the prerequisite, which prompts us to wonder, to what degree traffic flow on complex road networks is predictable with respect to high self-adaptivity of drivers and without any priori knowledge of their origins and destinations
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