关键词: 交通拥堵, 雾霾污染, 大数据, 收敛交叉映射, 因果关系

Abstract: Traffic congestion and smog pollution have become two stubborn diseases restricting the development of modern cities. Only by accurately identifying the causality between traffic congestion and smog pollution, can urban managers re-examine and improve the congestion policy and smog pollution policy. Via the big data platform, this paper collects daily data of Autonavi congestion delay index (CDI), air quality index (AQI) and six sub-pollutant concentrations in 99 cities in China mainland, and for the first time, this paper employs Convergent Cross Mapping (CCM) to explore the causal relationship between traffic congestion and smog pollution. The empirical results show that: There is no significant causal relationship between traffic congestion and AQI in most cities. Among the subpollutants that reduce air quality, there is no significant causal relationship between traffic congestion and major pollutants in most cities, but there is unidirectional or bidirectional causality between traffic congestion and secondary pollutants. Our results indicate that for most cities, traffic congestion and smog pollution should not be responsible for each other, but it is difficult to achieve a complete decoupling. Therefore they should be delat with together instead of separatly. This study enriches the discussion on the causal interaction between traffic congestion and smog pollution, and proves significant for unban manages to make policies against congestion and smog more discreetly and reasonably in reality.

Key words: Traffic Congestion, Smog Pollution, Big Data, CCM, Causality