This paper adopts a dynamic general nesting spatial panel data model with common factors to explore the effect of population density, real household income per capita, car fleet per capita, and real price of gasoline on departmental traffic per light vehicle in France over the period 1990–2009. Spatial heterogeneity is modeled by a translog function in the first three explanatory variables, which are dominated by variation in the cross-sectional domain, while the real price of gasoline, which is dominated by variation in the time domain, is treated as an observable common factor. Additional unobservable common factors are controlled for by principal components with heterogenous coefficients, building on previous work of Shi and Lee (2017a), thereby, generalizing the dynamic spatial panel data model with spatial and time period fixed applied in recent studies. It is found that the spatial lag in the dependent variable becomes insignificant due to these extensions. This paper explains the wider implications of this finding for spatial econometric modeling of cross-sectional dependence. In addition, the elasticities of the first three explanatory variables are shown to vary across space and time and to follow a plausible structure. Among other, an important result is that the long run income elasticity of car traffic diminished from 1.0 in 1990 to 0.4 in 2003, and then remained almost constant until the end of our sample period in 2009, i.e., during the peak-car traffic period. [ABSTRACT FROM AUTHOR]