Okay this might be a very weird question. I just started reading about DiD, and was wondering if it can be applied to my problem. Unable to find any good references on this.

I am considering the effect of introducing late-night taxis to cities on reduction in drunk driving arrests. However, the treatment here (introducing late-night taxis) had many characteristics i.e. the intensity at which it was introduced (#cars introduced), type of cars introduced (sedans, SUVs, yellow cabs, etc.), the fares at which they were introduced (diff. cities had diff. prices). I want to understand the effect of all of these treatment group.

Is it possible to model all of them in one equation itself i.e. having multiple treatment variables?

I just started reading about difference-in-differences (DiD), and was wondering if it can be applied to my problem. I am unable to find any good references on this.

I am considering the effect of introducing late-night taxis to cities and whether it resulted a reduction in drunk driving arrests. However, the treatment here (introducing late-night taxis) had many characteristics, i.e., the intensity at which it was introduced (# cars introduced), type of cars introduced (sedans, SUVs, yellow cabs, etc.), the fares at which they were introduced (different cities had different prices). I want to understand the effect of all of these treatment groups.

Is it possible to model all of them in one equation itself? Can I have multiple treatment variables?