Number of states in taxi environment (Dietterich 2000) Dietterich, who introduced the taxi environment (see p. 9), states the following: In total there “are 500 [distinct] possible states: 25 squares, 5 locations for the passenger (counting the four starting locations and the taxi), and 4 destinations” (Dietterich, 2000, p. 9).

However, in my opinion there are only 25 (grid) * 4 (locations) * 2 (passenger in car) = 200 different states, because for the agent it should be the same task to go to a certain point, regardless of whether it's on its way to pick up or to drop-off. Only the action at the destination is different which would be stored binary (passenger in car or not)

Why does Dietterich come up with 500 states?
This is more of a combinatorics than AI question but regradless, the full state information for the environment is:  

$(taxi \space position, passenger \space position, destination \space position)$

There are 25 possible taxi positions, 5 passenger positions and 4 destination positions making it $25 \cdot 5 \cdot 4 = 500$, so the paper is correct.  

You are also correct but you divided 1 objective into 2 objectives and you have 2 separate policies, a pickup policy and a dropoff policy. So your state information would be for each policy:

$(taxi \space position, destination \space position)$  

There are 25 possible taxi positions and 4 possible destination positions making it $25 \cdot 4 = 100$. You have 2 policies so you have $200$ states.

EDIT

Actually in the second case, I think you could get away with only 1 policy where you would simply change the destination position once you pick up the passenger so you dont need 2 separate policies and you would have only $100$ states