Comparing a single point in two time series I have two time series of males and females traveling from point A in between 1979 to 2001. The question is, is there a significant difference in the number of male and female travelers in year, say, 2000?
How can I answer this question?
Also, would it be different if I had the data divided by destination, i.e females and males traveling from A to B, from A to C, etc.?
 A: 
Is there a significant difference in the number of male and female travelers in year, say, 2000?

Your question could be interpreted in two ways: 


*

*assessing a difference between the number of male and female travelers in a given year;

*assessing the variation in the male and female traveler series over time, focusing on a particular data point (is it an "outlier"?).


I will focus on interpretation 1.
In interpretation 1., the time series dimension becomes irrelevant since you are only considering one time point.
Denote the number of male and female travelers $n_m$ and $n_f$, respectively. Then your question is, is $n_m$ significantly different from  $n_f$? 


*

*If the numbers are measured without error, then we cannot really talk about statistical significance. $n_m$ is simply different from $n_f$ unless $n_m=n_f$. If you mean subject-matter significance, you have to decide yourself what a significant difference is (10 units, 1% or yet something else). 

*If the numbers are measured with error and the error distribution is know or can be estimated from the data (additional data would be needed besides the two time series you mentioned), you could apply a statistical test (something like a $t$-test) to determine whether the difference is statistically significant.



Would it be different if I had the data divided by destination, i.e females and males traveling from A to B, from A to C, etc.?

There would be no major conceptual difference, but in the case of numbers measured with error you could probably have richer information on the error distribution (if that is available for each destination and jointly). Also, you could test a broader variety of hypotheses (destination-specific, combining destinations) if that interests you.
