There are a couple of different approaches you can take, depending on how you want to slice the data.
If you keep your groupings, then you could create a crosstab or contingency table, and use a $\chi^2$ test. This is a very basic test that compares the expected counts to the observed counts in each cell. The expected counts are what you would expect if there is no difference between seasons; the observed is what was actually obtained. If the difference is large enough to not be from random chance, then the test will show you it is statistically significant (based on a $\chi^2$ distribution).
If you want to work with the raw data, then you need to think about the process that created the counts. It could be represented by a Poisson distribution, which is often used for number of occurrences within a period. You could use a simple Poisson regression, where the unit of analysis is the day, dependent variable is the number of cars, and the independent variable is a single dummy variable for season (1=Winter, 0=not winter); or you could include all four seasons with 3 dummies (e.g. one for Fall, one for Spring, one for Summer, and the ommitted category is Winter). If the season coefficient is significant, then the expected number of cars is statistically different.
Depending on the actual distribution of the data, you may be able to approximate the Poisson distribution with a Normal distribution, and you could use a t-test, analysis of variance (ANOVA), or linear regression. The interpretation of these tests are much simpler than a Poisson model, so many analysts prefer to do this if possible. Look at the histogram of your data and examine the skew and kurtosis.
Most of these tests are available in standard statistical software like SPSS, Stata, SAS, and R. Excel can do them as well.
If I were you, I would actually start with the raw data, unless you have strong theoretical reasons for wanting to group the data. You could do both, of course, and see which result makes more intuitive sense.