Let $\lambda_0$ be the true rate at which the accidents occur. Let ${\mathbf X}=\{X_1, X_2, \ldots\}$ be the number of accidents on day 1, day 2, etc. Then the posterior distribution is given by Bayes' theorem $ p(\lambda|\mathbf{X}) = \frac{p(\mathbf{X}|\lambda)p(\lambda)}{p(\mathbf{X})}\propto p(\mathbf{X}|\lambda)p(\lambda) $
The prior is an exponential distribution with rate $\phi = 2$ such that $ p(\lambda) = \phi \exp(-\phi\lambda) $
We furthermore assume that the numbers of accidents per day are independent (this is probably not quite true). Consequently, we may write $ p(\mathbf{X}|\lambda) = \prod_{i=1}^N p(X_i|\lambda), $ where $p(X_i|\lambda)$ is the probability that $X_i$ accidents occur on day $i$ given an accident rate $\lambda$. Assuming a Poisson distribution we have $ p(X_i|\lambda)=\frac{\lambda^{X_i}}{X_i}\exp(-\lambda). $
Plugging into the product in the equation above we find $ p(\mathbf{X}|\lambda) = \exp(-N\lambda) \frac{\lambda^{X_1+X_2+\ldots}}{X_1!X_2!\ldots} $
The posterior is thus proportional to $ p(\lambda|\mathbf{X})\propto \exp(-N\lambda) \frac{\lambda^{X_1+X_2+\ldots}}{X_1!X_2!\ldots}\times \phi \exp(-\phi\lambda) $ and we can find estimates of $\lambda_0$ by e.g. finding the maximum of the posterior.
In particular, you asked for the mean and median. Note that the median is not the same as the maximum. You can read up on the topics here: Mean, Median, Mode (Maximum)
We define a normalized posterior distribution $ p'(\lambda)\equiv A p(\lambda|\mathbf{X}), $ where $A$ is chosen such that $\int_0^\infty d\lambda \, p'(\lambda) = 1$. I will leave it for you to carry out the integration.
Then the median $m$ is given by $ \frac{1}{2}=\int_0^m d\lambda \, p'(\lambda). $
The mean $\mu$ is given by $ \mu = \int_0^\infty d\lambda \, p'(\lambda)\lambda. $
Edit 1: Corrected a factor of $N$ that was missing in the expression for the conditional probability $p(\mathbf{X}|\lambda)$.
Edit 2: Added methods to estimate the mean and median of the posterior.