Parking Prediction based on Mobile application
If this data is coming in real-time then you don't need a model -- simply check how many spots have an ExpiryDate greater than now (i.e. when you need to provide a prediction) and subtract this from the total capacity of spots.
If the data is not coming in real-time, then you could use time of day and day of week as predictors. You might even want to make them into interaction terms. You would also need to decide how often you want to call your model and group your data so that each row represents how many tickets were active during that timeframe; this will define your target variable (what you're trying to predict).
By the way, I think you're referring to a survival model. I would recommend gradient boosting instead; it's much more powerful. Gradient boosting models (GBM) are part of caret in R and scikit-learn in Python by the way.