The short-term traffic forecasts are very crucial for road operators to plan different services they provide for travelers and also important for passenger to appropriately plan their trips in terms of the departure time and route selection. The data continuously collected by loop detectors embedded in road surface and cameras installed in the roadside are traffic volume and speed in 15-minute intervals that lead to traffic state (light, semi-heavy, heavy jam, blockage). There are also some Bluetooth detectors that are used for collecting travel times in pre-specified sections in roads. The software uses the aforementioned data for estimating a mathematical model and two artificial intelligence models to forecast traffic volume, speed and state as well as travel times in future hours and days. The data is a big data because of its variety, velocity and volume (3V). Variety means diverse factors that affect the traffic state including dates, weather conditions, unexpected incidents and etc. and velocity means the high rate of data generation and the volume is the high number of rows in data base, thus it is needed to use techniques compatible with the big data. These models have estimated incrementally because the data are continuously gathered and enter into the data base.